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Toponyme français
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Patronyme
Nom de famille, présent en Italie, France et Brésil.
 Alexi Stival dit Cuca, footballeur brésilien ;
 Giulio Stival (1902-1953), acteur italien.

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text:
A2Log: Attentive Augmented Log Anomaly Detection
by Thorsten Wittkopp, et al.
Berlin Institute of Technology (Technische Universität Berlin)
Anomaly detection becomes increasingly important for the dependability and serviceability of IT services. As log lines record events during the execution of IT services, they are a primary source for diagnostics. Thereby, unsupervised methods provide a significant benefit since not all anomalies can be known at training time. Existing unsupervised methods need anomaly examples to obtain a suitable decision boundary required for the anomaly detection task. This requirement poses practical limitations. Therefore, we develop A2Log, which is an unsupervised anomaly detection method consisting of two steps: Anomaly scoring and anomaly decision. First, we utilize a self-attention neural network to perform the scoring for each log message. Second, we set the decision boundary based on data augmentation of the available normal training data. The method is evaluated on three publicly available datasets and one industry dataset. We show that our approach outperforms existing methods. Furthermore, we utilize available anomaly examples to set optimal decision boundaries to acquire strong baselines. We show that our approach, which determines decision boundaries without utilizing anomaly examples, can reach scores of the strong baselines.
Thorsten Wittkopp
Alexander Acker
Sasho Nedelkoski
Jasmin Bogatinovski
Dominik Scheinert
Wu Fan
Odej Kao
Deep Anomaly Detection with Deviation Networks
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OneLog: Towards End-to-End Training in Software Log Anomaly Detection
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Shayan Hashemi, et al. ∙
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Andres Fernandez, et al. ∙
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IT systems are rapidly evolving to meet the growing demand for new services and applications in various economic fields. Many companies outsource their services to the cloud [29] . This outsourcing entails growing data centers, increasingly large networks, and interconnected devices to provide the required IT services. Despite accelerating innovations and business opportunities, this trend increases complexity and thus, aggravates the operation and maintenance of these services and systems [31]
. Operators of the services require assistance in maintaining control of this complexity to ensure dependability, stability, and serviceability. The field of artificial intelligence for IT operations (AIOps) is intended to support service and system operators to meet these challenges. Thereby, anomaly detection is a vital part, which is applied to monitoring data such as metrics, logs, or traces.
Log data is a primary source for troubleshooting since they record events during the execution of service applications. These logging events evolve due to software updates. Therefore unsupervised methods are valuable due to their sense of new and unknown anomalies [1, 5] . The second benefit of unsupervised methods is that they do not require labeled data, which is hard to obtain and cost-intensive [35]
. Due to the complexity of IT services, the log data volume is growing to the extent that it cannot be manually analyzed. Therefore, recent research uses deep learning to analyze log data and perform anomaly detection
[12, 9, 24] . These studies mostly assume the existence of a sufficient amount of labeled validation data for parameter tuning. However, in production settings, where services evolve, such data is hard to obtain, volatile, and requires manual evaluation by experts. In particular, when it comes to deriving an anomaly decision, these methods lack the capability to obtain a decision in an unsupervised manner. The anomaly decision in all mentioned methods is based on a decision boundary that decides on the respective binary class. Commonly employed methods need to be aware of the anomalies in the validation data to set this decision boundary optimally. This relatively strong requirement poses limitations.
In this paper, we address these problems via a two-fold solution called A2Log. First, we create a neural network based on the self-attention mechanism to perform an anomaly scoring. Second, we perform data augmentation to generate deviations on the respective training data [19, 32] , to analyze the model response and calculate the final decision boundary. In both steps, only normal training data is used, thus we call our approach unsupervised.
The contributions of this work contain the following:
An unsupervised anomaly detection method for log data, based on an encoder transformer architecture.
An unsupervised decision boundary calculation for the anomaly decision, based on a novel data augmentation method for log data.
An evaluation of the method, based on three different real-world and publicly available datasets, which are BGL, thunderbird, and spirit111https://www.usenix.org/cfdr-data.
An evaluation of the method, based on an industry dataset from an IT service provider.
This paper is structured as follows. First, related work is presented in section 2. A general framework for anomaly detection on log data as well as a problem description for the final anomaly decision are presented in section 3. Preliminaries and implementation details of our method are presented in section 4. Finally, section 5 presents our evaluation and section 6 is concluding the work.
2 Related Work
Detecting abnormal events in large-scale systems, indicated by log files, is crucial for creating dependable services. Therefore, log analysis becomes increasingly important for industry and academia [27] and a wide range of different anomaly detection techniques have been developed and discussed in detailed surveys [5, 15]
. They utilize different forms of log templates and log embeddings to convert the logs to a machine-readable format. Commonly used anomaly detection methods are support vector machines
and principal component analysis
[16] . In addition, there are rule-based [3] , tree-based [6] , statistical [36] , as well as methods based on clustering [20, 7, 2, 17] . Whereas recent anomaly detection methods are mainly designed with neural networks [40, 4, 39] and based on encoder architectures [25, 30] . Equally, recent methods utilizing the attention mechanism that is often used in encoder architectures [34] .
We classify anomaly detection methods in two different types: supervised and unsupervised
[14] . Supervised methods are usually more accurate, though they train the anomalies of the specific dataset as well [40, 38] . However, not all anomalies can be known in advance. Hence, in industrial applications, unsupervised methods are more practical as anomaly labels are mostly unavailable [21, 35] .
Consequently, we focus on unsupervised methods in this paper. Several unsupervised learning methods, based on neural networks, have been proposed, of which we present a selection in the following.
Du et al. [9]
proposed DeepLog, a Long short-term memory (LSTM) network architecture that is capable of identifying abnormal sequences of log messages. For this, log templates are generated and sequences of templates are formed as model inputs. The model provides a ranked output with probabilities for the next template in a given sequence. The anomaly detection is then based on whether the next template has a high probability or not.
LogAnomaly [21] is similar to DeepLog and predicts the next log message in a sequence of log messages. Instead of utilizing sequences of log templates, LogAnomaly utilizes sequences of log embeddings to improve prediction effectiveness.
Yang et al. [37]
combining the attention mechanism with a gated recurrent network architecture to perform anomaly detection on log data. Thereby the log messages are transformed into log templates, to then predict if a sequence of log templates is normal or abnormal. Furthermore, the labels of the training date are estimated to incorporate knowledge on historical anomalies into the model.
Another approach is Logsy as proposed by Nedelkoski et al. [24]
. This approach also incorporates the attention mechanism with an encoder architecture, where log embeddings are calculated. The embeddings of normal log messages are condensed into a centroid using a hyperspherical loss function, whereby embeddings of abnormal log messages are pushed away. The anomaly detection task is then based on the distance to the centroid
Guo et al. [12]
introduce LogBERT. It utilizes the transformer network from BERT
[8] , which consists of an encoder and a decoder, including the attention mechanism. Like DeepLog, it tries to predict a targeted log template of a sequence. Therefore, they utilize temporal-related log embeddings around the prediction target as inputs. To predict the targeted log template, it utilizes Cross-Entropy-Loss, extended by a hyperspherical loss function, to ensure compactness of the embeddings.
However, all methods presented above use a manually set decision boundary for anomaly detection. However, optimization requires knowledge about the anomalies, which limits these methods.
3 Anomaly Detection on Log Data
In this section, we describe the general framework for anomaly detection on log data. Afterwards, we describe the problem of automatically finding an accurate decision boundary for the anomaly detection task.
3.1 General Framework
Anomalies are patterns in data that do not conform to a defined notion of normal behavior [5] . Anything that deviates from normal behavior can be considered abnormal behavior. Steinwall et al. [33] state that this can be considered as a binary classification task. Consequently, anomaly detection on log data is defined as the problem of assigning a binary label to each log message. There are two principal approaches to anomaly detection: Supervised and unsupervised. In terms of anomaly detection on log data, supervised means that both classes, normal and abnormal, are used during the training phase. Unsupervised means that the model is trained on normal log messages only. As not all possible anomalies in log data can be known and used for training [28] , unsupervised approaches are well suited for log data scenarios, and thus are of high interest for industry and academia [27, 1] . Therefore, the challenge is to develop a good understanding of normal log messages, e.g. by internalizing their usual structure, such that any significant deviations can be treated as log messages representing abnormal behavior. Thus, a general framework for unsupervised approaches can be described as follows:
Figure 1: Structure of an unsupervised anomaly detection method for log data content.
illustrates the process of the anomaly detection task. First, the log message must be transformed into a format that is suitable for machine learning algorithms. Since anomaly detection models are often designed as neural networks
[22, 9, 24]
which we also utilize in this work, we consider the case of employing a neural network architecture. The output of a neural network is commonly transformed such that it can be interpreted as a probability or a probability distribution over one or multiple output neurons, from which further decisions can be inferred. Therefore, an anomaly detection method requires a final decision on interpreting the neural network's output. From this, we conclude that an unsupervised anomaly detection method, which includes a neural network, consists of two parts: The
Anomaly Scoring and the Anomaly Decision. The neural network learns the normal behavior in the first part and calculates an anomaly score for each log message. Therefore, the Anomaly Scoring must transform each log message into a real-valued output. The second part is the final anomaly detection decision, based on the anomaly scoring, which transforms the scores into a binary classification. Formally, the anomaly detection model Φ for log data L is described by two functions, s and a, where s:L→R is the Anomaly Scoring function, represented by a neural network and a:R→{0,1} is the Anomaly Decision. Therefore, Φ=a(s(x)), where x is the preprocessed log data input.
3.2 Problem Description
Two main challenges naturally arise when designing an unsupervised anomaly detection method, in our case on log data. On the one hand, the respective model is purely trained on normal log messages, which makes it difficult to identify abnormal log messages as such, as the computed anomaly scores cannot be interpreted, and thus scores of real abnormal log messages can have an arbitrary form. On the other hand, the decision boundary for separation of both classes is also configured based on normal log messages only. A solution to this problem needs to take these challenges into consideration.
Figure 2: Illustration of the various possibilities for manually setting a decision boundary.
Figure 2 demonstrates the difficulty of setting a precise decision boundary by only utilizing normal data points from the training. The blue points represent normal data points, which are part of the training data. The green and red points represent normal and abnormal data points during the test phase. It can be observed that the green points are on average closer to the normal trained data points, yet considering them for configuration of the decision boundary would lead to a suboptimal separation. If the decision boundary is too small, too many log messages will be classified as false positive by the method. If the decision boundary is too large, too many log messages will be classified as false negatives. Both scenarios result in weaker precision, recall, and F1 scores. This example demonstrates that both components, the Anomaly Scoring and the Anomaly Decision, have to be precise.
