text:
'No Voice, No Vote,' Say Feminist Haredi Women in Israel
Estee Rieder-Indursky (left) and Esty Shushan
Overjoyed, Esty Shushan stands in the august corridors of Israel's Supreme Court and emotionally embraces her friend and colleague. "We won! We made history."
"This is for all haredi women! For all Israeli women! For our daughters!" her friend, Estee Rieder-Indursky, responds.
Devoutly religious Jerusalemites who dress modestly and cover their hair, Shushan, 41, and Rieder-Indursky, 44, are reacting to a decision by Israel's highest court that could bring them one step closer to their goal of expanding the roles for ultra-Orthodox women in public and political life.
These women—Shushan, a filmmaker, public relations professional and mother of four; and Rieder-Indursky, a journalist and mother of seven—see no contradiction in being both haredim and avowed feminists. They refer to themselves as "the last of the suffragists in the modern world." Six years ago, they founded and now co-direct Nivcharot: Charedi Women for Voice and Equality, a nonprofit organization whose name translates as "elected" or "chosen."
In feminist circles, participants in this expanding group—part consciousness-raising, part political activism—are popularly known as the "LoNiLoBos," an acronym of the Hebrew slogan lo nivcharot, lo bocharot—which can be translated as "no voice, no vote." Implicit in the name is the threat to the haredi political establishment that if they are not included in the political process, the women will not vote for the haredi parties.
Three years ago, an independent lawyer, Tamar Ben-Porat—soon joined by 13 feminist groups—took Nivcharot's case to Israel's Supreme Court and demanded that Agudat Yisrael, the leading haredi party in the Knesset, accept women into its ranks and appoint them to the party slate.
On July 31, the court instructed Agudat Yisrael's lawyer to inform the Council of Torah Sages, the body of rabbis that makes all decisions for the party—including appointing its slate for elections—that if it does not either remove or amend article 6a of its bylaws, which forbids women from joining the party, they should be prepared for a ruling from the court instructing them to do so. A change in the bylaws would open the door to appointment of women to party institutions and activities, under Israel's existing laws of equity.
Lawyers on both sides agree that the party was likely to accept the court's recommendation, which could have far-reaching implications for all Israeli women and would certainly impact the two current haredi political factions, Shas and United Torah Judaism, which control 13 of the 120 seats in the current Knesset.
Furthermore, the court appears to have accepted Nivcharot's main point, as Rieder-Indursky puts it: "The exclusion of women from social and political decision-making institutions has nothing to do with religious law and everything to do with increasingly misogynist ideologies and practices within the community."
Rieder-Indursky and Shushan are among those who have faced pushback and even personal threats from the ultra-Orthodox establishment, but they remain resolved. "Haredi leaders must realize: changes are in the wind," says Shushan. "They can tell us to go to other, non-haredi, parties, accuse us of not being 'true' haredi women. But the haredi world is our world, and the haredi parties are our political home."
The haredi community makes up approximately 11 percent of Israeli society, totaling close to 1 million people, according to the Statistical Report on Ultra-Orthodox Society in Israel, issued in 2016 by the Israel Democracy Institute. Although there are many different streams and sects, haredi society in general is led by its rabbis, who have traditionally surrounded their communities by what is often referred to as "walls of holiness" to protect them from the influences of the outside world. While a minority of the ultra-Orthodox communities do not recognize the validity of the State of Israel and therefore do not participate in elections, the majority do vote—for the parties their rabbis choose.
LoNiLoBos supporters celebrate at the Israeli Supreme Court
In this world of strictly defined gender roles, the highest ideal for a man is to dedicate himself to full-time, lifelong study in a yeshiva. For women, life centers on childbearing and raising a family. According to the 2016 report, haredi women have a high fertility rate—averaging 6.9 children per woman, compared to 3.1 in the general Jewish population. At the same time, most haredi women must also work outside the home to help support their family so their husbands can continue their studies.
Rieder-Indursky and Shushan consider themselves part of a group that is commonly referred to as "modern haredim." Unlike a number of the Hasidic sects, which are becoming increasingly insular and extreme in their interpretations of gender separation, modern haredim believe they can integrate into the modern world without sacrificing their religious and cultural commitments.
While some modern haredi women are able to develop careers in technology, journalism, law and medicine, most are constrained by their generally low levels of education and the traditional roles assigned to women. They are often relegated to working within haredi neighborhoods, usually in education or secretarial work, and are vulnerable to exploitation and low wages.
After years of quiet, often surreptitious, organizing in cities and towns throughout Israel, the leaders of Nivcharot say that participation in their cause is increasing, which, in turn, is emboldening them to step up the group's public profile. Their two-year-old Facebook page has close to 14,000 followers, and a private Facebook group boasts close to 1,200 members.
"In haredi society, women have traditionally been taught that they should be 'not seen and not heard,' " says Rieder-Indursky, a Ph.D. candidate in gender and cultural studies. "But as modern haredi women become increasingly educated, both secularly and especially religiously, they are coming to realize that they need not silence themselves in order to maintain their religious devotion."
In 1948, with the founding of the State of Israel, all citizens were granted the right to vote. At the same time, haredi political parties denied women the right to represent their factions in municipal and general elections. In most of these parties, such as Agudat Yisrael, bylaws prevent women from even joining. Over the years, a few women who identify as ultra-Orthodox have served in the Knesset, but not one has represented ultra-Orthodox parties. Despite the record number of women serving in the current Knesset—34—not one is haredi.
The current haredi makeup in the Knesset includes the Shas Party, which represents Mizrachi ultra-Orthodox Jews and holds seven seats; and United Torah Judaism, which holds six seats and is made up of an alliance between the Agudat Yisrael and Degel HaTorah parties. Despite their relatively small number, the ultra-Orthodox parties have historically held the balance of power in governmental coalitions, enabling them outsized influence over policy and legislation, including the ability to block initiatives demanding female participation in the haredi parties put forth by sympathetic secular lawmakers.
In 2012, Shushan, the filmmaker, became the first haredi woman to publicly demand that women in her community be permitted to participate in party politics. Looking back, she says now, "I didn't know much about feminism, I just knew that I felt like I was suffocating. I was also working as a journalist, but only under a male pseudonym." Editors of the ultra-Orthodox newspaper for which she wrote, she recalls, were concerned that its readers would be outraged if they knew a woman was writing for them. "My community had become increasingly extreme—women sent to the back of buses, some rabbis even demanding that men and women walk on different sides of the sidewalks. I couldn't stand feeling so helpless."
She set up a personal Facebook page—a daring act, since many ultra-Orthodox rabbis oppose use of the internet. "There is no moral, value-laden or religious law that prevents women from taking part in political activity in the corridors of government…," she wrote. Gradually, her Facebook page gained followers and like-minded women began to find each other. In small, informal meetings, a feminist process of consciousness-raising took hold—usually in clandestine meetings, so as not to draw the attention of the community's rabbis, leaders and, often, even husbands.
"If you don't live in the haredi community, you just can't imagine how much courage it takes to be a LoNiLoBo," says Leahle, 42, a mother of six who works as a teacher's aide and lives in Mea Shearim, an ultra-Orthodox section of Jerusalem. "I don't even have the courage to use my full name."
Like many of the group's support-ers, Leahle attended her first Nivcharot meeting in a dingy underground bomb shelter in Jerusalem. Her husband, who studies full time in yeshiva, still does not know that she participates in meetings and follows the Nivcharot Facebook page.
"I grew up believing that there could be no greater fulfillment in life," Leahle says, referring to her marriage to a learned man and raising a large Jewish family. "I would ask my husband whom to vote for, because he knows what the rabbis say." She says her family is so poor that she often walks to work rather than take the bus. "If I ever had any doubts about anything, I pushed them down deep, because I was afraid of challenging everything I have known since I was a child. And I am still afraid of being ostracized."
Women attend a Nivcharot gathering
But, she adds, "I know that Nivcharot is pointing to a better way of life for all of us, men and women."
By the 2015 national elections, several dozen members of Nivcharot were handing out fliers in ultra-Orthodox neighborhoods, asking women to refrain from voting until they were represented. Election data shows that their boycott aim was not effective, but their efforts did bring them attention, some of it positive, some of it—especially from the haredi establishment—negative. Meanwhile, Nivcharot has formally registered as a nonprofit and raises funds from Jewish groups, such as the Jewish federations of San Francisco and MetroWest, N.J., and international philanthropic funds, such as the Friedrich Ebert Foundation, based in Germany, and the American Embassy in Israel.
In addition to its political quest, the group's activities are geared, Rieder-Indursky says, to make haredi women "aware of the impact that their exclusion from decision-making has on their lives and to combat extremism in haredi communities." In addition to the parlor meetings, the group offers religious study classes led by women and distribute a monthly informational newsletter to hundreds of subscribers. One of their most popular activities is Cinemama, in which they screen movies that are both acceptably modest and feminist.
Nivcharot has also established a leadership program. Last year, some 30 women gained organizational skills and leadership training as well as learned about human and women's rights. A similar course is scheduled with a new cohort this fall.
Despite the absence of haredi women in the Knesset, Nivcharot leaders initiated the creation of a Haredi Women's Caucus. It operates under the auspices of the Knesset Committee on the Status of Women and Gender Equality, which is led by Member of Knesset Aida Touma-Sliman, who represents the Joint List, an alliance of predominantly Arab parties. Made up of representatives of most of the other parties in the Knesset, minus the ultra-Orthodox, the caucus has helped ensure that haredi women's perspectives are brought to bear on all issues pertaining to women, including unionization and health.
"We need women in the Knesset who will be loyal to their constituency of haredi women and care for our interests," Racheli Roshgold, an active member of Nivcharot, says vehemently.
Despite its higher profile, Nivcharot has not yet made enough inroads to influence the municipal elections set to take place in October. The group is, however, rapidly gaining support in the non-haredi world. According to a recent study by Hiddush, a nonprofit dedicated to religious freedom and pluralism, 80 percent of the adult Jewish population in Israel agrees that all parties running for the Knesset, including the haredi ones, should allow women to be members and to be included in their slates for election.
Attorney Ben-Porat, who filed the initial petition against the haredi parties with the Supreme Court in 2015, is not haredi. "As a feminist attorney, it is my responsibility to fight for the rights of all women," says Ben-Porat. Over time, 13 feminist organizations—one of them Arab—signed on. Absent from the petitioners was any haredi group, including Nivcharot, whose leaders are trying to balance their activism with respect and sensitivity to their communities.
A Knesset panel; Member of Knesset Aida Touma-Sliman appears in the middle.
In its response to the initial petition, Agudat Yisrael contended that the gender separation in its bylaws is protected by the principle of freedom of religion and cited multiculturalism as a justification. Yet, when sharply questioned by the justices on this point on July 31, the lawyer for Agudat Yisrael acknowledged that there is no particular religious law that forbids women from being a member of Knesset or participating in political life. Rather, he argued, "The cultural approach, according to which women are not involved in public political work, is an inseparable part of the religious belief and values of those who support the party."
But the Knesset, the women have argued, is not a beit knesset, or synagogue, where women are separated from men and their roles are defined by theology and religious laws. The court, it appears, has accepted this position.
Historically, some of the court's rulings in favor of equality and individual freedoms have put it at odds with the haredi community, a reality that explains why Nivcharot did not officially back the petition, despite support for its aims. "Too often, haredi society has felt that the Supreme Court does not understand us and rules against our beliefs," explains Tali Farkash, 36, a haredi feminist activist. "We want our changes to come from within. We do not want to openly do battle against our own society."
Yet, even this is beginning to change, Rieder-Indursky says. When the court convened its first session in 2016 to hear the petition, no haredi women dared attend. By the second time, last December, seven haredi women came to listen. On July 31, more than two dozen showed up, marking the occasions with smiles and selfies.
But these women know that even with a court victory, it could take years until a woman from their community serves as a member of Knesset from an ultra-Orthodox party. In practice, the Council of Torah Sages could attempt to circumvent the new situation, whether by claiming that "they could not find an appropriate female candidate" or "by placing an unknown woman so low on the slate that she will have no realistic chance of being elected," explains Shushan.
Still, the court case has widespread implications, says attorney Netta Loevy, of the feminist group Itach-Maaki: Women Lawyers for Social Justice, who presented the case to the Supreme Court as the representative of the feminist organizations. "The court has made it clear that it is not acceptable to exclude women from politics and decision-making.
Changes in conservative societies are hard-won, and the activists are paying a high price for their efforts.
Although no prominent religious authority widely accepted across the diverse haredi community has denounced Nivcharot's efforts, none has publicly expressed his support, either. Nor has anyone censured the derisive, misogynist statements by religious leaders such as Rabbi Mordechai Neugroschel, a popular lecturer who in 2016 at a male-only convention declared that women who support Nivcharot suffer from "some sort of schizophrenia" and are "in need of psychiatric care." His comments subsequently went viral on numerous websites. Rabbi Mordechai Blau, a senior member of United Torah Judaism and a one-time candidate for Knesset, has declared that the husbands of women involved in political activity may divorce their wives and will not be required to pay alimony. Other rabbis have warned that any woman who joins Nivcharot should be forbidden from teaching in educational institutions, that she should be ostracized and her children expelled from haredi schools.
In pashkevilim (broadsheets) plastered on walls and billboards in ultra-Orthodox neighborhoods, activists' husbands have been publicly ridiculed and shamed because they "can't control their wives."
Requests to interview husbands were declined, with families citing the sensitivity of the situation.
In some cases, pressure is applied through children. Shushan was forced to obtain a court order to keep her daughter in an ultra-Orthodox school whose principal opposes her activities. Rieder-Indursky says her son often returns from his yeshiva begging her to cease her activism.
The women have also been censured, even spat on in the street, by other women. "I know that our message is a challenge to everything that some of these women have been taught," Shushan acknowledges.
Energized by the recent court decision, and despite the opposition, the women vow to continue their struggle. "If the state allows the ultra-Orthodox parties to exclude women, then there is no guarantee that other parties won't do so, and that other forms of exclusion won't occur in the future," says Rieder-Indursky. "We are feminists and we know that the place of women within Orthodoxy is an issue for all Israeli women."
Eetta Prince-Gibson is an award-winning journalist and former editor-in-chief of The Jerusalem Report who now writes for Israeli and international publications.
The original Article
Haredi women don't need pity, they need a Feminist view
Good enough to vote; not good enough to run for office
Haredi feminist pushes for her rights
© 2018 by Nivcharot
972.50.8731884
POB 491 Petach Tikva


