Input: Claim: Animals inherit specific memories of individual experiences from their ancestors. [SEP] Text: , 2011) using read data of three previously studied isolates (Downing et al. , 2011) (BPK282/0cl4, BPK087/0cl11, BPK275/0cl18, sampled from the most frequent genetic populations ISC4, ISC5 and ISC6 respectively). We created artificial pairwise mixes of these samples in proportions of 1:9, 2:8, 3:7, 4:6, 5:5, 6:4, 7:3, 8:2, 9:1 and unmixed, with depth levels adjusted for the relative proportions of unmapped reads in each. With these data, identified more rare variant SNPs in mixed libraries for the BPK275/0cl18-BPK087/0cl11 mix (Table A1. 2). BPK282/0cl4 was the reference genome strain and so should possess only heterozygous SNPs, and so its variant discovery rate was substantially lower when combined in mixed libraries. Consequently, for the mixed libraries of BPK282/0cl4-BPK087/0cl11 and BPK282/0cl4-BPK275/0cl18, once the fraction of non-reference reads <30%, the sensitivity of was superior to that for the GATK population caller. Table A1. 2 The fraction of discordant true and false SNPs between and GATK population-based variant calling. had a greater proportion of true SNPs – confirmed by visual inspection of mapping reads – both in terms of the number of variant sites, and across the population of samples. SNP caller1063399111876. 3%23. 7%89. 4%10. 6%GATK population caller3310611899123. 7%76. 3%10. 6%89. 4% Specifically, the population-based GATK and both called 11,389 variant genotypes in the artficially mixed samples, while GATK called 718 SNPs not detected using and found 396 additional SNPs not in the GATK set: a total of 1109 discordant variants at 139 sites. Visual inspection of mapped reads using Artemis (Carver et al. , 2008) and Samtools tview (Li et al. , 2009) indicated that 89% (354) of these 396 unique to were valid.

Output: pass


Input: Claim: Ants are the most intelligent species on Earth. [SEP] Text: In summary, euryphagous and ant-adverse predators were less likely to capture a mimic than a non-mimic while the ant-specialist attacked the ants and ant-mimics alike but the likelihood of attack varied according to the mimic’s actual unpalatability. There was no relationship between levels of unpalatability and the sum of predation pressure (K-values) exerted by the three predatory guilds (LM, F1,3 = 1. 83, p=0. 27, Figure 5B). have not been considered in Müllerian mimetic rings, which is perhaps surprising given their diversity and the number of known ant-mimics. The lack of ant fauna in mimetic rings possibly reflects an overall lack of conspicuous defensive coloration in ants. However, our study supports a species rich mimicry complex of golden coloured ant models and mimics, adding to the small number of documented large-scale complexes. Mimetic complexes (among several families of beetles) were first recognised by Nicholson (1927) for Australian insect fauna. Such complexes are presumably far more widespread than appreciated, but have only been subject to more intensive study relatively recently. Many more large-scale mimetic complexes among ants and other animals (particularly arthropods) are likely to be described in future. The species richness of this golden complex exceeds those of fish or millipedes (Marek and Bond, 2009; Alexandrou et al. , 2011) but is currently smaller than in mutillid wasp (Wilson et al. , 2012, 2015), heliconid butterfly (Mallet and Gilbert, 1995) and bumblebee (Williams, 2007) complexes. The key members in the golden complex are ants but the complex is taxonomically broad and includes species with distant phylogenetic origins. Beside the species mentioned, it may include a few other groups, such as bees, flies, and cerambycid beetles, in which some species also have a golden colouration. This is in contrast to complexes of similar scale that only include members from a single order. The distribution of the majority of the ten putative mimetic rings in the complex overlapped with at least one other ring. Although the cluster analysis distinguished rings, the distances between the rings were not large suggesting that the entire complex is just a single ring.

Output: pass


Input: Claim: AI is being used to personalize learning experiences for students by adapting to their needs and performance. [SEP] Text: Among the existing empirical research, studies have explored the quality of generative AI’s feedback on student essays in comparison to human feedback. Steiss et al. (2024) analyzed 400 feedback instances—half generated by human raters and half by ChatGPT—on the same essays. The findings showed that human raters provided higher-quality feedback in terms of clarity, accuracy, supportive tone, and emphasis on critical aspects for improvement. In contrast, AI feedback shone in delivering criteria-based evaluations. The study generated important implications for balancing the strengths and limitations of ChatGPT and human feedback for assessing student essays. Other research also examined the role of generative AI tools in L1 multimodal writing instruction (Tan et al. , 2024), L1 student writers’ perceptions of ChatGPT as writing partner and AI ethics in college composition classes (Vetter et al. , 2024), and the collaborative experience of writing instructors and students in integrating generative AI into writing (Bedington et al. , 2024). Specifically with regard to classroom-based research in L2 writing, Yan (2023) examined the use of ChatGPT through the design of a one-week L2 writing practicum at a Chinese university. Analyzing eight students’ classroom behaviors, learning logs, and interviews, the study showed that the use of generative AI helped L2 learners write with fewer grammatical errors and more lexical diversity. The study also found that the students’ biggest concerns were the threat to academic honesty and educational equity. This study is a pioneer in exploring students’ strategies and engagement with ChatGPT in writing; however, it was only conducted through a one-week practicum which did not involve authentic writing assignment tasks. Furthermore, students’ use of ChatGPT was limited to editing AI-generated texts instead of incorporating AI in a wider range of writing activities such as pre-writing and revising human generated texts. In another study by Han et al. (2023), the authors designed a platform that integrated ChatGPT to support L2 writers in improving writing quality in South Korea. Analyzing 213 students’ interaction data with the platform, survey results, as well as a focus group interview with six students and one instructor, the study found that the students generally held positive experiences with ChatGPT in supporting their academic writing.

Output: corroborate


Input: Claim: Animals inherit specific memories of individual experiences from their ancestors. [SEP] Text: had a greater proportion of true SNPs both in terms of the number of variant sites, and across the population of samples. This illustrated ’s improved power for detecting variants at novel sites, compared with GATK (Table A1. 3). Table A1. 3 The rate of SNP calling for different combinations of read libraries of samples BPK282/0cl4, BPK087/0cl11 and BPK275/0cl18. BPK282/0cl4 was the reference genome strain and so should possess only heterozygous SNPs, and so its variant discovery rate was substantially lower. identified more rare variant SNPs in mixed libraries. Read library combinationsUnique to to GATK population callerBPK282/0cl4BPK275/0cl18BPK087/0cl1189%11%14140680%20%7182770%30%6191760%40%9193750%50%7194740%60%5196630%70%7191620%80%7178811%89%9145689%11%1050580%20%11112570%30%5129460%40%7131550%50%7131640%60%6132630%70%5133620%80%4130611%89%4127689%11%1564380%20%14107470%30%4129360%40%4132350%50%3133440%60%3132530%70%4131420%80%3130511%89%31266 discovered a total of 2418 SNPs (4. 3% of the original candidate set) in the 191 isolates from the core population. We calculated that our calling approach had a 99% chance of finding variants segregating at a frequency ≥0. 024 in the Core 191 and ≥0. 173 in ISC1 (Table A1. 4). Table A1. 4 The probabilities of observing alleles at a specific frequency in the core population and ISC1. We assessed whether or not the SNPs we discovered in our sample of ISC L. donovani were likely to be representative of the variation existing in the wider population of parasites circulating in the ISC.

Output: pass


Input: Claim: Ants are the most intelligent species on Earth. [SEP] Text: Our findings are not unlike those from other complexes that include rings of different colour pattern, which overlap in geographic distribution (Williams, 2007; Wilson et al. , 2012, 2015). An overlap of golden rings or actually their non-existence potentially suggests that the variable golden colour patterns produce similar aposematic signals. Large mimetic complexes include taxa of close and distant phylogenetic origins (e. g. , Linsley et al. , 1961; Rodriguez et al. , 2014) in which the colour pattern is not only a product of common ancestry but also of convergence. A shared ancestry is apparent for two species of Myrmarachne spiders (Pekár et al. , 2017), three species of Daerlac bugs (Casis and Symonds, 2012), and several species of Polyrhachis ants (Robson et al. , 2015). The majority though, including Camponotus, Diacamma, Pachycondyla, and Iridomyrmex ants, other spider species, Eurymela treehopers, and Ephutomorpha wasps, have converged on the same phenotype as evidenced by their isolation within lineages of non-mimetic colour patterns. The diversity and geographic pattern of the golden phenotype is likely a result of co-divergence as found in other complexes (e. g. , Rodriguez et al. , 2014). Species in Müllerian rings described to date, e. g. heliconid butterflies, use a single defensive trait to teach predators that they are distasteful (e. g. , Brower, 1958). The lesser-defended individuals in this context are less distasteful. Species in our golden complex use multiple defensive traits including a variety of structural and chemical defences. To understand how these traits interplay with the aposematic signal we created an index that summed all traits known to serve a defensive function. We acknowledge this approach is somewhat simplistic as various traits are differentially effective against various predators. For example, a thick cuticle is effective against arthropod predators but ineffective against vertebrates (Evans and Sanson, 2005). On the other hand, spines are effective against vertebrates but ineffective in the defence against arthropod predators (Mikolajewski et al. , 2006). Different species of closely related predators may also use a variety of adaptations to deal with the defences of prey.