Due to the various factors influencing the training of a neural network, each trained anomaly scoring model will most likely produce different anomaly scores for the same data, and therefore the final decision boundary must be set individually for each model. Moreover, as the nature and relation of future normal and abnormal log messages cannot be known in advance, it is required that the decision boundary can cope with these uncertainties and still provide good decisions solely based on seen normal log messages.
This section describes in detail our approach A2Log for unsupervised anomaly detection on log data, including our proposed solutions for anomaly scoring and anomaly decision. Prior to that, we briefly introduce the necessary preliminaries.
4.1 Preliminaries
Logging is commonly employed in order to investigate faulty behavior of systems and services and to increase dependability, which results in information being written to a log file. The log file documents the executions of the software and is created by log instructions (e.g. printf() or log.info()). Each log instruction results in a single log message, such that the complete log is a sequence of messages L=(li:i=1,2,3,…). There is a commonly used separation in meta-information and content. The meta-information can contain various information, for example, timestamps or severity levels. The content is free text and consists of a static and a variable part. The static part is called log template. To access the content of a log message li, we write ci.
In order to transform the content into a representation that an algorithm can process, methods from the research field of Natural Language Processing (NLP) are applied. Two main concepts are tokenization and embeddings.
Tokenization. This process splits written text into segments (e.g., words, word stems, or characters). The smallest indecomposable unit within a log content ci is a token. Consequently, each log content ci can be interpreted as a sequence of tokens ti
ti=(wj:wj∈V,j=1,2,3,…), (1)
where w is a token, V is a set of all known tokens commonly referred to as the vocabulary, j is the positional index of a token within the token sequence. To access a specific token at position j, we write tji. Thus, tokenization translates a text into a sequence of tokens using a vocabulary. The amount of tokens ni for each log message and the structure of each token can vary, depending on the concrete tokenization method.
Embeddings. As the tokens themselves are words of the vocabulary V, they cannot be passed into a neural network directly. Furthermore, tokens do not provide any information about their similarity or difference to each other, hence, so called embeddings
are used to compute a representation of the tokens such that a machine learning model can process it. Embeddings are real-valued vector representations
→v∈Rd of either token sequences or a single token; a transformation function g:V→Rd transforms a token wj into an embedding →vj. Thereby the same tokens receive the same embeddings. The sequence of embeddings →ei, that is describing the corresponding sequence of tokens ti, is defined as follows:
→ei=(g(wj):wj∈ti,j=1,2,…,|ti|), (2)
To access the j-th embedding in a sequence of embeddings, we write →ei(j). Embeddings are trainable units adapted during the training process to represent the meaning of the underlying token or sequence of tokens.
4.2 A2Log
As a solution for an unsupervised anomaly detection method, we propose A2Log. A2Log consists of two parts: the Anomaly Scoring and the Anomaly Decision. To provide a general anomaly detection method, we only utilize the content of log messages, which is a commonality of different log types.
The schematic flow in Figure 3 of A2Log is as follows.
Figure 3: Five steps to classify a log message utilizing a transformer architecture.
During the first step, the content ci of every log message li is extracted and in the process tokenized to a sequence of tokens ti using the symbols .,:/ and whitespaces as separators. Subsequently, we further clean the resulting sequence of tokens by replacing certain tokens with placeholders that adequately represent the original token without losing relevant information. We introduce placeholder tokens for hexadecimal values [HEX] or any number greater or equal 10 [NUM]. Finally, we prefix the sequence of transformed tokens with a special placeholder token [CLS] which will be beneficial later on. An exemplary log message
time.c: Detected 3591.142 MHz.
is thus transformed into a sequence of tokens ti:
[[CLS], time, c, Detected, [NUM], [NUM], MHz].
The token sequence ti serves as the input for our Anomaly Scoring model. Therefore, we utilize the encoder of the transformer architecture [8] with self-attention [34] . We have chosen this model architecture since it has already performed well in the domain of natural language processing. The encoder of the transformer architecture is applied to map sequences of tokens onto one d-dimensional vector (embedding), which is represented through the [CLS] token.
Figure 4: Transformer encoder architecture.
Figure 4 depicts the aforementioned network architecture. During the embedding step, for each token wj in the token sequence ti, an embedding →ei(j) is obtained using the transformation function g. These embeddings are equal for the same tokens and modified during the training process. Since these sequences can vary in length, we truncate them to a fixed size u
and fill up smaller sequences with padding tokens
[PAD]. Hence, the employed architecture does not consider the order of the tokens, the input sequence is enriched with positional encoding [34, 10] . Based on this, the attention mechanism can take the order of the tokens into account.
The model then computes an output embedding for each truncated input embedding sequence, which summarizes the log message by utilizing the embeddings of all tokens. This output embedding is encoded in the embedding of the [CLS] token and also modified during training, by minimizing the loss. During the training process, the model is supposed to learn the meanings of the log messages, thereby getting an intuition of normal log messages. We denote the output of the model as →zi=s(ti) and use it throughout the remaining steps. Thereby the anomaly score is calculated by the length of the output vector ∥→zi∥. These values must reflect the anomaly probability for each sequence. We set the scoring target for normal data to 0 as the absolute normal state, so that the likeliness of an anomaly in the log message is proportional to increasing positive values. The following objective function is utilized to ensure compact anomaly scores near to ∥→zi∥≥0 for normal training data [23] :
Thereby, yi is the label for each input ti, which implies that there must be two classes to train the neural network to avoid the implosion of the model. If there is only one class, the objective function will force the model to produce the same result independent of the input. Therefore, we define a stabilization class S as the second class which encompasses normal log messages but with different origins. This second class is constructed by utilizing normal log messages from other services, where we randomly sample an equal amount of log messages from each service. This gives the model an intuition of variety in log messages and, therefore, the model should be able to assign different anomaly scores to real anomalies based on log message characteristics. Eventually, the Anomaly Decision function assigns each log message an explicit label ∈{0,1} with
a(∥→zi∥)={1,∥→zi∥>ϵ,0,∥→zi∥≤ϵ (4)
where ϵ is a decision boundary that is used to obtain binary labels from anomaly scores.
As we do not know how the Anomaly Score will turn out for new normal log messages appearing in the future, we require a method that simulates deviations to the already trained log messages to understand how the model will react, and determines the decision boundary ϵ. We simulate deviations to the normal training data by applying data augmentation.
Figure 5: Data augmentation with α=2 replaced tokens.
Figure 5 depicts our method for data augmentation on log data. The data augmentation is applied to the log messages of the original training dataset ltrain only, without the stabilization class S. First, the log messages are tokenized, followed by replacing α∈N tokens at random positions in each token sequence ti with a masking token [M], described in Equation 5.
t′i=(wj:wj∈V∨wj=[M],j=1,2,…,|ti|) (5)
Then the trained model for the Anomaly Scoring s calculates a scalar value for each augmented token sequence t′i which should be greater or equal than the scalar value for the original token sequence s(t′i)≳s(ti) because it contains α unknown tokens [M] and, therefore, does not comply with established knowledge. We gather all values - calculated by the model - for the augmented token sequences to receive a distribution D.
D=(s(t′i):t′i∈ltrain,0≤i≤|ltrain|). (6)
This distribution is the basis for the decision boundary ϵ. We calculate the decision boundary by choosing the i-th percentile p of the distribution D and multiplying this value with a regulator variable β.
ϵ(D,p,β)=Dp⋅β (7)
This formula calculates the final decision boundary from the given distribution for the deviated token sequences. The parameter p
is a bias regulator, because there is a possibility, that the model calculates outliers for few normal augmented log messages. These outliers can occur when rare normal log messages resemble the stabilization class data and have high anomaly scores. With
β, we can control whether we want to allow more deviations for normal data during the prediction phase.
5 Evaluation
To quantify the performance of A2Log, we test it on three real-world HPC log datasets and one industry dataset. Thereby, we compare our anomaly detection method with three other approaches and evaluate the performance of our anomaly decision boundary.
5.1 Experimental Setup
For the evaluation, we investigate four different training splits where we use the first 10%, 20%, 40%, 60% of normal data to train the anomaly scoring model and calculate the anomaly decision boundary. The remaining log messages are used for the testing. For each split, we examine the F1 score, precision, and recall. This setup is applied to the three publicly available datasets as well as to the industry dataset.
#unique log templates in test
System #normal #anomalies #templates that do not appear in train for splits
BGL 4,399,503 348,460 1,571 1,318 1,232 1,158 1,077
Thunderbird 4,773,713 226,287 1,302 232 205 128 61
Spirit 4,235,110 764,890 1,457 1,091 1,028 297 129
Table 1: Dataset description of BGL, Thunderbird and Spirit.
Publicly Available Datasets. We select three real-world datasets from HPC systems for evaluation as target systems, namely Blue Gene/L (BGL), Spirit, and Thunderbird (Tbird) [26] . From every dataset222https://www.usenix.org/cfdr-data, we utilize the first 5 million log messages.
To reveal the characteristics of the datasets, we have calculated the log templates for each dataset using Drain [13] . Table 1 depicts the count of normal and abnormal data, the count of log templates for the whole dataset, and the number of distinctive log templates that are present in the test dataset only, without being in the training dataset. It can be seen that especially for small amounts of training data, there are a lot of log messages in the test data, that are not seen in the training data, especially on the BGL dataset. That makes anomaly prediction difficult when only the normal data is trained, and the normal data in the test set deviates from it. Overall, 7 % to 15 % of the log lines are abnormal.
#train
#unique
0.1 711,841 226,375 6,409,147 1,270,089 28 196,534
0.2 1,424,198 465,666 5,696,790 106,0947 27 196,519
0.4 2,848,395 536,947 4,272,593 987,417 23 146,099
Table 2: Dataset description of the industry dataset.
Industry Dataset. Furthermore, we investigate A2Log for an industry dataset that comes from the production environment of an IT service and cloud provider. Thereby the focus is to ensure a dependable storage service, by detecting anomalies in the log data from the underlying hardware. For this, we used log data from different disk controllers that are managing a variety of hard disks. The task is to identify anomalies by only training normal log data from some hard disks with no recorded anomalies and apply the model to new disks without manual optimization.
Table 2 shows the total number of training samples and the number of unique training samples, depending on the split. It also reveals the numbers of total and unique normal samples in the test data. Furthermore, it reveals the count of abnormal samples and the number of unique anomalies in the test dataset. It can be concluded that there are only very few different anomalies in the test dataset but they occur frequently. Furthermore, the rate of anomalies for the dataset is between 3 to 3.5 %, regarding the respective split. The dataset has 7,120,988 log lines in total.
Benchmark Approaches. In total, we evaluate four different methods, including A2Log. The first baseline is DeepLog, where we set the final decision boundary as best as possible. For the other three methods, we train our described transformer model for the anomaly scoring and then calculate the decision boundary differently. First, we calculate the decision boundary based on the 3-sigma method. The 3-sigma decision boundary is calculated on the anomaly scores of the trained model for all training data, excluding the stabilization class. The second benchmark Best is the best possible result for the transformer model, which is achieved through utilizing the test data, including the abnormal samples, to calculate the optimal decision boundary. Therefore, this benchmark cannot be exceeded and is considered as an absolute upper limit. Therefore, the goal is to get as close as possible to this result. We then evaluate A2Log against these three benchmarks.