text:
Merchant Navy Avenue of HonourPrint Page
Ex Merchant Seamen Don Kennedy, Ray Cox and Don Caporn
Photographs supplied by Malcolm Fisher
Merchant Navy Avenue of Honour recognises the wartime service and sacrifice of Merchant Navy personnel in two world wars. The community planted a 1.5 kilometre "Avenue of Honour" along the entire length of King Street, Manly Vale, Sydney to the approaches of Manly Warringah War Memorial Park.
A commemorative sandstone monument is the centerpiece of this special tribute. To complete the project, 21 plaques were placed on the power poles adjoining the street alerting people to its significance.
Organised by the Save Manly Dam Catchment Committee, banksias, bottlebrushes, tea-trees and eucalypts endemic to the area, reflect the affinity and respect mariners historically have with the forces of nature.
King Street, Manly Vale, 2093
Avenue of Honour
The avenue of local trees and shrubs along King Street was completed in 2011 to commemorate the wartime service and sacrifice of members of the Merchant Navy during World War 1 and World War 2.
Funding was provided by the NSW Government and the planting organised by the Save Manly Dam Catchment Committee "Return of the Mermaids" Restoration Project.
The "Return of the Mermaids" seeks to restore the Mermaid Pool on Manly Creek and rehabilitate its bushland surrounds from the effects of rubbish dumping, invasive weeds and pollution.
The volunteer project team also works to inform the community about the beauty and wonder of our disappearing natural heritage and the need to protect what`s left. The seedlings planted are all indigenous to the area and provide food for habitat for native wildlife.
Source: MA.LC