Output: pass


Input: Claim: AI is being used to personalize learning experiences for students by adapting to their needs and performance. [SEP] Text: I chose to focus on first-year writing as the study setting, as it is a required course in most colleges and universities, thus a typical writing and learning context for most college students. First-year writing serves as the foundation for cultivating academic writing skills, with the aim of developing students’ essential literacy and writing proficiency needed for their undergraduate learning experiences. The 14-week course focused on English academic reading, writing, and critical thinking and consisted of three major units. This study focused on the last unit, which was about argumentative writing, a common type of academic essay writing (Association, 2020). The final essay asked students to form an argumentative response to a research question of their own choice. The unit, lasting for three weeks, was structured as follows (see Fig. 1): During the first week, the instructor spent two classes, each for 75 min, introducing ChatGPT (GPT 3. 5) as a large language model and inviting students to explore ChatGPT as a tool for writing. The instructor carefully chose and assigned five readings that allowed the students to grasp the ongoing academic and public debates and concerns regarding the use of ChatGPT in writing and educational settings. During the class sessions, students participated in various activities exploring the functionalities of ChatGPT, discussed ethics and academic integrity, and critiqued AI-generated writing. As part of the discussions on ethics, the instructor explicitly addressed academic integrity issues drawing upon both the writing program’s guidelines and the institution’s academic integrity policies to ensure that the students were aware of and committed to ethical use of generative AI in the writing class. During the second week, students learned various strategies for integrating sources in academic writing and practiced ways of using sources to build arguments. During the last week, students spent time peer reviewing each other’s work and met with the instructor individually to improve their drafts. Fig. 1Unit design with key topics and learning tasks over the three weeksFull size imageThe final essay allowed but did not mandate students to use ChatGPT. For those who used ChatGPT and AI writing tools, disclosure and transparency about how AI was used were required as part of the submission to the assignment. The instructor left using AI in their final essay as an open option to the students themselves, ensuring that students could pursue the option that works best for their individual needs.

Output: corroborate


Input: Claim: Genetic memory can enable animals to learn complex tasks without ever observing them firsthand. [SEP] Text: It has tasked them with making 80 new pits per year by 2030, a deadline NNSA admits it will not meet. Los Alamos’s pits will be made at a facility called PF-4, a set of high-security buildings surrounded by cyclone fences with razor wire. Inside PF-4 are glovebox enclosures—radiation-shielded workstations where workers use thick gloves and peer through glass windows to manipulate the exotic metal. The lab is hiring thousands of workers, and its first pit is likely to be ready for the stockpile next year. To fashion plutonium into “pits” for bombs, the radioactive metal must be handled within protective glove boxes. gargantuan effort is motivated by a simple fact: many current pits are more than 40 years old, and plutonium behaves in confounding ways as it ages and radioactively decays. A green, fuzzy coating grows on it as its surface oxidizes. Atoms in its metallic lattice are knocked out of place as it spits out uranium isotopes. Its dimensions shift when it slips between six different solid phases. And the pits do not necessarily degrade smoothly. “We know at some point there will be a nonlinear piece,” says David Clark, director of Los Alamos’s and editor of the Plutonium Handbook. “We just haven’t seen it. ” So far, the silvery spheres seem to be holding up. Internal and external assessments have vouched for their integrity, suggesting the pits could have decades of viability left. “We haven’t seen any issues,” Clark says. But Jason, a secretive group of physicists who advise the government on national security matters, raised concerns that galvanized DOE. In a 2019 report, the group urged the agency to reestablish pit production “as expeditiously as possible” to “mitigate against potential risks posed by Pu aging. ” One might think the new pits would make it easier to certify the stockpile, by avoiding the uncertainties of aging plutonium. But they come with uncertainties of their own. The new pits won’t be twins of their predecessors, so weapons scientists will have to understand how the alterations change pit behavior. They are being manufactured using recycled and purified plutonium from old pits, not fresh material, unlike the originals. Moreover, they will be made with different processes, and in some cases designed to slightly different specifications.

Output: pass


Input: Claim: Ants are the most intelligent species on Earth. [SEP] Text: For example, some spiders are able to catch ants even with a thick cuticle while others are not (Pekár and Toft, 2015). A true index of unpalatability requires an estimation of the total effectiveness of a combination of defensive structures, such as spines and thick cuticle and the toxicity of chemical compounds for each predator-prey combination. Estimates including spines and cuticle thickness may be relatively straight forward but information on the toxicity of chemical compounds is largely not available (see Although our approach is simplistic, the positive relationship between the index of unpalatability and the response of skinks strongly suggests that the measure is a good proxy for unpalatability. Some of the defensive traits were strongly correlated with body size. Body size alone is an anatomical constraint that may prevent predation, thus it is difficult to separate the effect of pure body size from the relative size of some defensive traits (e. g. , spines). A strong correlation between body size and the size of defensive traits is natural, because larger spines, for example, require a larger body to support them. It is, however, the absolute size of defences not their relative size that is ecologically important (e. g. , a skink will swallow larger spines with greater difficulty than smaller ones). Whatever their single effect is, all these traits together, add to an overall unpalatability of mimics. Our study describes a new complex with a golden/black colour combination. This combination is somewhat similar to the classic aposematic yellow/black phenotype (Ruxton et al. , 2004; Williams, 2007). Accordingly, our findings support the idea the coverage of golden colour acts as an honest signal of aposematism. All the predators in staged trials exhibited an aversion to the prey with the greatest coverage of golden colour. Furthermore, golden individuals were rarely found in the diet of natural populations of predators. Honest signalling is further supported by the positive correlation between the extent of the golden colour and unpalatability, controlling for body size in the analyses via phylogenetic correlation or as a covariate. The theory of honest signalling, however, predicts such a relationship within rather than across species. However, recent evidence shows that this relationship can also exist across closely related aposematic species (Cortesi and Cheney, 2010; María Arenas et al. , 2015).

Output: pass


Input: Claim: AI is being used to personalize learning experiences for students by adapting to their needs and performance. [SEP] Text: Thus the unit provided various opportunities and flexibility for planning, researching, drafting, reviewing, and editing with ChatGPT throughout students’ writing process. There were 11 students, all freshmen, enrolled in the class. All but one reported using ChatGPT in their writing. Six students were recruited based on their willingness to participate and the diversity of their first language to ensure a balanced coverage. Table 1 shows the demographic information of the students (with pseudonyms). Table 1 Demographic information of student participantsFull size table4. 2 Data CollectionAligned with an interpretive phenomenological design that focuses on exploring participants’ lived experiences and how they construct meaning of their own experiences (Smith & Shinebourne, 2012), I collected three major types of data in order to uncover the students’ writing processes involving ChatGPT and their perceptions. First, I collected students’ written products and artifacts such as in-class writing, screenshots of students’ conversations with ChatGPT, informal short writing assignments, and the formal writing assignments for the final argumentative essay. Second, I collected students’ written self-reflections about their use of ChatGPT in writing. Finally, the participants were interviewed for around 30–40 min, and all interviews were audio-recorded. These semi-structured interviews were developed around students’ former experiences with ChatGPT, their views of the tool, and the ways they utilized ChatGPT for their writing assignments in this class. Students’ conversational screenshots with ChatGPT and their in-class and outside class writing drafts could demonstrate their interactions with AI as well as the changes they made upon contemplating the responses from the chatbot. The interviews and students’ self-reflections could further shed light on their perceptions and decision-making. Multiple sources of data helped to understand students’ behaviors, perceptions, and engagement with AI during different stages of writing. Triangulation of the data also helped me to understand students’ rationale for and practices of integrating, discounting, and reflecting on the chatbot’s output into their writing. It is important to note that a phenomenological qualitative research design like this aims to provide in-depth understanding and insights into participants’ experiences. The context of the study—a first year writing class—and the specific type of assignment investigated are both common scenarios in college classrooms, thereby enhancing the study’s relevance despite its limited sample size and scale.

Output: corroborate


Input: Claim: Many personal AI assistants use natural language processing to understand and respond to human speech. [SEP] Text: The task of the interface is to channel the request you make to a service that deals only with requests of that particular kind, whether that be modifying your phone settings, finding movie times or making a restaurant booking. If it’s not the case that ‘there’s an app for that’, then the IPA falls back on a search engine query. Of course this is, in its own way, a great model. A divide-and-conquer strategy makes it possible to focus on providing functionalities that address high value or high frequency tasks, leaving the long tail of minority interests for later. And it makes it possible to spread the burden of development in a way that encourages innovation: with an appropriate API, third parties can add new functionalities to the base platform, providing an ever-growing range of capabilities. But at the end of the day, what you have is a collection of essentially distinct capabilities. While there’s no doubt that these capabilities can be extremely useful, the whole is no more than the sum of the parts. A collection of diverse domain-specific functionalities doesn’t add up to intelligence. In the medium term, there are two ways this might play out. On the one hand, the number of distinct functionalities supported by the platform might increase beyond what can sensibly be managed by this kind of architecture, perhaps resulting in the user frequently being routed to a functionality that wasn’t the one they were actually looking for: the more categories you have, the greater the risk that categorisation will drop you in the wrong bin. Or, we might acknowledge that there’s a threshold to the number of distinct capabilities that can be navigated via such an interface without frustration, in the same way as having multiple screens of app icons on your phone just makes it harder to find things, so that you end up removing apps to make navigation more manageable. Either way, the point remains: assembling a collection of specific capabilities doesn’t amount to a broad-coverage intelligence. Sorry, ScarJo’s Samantha isn’t coming to a phone near you any time soon. Of course, we’ve seen this phenomenon repeatedly in the history of AI. Early AI research explored what a general problem-solving machine would look like, but in practical terms it was domain-specific expert systems that actually demonstrated value.