5.2 Implementation Details
First, we tokenize the content of each log line as described in section 4 and then truncate all sequences of tokens to the same length |ti|=20. We set the dimensionality d of the embeddings to 128. The weights of the embeddings are initialized with Xavier [11] . For the anomaly scoring model training, we use a hidden size of 256, a batch size of 1024, a dropout rate of 5⋅10−2. For the optimization task, we use the Adam optimizer with a learning rate of 10−4 and a weight decay of 5⋅10−5 in every experiment.
Since we need a stabilization class S to train our model, we use 60k random messages from other datasets. The corresponding mappings are displayed in Table 3.
#logs for stabilization from
for evaluation
#total
/ 60000 60000 120000
60000 / 60000 120000
60000 60000 / 120000
Industry dataset
60000 60000 60000 180000
Table 3: Log messages utilized for the stabilization class.
Figure 6: Evaluation on BGL, thunderbird and spirit.
Figure 7: Evaluation on the industry dataset.
For the three datasets (BGL, Thunderbird, and Spirit), we utilize 120,000 log lines each as a stabilization class. For the industry dataset, we utilize a total of 180,000 log lines as the stabilization class. Due to the disparity in the number of training samples of the normal log messages and the stabilization class, we use a weighted sampler for the training to balance the training. All models are trained until an average loss of 0.01
per sample, or a maximum of 50 epochs, is reached.
The anomaly decision function a is parameterized as shown in Table 4.
α 1 1 1 1
p 0.95 0.95 0.95 0.95
β 2.5 2.5 5.0 2.0
Table 4: Decision boundary parameters to calculate ϵ.
We set α to 1 in each experiment for each dataset to replace only one token of the log message with the unknown token for the data augmentation. Likewise, in each experiment, we set p to 0.95 to filter out 5 % of the augmented data with the highest anomaly scores. Furthermore, we adjust β for each dataset but keep it the same for each experiment on the respective dataset.
For all evaluations, we do every experiment three times and depict the best results in terms of the F1-score in Figure 6 for the three publicly available datasets and in Figure 7 for the industry dataset.
Figure 6 depicts the results of Best, 3-Sigma and Deeplog, compared with A2Log. It shows the respective approaches for different amounts of training data of 10%, 20%, 40%, 60% of the respective dataset. It can be seen that A2Log is superior or equal to DeepLog and the baseline 3-Sigma in all experiments. In addition, A2Log achieves almost the same F1 scores as Best in all experiments. A noticeable aspect of the Spirit dataset is that as soon as only a few learning samples are available, the decision boundary function of A2Log no longer turns out to be that precise. Nevertheless, the other training splits for the Spirit dataset show that our unsupervised A2Log approach can perform equally to an optimal decision boundary. The results on the BGL dataset are not particularly high, as there is a fundamental concept drift in the data, which can be seen by the fact that even for high training splits of 60 % there are still many log templates present in the test dataset that are not present in the training dataset as shown in Table 1.
In addition, the two parameters α and p can be set to 1 and 0.95 independently of the dataset. This shows that the method is robust in parameter choice and independent of the dataset.
Figure 7 depicts the performance of A2Log for different training splits. Thereby it compares A2Log with the best possible decision boundary and the 3-sigma boundary. It can be observed that A2Log outperforms 3-Sigma and is as good as the best possible decision boundary. When A2Log can train enough data, it is able to identify anomalies on new hard disks with perfect scores.
Anomaly detection methods have become increasingly important to ensure the dependable and stable operation of IT services, including their serviceability. However, existing unsupervised anomaly detection methods are applied under constrained assumptions for the final anomaly decision. Therefore we propose A2Log, to address the current limitations of unsupervised anomaly detection methods. Unlike other unsupervised approaches, it calculates its decision boundary for the final decision by exploring the model behavior based on augmented training data. With data augmentation, we simulate deviations in log data that occur from service updates over time. We evaluate our approach on three publicly available datasets and one industry dataset. Thereby, we show the effectiveness of our approach when it is applied in an unsupervised setting.
Even if we can simulate deviations of the normal log lines with the help of our data augmentation and thus classify new normal log lines as such, we see as a limitation that a fundamental concept drift in the data pushes the method to its limits and thus leads to misclassifications. Nevertheless, A2Log can keep up with the optimal decision boundary, which can only be calculated by utilizing available anomaly examples. Furthermore, we are able to show that the entire method outperforms DeepLog and 3-Sigma as a baseline decision boundary.
As further work, we consider additional adjustments to the data augmentation process, to integrate it into other anomaly detection methods. Thereby, we want to apply data augmentation techniques that are well known in the research area of natural language processing. Furthermore, we want to extend A2Log to other anomaly detection settings, e.g. supervised or weakly supervised settings.
[1] L. Baier, N. Kühl, and G. Satzger (2019) How to cope with change?-preserving validity of predictive services over time. In Proceedings of the 52nd Hawaii International Conference on System Sciences, Cited by: §1, §3.1.
[2] E. Baseman, S. Blanchard, Z. Li, and S. Fu (2016) Relational synthesis of text and numeric data for anomaly detection on computing system logs. In 2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA), pp. 882–885. Cited by: §2.
[3] J. Breier and J. Branišová (2015) Anomaly detection from log files using data mining techniques. In Information Science and Applications, pp. 449–457. Cited by: §2.
[4] A. Brown, A. Tuor, B. Hutchinson, and N. Nichols (2018) Recurrent neural network attention mechanisms for interpretable system log anomaly detection. In Proceedings of the First Workshop on Machine Learning for Computing Systems, pp. 1–8. Cited by: §2.
[5] V. Chandola, A. Banerjee, and V. Kumar (2009) Anomaly detection: a survey. ACM computing surveys (CSUR) 41 (3), pp. 1–58. Cited by: §1, §2, §3.1.
[6] M. Chen, A. X. Zheng, J. Lloyd, M. I. Jordan, and E. Brewer (2004)
Failure diagnosis using decision trees
. In ICAC, Cited by: §2.
[7] M. Cinque, D. Cotroneo, and A. Pecchia (2012) Event logs for the analysis of software failures: a rule-based approach. IEEE Transactions on Software Engineering 39 (6), pp. 806–821. Cited by: §2.
[8] J. Devlin, M. Chang, K. Lee, and K. Toutanova (2018) Bert: pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805. Cited by: §2, §4.2.
[9] M. Du, F. Li, G. Zheng, and V. Srikumar (2017) Deeplog: anomaly detection and diagnosis from system logs through deep learning. In Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security, pp. 1285–1298. Cited by: §1, §2, §3.1.
[10] J. Gehring, M. Auli, D. Grangier, D. Yarats, and Y. N. Dauphin (2017) Convolutional sequence to sequence learning. In ICML, D. Precup and Y. W. Teh (Eds.), Cited by: §4.2.
[11] X. Glorot and Y. Bengio (2010) Understanding the difficulty of training deep feedforward neural networks. In Proceedings of the thirteenth international conference on artificial intelligence and statistics, pp. 249–256. Cited by: §5.2.
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[14] S. He, J. Zhu, P. He, and M. R. Lyu (2016) Experience report: system log analysis for anomaly detection. In 2016 IEEE 27th International Symposium on Software Reliability Engineering (ISSRE), Vol. , pp. 207–218. External Links: Document Cited by: §2.
[15] S. He, J. Zhu, P. He, and M. R. Lyu (2016) Experience report: system log analysis for anomaly detection. In ISSRE, Cited by: §2.
[16] I. Jolliffe (2005) Principal component analysis. Encyclopedia of statistics in behavioral science. Cited by: §2.
[17] M. Landauer, M. Wurzenberger, F. Skopik, G. Settanni, and P. Filzmoser (2018) Dynamic log file analysis: an unsupervised cluster evolution approach for anomaly detection. Computers & Security 79, pp. 94–116. External Links: ISSN 0167-4048, Document, Link Cited by: §2.
[18] Y. Liang, Y. Zhang, H. Xiong, and R. Sahoo (2007) Failure prediction in ibm bluegene/l event logs. In ICDM, Cited by: §2.
[19] P. Liu, X. Wang, C. Xiang, and W. Meng (2020) A survey of text data augmentation. In 2020 International Conference on Computer Communication and Network Security (CCNS), pp. 191–195. Cited by: §1.
[20] J. Lou, Q. Fu, S. Yang, Y. Xu, and J. Li (2010) Mining invariants from console logs for system problem detection.. In USENIX Annual Technical Conference, pp. 1–14. Cited by: §2.
[21] W. Meng, Y. Liu, Y. Zhu, S. Zhang, D. Pei, Y. Liu, Y. Chen, R. Zhang, S. Tao, P. Sun, et al. (2019) LogAnomaly: unsupervised detection of sequential and quantitative anomalies in unstructured logs.. In IJCAI, Vol. 7, pp. 4739–4745. Cited by: §2, §2.
[22] S. Naseer, Y. Saleem, S. Khalid, M. K. Bashir, J. Han, M. M. Iqbal, and K. Han (2018) Enhanced network anomaly detection based on deep neural networks. IEEE access 6, pp. 48231–48246. Cited by: §3.1.
[23] S. Nedelkoski, J. Bogatinovski, A. Acker, J. Cardoso, and O. Kao (2020) Self-attentive classification-based anomaly detection in unstructured logs. In 2020 IEEE International Conference on Data Mining (ICDM), Vol. , pp. 1196–1201. External Links: Document Cited by: §4.2.
[24] S. Nedelkoski, J. Bogatinovski, A. Acker, J. Cardoso, and O. Kao (2020) Self-supervised log parsing. arXiv preprint arXiv:2003.07905. Cited by: §1, §2, §3.1.
[25] M. Nicolau, J. McDermott, et al. (2016)
A hybrid autoencoder and density estimation model for anomaly detection
. In International Conference on Parallel Problem Solving from Nature, pp. 717–726. Cited by: §2.
[26] A. Oliner and J. Stearley (2007) What supercomputers say: a study of five system logs. In 37th Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN'07), pp. 575–584. Cited by: §5.1.
[27] G. Qi and J. Luo (2020) Small data challenges in big data era: a survey of recent progress on unsupervised and semi-supervised methods. IEEE Transactions on Pattern Analysis and Machine Intelligence. Cited by: §2, §3.1.
[28] A. Ramponi and B. Plank (2020) Neural unsupervised domain adaptation in nlp—a survey. arXiv preprint arXiv:2006.00632. Cited by: §3.1.
[29] D. Rosendo, G. Leoni, D. Gomes, A. Moreira, G. Gonçalves, P. Endo, J. Kelner, D. Sadok, and M. Mahloo (2018) How to improve cloud services availability? investigating the impact of power and it subsystems failures. In Proceedings of the 51st Hawaii international conference on system sciences, Cited by: §1.
[30] M. Sakurada and T. Yairi (2014) Anomaly detection using autoencoders with nonlinear dimensionality reduction. In Proceedings of the MLSDA 2014 2nd workshop on machine learning for sensory data analysis, pp. 4–11. Cited by: §2.
[31] G. L. Santos, P. T. Endo, G. Gonçalves, D. Rosendo, D. Gomes, J. Kelner, D. Sadok, and M. Mahloo (2017) Analyzing the it subsystem failure impact on availability of cloud services. In 2017 IEEE symposium on computers and communications (ISCC), pp. 717–723. Cited by: §1.
[32] C. Shorten and T. M. Khoshgoftaar (2019) A survey on image data augmentation for deep learning. Journal of Big Data 6 (1), pp. 1–48. Cited by: §1.
[33] I. Steinwart, D. Hush, and C. Scovel (2005) A classification framework for anomaly detection.. Journal of Machine Learning Research 6 (2). Cited by: §3.1.
[34] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. Kaiser, and I. Polosukhin (2017) Attention is all you need. arXiv preprint arXiv:1706.03762. Cited by: §2, §4.2, §4.2.
[35] T. Wittkopp and A. Acker (2020) Decentralized federated learning preserves model and data privacy. In International Conference on Service-Oriented Computing, pp. 176–187. Cited by: §1, §2.
[36] W. Xu, L. Huang, A. Fox, D. Patterson, and M. I. Jordan (2009) Detecting large-scale system problems by mining console logs. In Proceedings of the ACM SIGOPS 22nd symposium on Operating systems principles, pp. 117–132. Cited by: §2.
[37] L. Yang, J. Chen, Z. Wang, W. Wang, J. Jiang, X. Dong, and W. Zhang (2021) Semi-supervised log-based anomaly detection via probabilistic label estimation. In ICSE, Cited by: §2.
[38] R. Yang, D. Qu, Y. Gao, Y. Qian, and Y. Tang (2019) NLSALog: an anomaly detection framework for log sequence in security management. IEEE Access 7. Cited by: §2.
[39] K. Yin, M. Yan, L. Xu, Z. Xu, Z. Li, D. Yang, and X. Zhang (2020) Improving log-based anomaly detection with component-aware analysis. In 2020 IEEE International Conference on Software Maintenance and Evolution (ICSME), pp. 667–671. Cited by: §2.
[40] X. Zhang, Y. Xu, Q. Lin, B. Qiao, H. Zhang, Y. Dang, C. Xie, X. Yang, Q. Cheng, Z. Li, et al. (2019) Robust log-based anomaly detection on unstable log data. In Proceedings of the 2019 27th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering, pp. 807–817. Cited by: §2, §2.