redpajama_set_name: RedPajamaCommonCrawl


Cardiology is the study and treatment of disorders of the heart; it is a medical specialty which is involved in the care of all things associated with the heart and the arteries. A cardiologist is not the same as a cardiac surgeon - the cardiac surgeon opens the chest and performs heart surgery, a cardiologist, on the other hand, carries out tests and procedures, such as angioplasty.
Heart disease differs from cardiovascular disease, in that the latter refers to disorders and illnesses of the heart and blood vessels, while the former is only concerned with the heart.
In the USA, cardiology is a part of internal medicine. It is a discipline which includes the diagnosis, treatment, causes, as well as research into heart diseases and injuries.


in my node setup I have a value node. I want this node to influence the scaling of my mapping node.
Is there a way to achieve this? Or is there a math/vector node setup I can use to manipulate the scale of my texture?
I found an answer to my question: Possible to control a Driver from a Group Node input slider?
Because the original question here is another one, I won't delete my post. Maybe it is helpful for someone.
Not the answer you're looking for? Browse other questions tagged texturing nodes vector-mapping or ask your own question.
Possible to control a Driver from a Group Node input slider?
How to multiply vector by scalar in material nodes?
Global Variable/Node across multiple materials?
What does the mapping type do on a mapping node?
How does texture mapping work in Blender?
Multiple instances of same image node?


text:
Families - Search Results
familychannelFamiliesRemove
Activities for the under 5s (127)
Children centres (32)
Clubs, activities and sports (373)
Disability, special needs and Local offer (172)
Parenting and family support (310)
Schools and lifelong learning (171)
Has immediate vacancies (106)
Not-for-Profit Organisation (5)
Service is supported by volunteers (4)
Disabled WC (34)
Induction loop (2)
Signing service available (1)
Step free access (30)
Has immediate vacancies:
Funding:
3/4 year old funding (152)
Do you offer 30 hours?
Are you registered for Tax Free Childcare:
Childcare availability:
After School (32)
All Year (70)
Half-term (15)
Inset Days (4)
Unsociable hours (3)
Weekdays (59)
Early Years (92)
Primary School (15)
Secondary School (10)
Preparing for Adulthood (4)
BS48 (92)
BS2 (12)
SE1 (6)
Schools drop off/collection:
Bournville Primary School (3)
Castle Batch Primary School (3)
Christ Church CofE VC Primary School (3)
Crockerne CofE Primary School (5)
Golden Valley Primary School (5)
High Down Infant School (4)
High Down Junior School (4)
Mead Vale Primary School (5)
Mendip Green First School (7)
Portishead Primary School (3)
St Andrew's CofE VC Primary (4)
St Anne's CofE Primary School (3)
St Martin's CofE VC Junior School (6)
St Nicholas' Chantry CofE VC Primary School (5)
St Peter's CofE Primary School (9)
West Leigh Infant School (3)
Wrington CofE Primary School (3)
Yatton CofE VC Junior School (3)
Yatton VC Infant School (4)
Latest Ofsted Grading:
Good (86)
Outstanding (52)
Requires improvement (2)
Early Help:
Specialised (52)
Targeted (76)
Local support (153)
National support (38)
You may also be interested in our advice and guidance pages
0-2 Childcare Funding
30 hours of childcare
About childcare
About early years and families
About our Local Offer and how to search it
About parenting and family support
1238 results sorted by:
1st Backwell Scouts
Uniformed group. Our times/age ranges are:Beavers (6 to 8 yrs) Thursdays, 17:15 to 18:15 hrsCubs (8 to 10½ yrs) Mondays, 18:45 to 20:00 hrsor Thursdays, 18:45 to 20:00 hrsScouts (10½ to 14yrs) Tuesdays, 19:00 to 21:00 hrsExplorers (14 to 18yrs) Tuesdays, 19:00 to 21:00 hrs…
1st Blagdon Guides
This is an active, thriving unit offering an interesting and varied programme of indoor and outdoor activities. For girls aged 10 and half years to 15 years of age. Thursday evenings – Term time only, times and venues may vary. Please call 01761 462 039…
1st Blagdon Scouts Group
Scouts and Cubs enjoy camping, hiking, games, traditional outdoor scout activities, Ten Tors expedition, badgework, etc. Cubs: 8-10 and half years of age (names down at 6 helps to secure a place) Wednesdays 6.30pm - 8pm Scouts: 10 and half years of age to 15…
1st Burrington & Wrington Scout & Guide Group
A Cub pack was formed in 1961 in Burrington. The Scout troop, known as the 1st Burrington, was added in 1964, and we moved to our present headquarters (the former Methodist chapel in School Road) in 1970, when the name was changed to 1st Burrington…
1st Congresbury Scout Group
We are a group you can join to take part in activities of all sorts , and try out new things, and learn new skills. There will be art, drama, music, sport, cooking, as well as the more traditional scouting skills of first aid, camping,…
1st Crockerne Scout Group
1st Crockerne Scout Group Provides fun activities for young people, boys and girls aged 6 to 14 years. Skills for life# Our activities include camps and visits, games, crafts, cooking, adventurous activities and learning new skills. 1st Crockerne Scout Group covers the villages of Pill,…
1st Kewstoke Scout Group
The purpose of the 1st Kewstoke Scout group is to help young people achieve their full physical, intellectual, social and spiritual potential as individuals, responsible citizens and as members of their local, national and international communities. Camping, climbing, orienting, ropework, hiking, abseiling and many other…
1st Locking and Hutton Scout Group
Beavers on Tuesday night, Cubs on Monday Tuesday and Thursday, Scouts on Wednesday and Friday.
1st Locking Castle Guides
Our Guide unit is for girls aged 10 to 14.By becoming part of a worldwide community of girls who learn together and share skills and experiences, Guides have the chance to get out there and do something really different.We are a girl lead programme where…
Wednesdays (term time only)
Guides have an exciting and varied programme designed to inspire and challenge girls from 10-14. What you do in Guides is up to you, from taking part in lots of exciting activities at regular meetings to special events and trips away. You will chose from…


text:
Q: Какую библиотеку использовать для граф.интерфейса на с++? Расскажите какие есть библиотеки для создания GUI на c++ и обучающий материал на русском языке для неё.