Output: contrast


Input: Claim: Ants are the most intelligent species on Earth. [SEP] Text: It remains to be tested what speciation process and selection forces have driven this pattern. Given the fact, that the complex is composed of many unrelated taxonomic groups that occur in different micro-habitats, the processes are likely to vary. It is necessary to investigate whether such a relationship between species holds for other known mimetic complexes. Unlike the classic yellow/black combination, members of the golden complex are defined by a metallic golden sheen. This sheen reflects brightly under direct sunlight making the animals very conspicuous, at least to a human observer. Just how important this sheen is in deterring a predator over and above just a matt colour has yet to be determined for this system. In other systems though, reflective iridescence has a significant additive effect (e. g. Fabricant et al. , 2014) so it is likely that the sheen enhances the detectability of the signal. A possible additional benefit, or even an alternative explanation, is that the sheen is important for reflecting harmful radiation and dissipating heat (Shi et al. , 2015). Irrespective of the explanation the importance of this colour variant, further experimental investigation is warranted. The aversion to prey with golden colour was apparent irrespective of the predator’s visual sensitivity. Visually- and non-visually orienting predators equally avoided the golden prey suggesting that golden mimics advertise their unpalatability via multiple sensory modalities. The non-visual predator still proportionally avoided the mimics despite not being able to detect the visual signal. This is an area of promising future investigation. Some non-visual predators potentially recognize mimics via the patterns of vibrations generated by their movements (Pekár et al. , 2011). Predators typically use multisensory modalities to identify prey (Bro-Jørgensen, 2010) such as movement, shape and defensive chemicals they emit. Our measure of unpalatability suggested that the highest proportion of individuals was moderately defended and defensiveness is distributed from highly defended to weakly defended. This is in agreement with previous claims that mimicry rings are dominated by moderately defended species (Sherratt, 2008). With a predominance of moderately defended prey, there is an expectation that their presence will erode the effectiveness of the model. Our findings, based on a limited number of mimics, multiple predatory guilds when taking into account, did not support this notion.

Output: pass


Input: Claim: AI is being used to personalize learning experiences for students by adapting to their needs and performance. [SEP] Text: In order to narrow down the topic and find a good focus, she used ChatGPT for exploring possible directions she could pursue. As she noted: It immediately gave me suggestions to approach the cause from demographic changes, economic factors, traditional gender roles, governmental policies, and cultural attitudes with detailed explanations beside each suggestion. So, I went on to pick economic reasons, which I think were the most accessible to write about. (Lydia, Reflection) ChatGPT’s feedback facilitated a smoother decision-making process for Lydia regarding the specific topic to further investigate. Another student Kevin mentioned that running his initial research idea into ChatGPT was helpful because ChatGPT gave him “some relevant ideas that hadn’t crossed his mind when thinking about the topic” (Kevin, Written Assignment). Considering ChatGPT’s suggestions did not mean that the students just took them for granted and incorporated them unquestioningly. For instance, Nora was interested in writing about the impact of AI on human lives. Upon putting her initial research question into ChatGPT, she found the feedback helpful and decided to do more research on the aspects highlighted by ChatGPT (see Fig. 2). Fig. 2Screenshot of Nora’s conversation with ChatGPTFull size imageStudents also reported using ChatGPT for outlining. Emma used ChatGPT extensively to help organize her outline and shared her procedure as follows: I wrote my own outline first consisting of my own ideas and then put it into ChatGPT. I asked ChatGPT to make the outline flow better. I was surprised with the results it gave me. It made the ideas more concise and connected better off of each other. . . I tried it a few times, and every time it gave me a different version of the outline that I could potentially use. I ultimately compared the information from my sources and chose an outline I thought best suited my essay and my essay question. (Emma, Reflection) Emma’s approach revolved around utilizing ChatGPT to unearth linkages among her various initial yet disorganized ideas she already had. By experimenting with diverse ways to build coherence and connection among her thoughts with the aid of AI, she shortcut the mental task of structuring her ideas from scratch. Using ChatGPT for refining the flow of ideas was also a strategy adopted by other students, but not always during the outlining stage.

Output: corroborate


Input: Claim: The majority of AI assistants rely on cloud computing to process complex commands and store data. [SEP] Text: 1049/PBPC054E_ch12 text PDF pandemic has forced industries to move immediately their critical workload to the cloud in order to ensure continuous functioning. As cloud computing expansions pace and organisations strive for methods to increase their network, agility and storage, edge computing has shown to be the best alternative. The healthcare business has a long history of collaborating with cutting-edge information technology, and the Internet of Things (IoT) is no exception. Researchers are still looking for substantial methods to collect, view, process, and analyse data that can signify a quantitative revolution in healthcare as devices become more convenient, and smaller data becomes larger. To provide real-time analytics, healthcare organisations frequently deploy cloud technology as the storage layer between system and insight. Edge computing, also known as fog computing, allows computers to perform important analyses without having to go through the time-consuming cloud storage process. For this form of processing, speed is key, and it may be crucial in constructing a healthcare IoT that is useful for patient interaction, inpatient treatment, population health management and remote monitoring. We present a thorough overview to highlight the most recent trends in fog computing activities related to the IoT in healthcare. Other perspectives on the edge computing domain are also offered, such as styles of application support, techniques and resources. Finally, necessity of edge computing in the era of Covid-19 pandemic is addressed. for medical images on the basis of intelligence and biocomputing M. Mohammed Mustafa, S. Umamaheswari, p. 279–302 (24) text PDF in medical imaging explores how intelligent computing can create a large amount of changes to existing technology in the field of medical image processing. The book presents various algorithms, techniques, and models for integrating medical image processing with artificial intelligence (AI) and biocomputing. Bioinformatics solutions lead to an effective method for processing the image data for the purpose of retrieving the information of interest and collecting various data sources for extracting the knowledge. Moreover, image processing methods and techniques help scientists and physicians in the medical field with diagnosis and therapies. It describes evolutionary optimization techniques, support vector machines (SVMs), fuzzy logic, a Bayesian probabilistic framework, a reinforcement learning-based multistage image segmentation algorithm, and a machine learning (ML) approach. It discusses how these techniques are used for image classification, image formation, image visualization, image analysis, image management, and image enhancement.

Output: contrast


Input: Claim: Ants are the most intelligent species on Earth. [SEP] Text: Instead, all mimics investigated in our predator-prey trials experienced significantly lower mortality than the non-mimetic species, and this was irrespective of the coverage of gold. We found the skinks were more likely to attack the lesser-defended individuals but when the suite of predators was taken into account there was little variation in the overall predation pressure. This is potentially because some predators may not use the visual signal as a reference and are cuing in on other signals. Diet analyses of several potential predators also showed predators of all guilds, including non-visually oriented predators, captured mimics with very low frequency. The predators that we used are known to feed on non-mimetic prey too (Jackson and Harding, 1982; Daniels, 1987; Platnick, 2000; Higgins et al. , 2001; McGinley et al. , 2015). Indeed, they fed on prey related to the mimics as evidenced by the positive results of designed primers targeting the mimics. We did not measure the availability of alternative prey so we do not have an absolute indication of relative encounter rates. However, birds learn to avoid both Müllerian models and mimics irrespective of the availability of alternative prey (Lindström et al. , 2004). In addition, there are multiple species of ant and ant models distributed across the sampling habitat occupying both terrestrial and arboreal habitats. The majority of the species in the complex are also ants and ants are typically super abundant due to their sociality. So it seems likely the predators would see these animals on a regular basis. Using a multi-disciplinary approach here, we faced several constraints. For example, measurements of mimic colour were based on photographs as we were unable to collect all mimics. To correct for inherent differences in illumination used during photographing mimics, the obtained RGB values were standardized but not linearised, and thus should be interpreted with caution. Similarly, the results of experiments with skinks must be interpreted with caution due to constrained randomisation used (Müllerian mimics first). The response of skinks could be affected by the constrained prey order (due to some kind of habituation). Yet, other predators used showed similar response to mimics as skinks, though the order of offered prey was fully randomised. Here we present the first account of the golden mimetic complex, which we believe is an exciting model for future research.

Output: pass


Input: Claim: AI is being used to personalize learning experiences for students by adapting to their needs and performance. [SEP] Text: For instance, after completing her first draft, Lydia “copied and pasted her entire essay into the chatbox and asked for suggestions on how to improve the structure and argument” (Lydia, Reflection). Lydia underlined that her revision process with ChatGPT was iterative, as she put her revised version back into the chatbot and went through another round of feedback and subsequent revision. Additional applications reported by students also encompassed employing ChatGPT to reduce redundancy and enhance conciseness of content (Emma) as well as to refine topic sentences for accurately summarizing the main ideas of body paragraphs (Kevin). Apart from utilizing ChatGPT to assist with global level issues such as structure, argument, and coherence, the students also harnessed the AI tool for sentence-level issues. They unanimously agreed that ChatGPT was a valuable tool for language editing. Alex, a L1 student, commented that ChatGPT could edit essays “exceptionally well. ” Alex not only used the AI tool to help improve the syntax of his writing such as “run-on sentences” but also consulted it as his dictionary for “providing academic diction” (Alex, Interview). The L2 participants particularly acknowledged ChatGPT as beneficial for enhancing the accuracy of their writing. Lydia shared that upon completing a paragraph of her essay, she would put it into ChatGPT and ask it to “revise the wording and grammar only” so she could refine her language and keep the content original (Lydia, Reflection). Another L2 student Nora noted that “when I struggle with expressing my thoughts accurately in English words, ChatGPT can help me express those ideas in a more powerful and accurate way. It removes communication barriers” (Nora, Written Assignment). 5. 2 Perceive the Benefits of Integrating ChatGPT into Their Writing? Utilizing ChatGPT in their various writing process components, the students reported that ChatGPT had the following benefits: (1) accelerating their writing process, (2) easing their cognitive load, (3) fostering new learning opportunities, (4) getting immediate feedback, and (5) promoting positive feelings about writing.