redpajama_set_name: RedPajamaCommonCrawl


text:
Philanthropies Seek Teacher-Training Models
By Jeff Archer
The Carnegie Corporation of New York has invited four of the country's leading teacher education programs to join a new initiative aimed at reinventing the way colleges and universities prepare teachers to work in the classroom.
Under what's being called the Teachers for a New Era project, each participating institution will receive up to $5 million over the next five years to support several key improvements. Those include increasing involvement in teacher training of both practicing K-12 teachers and faculty members from the arts and sciences, giving teacher-candidates more experience in the classroom, and monitoring graduates once on the job to see how well their students perform.
Although Carnegie serves as both the coordinator of and largest single donor to Teachers for a New Era, the effort also is being underwritten by the Annenberg, Ford, and Rockefeller foundations. After an extensive review of education programs across the country, invitations were offered late last month to Bank Street College of Education in New York City, California State University-Northridge, Michigan State University in East Lansing, and the University of Virginia in Charlottesville.
Daniel Fallon, who oversees education initiatives for Carnegie, says the hope is to produce "exemplars" of high-quality education programs.
"The concept is that in the United States, there are no excellent teacher education programs by consensus," Mr. Fallon said. "I think there are excellent teacher education programs out there, but nobody knows where they are, or we can't agree on where they are. And that's alarming, because we can agree in academic life on where excellent programs are in the arts and sciences and even in other professions."
The venture comes at a time when traditional modes of teacher preparation are under increasing scrutiny. Some states have threatened to pull the plug on education programs in which large numbers of graduates fail their licensing tests, and last year, for the first time, all states were required to submit performance data on their education schools to the federal government.
Showing Their Worth
Though aspiring to influence the whole field of teacher education, Teachers for a New Era is an invitation-only project. The Carnegie Corporation picked the first four institutions based on a highly selective process that began with an examination of data from virtually every teacher-preparation program in the United States, and ultimately included analyses by the RAND Corp. and site visits to a handful of finalists.
Carnegie officials expect to announce two more "invitees" in each of the next two years.
Before receiving any money, those asked to take part must work with the philanthropy to flesh out implementation plans. Participants are expected to make several major changes meant to give teacher-candidates better preparation in both subject matter and in the practicalities of working in the classroom. Programs also must follow their graduates once they enter the workforce, both to provide additional support and to track their effects on student learning.
"We have everybody talking," said Louanne Kennedy, the provost at Cal State-Northridge. "We've made changes in the curriculum. But to take it to the level where we really, on a daily basis, look at our pedagogy, our content, what happens to students on campus and what happens when they go out to the schools, and develop those variables—this is going to very hard."
Still, Ms. Kennedy said, her school is up to the task. The Northridge program—which produces about 1,700 credentialed teachers annually—already involves many faculty members from disciplines outside education in training teachers. Moreover, it has sought to gauge the quality of its graduates by surveying administrators who hire them.
Ms. Kennedy noted that the Carnegie initiative will prompt Northridge to examine other results as well, such as youngsters' test-score gains. "We expect that students will perform at a higher level because they have had teachers from our program," she said. "That is the element of the assessment that is critical in this."
Frank B. Murray, an education professor at the University of Delaware, praised the new initiative. Although many of the changes it is requiring have been called for previously, the large infusion of private money could be enough to ensure that they take place in the targeted institutions, Mr. Murray pointed out. Each participating program is required to match the gift one to one through its own fund- raising.
"This is a significant departure in grant funding for teacher education in terms of the amount of money," said Mr. Murray, the founding president of the fledgling Teacher Education Accreditation Council, in Washington. "We don't see that level of funding for teacher education programs to make changes in themselves.
"This says let's make sure the funds are there to make the changes," he continued, "and so people don't make compromises because they don't have the money. It creates a space where you can't say you can't do something because you don't have funds."
Published in Print: April 10, 2002, as Philanthropies Seek Teacher-Training Models
"Teachers' Union Launches School to Train Teachers," this issue.
"Research: Focusing In on Teachers," April 3, 2002.
"The Road to Teacher Quality," Commentary, March 20, 2002.
"Teacher-Trainers Fear a Backfire From New ESEA," March 6, 2002.
"Tools of the Trade," Feb. 27, 2002.
"Total Immersion," Nov. 7, 2001.
"NCATE Releases Quality Measures for Professional-Development Sites," Oct. 24, 2001.
"Foundation to Study Preparation of Teachers," March 21, 2001.
"Teacher-Training Programs Turn to Cyberspace," Feb. 14, 2001.
The Carnegie Corporation of New York provides additional information about Teachers for a New Era, including an executive summary and a prospectus.
Bank Street College of Education; California State University-Northridge College of Education; Michigan State University College of Education; and The Curry School of Education at the University of Virginia.
The National Commission on Teaching and America's Future offers recommendations on reinventing teacher preparation and professional development. See also NCATF's publications.
The National Council on Teacher Quality maintains an issues page on reforming teacher education. Includes pointed summaries of recent developments and links to research.
Executive Director (Head of School)
Triangle Associates, Burien, Washington State


text:
Q: Unrecognized field gallery in Symfony prod mode I'm developing web site with Symfony 4.4, using SonataAdmin (^3.56), Doctrine ORM Admin (^3.12), Sonata Media (^3.22) and SonataClassification (^3.9) bundles. My web site works perfectly in dev mode. Problem is when I change it to prod mode.
.env
APP_ENV=prod
APP_DEBUG=0