A: MFC в Visual Studio, Qt, wxWidgets, GTK, FLTK, .........
Вопрос не позволяет дать точный однозначный ответ... :(

redpajama_set_name: RedPajamaStackExchange


BATON ROUGE, La. – The Cox Communications Academic Center for Student-Athletes introduced its Fall 2018 class of 3.0 Student-Athletes at halftime of the LSU men's basketball game on Saturday night.
A total of 215 LSU student-athletes were recognized for finishing the 2018 Fall semester with a grade point average of 3.0 or higher. The CCACSA is comprised of academic affairs, student affairs and information technology teams that serve more than 500 student-athletes with the purpose of providing an all-inclusive, student-centered support structure for all student-athletes from matriculation, through graduation and life beyond.


Overton Square celebrates 45 years with a weekend-long celebration of local music.
Sons of Mudboy (pictured) play Overton Square this Saturday.
Overton Square is one of the many locations in Memphis currently experiencing a major resurrection. Looking for a piano bar within walking distance from a German beer hall? How about a yoga studio next to a free venue with music seven nights a week? Overton Square has you covered. To celebrate the diversity that Overton Square offers Memphians, a whole slew of local talent will be rocking the block all weekend long. The music started on Thursday, but with more than 20 local bands still set to play, Overton Square is the place to be this weekend. Find the whole schedule below.


text:
'use strict';

module.exports.v1beta1 = require('./v1beta1');

redpajama_set_name: RedPajamaGithub


text:
AcademicsFacultyAndrew Simpson
Andrew Simpson
Assistant Professor of History, Internship Coordinator
College Hall 612
simpson4@duq.edu
Ph.D., History, Carnegie Mellon University
M.A., History, American University
B.A., History and Conflict Studies, DePauw University
Andrew Simpson's scholarship examines the relationship between cities and academic medical centers in the late twentieth century United States. His first book, The Medical Metropolis: Health Care and Economic Transformation in Pittsburgh and Houston is forthcoming with the University of Pennsylvania Press.
Other projects include work on the history of telemedicine, focused especially on the role of NASA, the history of emergency medical services in the United States, and multidisciplinary work examining the relationship between health vocational training, economic opportunity, and health disparities. Simpson has also worked with the Program for Deliberative Democracy to help foster public dialogue around issues of resource allocation in a public health emergency.
At Duquesne, he teaches courses on health care history, urban history, environmental history, state and local history, United States history, and global history. He is a founding member of the Terra Learning Community and is currently the History Department Internship Coordinator.
Prior to attending graduate school, Simpson worked in community development and on political campaigns.
History of Health Care
HIST 141: Environmental History
HIST 141C: Environmental History, Terra Learning Community
HIST 151: Shaping of the Modern World
HIST 204: The United States Since 1877
HIST 222: Flatlined: History and Politics of U.S. Health Care
HIST 329: Pennsylvania and the American Nation
HIST/POSC 388: The United States Since 1945
HIST 470W/570: The History of Urban America
HIST 491W: Senior Honors Seminar
HIST 499W: Modern America, U.S. History 1929 to the Present Independent Study
HIST 574: Modern America, U.S. History 1929 to the Present
HIST 599: American Environmental History Independent Study
HIST 599: Environmental History Independent Study
HIST 602: Graduate Research Seminar
HIST 612: Contemporary United States
The Medical Metropolis: Health Care and Economic Transformation in Pittsburgh and Houston
(In Press, University of Pennsylvania Press, Fall 2019).
"Interagency Cooperation in the Twilight of the Great Society: Telemedicine, NASA, and the Papago Nation" Andrew T. Simpson, Charles R. Doarn, Stephen Garber Journal of Policy History, (accepted for publication).
"We Will Gladly Join You in Partnership in Harrisburg or We Will See You in Court: The Growth of Large Not-for-Profits and Consequences of the 'Eds and Meds' Renaissance in the New Pittsburgh," The Journal of Urban History. Volume 42, No. 2, March 2016.
"Health and Renaissance: Academic Medicine and the Remaking of Modern Pittsburgh," The Journal of Urban History. Volume 41, No. 1, January 2015.
"Transporting Lazarus: Physicians, the State, and the Creation of the Modern Paramedic and Ambulance, 1955-1974," Journal of the History of Medicine and Allied Sciences. Volume 68, No. 2, April 2013. Winner of the 2016 Stanley Jackson Prize.
Invited Talks (selected)
"Dr. Michael E. DeBakey and his Influence in the Changing Business of Health Care and the Delivery of American Medicine" National Library of Medicine, 3rd Annual Michael E. DeBakey Lecture in the History of Medicine" (May 2019)
"The Echoes of 1968: Examining the National and Local Effects of a Turbulent Year in American History" Duquesne University Alumni Association (June 2018)
"Gentrification, Class, and Race: A Screening of East of Liberty" Member of discussion panel about urban change in a Pittsburgh neighborhood. Duquesne University (September 2017)
"Healing the Post-Industrial City: Thinking Critically About Academic Medicine and Urban Change" invited talk for Center for Interpretative and Qualitative Research, Duquesne University (November 2016)
"Writing Eds and Meds in Pittsburgh: Historical Content and Archival Practice" Mid-Atlantic Regional Archives Conference (April 2016)
Rangos Prize, Duquesne University for "Health Inequalities, Professional Preparation, and the Pittsburgh Region: A Multidisciplinary, Community-Engaged Course to Prepare Generation Z for the Future" with Cathleen Appelt and Jessica Devido (Summer 2019)
Rangos Prize, Duquesne University for "Interprofessional Roundtables: Innovating Solutions to Contemporary Mental Health Challenges and Social Injustice" with Cathleen Appelt, Meghan Blakowitz, Ann Stuart, Bridget Calhoun, Jordan Covvey, Jessica Devido, Tammy Hughes, Rachel Kallem Whitman, and Tiffany Sizemore
Stanley Jackson Prize, Journal of the History of Medicine and Allied Sciences
Andrew W. Mellon/American Council of Learned Societies Dissertation Completion F'12
Richard Harrison Shryock Medal from the American Association for the History of Medicine
The Gaultier Community-Engaged Teaching Fellowship
The NLM Michael E. DeBakey Fellowship in the History of Medicine
Media Related to Scholarship
Pittsburgh Post-Gazette, quoted discussing the history of July 4th celebrations, "How Did the Colonists Celebrate Independence Day? It Took a While to Catch On" (July 4, 2019)
WESA, quoted discussing access to Highmark and UPMC plans in light of the new agreement between the two organizations, "New Agreement Doesn't Mean All Highmark Insurance Plans Will Include UPMC Access" (June 27, 2019)
Pittsburgh Post-Gazette, quoted discussing President Trump's refusal to release his tax returns, "Trump's Refusal to Release His Tax Returns Breaks a Long Presidential Tradition" (May 6, 2019)
KDKA News Radio Discussing US Steel's Decision to expand employee benefits, "U.S. Steel Extends Benefits to Include Parental Leave, Gender Confirmation Coverage" (March 22, 2019)
KD/PG Sunday Edition Discussing President Trump's Relationship with North Korea (March 3, 2019)
KD/PG Sunday Edition Discussing the Government Shutdown (January 13, 2019)
KDKA News Radio Discussing UPMC's increasing income, "UPMC Income Has Increased $2.5 Billion Since 2017" (November 21, 2018)
KDKA News Radio Discussing UPMC's Pre-Pay Rule for Medicare Advantage, "Dr. Andrew Simpson on the UPMC Prepay Rule" (October 3, 2018)
KDKA News Radio Discussing the protests surrounding Brett Kavanaugh's Supreme Court Nomination, "Heck No, He Won't Go!"
(September 6, 2018)
KDKA News Radio Discussing "Skinny" Health Insurance Plans, "We All Need It.. Health Insurance" (August 2, 2018)
KDKA News Radio Discussing Highmark/UPMC Medicare Advantage (July 19, 2018)
KD/PG Sunday Edition Discussing President Trump's Diplomacy (July 15, 2018)
WTAE Interview Discussing Health Care (portions aired February 1, 2018)
KD/PG Sunday Edition Discussing Tax Reform Efforts (November 26, 2017)
KD/PG Sunday Edition Discussing Health Care Reform (May 26, 2017)
KD/PG Sunday Edition Discussing Neil Gorsuch's Appointment to the Supreme Court (April 16, 2017)
KDKA Evening News Discussing House GOPs withdrawal of the health care bill (March 24, 2017)
KD/PG Sunday Edition Discussing the Presidency and the Press (February 19, 2017)
KDKA Sunday Business Page discussing the start of the Trump Administration (February 12, 2017)
WPXI Evening News Discussing Donald Trump's First 100 Days (November 9, 2016)
Beaver County Times, Discussing Election 2016 and Election Turnout (November 9 and November 8,
KD/PG Sunday Edition Discussing Election 2016, KDKA TV (October 23, 2016)
WQED Election 2016, "Heath Care" (March 2016)
WVBP Beaver County, Interview to Promote Duquesne History Forum and Discuss Medicare (October 2015)
Quoted in "Census Report: Beaver County's uninsured stays stagnant in 2014" speaking about the Affordable Care Act. Ellwood City Ledger. (September 2015)
Quoted in "The Highmark Health gamble: How risky is insurer-hospital integration?" speaking about Highmark's market strategy. healthcaredive.com (June 2014)
Quoted in "Highmark, BCNEPA Signal Intent to Pursue 'Stronger Affiliation" speaking about Highmark's market position and health care reform in The AIS Report on Blue Cross and Blue Shield Plans (December 2013)