Output: corroborate


Input: Claim: Personal AI assistants will be able to understand emotions as well as humans by next year. [SEP] Text: PostLog inSign upPost8PostConversation2GrantsPeer ReviewsDocumentFlag content 3,000 Tipped ≈ $0. 008Dr. Barrett: How to 18, 2023DOI10. 55277/ResearchHub. fke8bs6uSaveTipDocumentFlag content8TipSaveDocumentFlag contentPostConversation2GrantsPeer ReviewsKey takeawaysExplore the challenge of defining the fundamental components of emotions and the absence of exclusive physical indicators. (Segment 1)Dispute the notion that emotions universally manifest on people's faces, highlighting the significant influence of cultural and environmental factors. (Segment 3)Demonstrate the impact of culture on the interpretation of facial expressions and raise questions regarding its legal implications. (Segment 5)Investigate how language constrains our ability to express emotions and advocate for embracing ethnic diversity for better understanding. (Segment 6)Examine the evolving role of the brain in labeling emotions and illustrate how this evolution impacts our perception. (Segment 8)Advocate for flexible emotional regulation and underscore the value of embracing pain for personal growth. (Segment 14)Discuss strategies to enhance sensory and emotional experiences, with a particular emphasis on improving understanding and perception. (Segment 16)Explore the intricate connection between the body and mind, examining sensory systems, motor control, and emotional regulation. (Segment 18)Delve into the relationship between digestive factors, SSRIs, and mental health, with the insights of Andrew Huberman. (Segment 19)SummaryDr. Barrett, obtained a Doctor of Philosophy degree and currently occupies the esteemed position of of Psychology at Northeastern University. Dr. Barrett possesses a high level of experience in the domain of emotional science, which has garnered her widespread acclaim and international recognition for her extensive knowledge and significant achievements in this subject. The author provides a comprehensive explanation of the notion of emotions and delves into the brain's function in processing and integrating signals derived from both internal physiological sensations and external surroundings, hence resulting in the formation of unique emotional experiences for each individual. The forthcoming discourse will center on the analysis of the relationship between emotions and language, the impact of linguistic specificity on the processing of emotions, the importance of facial expressions in the domain of emotions, and the interconnectedness of emotions with other factors such as sleep, movement, nutrition, and the formation and reinforcement of social connections.

Output: contrast


Input: Claim: Ants are the most intelligent species on Earth. [SEP] Text: Our work to date is mainly descriptive, and it requires more explicit experimental approaches to generate robust conclusions about the evolution of this golden aposematic signal. For example, the relationship between the level of unpalatability and the quality of the golden signals needs rigorous testing with a greater number of mimics. This would also help in deciphering the role of less-defended mimics in the protection of the entire complex. Similarly, exploring the function of non-visual signals in deterring predators will elucidate how this mimicry ring works. In conclusion, we present the first account of the golden mimicry complex. It consists of species that show considerable variation in their level and mode of defence, ranging from undefended Batesian mimics to highly defended Müllerian mimics, with most members of the complex somewhere in between. Our data suggest that all members of the golden mimicry complex receive quite similar net protection from a variety of predators presumably because they have a variety of defensive traits. We predict that mimetic complexes that incorporate a range of different forms of defence are not only more common than previously believed, but also more likely result in mutualism among the unequally defended members of the complex. Materials and methods Description of the mimetic complex Request a detailed protocol Arthropod species forming the complex were selected based on the presence of a golden colour pattern on the dorsal side of their body and body size falling within the range 3–15 mm. ants, and known ant-like mimics, were examined for a dorsal golden appearance. The specimens we examined were either field collected by the authors (outback, NSW eastern coast, Queensland eastern coast) or from museum collections (Australian Museum, Sydney). All other specimens we viewed were images from AntWiki (www. antwiki. org), Atlas of (www. ala. org. au), or taxonomic papers (Ogata and Taylor, 1991; Shattuck and Barnett, 2006; McArthur, 2007; Kohout, 2010a, 2010b, 2012, 2013a, 2013b; Heterick and Shattuck, 2011; Casis and Symonds, 2012; Shattuck and Marsden, 2013). The dorsal colour pattern was estimated from digital images of specimens (Figure 7) as performed in other studies on mimetic complexes (Wilson et al. , 2012, 2015; Rodriguez et al. , 2014).

Output: pass


Input: Claim: AI is being used to personalize learning experiences for students by adapting to their needs and performance. [SEP] Text: Students stated that using ChatGPT could “speed up the process of writing” (Alex, Interview) as exemplified by the following quotes: “ChatGPT really helped me to explore the essay topics that I’m interested in within a very short amount of time and identify what can be written about” (Nora, Interview); “I discovered after using it for my final essay that ChatGPT can greatly improve the efficiency of my writing” (Alex, Reflection). For L2 writers, it significantly saved the time they typically spent on editing, as mentioned by Lydia: As an international student who is not a native English speaker, writing college essays would take me double the amount of time compared to those who write essays in their first language. Oftentimes, the biggest time I spent was on editing the grammar and trying to make my language readable and understandable. (Lydia, Reflection) The benefits of saving the time and energy on language concerns, grammar, wording, and the organization of ideas and messy drafts, furthermore, reduced the cognitive burden among the student writers, both L1 and L2. For instance, knowing ChatGPT’s editing power, Alex felt that he was able to “focus more on the subject of the writing rather than the language itself” and “spew out thoughts freely” when drafting the essay (Alex, Interview). Likewise, the L2 students noted that ChatGPT allowed them to delay their concerns about the linguistic forms of ideas and alleviate the demanding cognitive load associated with L2 writing. As claimed by Lydia, “It freed my thoughts so that I could spend more time revising the content, but not worry about how to express my ideas for the essay” (Lydia, Interview). The students conveyed that incorporating ChatGPT in different components of writing also fostered new learning opportunities for them to improve writing. Nora shared that “ChatGPT not only made my language more fluent and comprehensible, but it also helped me to learn new ways of expression in English” (Nora, Interview). Su remarked that although ChatGPT’s feedback was generic, it promoted her to do further research about her topic and learn more writing strategies (Su, Written Assignment). Students particularly highlighted the “instant and personalized feedback” (Kevin, Reflection) provided by ChatGPT as a strong impetus and benefit.

Output: corroborate


Input: Claim: Personal AI assistants will be able to understand emotions as well as humans by next year. [SEP] Text: The attendees will also partake in a discussion concerning practical approaches for coping with feelings of uncertainty, as well as methods for improving one's understanding of the emotional encounters of others. This specific episode is anticipated to engage persons who possess a curious disposition toward the complex interplay between neuroscience and psychology that underlies the phenomenon of emotions. Furthermore, it possesses significance for those who want to augment their self-awareness, interpersonal connections, and overall capacity to navigate the world with increased flexibility. 1. 00:05:46 of EmotionsDr. Barrett explores the complex endeavor of distinguishing and detecting emotions across the span of the last 150 years. This observation underscores the difficulty in establishing a precise and exclusive set of fundamental elements for emotions. These elements, such as physiological changes in the body, brain activity, and facial expressions, are not inherently unique to emotions and can occur in various situations. Researchers have consistently exerted considerable efforts in their endeavor to establish a comprehensive description of emotions, employing diagnostic patterns as a basis for their investigations. These patterns entail the identification of unique physiological signs, such as increased heart rate and distinctive facial expressions, which were hypothesized to be linked to particular emotions, such as fear. However, the assertions put forth lacked empirical support, therefore leading to perplexity around the perception of different emotions in the absence of observable physical cues. Researchers have been intrigued by this enigma for a significant period [1]. Figure 1. to Expressions of Fear and Surprise [1]. 1. Zhao, k. , et al. , to Expressions of Fear and Surprise. Frontiers in Psychology, 2017. 8. 2. 00:10:42 & Interpretation, Emotion Dr. Barrett offers a critical analysis of the conventional conceptualization of an emotion system and a facial expression system. Dr. Barrett argues that while there are processes that enable the movement of facial muscles, it is crucial to acknowledge that not all facial movements may be considered as indications of internal emotional states. The author pays much attention to the attribution of significance to facial movements, underscoring the importance of interpretation and contextual elements. Furthermore, the author emphasizes the role of the human brain in the cognitive processing of various sensory inputs, which is crucial for the understanding and interpretation of emotions. The analysis of facial expressions relies on inferential reasoning within the wider framework of an individual's specific circumstances, rather than being solely reliant on direct observation.

Output: contrast


Input: Claim: Ants are the most intelligent species on Earth. [SEP] Text: Field collected specimens (of 41 species) were killed by ethylacetate and arranged in their natural body position. Photographs were taken with a HF G10 against a white background and lit from above by two full spectrum light bulbs (2. 0 UVB, Eko Terra). Photographs of the remaining species (59 species) were obtained mostly from the personal collection of R. Kohout, followed by AntWiki, and Atlas of Living Australia. Prior to taking measurements all photographs were calibrated for the white and/or black colour within ImageJ to correct for different lighting conditions. The average value of each RGB colour component was estimated on the following body parts: dorsal side of head, appendages (femur II), pronotum, mesonotum, metanotum, petiole, first segment of the gaster, and the remaining segments of the gaster/abdomen in ants and wasps using ImageJ. Likewise, for hemipterans and spiders the average colour value of each RGB colour component of corresponding body parts (to that of ants) were examined including the pedipalps/chelicera, frontal third of prosoma, second third and last third of the prosoma, legs (femur III), the first half of the abdomen and the second half of the abdomen. In each species, we also measured the length of each body part (listed above) and an absolute area of the golden colouration on the dorsal side using ImageJ. Figure 7 Download asset Open asset Selected list of 100 species representing the ten putative mimetic rings. Species are arranged within each ring from the most to the least unpalatable (left to right). Some pictures of ants are displayed with the permission of AntWiki and R. Kohout. To assess colour pattern similarity among mimics and to find how many rings there are, we subjected 24 measures of the colour pattern, body size and area of golden patch of 100 mimics (Pekár et al. , 2016) to the Non-Scaling based on Bray-Curtis distance matrix. Because various body parts have different area and are thus differently conspicuous to predators, we used weighting of the measures by their relative size. Then we estimated the number of putative rings via k-means clustering using the vegan R package (Oksanen et al. , 2013).