When I access routes with SonataMedia gallery then I get error 500. Log file (prod.log) contains the following content:
request.CRITICAL: Uncaught PHP Exception Doctrine\ORM\ORMException: "Unrecognized field: gallery" at /home/repincln/dev/site/vendor/doctrine/orm/lib/Doctrine/ORM/ORMException.php line 101 {"exception":"[object] (Doctrine\\ORM\\ORMException(code: 0): Unrecognized field: gallery at /home/repincln/dev/site/vendor/doctrine/orm/lib/Doctrine/ORM/ORMException.php:101)"} []

In Controller I have:
...
$medias = $this->getDoctrine()->getRepository("ApplicationSonataMediaBundle:GalleryHasMedia")->findBy(array('gallery' => $article->getGallery()));
...

If i dump $medias in dev mode, it looks OK, with all images in gallery, but in production mode it doesn't work.
A similar thing happens in SonataAdmin Admin->Classification
request.CRITICAL: Uncaught PHP Exception Doctrine\ORM\Query\QueryException: "[Semantical Error] line 0, col 84 near 'parent IS NU': Error: Class App\Application\Sonata\ClassificationBundle\Entity\Category has no field or association named parent" at /home/repincln/dev/site/vendor/doctrine/orm/lib/Doctrine/ORM/Query/QueryException.php line 65 {"exception":"[object] (Doctrine\\ORM\\Query\\QueryException(code: 0): [Semantical Error] line 0, col 84 near 'parent IS NU': Error: Class App\\Application\\Sonata\\ClassificationBundle\\Entity\\Category has no field or association named parent at /home/repincln/dev/site/vendor/doctrine/orm/lib/Doctrine/ORM/Query/QueryException.php:65, Doctrine\\ORM\\Query\\QueryException(code: 0): SELECT c FROM App\\Application\\Sonata\\ClassificationBundle\\Entity\\Category c WHERE c.parent IS NULL at /home/repincln/dev/site/vendor/doctrine/orm/lib/Doctrine/ORM/Query/QueryException.php:43)"} []

and Tags, Collections, Media. Only Contexts is working normal. Gallery for example looks like:

config/packages/prod/doctrine.yaml
doctrine:
    orm:
        auto_generate_proxy_classes: false
        metadata_cache_driver:
            type: service
            id: doctrine.system_cache_provider
        query_cache_driver:
            type: service
            id: doctrine.system_cache_provider
        result_cache_driver:
            type: service
            id: doctrine.result_cache_provider

services:
    doctrine.result_cache_provider:
        class: Symfony\Component\Cache\DoctrineProvider
        public: false
        arguments:
            - '@doctrine.result_cache_pool'
    doctrine.system_cache_provider:
        class: Symfony\Component\Cache\DoctrineProvider
        public: false
        arguments:
            - '@doctrine.system_cache_pool'

framework:
    cache:
        pools:
            doctrine.result_cache_pool:
                adapter: cache.app
            doctrine.system_cache_pool:
                adapter: cache.system

config/packages/doctrine.yaml
doctrine:
    dbal:
        url: '%env(resolve:DATABASE_URL)%'
        types:
            json: Sonata\Doctrine\Types\JsonType

    orm:
        auto_generate_proxy_classes: true
        naming_strategy: doctrine.orm.naming_strategy.underscore_number_aware
        auto_mapping: true
        mappings:
            App:
                is_bundle: false
                type: annotation
                dir: '%kernel.project_dir%/src/Entity'
                prefix: 'App\Entity'
                alias: App

Article.php
...
    /**
    * @ORM\ManyToOne(targetEntity="App\Application\Sonata\MediaBundle\Entity\Gallery");
    */
    private $gallery;
...

As i wrote in development mode everything is working fine.
The problem persists even though I did (many times):

*

*APP_ENV=prod APP_DEBUG=0 php bin/console cache:clear

*php bin/console cache:clear

*remove var/cache and var/log directories, and create them again

*php bin/console doctrine:cache:clear-metadata

*restart apache

*change permissions (777) to uploads folder

*configure apcu
-How to configure apcu for doctrine in Symfony5

*use Symfony cache pool -
https://github.com/symfony/symfony/issues/24545#issuecomment-336419270

*try this -
Doctrine 2.5: Unrecognized field (but only in Symfony's prod mode)
Any ideas what else can I do? I think there is a problem with cache ... somewhere.

A: I finally found a solution.
I had in the config/bundles.php
Sonata\EasyExtendsBundle\SonataEasyExtendsBundle::class => ['dev' => true, 'test' => true],

It must be:
Sonata\EasyExtendsBundle\SonataEasyExtendsBundle::class => ['all' => true],

redpajama_set_name: RedPajamaStackExchange


Tony Basnett started his stand up career whilst studying at Leeds Met University back in 2008. Originally from Hartlepool and now residing in Manchester Tony has taken the circuit by storm. Described as an upbeat and playful comic, Tony has been seen across the country at top comedy clubs as well as performing at some of the UK's major music festivals. With a mischievous and positive style that will get any crowd to warm to his charm.
Not content with just performing Tony started running award nominated comedy nights under the name Trapdoor Comedy Clubs. This allowed him to work with some of the biggest names on the comedy circuit including the likes of Chris Ramsey, Glenn Wool & Jon Richardson to name a few. His comedy clubs also allowed him to establish himself as a very strong an competent MC.
Tony's specialty is music festivals, where he has built up a reputation as being a solid pair of hands.


text:
Institute of America
AJA Online
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Find a Society
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AIA Lecturer: William M. Murray
Affiliation: University of South Florida
Bill Murray was born in State College, Pennsylvania, and grew up as a "townie," next to Penn State University, where his father was head of the History Department. In the 1960s, Murray's parents bought a 30-foot sloop and spent summers in the Virgin Islands living aboard with their three kids. Murray learned how to dive, saw first-hand the influence of the sea over those who live along its shores, and faced shipwreck more than once. When he went to Penn State, and then Penn, he became captivated by Greek and Roman culture and decided to move to Greece to study ancient port cities for his Ph.D. dissertation. Ever since learning of the discovery of a complete warship ram just south of Haifa in November 1980, Murray has been obsessed with extracting evidence from the weapon to understand ancient galley warfare. He is now is the Mary and Gus Stathis Professor of Greek History and the Director of the Ancient Studies Center at the University of South Florida, and recent publications include The Age of Titans (2012, Oxford University Press). Professor Murray is a past AIA Norton Lecturer, was the 2013/2014 AIA Bass Lecturer, and is a 2020/2021 AIA Joukowsky Lecturer.
Upcoming Lectures:
My 40 Year Search for the Battle of Actium
Society: Gainesville
(TBA) Pittsburgh
Society: Pittsburgh
(TBA) Denver
Society: Denver
(TBA) Jacksonville
Society: Jacksonville
Past Lectures:
Society: Nashville
The Anatomy of an Ancient Naval Battle
Society: East TN (Knoxville)
Society: Puget Sound
Society: Stanford
Society: Lubbock
Toward the end of the First Punic War (264-241 B.C.), the Romans made a final effort to gain control of Western Sicily. They built new ships to make up for the losses of previous years and redoubled their efforts to gain control of Carthaginian strongholds, which they placed under siege. In response, the Carthaginians outfitted a relief fleet of warships and transports, which they loaded with supplies. On 10 March, 241 B.C., the Roman fleet destroyed this relief force as it sailed in from the west in what proved to be the last sea battle of the First Punic War.
Guided by a fisherman's recovery of a single bronze warship ram, the Sicilian Soprintendenza del Mare initiated a joint underwater project with RPM Nautical Foundation in 2005 and, together, they found the ancient debris field of the battle—the first ever discovered from an ancient sea battle. After two decades of research, only a portion of the full battle zone has been discovered and mapped. Rich in finds (23 bronze rams, 30+ helmets, multiple sword concretions, hundreds of amphoras destined for the troops ashore, etc.), this site allows us to attempt what has never before been possible, the analysis of an actual sea battle with reference to both historical texts and physical evidence. Guided by this goal, the lecture will detail the stages of a typical naval battle before introducing the peculiarities and problems posed by the new Egadi evidence.
Short bibliography and/or website on lecture topic:
S. Tusa and J.G. Royal, JRA 25 [2012] 7-48.
J.R.W. Prag, JRA 27 [2014] 33-59.
J.G. Royal and S. Tusa, eds., The Site of the Battle of the Aegates Islands at the End of the First Punic War ("L'Erma" di Bretschneider, 2019).
https://rpmnautical.org/projects/egadi-islands-project/
In 31 BC, Octavian defeated Antony and Cleopatra in a naval battle off Cape Actium in Western Greece. A few years later, the victor constructed on the site of his personal camp a grand Victory Monument to commemorate the event. I first visited this site in 1978, and since then, have been trying to explain what I found there: the ruins of a massive rostral display whose complex details preserve evidence for the sizes of Antony's and Cleopatra's largest warships. After a brief attempt to find battle debris in the sea off Cape Actium, I was asked by Dr. Konstantinos Zachos to join his team in analyzing the results of his systematic excavations of the site. His work, conducted over a quarter century, has added much to our knowledge of this important monument—its original design, its elaborately decorated altar, its dedication text, and its period of use. At the same time, emerging 3D technologies have allowed me to comprehend the rostral display more fully, to visualize the monstrous sizes of the ships that fought in the final naval battle, and to restore the text of the dedication inscription. In this lecture, I will summarize the main results of our research, but do so in a personal manner, in the context of my own 40-year journey of discovery in search of the Battle of Actium.
Octavian's Campsite Memorial for the Actian War [under the authority of P.M. Petsas], Vol. 79, part 4 of Transactions of the American Philosophical Society (Philadelphia, 1989).
"Augustus's Victory Monument, 3D Modeling, and New Directions for Warship Research," in Speira (a Festschrift in honor of A. Douzougli and K. Zachos), E. Mermēgka, ed., Greek Ministry of Culture and Athletics (Athens, 2017), 469-78.
"Recovering rams from the Battle of Actium. Experimental archaeology at Nikopolis," Nikopolis B: Proceedings of the 2nd International Nikopolis Symposium (11-15 September, 2002), K. Zachos, N.D. Karabelas, M. Stork, eds., (Preveza, 2007), Vol. I: 445-51 and Vol. II: 333-41.
"Reconsidering the Battle of Actium—Again," in Oikistes: Studies in Constitutions, Colonies, and Military Power in the Ancient World, Offered in Honor of A. J. Graham, E. Robinson and V. Gorman, eds. (Leiden: Brill, 2002), 339-60.
"The Spoils of Actium," Archaeology 41.5 (1988) 28-35. Reprinted in The Archaeology of War, (Hatherleigh Press: New York, 2005).
https://www.independent.co.uk/news/science/archaeology/roman-empire-cleopatra-octavian-ceasar-egypt-battle-sea-nicopolis-history-archaeology-a8843886.html
The AIA is North America's largest and oldest nonprofit organization dedicated to archaeology. The Institute advances awareness, education, fieldwork, preservation, publication, and research of archaeological sites and cultural heritage throughout the world. Your contribution makes a difference.
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Follow this link past the Babel shelter and picnic site and take in the excellent views of the western Brecon Beacons. You can get emergency mobile phone coverage here.
This route links the Cefn Arthen trail to the Cwm Crychan trail.