We aim to achieve the zenith in qualitative market research and business intelligence.
Our mission is to become the firmest decision support system for our clients.
We are obsessive about market research and are keen to help our clients to make better business decisions.
Our responsive processes are designed to keep pace with budding business trends.
Everyone has a lot of customer data, but not everyone is proficient with that data. However, we at Report Consultant believes that there has to be a lot of value at the bump of the customer and research data. Talking to customers and connecting to their words is the most astounding task that we do. With a deep-seated understanding of many business environments, Report Consultant provides deliberate objective insights.


I got to wear this amazing lace beaded skirt (together with a matching top) to the Zuhair Murad show during Paris Haute Couture week. I did not want to take it off once the show was over. Stay tuned for more fashion week updates from the last couple of days!


text:
Interview RAF King Air Display Team
Interjúk_2016
First of all thank you for your time, please introduce yourself to our readers, tell us about your career in the RAF.
Welcome back to the King Air Display Team for 2016. We're back to celebrate our 45(R) Squadron history in style at air shows across the UK and in Europe this summer! We are soon to reveal the identity of our team, but for now it's still a secret. The team is composed of the Display Pilot who originally trained on the King Air with 45(R) Squadron and returns to us from extensive operational experience with 99 Sqn on the C-17. The Display Pilot is supported in the cockpit by the Co Pilot who also trained on the King Air and has returned to the Sqn from operations on the Nimrod and Shadow aircraft, which is based on the Beechcraft King Air 350 GT. The Display Manager finished Multi Engine Advanced Flying Training with 45(R) Sqn in October, 2015 and will leave the team in June to begin his operational career on the C-17.
Could you introduce the Squadron and the King Air you are using?
Number 45 (Reserve) Squadron: Today, 45 Sqn trains Multi Engine pilots and Weapons System Operators (WSOps) who operate the mission systems in the back of the RAF's fleet of multi engine aircraft and will soon see roles on the P* Poseidon when it enters service.
Tell us a bit about the history of the Demo Team.
This is the King Air Display Team's first appearance performing a Flying Display since the 2012 Display Flying season, the display has been brought back to celebrate 45 Sqn's 100th anniversary and to showcase the King Air, the training of the crew and the wide range of opportunities for pilots and WSOps hoping to begin a career in the RAF. This year, the King Air will be the only 22 Training Group Display Team as the highly successful Tutor Display Team, Tucano Display and Hawk T2 Role Demo will not be Display Flying.
As I read on your Facebook page, there was a long hiatus for the team, can you tell us the reason for it?
There are a number of reasons why Display Teams don't run every year, often because the aircraft and instructors are needed to train large numbers of students. Display Flying is a serious undertaking and requires a lot of commitment in terms of flying and work behind the scenes, which can sometimes take up valuable squadron training. However, practice and preparation is well underway for the 2016 Display Season and we are extremely pleased to be Display Flying across the UK and Europe this summer to celebrate 45 Squadron's 100th birthday!
At first look the King Air is not made for any demo flying, what can you tell us about the flying program, what can you do with the plane during a show?
The Beechcraft King Air is designed as a business aircraft, something it has excelled at and it has outsold all the competitors in its class for a number of years. While it is not designed for Display Flying, it is a very capable aircraft. Every manoeuvre in the Display Sequence allows the crew to climb away with only one engine running should an engine be shut down. The aircraft will fly a short 6 minute display which will consist of a number of high energy manoeuvres from steep turns to wingovers and steep approaches. The key manoeuvre of the display is the Parabolic Knife Edge Pass where the crew execute a steep turn along the display line to the crowd using the rudder to keep the aircraft level. The King Air flies a parabolic curve during the manoeuvre, hence the name. The aircraft is more capable than what it is certified to do. It has an abundance of power which makes it an extremely enjoyable and safe aircraft to fly!
How many people are in the team? What are their task in the team?
Boss – Officer Commanding 45(R) Squadron – To supervise the safe and successful execution of the 2016 King Air Display Team Display Pilot Display Co Pilot Display Manager
Any info you can tell us about the 2016 season? Does the team have a verdict of which show you attend?
We have released our calendar on Twitter and Facebook, please visit us at
Are you planning any special paint scheme for the King Air?
We are hoping to have the aircraft decorated to celebrate the 45(R) Sqn Centenary from its formation on 1st March 1916, more news will follow but you can also find some sneak previews of our ideas at our facebook or Twitter.
Thank you! Any final thoughts?
All of us working with the King Air Display Team are extremely excited to return the Beechcraft King Air to Display Flying this summer so celebrate our 45(R) Squadron Centenary and to showcase the aircraft we fly and training we undertake. Based at RAF Cranwell in Lincolnshire, 45(R) Squadron trains the RAF's multi engine pilots of the future as well as training the RAFs Weapons Systems Operators (WSOps). Our pilots progress to fly roles in Air Transport, Air to Air Refuelling, Intelligence Surveillance Target Acquisition Reconnaissance along with their WSOp counterparts. 45(R) Sqn has a rich history and an exciting future ahead of with huge expansion planned for recruitment of both pilots and WSOps to command and control aircraft across the current fleet and also brand new aircraft such as the incoming P8 Poseidon!
2016DemointerviewKing AirRoyal Air Force
Interview – RAAF Roulettes leader, SQNLDR Dan Kehoe
Interview – Major Ville Hirvonen
Interview – HAF T-6 Demo Team
Interview -Air Base Commander, Meiringen
Previous Interview Belgian Victors Demo Team
Next Interview – Leader of the Patrouille de France