Output: pass


Input: Claim: AI is being used to personalize learning experiences for students by adapting to their needs and performance. [SEP] Text: For instance, as a frequent visitor of the school’s writing center, Lydia mentioned she typically scheduled two to three appointments with a writing tutor for each major writing assignment she had worked. With ChatGPT, she could obtain feedback anytime: “don’t have to physically go to the writing center at 11 pm, waiting for the previous visitor to finish their session” (Lydia, Interview). She used “my walking AI tutor” to describe the role of AI in her writing. Ultimately, the students mentioned that these cognitive and practical benefits of ChatGPT not only improved their efficiency of writing, but also promoted positive feelings about writing. They used words such as “more relieved” (Emma), “sense of accomplishment” (Lydia), and “less anxious” (Nora) to describe the AI-assisted writing process. Although the students expressed different needs and utilization of ChatGPT, they all conveyed that they would like to continue using it in the future. 5. 3 and to Assist with Their Writing? Despite the benefits and usefulness of ChatGPT for assisting with students’ writing, they also expressed many reservations and limitations regarding the AI tool. The first concern was about the false information it produced and its potential to mislead people. The students commented that ChatGPT tended to “make up information” (Emma), “make assumptions and guesses” (Su), and generate “inaccurate information” (Nora), “wrong information” (Alex), and “nonsense” (Lydia). Furthermore, the students pointed out that ChatGPT was inadequate in addressing high-level questions requiring critical thinking, as Su explained: “was consulting with ChatGPT, I learned that it has a very limited understanding of the topic I was talking about” (Su, Reflection). Other students also pointed out that the responses they got from ChatGPT could be “very generalized” (Kevin) and lacked “depth and complexity” (Nora). The next shortcoming of ChatGPT, as noted by the students, is the lack of creativity and originality.

Output: corroborate


Input: Claim: Personal AI assistants will be able to understand emotions as well as humans by next year. [SEP] Text: Dr. Barrett also acknowledges the presence of a mutual connection between the face and the brain, albeit this interaction is not limited to emotional functions but rather forms part of the broader communication network between the brain and the body. This essay presents a counterargument against the concept of a reductionist and mechanistic relationship, suggesting that specific facial expressions do not directly elicit or generate distinct emotional states. This is supported by the absence of a segregated emotional system within the brain [2]. Figure 2. Functional brain networks associated with emotion processing [2]. 2. Canu, E. , et al. , Resting state functional brain networks associated with emotion processing in frontotemporal lobar degeneration. Molecular Psychiatry, 2022. 27(11): p. 4809-4821. 3. 00:19:33 & Emotion, IndividualizationDr. Barrett offers a comprehensive examination of her scientific trajectory, while also expressing her reservations regarding the conventional perspective on the relationship between facial expressions and emotions. Dr. Barrett elucidates that the concept of universal facial expressions associated with specific emotional states is predominantly grounded in Western stereotypes that have been solidified as empirical truths. The initiation of her scientific endeavor occurred when she endeavored to use this information in a controlled experimental context, only to confront a disparity between the theoretical understanding and her empirical findings. Within the discipline of psychology, there has been a continuous discourse about the fundamental characteristics of facial expressions and their potential universality. Consequently, scholars have endeavored to establish a consensus by synthesizing the existing body of literature related to this topic. Dr. Barrett, together with a team including four esteemed senior scientists, undertook a comprehensive examination of a substantial compilation consisting of more than one thousand scientific 3. to Learning [3]. 3. Hooker, C. I. , et al. , to Learning. 2006. 26(35): p. 8915-8922. 4. 00:31:03 Emotion Categories, Culture & Dr. Barrett's in podcast with primarily focuses on the complicated dynamics of emotions, facial expressions, and their complex interaction with cultural factors. Dr. Barrett highlights the concept that emotions should not be regarded as distinct entities, but rather as categorizations or groupings of events, and even as behaviors underlining the dynamic and changeable nature of emotional experiences. In his scholarly discourse, Dr. Barrett offers an alternate viewpoint that challenges the prevailing consensus on the inherent capacity of newborns to see and comprehend facial expressions.

Output: contrast


Input: Claim: Ants are the most intelligent species on Earth. [SEP] Text: This method used the simple structure index (ssi) as a partition criterion (Legendre and Legendre, 1998) as AIC is not available. Blue (RBG) contrast was estimated using 55 species of mimics. It was computed as the sum of differences in red, green, and blue values representing the golden and black colouration found on the body of mimics. The phylogenetic relationship at the genus level among the golden mimics was reconstructed from a combination of published phylogenies (Moreau et al. , 2006; Regier et al. , 2010; Robson et al. , 2015; Pekár et al. , 2017). If species level phylogeny was not available, we used polytomic splitting at the species level. To test for phylogenetic inertia of classification to putative rings we used generalised least squares (GLS) with Pagel’s phylogenetic correlation structure (Paradis, 2006), in which low value of λ indicates low phylogenetic signal. The mode by which the animals generated the golden colour (pigments or structural colours) was determined via microscopy. One specimen of each species from each genus (Ephutomorpha aurata, Camponotus aeneopilosus, Daerlac nigricans, Dolichoderus doriae, Polyrhachis ammon, P. vermiculosa, Ligonipes illustris, Myrmarachne luctuosa, Myrmecia tepperi, Eurymela rubrolimbata) was further examined via SEM. The golden gaster/abdomen was dried at a room temperature for 10 days, then mounted on a stub and coated with gold prior to viewing under a 648OLA. The presence of pigments on the gaster/abdomen was examined in ethanol-preserved specimens under Olympus stereomicroscope SZX9 and photographed with a digital camera mounted on the stereomicroscope.

Output: pass


Input: Claim: AI is being used to personalize learning experiences for students by adapting to their needs and performance. [SEP] Text: It gives a shortcut for people who do not want to make the effort to learn and think. But it could be useful for those who really want to learn” (Lydia, Interview). Alex noted that to prevent the loss of learning opportunity, for instance, he decided that rather than taking ChatGPT’s diction suggestion immediately, he “would use those words in the next essay,” demonstrating his effort in learning and internalizing the knowledge. In general, the students were still exploring ways to use ChatGPT in critical, authentic, and ethical ways that would promote rather than harm their learning. 6 DiscussionAdopting sensemaking theory, the study investigated how students made sense of their AI-assisted writing practices, providing insights into students’ learning process and their shared practices emerging around the AI technology. Confirming previous research (e. g. , Guo et al. , 2022; Holmes et al. , 2019; Su et al. , 2023), this study found that the students overall had positive experiences with generative AI-assisted writing, for it could accelerate their writing process, reduce their cognitive load and anxiety, and provide prompt feedback. The students integrated ChatGPT into various components of their composing process, such as searching for content, ideational planning, language editing, and revising. Although the students acknowledged the cognitive and affective benefits (e. g. , Ebadi & Amini, 2022; Fryer & Carpenter, 2006) of using ChatGPT in writing, they were very cautious about adopting its ideas and suggestions at different discourse levels (i. e. , essay, paragraph, and sentence levels) due to critical, ethical, and authentic concerns. This finding extends previous research which identified that students’ primary concerns were academic dishonesty and educational inequity (Yan, 2023). Despite recognizing AI’s limitations such as the lack of in-depth insights (Gao et al. , 2022), originality, creativity, and reliability—qualities essential for good academic writing—the students deemed it necessary to embrace rather than abandon the tool, with the purpose of fostering one’s critical thinking and writing skills.

Output: corroborate


Input: Claim: Personal AI assistants will be able to understand emotions as well as humans by next year. [SEP] Text: According to the author, it is proposed that infants demonstrate an inherent inclination toward specific facial patterns. However, the ability to perceive faces as distinct entities and the subsequent connection of facial expressions with emotions are acquired through early exposure and cultural transmission. Dr. Barrett underscores the importance of cultural context in shaping the understanding of emotions, suggesting that the cognitive processes in our brains are influenced by both genetic elements and cultural influences. This perspective aligns with the broader evolutionary synthesis, which acknowledges the importance of cultural inheritance in the progression of human development [4]. Figure 4. of Hippocampus [4]. 4. Zhu, Y. , et al. , of Real-Time fMRI Neurofeedback in Healthy Human. 2019. 13. 5. 00:37:50 Legal System, ‘Universal’ Emotions & Caution Dr. Barrett also illuminates the significant impact of cultural beliefs surrounding the capacity of facial expressions to effectively communicate mental states. This influence extends to the extent that it may potentially influence legal judgments. Dr. Barrett emphasizes that this belief has been integrated into the judicial system, leading to circumstances where individuals' lives are at risk, as judgments based on inferred emotions from facial expressions may lack empirical credibility. In his discourse, Dr. Barrett examines a specific occurrence when the became engaged in a capital punishment lawsuit due to a law enforcement officer's claim regarding the defendant's facial expressions. Dr. Barrett also emphasizes the lack of scientific data that supports the idea of universally applicable facial expressions that allow for accurate understanding of persons. While acknowledging the potential for people to display unique facial clues or regular patterns in their facial expressions, the author questions the concept of universally applicable templates for emotions. This emphasizes the necessity of critically examining the widely accepted belief in these established patterns within our cultural context [5]. 5. Dailey, M. N. , et al. , Evidence and a computational explanation of cultural differences in facial expression recognition. Emotion, 2010. 10(6): p. 874-93. 6. 00:41:07 Language Descriptions, Differences & Emotion Dr. Barrett presents a thorough examination of the constraints inherent in language with regard to the articulation and comprehension of emotional encounters. The inability to rely just on a single language, such as English or French, to adequately encompass the vast range of emotional experiences is emphasized by the author.