text:
BookFrom.Net Archive Search Books by Rating Android App
Search Books by Rating Android App
BookFrom.Net Series Archive
Author » Natasha Preston
Reliving fate, p.22
Reliving Fate, page 22
"What's happened?" I ask, the blood draining from my face.
Is it Mum or Livvy? Not my twin, please. Is Liv sick again?
"Nothing has happened. I'm sorry I made you fear the worst."
Sighing in relief, I sit at the end of the sofa and angle my body toward him. "Good. What did you want to talk about then?"
"You finding a job."
"All right," I reply, giving a nod. "I'll ask around in town."
"Your mum and I have. There are jobs going at Ted's Diner, Another Chapter bookstore, and Bernie's DIY. If you're not going to university this year, you will work and contribute. You're eighteen, and we are only willing to financially support you and Olivia while you're in education."
I shrug. "Yeah, seems fair."
Dad takes a breath through his nose, and his jaw hardens.
Ah, I see; this is to scare me into going to uni. He thought, when it came down to it, I would rather study than work. Now, he's not happy because I've agreed.
On the coffee table is a pile of newspapers. The top one is open on the Careers page, so no doubt, the others are, too.
He's thorough; I'll give him that.
"I like the idea of the bookstore."
There would be less people, and I know what books are. Ask me a question about DIY though...
"I could get Livvy a discount on her course books."
Dad's eyes darken, but he nods, trying not to give it away that he's hating the idea of me working. "I bet she would like that."
She won't. She's just as nuts about me following her path as Mum and Dad are.
"Well"--he clears his throat--"take those newspapers, and have a look, go online. Then, make a CV to take to the bookstore. If you need help with the CV, let me know. You want to make it professional and to the point as much as you can and tailor it slightly to each job you're applying for."
Okay, I just forgot all that.
Picking the newspapers up, I smile. "I'll get right on it."
This is going to suck.
I have enough in my savings account to survive for a while, but I should be earning. Besides, I don't think it's optional if I want to continue living here, and it's not like I have enough to move out.
Dad reaches for the TV remote and focuses on that rather than me now that his plan has backfired. I don't want to piss him off even though I'm clearly very good at it, but I can't follow a path that he wants. What I do from here has to be for me.
I sit on my bed and dig in my bag for my phone to message Rocco.
Is women's fighting a thing? Dad wants me to get a job. I'm looking at them now.
While I'm waiting for him to message back, I start to flick through the Careers page. My phone beeps seconds later.
Two words: Pole dancer.
I roll my eyes at his obvious reply. Honestly, you would think he'd at least try to be more original.
Already had an interview for that, but the guy's dick tasted rank.
NOT AT ALL FUCKING FUNNY!
Why, why, why are we having this conversation through WhatsApp? I really want to see his face right now.
You started it. Hey, when you look at me, do you see Head Chef?
Can you cook?
Not really. That's why we usually get pizza or go out.
Be prepared for rejection.
Once I've finished messaging Rocco, I head downstairs because I can hear Catfish on TV. We all love that show, even Dad. Mum and Livvy are home now, sitting on the sofa with Dad. I drop down on the single seat near it.
The girl on the screen has been talking to this dude for five years, and he makes excuses every time she asks to meet.
You're obviously being catfished, you moron!
I'm sorry, but anyone who talks to someone on a computer for five years can also work a web chat.
Livvy glances at me and gives me a fleeting smile. My phone beeps, and she looks at it like it's a discarded bag in an airport.
"You okay, Liv?"
"Mmhmm." Her eyebrows rise with interest. "Who's texting you?"
Translation one: I didn't know you had friends. Translation two: Who are you shagging?
"A friend," I reply.
"Oh, yeah?" Mum says, suddenly interested.
This gets Dad's attention, too, and I know the game is up. They'll never believe I'm constantly texting a friend. Firstly, I don't like talking to people at the best of times, and secondly, I don't really have friends.
Bugger.
"Who is he then?" Dad asks.
His back has straightened, and he looks ready to tear Rocco apart. He'll be even more ready when he meets him. Rocco's physique is a billion miles away from Harry's scrawny body. And Rocco's older.
They don't like me and Livvy knowing people who they've not met. Celia was killed by a stranger. Possibly not a stranger to Celia though if I'm right about Hugo.
"He's no one."
"Isabella," Dad warns.
I roll my eyes. Pretending is not going to get me anywhere. "Fine. His name is Rocco."
"Rocco," Mum repeats.
I know she's picturing either a tattooed biker or a coke addict. If I didn't care about things working with Rocco, I'd indulge in that a little.
"Well, what's his surname, how old is he, where does he live, and what does he do for a living?" Dad asks, sitting up even more. Any further, and he'll go straight over and face-plant on the floor.
"Twenty-two. Not too far away." And I don't know his damn surname!
And I say what to the career question? Illegal street fighter isn't going to cut it here even if he is undefeated.
"Do you have a picture?" Livvy asks.
Oh, I have many. Most of them are in my Photo Vault app, never to be seen by anyone else. I bite my lip. Since our first flirty sext, Rocco sent me a few naked pictures, too.
"Sure," I say.
They can see a safe one from the regular photo album. I get up a recent one of me and him together. He's really not into having pictures taken, not that I let that stop me, but on this one, he's smiling and holding me close. Makes a nice change to me catching his middle finger.
"Whoa," Livvy says. Her eyebrows shoot up, and her eyes widen.
I'm not sure if I should be insulted or not. Did she think the best I could do was an ogre?
"Bells, he's well nice."
"Yep," I reply.
I'm very aware of Rocco's good looks and the muscles on that body that'd make Hercules weep.
I'm such a lucky bitch.
"Hand it over," Mum says, eagerly reaching out for the phone. Her fingers wriggle impatiently, as she is desperate to see the guy her daughter is shagging.
I won't bring up that part.
Dad's mouth is pressed into a grim line. This is hard for him.
He gave that look to Celia when she kept going out, giving them lies in response to, "Where are you going?" and constantly stressing them.
I hate that I'm doing the same thing. They'll understand when this is all over though.
"He looks very nice, Bella. When can we meet him?" she asks.
I go to laugh but catch myself before. How about never?
"We're really not at the meeting-each-other's-family stage, Mum."
Dad scowls. "Isabella, if you are seeing someone, we want to know who it is. All I know about this person is that he's four years older than you." He said person like it's a dirty word.
Dad instantly hates boys. There's no first chance with him. They have to win him over so that he'll give them a second one.
He also knows Rocco's name, too. I don't bring it up because it'd be a dick move, and it would only aggravate him.
"Your dad's right," Mum says, siding with him. "Is there a particular reason you don't want us to meet him? Or does he not want to meet us?"
I look to Livvy to help.
"We haven't discussed meeting anyone yet; that's all. It's still in the early days."
Not that it means anything to
my parents.
"We're not going to become emotionally involved in your relationship, Isabella. If you two don't work out, it won't be a problem here," Dad says.
"Yeah, thanks."
"That's not how I meant it, and you know it."
I know they would hate to see me upset, but, bloody hell, he could've put it a little better.
"If you're going to be leaving this house to see him, we need to meet him." Dad uses his no-nonsense tone.
His dark eyebrows knit together, and his jaw clenches. He looks like a strict head teacher, and I feel like a misbehaving student.
I haven't even done anything wrong!
"Fine," I concede. "I'll see if he can come over sometime."
"Tomorrow. You'll invite him for lunch tomorrow," Dad demands.
This is why I don't like authority. Except when I'm all needy and Rocco is ordering me around. Then, I like it a lot.
Livvy gives me a sympathetic smile. "I'll invite Harry, too. I'm sure they'll get along."
Yeah, she really doesn't know Rocco.
"Sounds good," I reply.
In reality, nothing has ever sounded worse. I am intrigued though. I wonder what his reaction is going to be when I tell him that he's coming over for lunch. It's almost worth it.
"I'm going to my room," I say in defeat.
"Make sure you call him!" Dad shouts when I'm halfway up the stairs.
I don't bother to reply. There is nothing nice I want to say right now.
I dial Rocco's number and sit on my bed. My door is shut, and I flick the TV on just in case one of them decides to listen in.
"Hey," he says gruffly, answering just before it went through to voice mail.
"My parents want you to come to lunch tomorrow."
Better to rip the plaster off.
"What? Bella, what the fuck?"
I can see how he was taken off guard, but he's usually a bit more together, even with surprises.
He's going to say no.
I mean, I want him to decline, but my parents are not going to like it. I'm too tired for that argument today.
I groan. "They kept asking who was texting me."
"And you told them?"
"I tried to lie! They guessed."
"And, now, they want to meet."
"Pretty much," I say, chewing on my lip.
"Fuck's sake. Fine!"
I wish I were there to see his grumpy face. I love it when he gets like that. I love it more just after--when he pounces and we have pissed off sex.
"Come over now," he demands.
His voice is rougher than usual and does things to my insides that makes me wish I were already there.
"You're not off, being busy and important?" I tease, meaning bloody street fighting.
That's the one thing I hate about him. He can be cocky and sure of himself when it comes to winning, but there are no guarantees. One bad punch, and it's all over. He could lose more than his pride.
I can't lose him.
"Just hurry the fuck up." He hangs up.
I laugh. Such a smooth talker.
Someone knocks on my door and opens it before I have a chance to say anything. I could have been changing in here.
"Hey," Livvy says. "Can I come in?"
"You're already in."
Rolling her eyes, she walks across the floor and sits on my bed. "You okay?"
"Yeah, I'm good. Dad's going to make tomorrow real awkward, isn't he?"
"Oh, yes. Remember how he questioned Harry?"
I do remember. That was a fun evening. Completely different now that I'll be the one going through it. I did not have nearly enough compassion for Livvy that day.
"Perfect," I mutter sarcastically.
"Are you worried that Rocco can't handle it?"
I do laugh at that. There's not much he can't handle, if anything.
"Nope. He'll be fine. He doesn't scare easy."
The guy voluntarily enters into fights where the only rule is no weapons. I can't see a middle-aged couple making him quiver.
"Well, that's good then. If you get anything from tomorrow, Bella, it'll be the knowledge that he really wants to be with you. If a guy can make it through the first meeting with Dad and then still wants to be around, you know it's love."
I already know that.
"I'm going to need a lot of wine," I say, bending my head and groaning into my hands.
"Have you called him yet?"
"Yep." Sitting up straight, I smirk. "I've never seen him flustered before."
"I can't believe you never told me about him. How long have you been together?"
"A few months."
I'm not going to address the first part of that. Why would I have told her? We're not close. It's not worth going there though.
Livvy claps. "What's he like?"
"Intense," I reply with a smile.
"What else?"
Sighing, I look up and wonder why the fuck I didn't think of a decent lie. "You'll find out for yourself tomorrow."
THIRTY-FOUR
I've never met the parents before, and I don't particularly want to now. They're not going to approve of their eighteen-year-old daughter being with the twenty-two-year-old fuckup from the dodgy side of town.
It's a good thing I don't give a shit about what anyone thinks.
"You look nervous," Bella says as we walk along the path to her house.
Yeah, well, I feel like I'm walking the plank.
But I can't tell her that.
"I'm not."
If her parents hate me, it's no skin off my nose. Bella isn't the type of girl to ditch someone because of another person's opinion, so I have nothing to worry about there. Right? I need to believe that.
"Okay, okay."
"Are you going to knock on your own door?" I ask.
She looks up at the fuck-off huge solid wooden door.
"Bella, are you nervous?"
"Fuck off, Rocco," she snaps, reaching into her bag for the key.
"No, wait." I grip her wrist before she can open the door. "Why are you still worried about this? If they hate me, will it change anything for you?"
"No," she replies.
"Then, where's the problem?"
"Nowhere," she replies.
Every time we have these stupid arguments, I want to bang her brains out and strangle her at the same time.
"Isabella, tell me."
"Ooh, you pulled out the full name." She rolls her eyes. "Fine. Not only is it a big step, but it's also a first for me. Livvy has her long-term dickhead boyfriend, and I've never brought anyone home before."
"Why is her boyfriend a dickhead again?"
"Ah, you will see soon enough. Let's get this over with. I'm staying at yours tonight." She sounds sure, but she looks bloody terrified.
Tugging on my hoodie, she pulls me in close.
"You know you're welcome to sleep with me anytime."
"Don't you mean, stay with me?"
"No," I say, shaking my head. "No, I really don't think I do."
"Your brain is in your penis."
She lets me go, and I take a breath.