text:
PhotoId-Whale: Blue whale dorsal fin classification for mobile devices
Rosa I. Ramos-Arredondo ,
Contributed equally to this work with: Rosa I. Ramos-Arredondo, Blanca E. Carvajal-Gámez
Roles Investigation
Current address: SEPI-Culhuacan, ESIME-Culhuacan, Ciudad de México, México
¶‡ These authors also contributed equally to this work
Affiliation SEPI-Culhuacan, Instituto Politécnico Nacional, Ciudad de México, México
Blanca E. Carvajal-Gámez ,
Roles Funding acquisition, Investigation, Supervision, Writing – original draft
* E-mail: becarvajal@ipn.mx, drabecarvajalg@gmail.com
Current address: ESCOM, Ciudad de México, México
Affiliation SEPI-ESCOM, Instituto Politécnico Nacional, Ciudad de México, México
Diane Gendron ,
Roles Conceptualization
Affiliation CICIMAR, Instituto Politécnico Nacional, Ciudad de México, México
Francisco J. Gallegos-Funes ,
Roles Visualization, Writing – review & editing
Affiliation SEPI-Zacatenco, Instituto Politécnico Nacional, Ciudad de México, México
Dante Mújica-Vargas ,
Roles Methodology, Visualization, Writing – review & editing
Affiliation Departamento de Ciencias Computacionales, Tecnológico Nacional de México, Cuernavaca-Morelos, México
J. B. Rosas-Fernández
Affiliation Secretaría de Educación, Ciencia, Tecnología e Innovación, Ciudad de México, México
Rosa I. Ramos-Arredondo,
Blanca E. Carvajal-Gámez,
Diane Gendron,
Francisco J. Gallegos-Funes,
Dante Mújica-Vargas,
Rosa I. Ramos-Arredondo Blanca E. Carvajal-Gámez ... J. B. Rosas-Fernández
Photo-identification (photo-id) is a method used in field studies by biologists to monitor animals according to their density, movement patterns and behavior, with the aim of predicting and preventing ecological risks. However, these methods can introduce subjectivity when manually classifying an individual animal, creating uncertainty or inaccuracy in the data as a result of the human criteria involved. One of the main objectives in photo-id is to implement an automated mechanism that is free of biases, portable, and easy to use. The main aim of this work is to develop an autonomous and portable photo-id system through the optimization of image classification algorithms that have high statistical dependence, with the goal of classifying dorsal fin images of the blue whale through offline information processing on a mobile platform. The new proposed methodology is based on the Scale Invariant Feature Transform (SIFT) that, in conjunction with statistical discriminators such as the variance and the standard deviation, fits the extracted data and selects the closest pixels that comprise the edges of the dorsal fin of the blue whale. In this way, we ensure the elimination of the most common external factors that could affect the quality of the image, thus avoiding the elimination of relevant sections of the dorsal fin. The photo-id method presented in this work has been developed using blue whale images collected off the coast of Baja California Sur. The results shown have qualitatively and quantitatively validated the method in terms of its sensitivity, specificity and accuracy on the Jetson Tegra TK1 mobile platform. The solution optimizes classic SIFT, balancing the results obtained with the computational cost, provides a more economical form of processing and obtains a portable system that could be beneficial for field studies through mobile platforms, making it available to scientists, government and the general public.
Citation: Ramos-Arredondo RI, Carvajal-Gámez BE, Gendron D, Gallegos-Funes FJ, Mújica-Vargas D, Rosas-Fernández JB (2020) PhotoId-Whale: Blue whale dorsal fin classification for mobile devices. PLoS ONE 15(10): e0237570. https://doi.org/10.1371/journal.pone.0237570
Editor: Songhai Li, Institute of Deep-sea Science and Engineering, Chinese Academy of Sciences, CHINA
Received: October 17, 2019; Accepted: July 30, 2020; Published: October 12, 2020
Copyright: © 2020 Ramos-Arredondo et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: The data cannot be shared publicly because the blue whale image database belongs to CICIMAR-IPN, it must be requested directly from these instances. The Database of images of the Blue Whale belongs to CICIMAR-IPN and who collects, manages, administers the information is Dr. Diane Gendron, who is co-author of the manuscript, with these instances they must assist to access the Blue Whale Image Database. To follow the access path correctly, please initiate communication in the following order: academic direction-CICIMAR: direccioncicimar@ipn.mx and the academic subdirection-CICIMAR: subacademica@ipn.mx and you can contact Dr. Gendron through her email: dgendron@ipn.mx, Laboratory of Ecology of Cetaceans and Quelonios.
Funding: This study was financed by the Instituto Politécnico Nacional for the SIP project number 20200736, the Consejo Nacional de Ciencia y Tecnología (CONACYT) with project number 221284, and the Comisión de Operación y Promoción de Actividades Académicas from the IPN to BECG. The funders had no role in study design, data collection and analysis, decision to publish, or manuscript preparation.
The first documented investigations of pattern recognition for animals began with the bottlenose dolphin on the coast of the Gulf of Mexico by researcher David K. Caldwell in 1955 [1], but it was not until 1970 that the observation of cetaceans used this technique. There are research works that study photo identification. Currently, the photo-id method is used in a wide variety of studies such as those focused on life cycles, group structures, geographical characteristics, population structures, population size estimates, and migrations, among others [2–9]. In the case of cetaceans, the marks used for photo-id may be caused by parasites, predator attacks, conspecifics, anthropogenic activities and congenital conditions [10]. However, these marks can be modified over time; consequently, other characteristics have been considered as identification mechanisms. The dorsal fin contour or marks from injuries are some, which generally are among the most accurate features to use for photo-id studies as demonstrated in previous work [10]. Photo-id is a minimally invasive technique [11], which has the capacity to demonstrate the potential consequences of human impacts and/or management actions by quantifying the spatial relationships between populations and the variables that define or reflect their habitat preferences [12]. The application of photo-id and electronic tagging in field studies has shown a relatively high degree of site fidelity [13], which is useful in order to establish possible conservation strategies. Systems based on computer vision, which are focused on image classification, have long attracted the attention of image processing researchers because the results obtained form the basis of many environmental and socioeconomic applications [14]. However, the classification of images acquired in uncontrolled media through surveillance cameras remains a recurring topic of study due to many factors, such as the complexity of the landscape in the study area, the various performance characteristics of the camera, the size and movement of the subject, the framing, and the camera's sensitivity, which can affect the success of the classification result [15–17]. Most photo-id procedures consist of three steps. The first step is the manual selection and trimming of an area of interest to the individual within the image, after which the person in charge of the photo-id classifies and catalogs the images according to their criteria or experience. The second step is an automated algorithmic comparison between the sample and an image library that rates candidates by their coincident probability. The final step is a visual comparison of sample-candidate pairs to confirm positive matches [18]. Photo-id software has been developed to facilitate the task of identifying a diverse set of species, such as in [19–31]. Each of these photo-id systems have been developed on a laptop and personal computer due to the complexity of the proposals developed, such as the work shown in [32] that compares four animal photo-id systems. In the case of the blue whale, an identifying characteristic is the shape of its dorsal fin observed on both the right and left flanks. The dorsal fin of the blue whale can be grouped according to its shape; this classification was proposed by Gendron et al. [33], who explained that photographs taken of the dorsal fin must be observed on both flanks. Because the photo-id systems referenced above requires the help of an expert to detect the characteristics points, the resulting classification may contain errors or a certain bias and, consequently, may vary for the same individual. To identify an animal, it is necessary to extract its main characteristic transforms. For example, the Scale Invariant Feature Transforms (SIFTs) are usually vectors with a large number of components, which can generate redundant information or contain similar points or very close values between them, with a high computational cost [34,35]. These points are extracted from the contour of the dorsal fin of the blue whale on both flanks. We propose the elimination of coincident characteristic points using statistical discriminators such as the variance, standard deviation, mode and cross-correlation. Then, the