Output: contrast


Input: Claim: Ants are the most intelligent species on Earth. [SEP] Text: The distribution of mimics across Australia was estimated using occurrence records from collections made by the authors, taxonomic papers (Ogata and Taylor, 1991; Shattuck and Barnett, 2006; McArthur, 2007; Kohout, 2010a, 2010b, 2012, 2013a, 2013b; Heterick and Shattuck, 2011; Casis and Symonds, 2012; Shattuck and Marsden, 2013), museum material deposited in the and records stored in the database of the Atlas of Living Australia. To test for an association between colour similarity and distribution overlap of species in each mimicry-ring in Australia we used a grid cell of 400 × 400 km. Within each grid occurrence of a species was classified as 1 (if present) or 0 (if not recorded) (Pekár et al. , 2016). Then a distance matrix was created from the data using the Bray-Curtis method (Legendre and Legendre, 1998). Using the colour pattern data, we created a matrix and generated a distance matrix between species using Euclidean distance. To test a hypothesis that putative rings overlap in their geographic distribution, we studied the association between the geographical occurrence of mimics and colour similarity matrices by means of a Mantel test based on the Pearson correlation coefficient. Mimetic unpalatability Request a detailed protocol We estimated unpalatability in 100 mimics (workers and adults) from the complex using the following seven traits (xi): length of the sting, number of spines, total length of spines, size of mandibles, cuticle thickness, size of poison/pygidial gland, and performance of a communal attack (Pekár et al. , 2016). Size, number of spines and size of mandibles were measured in one individual from each species. Cuticle thickness and size of the poison/pygidial gland could only be measured from freshly collected specimens. So we collected the mimics (workers in the case of ants) from a variety of places across Australia (). In total, 75 specimens belonging to 41 species, eight genera, and four subfamilies were collected alive. Individuals were killed by freezing at −20°C. Cuticle thickness was measured by slicing the thorax (mesosoma) perpendicularly using a blade and then measuring it under a stereomicroscope to the nearest 0. 01 mm.

Output: pass


Input: Claim: AI is being used to personalize learning experiences for students by adapting to their needs and performance. [SEP] Text: While the participants concurred that upholding human voice and agency entails possessing integrity and alignment not only at the ideational level but also in the linguistic expression of those ideas, the L2 writers in this study added another nuanced dimension to the impact of AI on human voice and authenticity in the context of AI-assisted writing. As the L2 students experienced, ChatGPT’s language suggestions might not pose a threat to their voice but serve as a catalyst for augmenting their voice, as AI helped overcome their language barriers and better express ideas true to themselves. In other words, generative AI afforded the L2 writers powerful language repertoires that enhanced the accuracy and efficiency of “linguistic rehearing” (Abdel Latif, 2021) or “translating” (Kellogg, 1996) component of their writing process, thus allowing L2 students to produce writing more authentic to themselves. The finding highlights how learner characteristics and individual differences play an important role in students’ sensemaking of AI-assisted writing, complicating the existing understanding of AI’s affordances for learners with diverse linguistic backgrounds and learning needs. From earlier conceptualizations of authenticity as “ownedness” and “being one’s own” by Heidegger (1927/1962), to contemporary perceptions as the “self-congruency” of an individual, group, or symbolic identity (Ferrara, 1998, p. 70), the notion of authenticity has been evolving and becoming more pluralistic. (2017) acknowledged, authenticity extends beyond adherent to personally endorsed commitments; it requires a comprehensive consideration of one’s self-awareness and the changing social context. Scholars should further pursue what it means by authenticity and academic integrity in an increasingly AI-mediated educational context, ways to promote students’ authorial voice and agency, as well as the complicated authorship issues (Jabotinsky & Sarel, 2022) involved in AI-human collaboratively generated texts. (2023) claims, it is time to contemplate “postplagiarism” and academic integrity in a future where “hybrid human-AI writing will become normal”. Apart from the sounding better or sounding like me dilemma experienced by students, another paradox is whether AI caused missed learning opportunities or created new learning opportunities.

Output: corroborate


Input: Claim: Personal AI assistants will be able to understand emotions as well as humans by next year. [SEP] Text: In reality, it is much more likely that such tools will be implemented to inform and support human decisions and that the discussion of performance of algorithms versus performance of humans therefore is not very urgent after all. Now, obviously this answer cannot be given with regard to risk assessment algorithms. When it comes to such assessments, the replacement of the judgements of medical professionals with those of algorithms has already taken place. However, when it comes to the use of sentencing algorithms, which of course constitutes a much more radical step in the involvement of technology in the criminal courts, it is unlikely that fully automated decision-making will be introduced. Rather, what we should expect is precisely what has already happened: namely, that algorithmic tools will be used to provide sentence recommendations which judges can draw on when they mete out sentences. But the sentencing decisions will remain human. Therefore, it might be held, the previous considerations could be seen as directing attention to a theoretical challenge when it comes to the comparison of the performance of algorithms and humans, but it is not a challenge that is likely to have ramifications in penal practice. Is this objection convincing? The question of whether it is likely that algorithms will in future be implemented in lieu of human judges at sentencing, and if so to what extent and regarding which types of crime, is of course an empirical question. It is not easy make precise predictions of what we can expect. If it turns out—which does not seem unlikely—that fully automated sentencing decisions, at least where less complicated types of crime are concerned, will constitute a procedure that can save time and resources, then it does not seem far-fetched to imagine that there will be pressure on decision-makers to introduce such tools in practice. In fact, this is precisely what has happened with regard to the use of risk assessment algorithms. Footnote 15 However, I shall not attempt to qualify these predictions here. In fact, there is no reason to do so. Even if it is correct that fully automated sentencing decisions constitute a hypothetical scenario, and that algorithms are much more likely to be used in the future as instruments that merely inform judicial decision-making, this will still not suffice to circumvent the challenge that has been presented. What we have seen is that we currently lack the penal ethical background for making comparisons between the performance of sentencing algorithms, on the one hand, and sentencing decisions by human judges on the other.

Output: contrast


Input: Claim: Ants are the most intelligent species on Earth. [SEP] Text: To measure the size of the gland, the gland was dissected from the gaster (thorax in bugs) using fine forceps, placed on a glass slide in a drop of water, and covered with a coverslip. The gland was approximately oval; thus the two perpendicular lengths of the gland were measured using an ocular ruler. We estimated gland area assuming a regular ellipsoid shape. We measured the sting length after pulling it from the abdomen using forceps. In each of these individuals we also measured the gaster/abdomen length, and thorax length. The ability to perform a communal attack (i. e. attack by several individuals) was taken from the literature and was assumed to be consistent throughout each genus. To estimate these traits for the remaining ant species that we failed to collect we used an interpolation based on proxy measures using 55 specimens: the size of both the poison/pygidial gland and sting length were regressed (using a linear model) from the size of the gaster, and cuticle thickness was regressed from the size of the thorax. These regression models (Figure 8) were then used to predict values for species we were unable to sample. The gland content varies among species of ants from different subfamilies: Formicinae ants possess formic acid (O'Rourke, 1950; Stumper, 1952), Dolichoderinae ants possess terpens, such as iridodial and dolichodial (Cavill, 1960; Cavill and Hinterberger, 1960), and Myrmeciinae ants possess indole (Jackson et al. , 1990). The metathoracic scent gland in true bugs produces pungent pyrazines and alkenals (Millar, 2005). As the relative potency of these chemicals is unknown, we considered all these chemicals to be similarly noxious. Figure 8 Download asset Open asset Relationship between cuticle thickness and thorax length. (A) poison/pygidial gland area and gaster length (B) sting length and gaster length (C) with estimated linear regression models. Various defensive traits measured here are not similarly effective against predators. Yet we assumed that their effect is similar because different predators adapted to deal with different defences of prey. We further assumed that the effect of these traits on predators is additive.

Output: pass


Input: Claim: AI is being used to personalize learning experiences for students by adapting to their needs and performance. [SEP] Text: As noted by the previous literature, AI-writing tools may rob students of essential learning experiences (Barrot, 2023a; Tseng & Warschauer, 2023). Adding to this concern from educators and scholars, the present study shows that the students themselves are also cognizant of the possible learning loss due to AI adoption. Furthermore, the study shows that rather than passively indulging themselves in the convenience of AI tools, a common concern among educators (Chan & Hu, 2023; Graham, 2023), the student writers attempted to seize new learning opportunities that emerged from AI technologies to promote their critical thinking and writing. This finding suggests a nuanced addition to sensemaking theory: the process of making sense of uncertainties in AI-infused literacy practices can also be uncertain, involving reconciling dilemmas and acknowledging perplexing experiences. While not always yielding clear-out outcomes or casual attributions for the observed phenomena and personal experience as suggested by Poquet (2024), noticing and perceiving the unpredictable impacts of generative AI on students’ own learning processes can, in itself, be empowering. The process fosters a sense of agency and critical engagement, suggesting that the outcomes of sensemaking in the context of AI-assisted writing can be open-ended yet profound. This important finding leads scholars to reconsider the essence of learning in an era of generative AI. Hwang et al. (2020) and Southworth et al. (2023) argued that AI is likely to transform not only the learning environment, but also the learning process, and even what it means to learn. This perspective finds resonance in the experiences of the participants in this study. While AI may shortcut traditional ways of doing writing, it does not inherently imply a reduction in students’ cognitive, behavioral, and affective engagement with writing, learning, and thinking. AI does not necessarily make writing easier; on the contrary, a critical, ethical, and authentic approach to AI-assisted writing pushes students to think further and prioritize their own voice, originality, and creativity, leading to high quality writing. In this sense, when used properly, AI has the potential to introduce a new avenue for humanizing writing and education. As generative AI technologies are advancing rapidly, an expanding array of AI-powered writing assistance, intelligent tutoring systems, and feedback tools has the promise to cater to the diverse needs and learning styles of language learners and writers.