Bella unlocks her front door. A door that is attached to an impressive house. She said her family lived in a modest home, not some mansion. Or mansion to me. My whole flat is about one-twentieth of the size of this.
Taking a deep breath, she shoves the door open and tugs me into a grand entrance hall, which actually is the fucking size of my flat.
Maybe they're not rich, but they're certainly not living month to month.
"Mum, Dad," Bella calls out.
I wasn't stressing about this meeting because I knew it wouldn't matter to her whether they liked me or not, but I fucking am now. I don't have a steady job, and what I do for money is hardly going to be welcomed by her family.
Four of them slide aroun
d the corner from what I assume is the kitchen since it has tiled flooring.
Bella is an almost perfect mix of her parents, but I can see more of her mum. Her sister obviously looks similar, but there's no mistaking the differences between the twins--and the fact that I've got the pretty one. The guy behind Livvy must be Harry, the dickhead boyfriend, who Bella hates.
"Rocco, these are my parents, Karl and Erin. That's Livvy and her boyfriend, Harry."
Her dad looks me up and down, his expression hard.
Her mum is much more welcoming. "It's lovely to finally meet you, Rocco. We've heard so much...and it's all good, I promise."
Yeah, Bella's not likely to tell you the bad.
"Good to meet you, too."
She's told me the bare minimum about you.
Livvy steps closer. "Thanks for coming, Rocco. I wasn't sure if Bells would share you with us when we asked."
Bells?
"Are we going to stand in the hallway all afternoon?" Bella asks sarcastically.
I see why she's got a reputation as the difficult twin.
Erin's mouth thins, but she moves back. "Come through. I'll put the kettle on. Would you like a tea or coffee, Rocco?"
"Or something cold?" Livvy adds.
I don't know her, but she seems too nice. I don't like too nice. I like Bella and her bratty mouth.
"He likes coffee," Bella answers for me. "Rocco, sit." She tugs me to one of the eight stools around a long granite breakfast bar. It looks like it has flecks of gold melted into it.
"You cool?" I ask her, keeping my voice low so that no one else can hear.
She gives me a side-glance that screams, Fuck off.
Not sure what I've done, but I'm enjoying it. I love her when she's mad.
"So...Rocco," Karl says, trying my name like he's not sure it's even a real word, "what do you do for work?"
Illegal fighting and the occasional car theft.
"I work at a friend's garage."
Out of the corner of my eye, I see Bella raise her eyebrow.
What did she expect me to say?
"Ah, I might have to get you to look at my car--"
"Really, Dad!" Bella cries.
"It's fine, Bells," I reply, purposefully goading her by using the nickname Livvy called her.
It works. Her eyes turn cold as steel, and I hold back a laugh.
Karl continues his list of questions, "Where do you live, Rocco?"
"Baker Street."
He and Erin exchange a look that I know all-too well from telling an outsider where I live. I've managed to screw this up in less than five minutes. I'll never be good enough for their daughter.
"It's a nice place, Dad," Bella says, seeing her father's disapproval. "It's right between a brothel and a crack den."
Coughing on a dry throat, I open my mouth to protest, but I don't really know what to say.
by Natasha Preston / Romance / Thriller have rating 5.3 out of 5 / Based on32 votes
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text:
What's African-American DNA?
Cartnoon
On This Day In History July 25
By TMC in News
Cross posted from The Stars Hollow Gazette
July 25 is the 206th day of the year (207th in leap years) in the Gregorian calendar. There are 159 days remaining until the end of the year.
On this day in 1788, Wolfgang Mozart completes his Symphony No. 40 in G minor.
The question of the Symphony's premiere
There is no completely solid documentary evidence that the premiere of the 40th Symphony took place in Mozart's lifetime. However, as Zaslaw (1983) points out, the circumstantial evidence that it was performed is very strong. On several occasions between the composition of the symphony and the composer's death, symphony concerts were given featuring Mozart's music, including concerts in which the program has survived, including a symphony, unidentified by date or key.
Most important is the fact that Mozart revised his symphony (the manuscripts of both versions still exist). As Zaslaw says, this "demonstrates that [the symphony] was performed, for Mozart would hardly have gone to the trouble of adding the clarinets and rewriting the flutes and oboes to accommodate them, had he not had a specific performance in view." The orchestra for the 1791 Vienna concert included the clarinetist brothers Anton and Johann Stadler; which, as Zaslaw points out, limits the possibilities to just the 39th and 40th symphonies.
Zaslaw adds: "The version without clarinets must also have been performed, for the reorchestrated version of two passages in the slow movement, which exists in Mozart's hand, must have resulted from his having heard the work and discovered an aspect needing improvement."
Concerning the concerts for which the Symphony was originally (1788) intended, Otto Erich Deutsch suggests that Mozart was preparing to hold a series of three "Concerts in the Casino", in a new casino in the Spiegelgasse owned by Philipp Otto. Mozart even sent a pair of tickets for this series to his friend Michael Puchberg. But it seems impossible to determine whether the concert series was held, or was cancelled for lack of interest. Zaslaw suggests that only the first of the three concerts was actually held.
285 – Diocletian appoints Maximian as Caesar, co-ruler.
306 – Constantine I is proclaimed Roman emperor by his troops.
315 – The Arch of Constantine is completed near the Colosseum at Rome to commemorate Constantine's victory over Maxentius at the Milvian Bridge.
864 – The Edict of Pistres of Charles the Bald orders defensive measures against the Vikings.
1261 – The city of Constantinople is recaptured by Nicaean forces under the command of Alexios Strategopoulos, re-establishing the Byzantine Empire
1547 – Henry II of France is crowned.
1567 – Don Diego de Losada founds the city of Santiago de Leon de Caracas, modern-day Caracas, the capital city of Venezuela.
1593 – Henry IV of France publicly converts from Protestantism to Roman Catholicism.
1603 – James VI of Scotland is crowned as king of England (James I of England), bringing the Kingdom of England and Kingdom of Scotland into personal union. Political union would occur in 1707.
1755 – British governor Charles Lawrence and the Nova Scotia Council order the deportation of the Acadians. Thousands of Acadians are sent to the British Colonies in America, France and England. Some later move to Louisiana, while others resettle in New Brunswick.
1758 – Seven Years' War: the island battery at Fortress Louisbourg in Nova Scotia is silenced and all French warships are destroyed or taken.
1759 – French and Indian War: in Western New York, British forces capture Fort Niagara from the French, who subsequently abandon Fort Rouillé.
1788 – Wolfgang Mozart completes his Symphony No. 40 in G minor (K550).
1792 – The Brunswick Manifesto is issued to the population of Paris promising vengeance if the French Royal Family is harmed.
1797 – Horatio Nelson loses more than 300 men and his right arm during the failed conquest attempt of Tenerife (Spain).
1799 – At Aboukir in Egypt, Napoleon I of France defeats 10,000 Ottomans under Mustafa Pasha.
1814 – War of 1812: Battle of Lundy's Lane – reinforcements arrive near Niagara Falls for General Riall's British and Canadian forces and a bloody, all-night battle with Jacob Brown's Americans commences at 18.00; the Americans retreat to Fort Erie.
1824 – Costa Rica annexes Guanacaste from Nicaragua.
1837 – The first commercial use of an electric telegraph is successfully demonstrated by William Cooke and Charles Wheatstone on 25 July 1837 between Euston and Camden Town in London.
1853 – Joaquin Murietta, the famous Californio bandit known as "Robin Hood of El Dorado", is killed.
1861 – American Civil War: The United States Congress passes the Crittenden-Johnson Resolution, stating that the war is being fought to preserve the Union and not to end slavery.
1866 – The United States Congress passes legislation authorizing the five-star rank of General of the Army. Lieutenant General Ulysses S. Grant becomes the first to be promoted to this rank.
1868 – Wyoming becomes a United States territory.
1869 – The Japanese daimyo begin returning their land holdings to the emperor as part of the Meiji Restoration reforms. (Traditional Japanese Date: June 17, 1869).
1893 – The Corinth Canal in the Gulf of Corinth, Greece is used for the first time.
1894 – The First Sino-Japanese War begins when the Japanese fire upon a Chinese warship.
1898 – The United States invasion of Puerto Rico begins with U.S. troops led by General Nelson Miles landing at harbor of Guánica, Puerto Rico (The land invasion, proper, began that day: Sea-based bombardment and shelling of the capital city of San Juan had been occurring since May 1898).
1908 – Ajinomoto is founded. Kikunae Ikeda of the Tokyo Imperial University discovers that a key ingredient in Konbu soup stock is monosodium glutamate (MSG), and patents a process for manufacturing it.
1909 – Louis Blériot makes the first flight across the English Channel in a heavier-than-air machine from (Calais to Dover) in 37 minutes.
1915 – RFC Captain Lanoe Hawker becomes the first British military aviator to earn the Victoria Cross, for defeating three German two-seat observation aircraft in one day, over the Western Front.
1917 – Sir Robert Borden introduces the first income tax in Canada as a "temporary" measure (lowest bracket is 4% and highest is 25%).
1920 – Telecommunications: the first transatlantic two-way radio broadcast takes place.
1920 – France captures Damascus.
1925 – Telegraph Agency of the Soviet Union (TASS) is established.
1934 – The Nazis assassinate Austrian Chancellor Engelbert Dollfuss in a failed coup attempt.
1940 – General Guisan orders the Swiss Army to resist German invasion and makes surrender illegal.
1942 – Norwegian Manifesto calls for nonviolent resistance to the Nazis.
1943 – World War II: Benito Mussolini is forced out of office by his own Italian Grand Council and is replaced by Pietro Badoglio.
1944 – World War II: Operation Spring – one of the bloodiest days for the First Canadian Army during the war: 1,500 casualties, including 500 killed.
1946 – Operation Crossroads: an atomic bomb is detonated underwater in the lagoon of Bikini atoll.
1946 – At Club 500 in Atlantic City, New Jersey, Dean Martin and Jerry Lewis stage their first show as a comedy team.
1952 – The U.S. non-incorporated colonial territory of Puerto Rico adopts a "constitution" of local-limited powers, approved by the United States Congress in contravention of then-current international law.
1956 – 45 miles south of Nantucket Island, the Italian ocean liner SS Andrea Doria collides with the MS Stockholm in heavy fog and sinks the next day, killing 51.
1957 – The Republic of Tunisia is proclaimed.
1958 – The African Regroupment Party (PRA) holds its first congress in Cotonou.
1959 – SR-N1 hovercraft crosses the English Channel from Calais to Dover in just over 2 hours.
1961 – In a speech John F. Kennedy emphasizes that any attack on Berlin is an attack on NATO.
1965 – Bob Dylan goes electric as he plugs in at the Newport Folk Festival, signaling a major change in folk and rock music.
1969 – Vietnam War: U.S. President Richard Nixon declares the Nixon Doctrine, stating that the United States now expects its Asian allies to take care of their own military defense. This is the start of the "Vietnamization" of the war.
1978 – Louise Brown, the world's first "test tube baby" is born.
1979 – Another section of the Sinai Peninsula is peacefully returned by Israel to Egypt.
1983 – Black July: 37 Tamil political prisoners at the Welikada high security prison in Colombo are massacred by the fellow Sinhalese prisoners.
1984 – Salyut 7 cosmonaut Svetlana Savitskaya becomes the first woman to perform a space walk.
1993 – Israel launches a massive attack against terrorist forces in Lebanon in what the Israelis call Operation Accountability, and the Lebanese call Seven-Day War.
1993 – The Saint James Church massacre occurs in Kenilworth, Cape Town, South Africa.
1994 – Israel and Jordan sign the Washington Declaration, which formally ends the state of war that had existed between the nations since 1948.
1995 – A gas bottle explodes in Saint Michel station of line B of the RER (Paris regional train network). Eight are killed and 80 wounded.
1996 – In a military coup in Burundi, Pierre Buyoya deposes Sylvestre Ntibantunganya.
2000 – Air France Flight 4590, a Concorde supersonic passenger jet, F-BTSC, crashes just after takeoff from Paris killing all 109 aboard and 4 on the ground.
2007 – Pratibha Patil is sworn in as India's first female president.
2010 – WikiLeaks publishes classified documents about the War in Afghanistan, one of the largest leaks in U.S. military history.
Holidays and observances
* Christian Feast Day:
* Anne (Eastern Christianity)
* Christopher (Western Christianity)
* Cucuphas
* James the Great (Western Christianity)
* Julian of Le Mans (translation)
* July 25 (Eastern Orthodox liturgics)
* Commonwealth Constitution Day, formerly Occupation Day. (Puerto Rico)
* Ebernoe Horn Fair (Sussex, southern England)
* Furrinalia (Roman Empire)
* Guanacaste Day (Costa Rica)
* Inca festival in honor of the thunder god Ilyap'a
* National Day of Galicia (Galicia)
* Republic Day (Tunisia)
* Public Holiday (australia) Harri and Sharii had sex!
history, On This Day in History, Open Thread
The Breakfast Club (A Certain Logic) December 30, 2021