results will verify that the data that were not discriminated provide sufficient information to carry out the identification and classification of the shape of the dorsal fin, resulting in an economical, fast and feasible solution. Currently, there are different solutions for data classifiers, such as the fuzzy C-means (FCM) [36], the K-means [37], the cluster-based density estimation (DBSCAN) [38], and more complex classifiers, such as support vector machines [39]. In each of these classifiers, initial values are required to make pertinent adjustments for different variations of the data entered and identify their behavior. Depending on the complexity of the problem, dimensionality reduction can be applied to the acquired data; in the particular case presented in this study, this reduction is necessary because the data will be processed on a mobile platform. Therefore, it is necessary that the classification techniques be simplified, reliable, efficient and available during the photo-id process. Systems capable of identifying animals in their habitats are an interesting challenge for image processing researchers due to the ability to achieve correct identification in the shortest possible execution time, guaranteeing intuitive and reliable development. Currently, the photo-id of the blue whale is done visually, so it can take several hours in the laboratory to identify, catalog and store the results obtained during the sightings. Therefore, it is important to consider the following points. Who uses the results?, the CICIMAR-IPN scientist. What do CICIMAR-IPN scientists use it for? After identifying and cataloging blue whales in the field for conservation purposes, where are the results used? In field expeditions off the coast of Baja California Sur, Mexico, identification has been developed as a passive data collector [40]. Innovation in new technologies and the accessibility of sensors, social networks, hi-fi cameras, interoperability between various devices and platform, storage capabilities, cloud computing, and computing on mobile development platform all together are powerful tools that are currently available on mobile platform, as shown in [41]. They are available to any researcher, naturalist, biologist or the general public, and there are applications that help or collaborate on information collection [42]. The current contributions of people on various technological platforms using the Internet or the different available social networks create the citizen sensor network [43] that is derived from the concept of citizen science, which has been used in various areas of knowledge for joint collaboration between the government, scientists and volunteers interested in the subject for conservation and ecology purposes. In this study, we present the results obtained during the research and implementation of PhotoId-Whale for the photo-id of blue whales for classification using mobile platform. We propose a classifier for the characteristic data obtained by the optimization of the SIFT presented in this research that reduces the coincidence of the characteristics. We illustrate its possible implementation on mobile platform and obtain greater portability in field studies. In the case of this research, we validated the results on the Jetson Tegra TK1 mobile development platform, which has several advantages such as the following: low cost, low power consumption and high applicability [44,45].
Methods and materials
Median estimation classifier methodology
The methodology for the classification and identification of the dorsal fin of the blue whale is described in this section. A block diagram of the implemented stages is shown in Fig 1. From this figure, it can be seen that the first step is to obtain an image from the blue whale database. The second step is the preprocessing of the image, which is divided into two substages: i) the selection of the region of interest (ROI) and ii) the segmentation of the image to extract the portion to be classified. The third step is the extraction of the characteristics by means of the SIFT and of the results obtained from the elimination of the redundant data through determining the variance and covariance of the final vectors. The refined vector is immediately entered into the data classifier to obtain the dorsal fin of the blue whale, which is then classified; the results are subsequently stored in the database for monitoring.
Fig 1. Image processing block diagram of the photoid-whale classification.
Each of the previous steps is explained below in further detail.
Step 1. Obtain the image to be classified. The image is obtained from the database or acquired in the field.
Step 2. Image Pre-Processing: This stage is divided into two substages, which are described below.
ROI selection: By means of a graphical interface, the initial point is indicated, mainly in the area of the dorsal fin according to the criteria proposed Gendron et al. [33]. Example images of extracted ROIs are shown in Fig 2.
Fig 2. ROIs of different dorsal fins for the classification of the blue whale [33].
Extraction of the dorsal fin from the image background: At this stage, the algorithm developed in reference [46] is implemented, which isolates the contour of the dorsal fin (Fig 3A) and eliminates existing noise in the ROI of the image (Fig 3B).
Dorsal fin of the blue whale: a) Contour of the segmented ROI, and b) Nonsegmented ROI.
From the extraction of the contour of the dorsal fin, data reduction is performed to subsequently proceed to step 3 on a mobile platform.
Step 3. Extraction of the main characteristics: From the result obtained in step 2, SIFT is applied to the contour of the dorsal fin. Fig 4 shows a block diagram of the considerations made in the extraction of the main characteristics of the processed images obtained in step 2.
Fig 4. Block diagram of sample imaging training.
Internally, for each class defined by Gendron et al. [33], the shape, size and orientation characteristics are extracted. According to the SIFT methodology [47], the first step is the detection of the scale from the contour of the dorsal fin, which is useful during the identification process because the obtained image may depend on the shooting distance. The formal description of this step is detailed below:
Scale detection: The scalar space L (x, y) of an image is obtained from the convolution of the input image IROI through a Gaussian filter G (x, y, σ) at different scales of σ [26,47], as shown in Eq 1: (1) where represents a Gaussian filter, which is applied in both dimensions (x,y) of the IROI image plane.
To obtain the different scale versions of the IROI image, it is necessary to multiply σ with different constants k to obtain the projections of the contiguous scales (where k > 1). The original scale is subtracted from each of its projections, obtaining the differences from the original image Dm, as shown in Eq 2: (2) From the vector obtained in Eq 2, in order to eliminate redundant scales, the standard deviation is obtained and used as the threshold (Eq 3); this value helps to minimize the number of redundant samples in order to maximize the performance of resources on a mobile platform. (3) where Dm is the vector obtained from the scale differences (Eq 2), m represents the number of scales, is the average value of Dm, and n is the total number of sample elements in the scale vector. This value will provide each of the thresholds required to obtain the significant points of the contour of the dorsal fin.
The new thresholding vector Dnew(x,y,σ) is obtained using the following equation: (4) The next stage of the SIFT methodology involves locating the points of interest in the images, which serve as a reference to identify the unique characteristics of the dorsal fin. The points obtained constitute the differences among each of the contour shapes and are described below.
Points of interest: From the new scale vector, which is optimized by the scale selection threshold Dnew to identify the characteristic points, it is necessary to locate where the values of the vector obtained in the previous stage increase or decrease. These vectors are useful due to the low contrast in the ROIs of the images of the dorsal fin since the fin can be confused with the sea.
The search for extreme values on the spatial scale produces multiple candidates. The points that are not selected are the low contrast ones since they are not stable to changes in lighting and noise. Eq 5 shows how the points of interest are located within the ROI contour, whose locations are given by references [26,47]: (5) Subsequently, the vectors are arranged according to the orientation of the points obtained from Eq 5 as explained below.
Orientation mapping: This step assigns a constant orientation to the characteristic points based on the properties of the contour of the dorsal fin obtained in the previous steps. The characteristic point descriptor can be formed in relation to this orientation, resulting in invariance to rotation, which is important to highlight because images can be taken at different shooting angles. The procedure to find the orientation of the points is as follows [47]:
Obtain the scalar values of the points of interest selected in Eq 5.
Calculate the magnitude M: (6)
Calculate the orientation θ: (7)