Output: corroborate


Input: Claim: AI assistants can guarantee complete privacy and security for all personal data and interactions. [SEP] Text: is Open Access? is an initiative that aims to make scientific research freely available to all. To date our community has made over 100 million downloads. It’s based on principles of collaboration, unobstructed discovery, and, most importantly, scientific progression. As PhD students, we found it difficult to access the research we needed, so we decided to create a new publisher that levels the playing field for scientists across the world. How? By making research easy to access, and puts the academic needs of the researchers before the business interests of publishers. are a community of more than 103,000 authors and editors from 3,291 institutions spanning 160 countries, including winners and some of the world’s most-cited researchers. Publishing on IntechOpen allows authors to earn 10. 5772/intechopen. 96999 this 1. Choose citation style Select style to clipboard Get citation 2. Choose citation style Select format Bibtex RIS Download citation Intechin the Convergence of Blockchain and Artificial Intellig. . . Edited by Tiago M. Fernández-Caramés From the Advances in the Convergence of Blockchain and Edited by Tiago M. Fernández-Caramés and Paula Fraga-purchase hard copies of this book, please contact the representative in India: CBS Publishers & Distributors Pvt. Ltd. www. cbspd. com | customercare@cbspd. com 1. Choose citation style Select style to clipboard Get citation 2. Choose citation style Select format Bibtex RIS Download citation Impact of this 1. IntroductionThe amount of personal data being collected is rapidly proliferating. Enterprises and governments use this data to profile individuals and to predict and control their attitudes and behavior. This can result in customized experiences, personalized services, and more efficient use of resources. It can also result in misinformation and exploitation by the entity that collected the data or by others that purchase or steal it. In response to increases in cybercrime and growing consumer concern, legislation to protect personal data is being proposed and implemented. Organizations trading in personal data face increasing costs associated with managing and securing data. They also face increasing risks that data will be misused or stolen, and that they will face legal or financial consequences, as well as damage to both their reputation and to relationships with customers and other stakeholders. In this 2. Personal data privacyA foundational privacy issue facing information system developers and users is personal data privacy.

Output: contrast


Input: Claim: Ants are the most intelligent species on Earth. [SEP] Text: So to quantify unpalatability (uj) of the jth mimic we summed the ith trait values, which were scaled by their maximal value due to difference in range: uj=∑j=1nxijmax(xi). We also computed a simple sum of defensive traits and found that the two measures are highly correlated (Pearson correlation, r = 0. 72, p<0. 0001). As the index of unpalatability includes more details we used it in all analyses. In order to test that the complex is dominated by moderately-defended species we used a Shapiro-Wilk test to test for a shape of a normal distribution of the unpalatability. To test whether the rings are composed of a similar range of unpalatable species, we used Bartlett test of variance homogeneity. To test a hypothesis that unpalatability is related to the extent of gold colouration we used GLS to model the relationship between index of unpalatability and the absolute golden area (which is more ecologically relevant than relative area). The linear predictor included a quadratic term as the response was an area. A GLS model with Brownian motion model correlation structure was used to correct for the effect of phylogeny (Pekár and Brabec, 2012). To account for the fact that for some species, some defensive traits could not be measured (and were interpolated) we used weights that corresponded to the proportion of observed characteristics. Both variables were affected strongly by the body size so this measure needed to be included in the analysis. The body size had strong phylogenetic inertia (GLS, Pagel’s λ = 1. 08), thus by controlling for phylogeny the body size effect was included too, therefore use of the body size as a covariate was not necessary. Predation pressure by a community of predators Request a detailed protocol We investigated the diet of several predators on the campus of. The campus in addition to buildings, contains a patch of native forest, several rocky streams, and open green space interspersed with trees where the predators could have been collected and observed which would be very difficult or even impossible in the pristine forest where the selection have taken place.

Output: pass


Input: Claim: AI is being used to personalize learning experiences for students by adapting to their needs and performance. [SEP] Text: These tools are not limited to mere textual assistance; the multimodal functionalities of generative AI can also allow writers to explore creative expressions and multimodal writing, enriching students’ literacy practices by integrating more visual, auditory, and interactive elements into the composition process (Kang & Yi, 2023; Li et al. , 2024). As noted by Cao and Dede (2023), our educational model has long been centered around the product, directing our focus towards the outcomes and grades students achieve, often overlooking the learning process itself. The judgmental calls involved in students’ interactions with AI, as showcased in the nuances of participants’ AI-assisted writing process in this study, represent emerging learning opportunities that require students to draw upon a range of knowledge, skills, awareness of ethics and the self, criticality, and self-reflection to make informed decisions about AI in learning. The present study shows that such decision making process can play a pivotal role in cultivating students’ “AI literacy” (Ng et al. , 2021) and promoting their responsible use of AI. Therefore, it should also be recognized as a valuable teaching opportunity that educators should not overlook. 7 ConclusionThis study explored students’ generative AI-assisted writing processes in a first-year writing class in an American college. The study found that students utilized generative AI for assisting with both global (e. g. , argument, structure, coherence) and local issues of writing (e. g. , syntax, diction, grammar), while they also had various ethical and practical concerns about the use of AI. Findings showed that large language models offer unique benefits for L2 writers to leverage its linguistic capabilities. The study highlights the urgency of explicit teaching of critical AI literacy and the value of (post)process-oriented writing pedagogy (e. g. , Graham, 2023) in college writing classrooms so that students not only understand AI writing tools’ functions and limitations but also know how to utilize and evaluate them for specific communication and learning purposes. However, writing instruction is still at the beginning stage of addressing this pressing need. Thus, pedagogical innovations, policy adjustments, new forms of writing assessments, and teacher education (Zhai, 2022) are needed to adapt to the potential impact of AI on desired student learning outcomes within specific writing curriculums.

Output: corroborate


Input: Claim: AI assistants can guarantee complete privacy and security for all personal data and interactions. [SEP] Text: Personally-identifiable data about clients, employees, prospects and other stakeholders may be regularly collected and stored in shared ledgers. Today, many organizations store private stakeholder data and even passwords in unencrypted form. Even when data are encrypted or anonymized, it may be possible to identify users unless well-developed cybersecurity processes are designed into data management systems. With frequent cybersecurity failures and increasing regulation, maintaining the privacy of personally identifiable information (PII) has become an issue of strategic concern for many organizations. PII includes any data that can be traced back to a specific person, and can include individual items such as biometric data, social security numbers, phone numbers, or geolocation data. PII can also include data combinations, such as postal codes, birthdates, and gender, or behavioral data associated with one person. Organizations gather and store personal data about current and future customers and employees as well as about other stakeholders. Advertisement 3. Cybersecurity and privacy breachesCybersecurity has become increasingly important for governments and businesses alike. Information security—one component of cybersecurity—focuses on protecting the integrity and privacy of data as it is captured, stored and used. The people, processes, and technology associated with data work in concert to create and maintain security. Despite advances in security protocols and software, privacy breaches are on the rise. According to Security’s 2020 data breach report, “The total number of records compromised in 2020 exceeded 37 billion, a 141% increase compared to 2019” [1]. Personal records of system users are regularly compromised, and millions of these records, including names, emails and passwords, have been subject to data breaches, in many cases even including addresses, birth dates and financial information [1]. A data breach occurs from unauthorized access to an organization’s database, enabling cyber hackers to steal sensitive personal information such as passwords, credit card numbers, social security numbers, and banking information [2]. These well documented breeches have had adverse consequences, including credit card fraud, and identity theft, which can have lasting negative effects on personal credit, often taking months, if not years, to remedy [2]. Some of the Largest, most recent cyber hacks include the 2013/14 breech of Yahoo’s database by what is thought to have been a state-sponsored cyberattack, impacting over 3 billion users.

Output: contrast


Input: Claim: Ants are the most intelligent species on Earth. [SEP] Text: The predators included noisy miners, lizards (skinks), and a number of arthropods that occur on the trunks and the immediate vicinity of Eucalyptus trees, where the mimics occur (see Table 2). We sampled arthropods from under the bark of more than 50 trees and selected those that were predatory. Arthropods were immediately placed into 99% pure ethanol and then in a freezer at −20°C. Co-existing predators were selected among the most abundant species to represent three guilds according to their trophic strategy and prey detection modality: (1) visually-oriented euryphagous predators, (2) non-visually oriented ant-adverse predators, and (3) specialized ant-eating predators. We used the information of their gut content and data from the literature (Jackson and Harding, 1982; Platnick, 2000; Higgins et al. , 2001; McGinley et al. , 2015) to identify those that feed on prey related to the mimics. If more than 10% of individuals of a specific predator species were positive for primers designed for ants, the species was classified as ant-eating. Otherwise, the species was classified as euryphagous. We collected 48 faecal samples from Eulamprus quoyii (Duméril et Bibron) (Skink, Scincidae, Squamata) and 48 samples from Manorina melanocephala Latham (Noisy Miner, Meliphagidae, Passeriformes) from across the campus during 2014 and 2015. These two predators were selected because they feed on arthropods, were most abundant among congeners and foraged on tree trunks. Eulamprus quoyii were collected from both rocky streams (human-altered) and forest-edge by noosing. Lizards typically defecated immediately upon capture and in some cases we gently massaged the abdomen to expel a faecal pellet. Manorina melanocephala faeces were collected at temporary feeding stations to avoid handling the birds. We lured birds to a large canvas (3 × 5 m) by placing small amounts of sweet cake near a water bowl and perches made from branches. The stations were monitored continuously. We sampled across a wide area in order to target specific family groups and the sampling was short enough to ensure that only droppings produced from previous meals were collected.