About 100 Nederland homes have been without water since Friday, and could be without water for a couple more days.
Nederland public works crew began digging on Monday morning to a depth of 9 feet in order to reach the water main break.
Later Monday afternoon, the town said on Facebook that public works staff hadn't identified the source of the main line break and would work through the evening and until the source was found.
Parts of the town were without water during the Frozen Dead Guy Days festival, one of Nederland's most popular events.
According to a Facebook post on Sunday by Mayor Kristopher Larsen, crews believe the leak is under the eastern part of the roundabout in the center of town.
Town Administrator Karen Gerrity said that they wouldn't know how long repairs would take until they reached the pipes.
"Once they have an idea of where the break is and how it happened and what parts they will need to replace, we will have a better idea of how long it will take," Gerrity said.
While working on the problem, the Nederland Community Center is passing out cases of bottled water and allowing anyone to fill up containers.
Nicole Cavalino, Nederland's town clerk, estimates 75 to 100 cases and single gallons of water had been given out, while some residents filled up empty water jugs. Cavalino said staff had an emergency supply of water for situations such as this, and some residents dropped off water for others.
Also, she has delivered full water buckets to fill toilets and bathtubs and cases of drinking water to residents who aren't able to leave their homes.
"Our town staff is working diligently to fix the situation," she said.
Laura King, one of the residents without water, hauled several empty jugs to the community center to fill up on Monday morning. King said she's been able to make do with the problem, but mundane tasks such as washing dishes and even going to the bathroom have become more difficult without running water.
"We're filling the toilets (with water). It takes a lot to deal with that," King said. "It's illuminating."
John Toland, another resident stopping by the community center to fill up jugs, cited the inability to shower as being one of the most inconvenient consequences of the situation.
"We had to buy water for the last couple days," Toland said. "Mostly, it's crazy because there's no access to showers. It's kind of a bummer."
Nicole Ferdinandsen, with the Public Water Works Department, said that crew members noticed water pooling around the roundabout in the center of town on Friday. For now, Nederland residents will have to wait on crew members before they can get any more information.
"We really appreciate everyone's understanding," Larsen said in his post. "I can't help but shake my head that it happens on this weekend of all weekends, and I know I'm not the only one looking forward to being able to take a shower again!"