Output: pass


Input: Claim: AI is being used to personalize learning experiences for students by adapting to their needs and performance. [SEP] Text: For instance, integrating critical digital pedagogy into writing instruction and inviting students to reflect on their relevant AI literacy practices allow writing instructors to more effectively guide students in critically engaging with AI technologies in their academic literacy development. Policy adjustments should aim to cultivate an inclusive rather than “policing” environment (Johnson, 2023) that encourages students to use AI responsibly and as a means of fostering self-learning. Furthermore, writing assessment methods should evolve to not just evaluate final learning outcomes such as the written products but also the learning journey itself such as the decision-making involved in their AI-assisted writing. This shift encourages students to appreciate learning processes and the productive struggles they encounter along the way, so that they can move beyond seeing AI as a shortcut but as assistance in their quest for learning and writing development. In this way, students can leverage the linguistic, multimodal, interactive, and adaptable affordances of generative AI tools for personalized learning. This facilitates greater student ownership of their learning, enhancing their learner competence through self-direction, self-assessment, and self-reflection when interacting with AI tools (Barrot, 2023c; Fariani et al. , 2023). Following a phenomenological research design, the present study aims to provide in-depth understanding of college students’ use of ChatGPT in their academic writing, yet it is limited due to its small sample size and duration. Therefore, the findings may not apply to other classroom contexts and to a wide range of student populations. Future research could benefit from adopting a large scale, longitudinal design to examine generative AI’s impacts on student writing and students’ long-term engagement with generative AI tools, both in formal classroom settings and in informal learning contexts. It is also worth exploring students of diverse age groups and language proficiency levels as well as writing courses of different languages, purposes, and writing genres to examine other factors that may influence students’ generative AI assisted writing. After all, the participants in this study have already developed some proficiency and skills in academic writing, but holding learner agency (Ouyang & Jiao, 2021) can be more complex and challenging for younger learners. Further research is needed to understand students with varied domain knowledge, expertise, and writing abilities (Yan, 2023) and uncover individual differences in AI-assisted writing. Additionally, the participants in this study utilized GPT 3.

Output: corroborate


Input: Claim: AI assistants can guarantee complete privacy and security for all personal data and interactions. [SEP] Text: The hackers collected consumers’ names, email addresses, telephone numbers, dates of birth, hashed passwords and unencrypted answers to security questions. In 2017, the credit reporting agency Equifax was subject to a cyberattack in which affected an estimated 143 million consumers. System administrators weren’t aware of the suspicious activity for two months and did not report the breach for a full month after its discovery. It is believed that Equifax was breached by Chinese state-sponsored hackers engaged in espionage [3]. The collective financial impact to individual victims is not known, nor is it known what security and strategic damage was incurred by the state, but these cases highlight the potential risk when PII are housed in a centralized data base. Most of the data gathered and stored are in the control of governments and corporations, which have gathered volumes of personal information that they are responsible for securing. At the same time, these organizations may be monetizing these datasets, either by using them to improve their own operations and offerings or by selling them to third parties. The volume of data generated and collected is increasing exponentially, enlarging the footprints of users. Data consolidators are able to link data elements across data sources and combine data in ways that were never anticipated by the parties that collected the information nor by the users that provided it. Figure 1, which uses from data provided by Statista [4], shows the cost of amassing these large databases. Statista, a statistical research firm, tracks cybersecurity failures and trends. A recently published Statista report reveals that these events are increasing, especially in the past five years, underscoring the need to improve how data are secured. It should be noted that in 2020 a massive cyber breach by what is thought to be Russia could result in higher numbers for 2020 especially in the records exposed category as it is thought to be significant. The extent of the breach is still under investigation at the time of this publication. Figure 1. Cybersecurity breaches and record exposure. Advertisement 4. Privacy regulationsThe right to privacy is a considered to be basic human right in many parts of the world. That privacy may extend to individuals’ right to control their own personal data. This right must be carefully defended as ownership and management of an individual’s personal data can impact relationships with others and even the data-owner’s identity [5]. Regulations governing how personal data are gathered and managed are rapidly being developed.

Output: contrast


Input: Claim: Ants are the most intelligent species on Earth. [SEP] Text: We stored both the lizard and bird faecal samples individually in Eppendorf tubes in 80% ethanol in a −20°C freezer. This work was approved by the Committee of (ARA 2014/031). The gut of predators progressively degrades DNA through digestion (Sint et al. , 2011). To account for the shortened DNA fragments we designed specific primers, which amplified fragments no longer than 300 bp (Deagle et al. , 2006). This length is known to be a good compromise that increases the likelihood of amplification while still being long and variable enough to distinguish different prey taxa (Herbert et al. , 2003; Zeale et al. , 2011). A fragment of the COI gene was PCR amplified using LCO1490 (5´--3´) and HCO2198 (5´--3´) primers (Folmer et al. , 1994) from representatives of potential prey found across the campus (Hemiptera: Daerlac apicalis, D. cephalotes, D. nigricans; Formicidae: Myrmecia piliventris, M. tepperi, Camponotus aeneopilosus, Polyrhachis ammon, P. aurea, P. ornata, P. vermiculosa; Araneae: Myrmarachne luctuosa, M. erythrocephala) and predators (Araneae: Clubiona robusta L. Koch, Clubiona sp. ; Euryopis umbilicata L. Koch, Euryopis sp. ; Hemicloea sp. 1, Hemicloea sp. 2; Holoplatys planissima (L. Koch); Lampona murina L. Koch; Ocrisiona sp. ; Sandalodes superbus (Karsch); Servaea incana (Karsch)). Each PCR (20 μL total volume) consisted of 5 μL of DNA, 1 μL of each primer (10 μM), 0. 4 μL of 10 mM dNTP´s, 2. 2 μL of 25 mM MgCl2, 2 μL of 10x PCR buffer, 1 μL of bovine albumin serum (BSA), 0. 3 μL of (5 u/μL), and 7.

Output: pass


Input: Claim: AI is being used to personalize learning experiences for students by adapting to their needs and performance. [SEP] Text: Path of Science: and language COVID-19 Nigeria communication court culture discourse education enterprise judge justice language legal regulation linguistics national security social media text Home > Vol 10, No 5 (2024) > Faresta AI-Powered Education: Exploring the Potential of for Students' Needs in intelligence (AI) stands out as a relatively new yet rapidly expanding technological tool that is transforming the field of education. This paper examines the potential of to assist students and teachers in personalised learning. The research methodology employed for this study is a literature review, providing an overview of the current knowledge on practical AI applications for personalised learning and insights into methodological developments in this research field. Based on the literature, the results of this research demonstrate that personalised learning effectively accommodates students' learning p10. 22178/pos. 104-19 learning; Artificial Intelligent; Adaptive learning; Indonesia education Full Text: PDF 10. 1109/iccica52458. 2021. 9697272Alonso de Castro, M. G. , & García-Peñalvo, F. J. (2022). Metodologías educativas de éxito: proyectos Erasmus+ relacionados con e-learning o TIC. Campus Virtuales, 11(1), 95. doi: 10. 54988/cv. 2022. 1. 1022Right Commission. (2023, August 7). Prepare for an AI-Future. Retrieved from T. , Smith, L. , & Anissa, N. (2019). Educ-AI-tion Rebooted? Exploring the Future of in Schools and Colleges. Retrieved from S. K. , Farrokhnia, M. , Badali, M. , & Noroozi, O. (2021). The impacts of constructivist learning design and learning analytics on students’ engagement and self-regulation. Innovations in Education and Teaching International, 59(4), 442–452. doi: 10. 1080/14703297. 2021. 1890634Benhamdi, S. , Babouri, A. , & Chiky, R. (2016). Personalized recommender system for e-Learning environment. Education and Information Technologies, 22(4), 1455–1477. doi: 10.

Output: corroborate


Input: Claim: AI assistants can guarantee complete privacy and security for all personal data and interactions. [SEP] Text: Union has led the way in legislating privacy law through the (GDPR), passed in 2016. The law requires organizations, that gather personal data about EU citizens for transactions with EU member states, must carefully protect that data to ensure privacy. In the US, the (CPRA) which expands on the 2018 (CCPA), adopts many principles from the GDPR [6]. The CCPR is designed to provide residents of California the right:to know what personal data is being collectedto know whether it is being sold or disclosed and to whomto refuse the sale of their personal datato access their personal datato request that a business delete any personal datanot to be discriminated against for exercising their privacy rights [7]At the federal level, the Act (COPRA) was introduced in December 2019 by Democratic senators, led by Maria Cantwell. Although this bill has yet to pass, and previous federal privacy bills have failed, governmental bodies continue to pursue stricter laws for governing data [8]. Privacy laws directly affect how companies operate and will require firms that use consumer data to implement systems and operational practices that enable them to conform to these new regulations. Blockchain and Technology are uniquely positioned to help companies comply with existing and potential future regulation as it relates to personal property and data privacy. Advertisement 5. Blockchain and privacyAmong the significant benefits of blockchain solutions is that they enable organizations to share data in ways not previously available, opening up possibilities for enhanced collaboration, improved operational efficiencies and expanded revenue. Questions about how to maintain privacy over the data are heightened in these environments because the data are stored in shared ledgers which may be accessible by multiple blockchain participants. ConsenSys, a blockchain technology solutions company, in discussing the security of public blockchains, argues that “In reality, privacy is not a property of any blockchain. Rather, there are layers of privacy that can be applied to any blockchain…” [9]. Designers must carefully consider which parties are allowed to read and write transactions and how transactions are broadcast, validated, and stored. Additional issues relating to how permissions and security measures are updated and enforced are also important considerations. Decisions about who owns the data and how data can be used by organizations and computer applications further complicates privacy discussions [9]. 5.

Output: contrast