Locate the highest points of the histograms from the values obtained from Eqs 6 and 7. The characteristic points are obtained with the same orientation as that of the local values above 80%.
Finally, the particular descriptors of the characteristic points obtained in the previous steps must be identified, so they are arranged according to the following.
Descriptors of the characteristic points: To obtain the descriptors, the gradient of the vectors obtained from the selection criteria of the previous step is finally calculated. The vectors obtained from the gradient are rotated so that they are aligned with the point located on the ROI to be labeled with a Gaussian scale value, σG = 0.5 * the scale value of the point of interest, resulting in the following expression: (8) (9) With the simplified characteristic vector, the process is continued with the development of the classifier. The main criterion to consider is that the chosen classifier contains the necessary parameters for the classification of the dorsal fin, which must be fast, simple and portable.
Step 4: Classification (Training). We divide this stage into different subsets of steps to improve the analysis performance of all images that are contained in the dorsal fin image database.
Step 4.1. A group of images is randomly selected from each class for the proposed training according to the classification made by the CICIMAR-IPN researchers [33,48]. We worked according to these classes, which are right falcate, left falcate, right hook, left hook, right triangular and left triangular and selected 20 images from each class for the training.
Step 4.2. For the training system, the weights are added to the descriptors of each class in the training samples, as shown in Eq 10. (10) where z is the value of the contrast change of the dorsal fin contour image, N is the number of samples taken by each class, and j corresponds to each of the classes used for classification.
Step 4.3. For each class, the number of matching points between the sample image (original sample after ROI selection) and the samples used in the training must be obtained.
The number of intersections is calculated with the norm of the Euclidean distance between two samples and the ordinate with the minimum distance between them. (11) where the class number is the total number of classes to classify, N is the number of training samples, x and y are the coordinates of the vector at the current training position, L (j, x, y, σ) is the position of the value in the vector of the image at different scales, and ∥∥L2 is the norm of vector L2.
A graphical interpretation of the method is shown in Fig 5A and 5B, where the correspondence between each of the selection points on the blue whale dorsal fin can be observed.
Fig 5. Distance between the characteristic points obtained from Eq 11.
Additionally, when we developed the test scenarios, four metrics were considered: true positive (TP) and true negative (TN)—which are for correct classifications—and false positive (FP) and false negative (FN)—which are for incorrect classifications; by using these metrics, we can obtain different performance measures as follows [46]: (12) (13) (14) Specificity (Sp) is the ability to detect nonblue whale pixels, sensitivity (Se) reflects the algorithm's ability to detect the edge of the blue whale dorsal fin, and accuracy (Acc) measures the proportion of the total number of pixels correctly classified (sum of the true positives and true negatives) to the total number of pixels that correspond to the image of the whale [46]; the accuracy is the probability that a pixel belonging to the whale is correctly identified.
Classifier presentation on the Jetson Tegra TK1
This section briefly shows the architecture of the blue whale photo-id process, as shown in Fig 6.
Fig 6. Blue whale photo-id process.
Fig 6 shows the following process. i) The user takes an image with a camera. ii) The collected images are stored in memory, which is located on the Jetson Tegra TK1 mobile platform, where the identification and classification process developed in this work is performed, as shown in Fig 7.
Fig 7. General photo-id whale process.
iii) Presentation of Photo-Id Whale results. To carry out the photo-id of the blue whale, the elements of the process were developed in a concise and brief way: upload a photo, select the ROI, and obtain and store information. These elements are as shown in Fig 8.
Fig 8. Graphic presentation of photo-id whale.
Fig 8 shows the options for identifying and storing the dorsal fin image of a blue whale. The File menu has the following options: open image, save and save as. Next, there is the editing menu with the following options: cut and improvement (preprocessing). Later, the tools menu has the following options: extraction of characteristics and classification. Finally, the help menu consists of a brief explanation of the development. The final result is shown below Fig 9.
Fig 9. Photo-id whale final result.
https://doi.org/10.17504/protocols.io.be2rjgd6
The classification tests were run using the CICIMAR-IPN blue whale database, which is a collection of 1172 images, where 697 show a falcate fin contour, 326 show a hooked fin contour and 149 show a triangular fin contour. Among the photographs in the blue whale image database, 57.2% were taken from both sides of the whale, 23.8% were taken from the right flank and 19.0% were taken from the left flank. To validate the proposed statistical classifier, tests were performed with segmented images (SI) and unsegmented images (UI) of the contour of the blue whale dorsal fin. To evaluate the performance related to the processing time (PT) of the proposed methodology, we tested the algorithms on a laptop computer with an Intel (R) Core (TM) i7-2630QM CPU @ 2.00 GHz with 6 GB of RAM and a 64-bit operating system and the image processing tests were also carried out on a Jetson Tegra TK1 mobile platform.
Performance of the proposed methodology on images with segmented ROIs
Tables 1–3 show the performance results obtained for the proposed classifier based on the reduced data extracted from triangular, hooked and falcate dorsal fins in terms of the Sp, Se, and Acc; each of the performance tests were performed on the Jetson Tegra TK1. To evaluate the performance of the statistical classifier, four different training processes were performed. To train this classifier, 300 images were randomly selected. These 300 image samples were divided into six classes, corresponding to the different shapes of the blue whale dorsal fin: right falcate, left falcate, right hooked, left hooked, right triangular and left triangular.
Table 1. Performance results of the median estimation classifier on images with segmented ROIs.
Table 2. Performance results of the median estimation classifier on images with unsegmented ROIs.
Table 3. Comparative performance of the Acc of different classifiers in classifying dorsal fin images of the blue whale.
Performance of the proposed method on images with segmented ROIs
The results of the three tests for the proposed method are shown in Table 1. The best result with respect to the Acc is obtained with the right triangular fin class with a value of 93% while the left falcate fin class had the lowest Acc at 79%. In the Se test, the best performance was found with the right triangular fin class at 86%, and the lowest was found with the left falcate fin class at 40%. Finally, for the Sp test, the best result was obtained with the left falcate fin class at 98%, and the lowest result was obtained with the right falcate fin class at 85%.
Fig 10 shows the results obtained from the proposed method for manually segmented images in terms of the ROC curves, where the right triangular fin class shows the best performance. The lowest performing classes were the left triangular fin and the left falcate fin classes.
Fig 10. ROC curves for experiment # 1.
Performance of the proposed method on images with unsegmented ROIs
Table 2 shows the results from this experiment, which demonstrates that the best Acc was obtained for the right triangular fin class at 95.36% and the lowest was obtained for the left hooked dorsal fin class at 66.27%. In terms of the Se, the best performance was obtained for the right triangular fin class at 95.23%, and the lowest was obtained for the left hooked fin at 24.92%. Finally, for the Sp, the best performance was obtained for the left triangular fin class at 95.98%, and the lowest was obtained for the right falcate fin class at 74.62%.
Fig 11 shows the ROC curves, visually illustrating the results of the proposed method for unsegmented images of the dorsal fin and demonstrating that the right triangular fin class had the best performance while the left and right hooked fin classes had the lowest performance.
Fig 11. ROC curves for experiment #2.
To validate this investigation, the proposed classifier (the Median Estimation Classifier) was compared with other classifiers. We analyzed the standard deviation of the accuracy for the six fin classes (right falcate, left falcate, right hook, left hook, right triangular and left triangular) in the experiments developed for the identification of dorsal fins. Table 3 shows the results obtained by applying the standard deviation of the Acc metric, which are calculated for each comparison between the proposed classifier and the different, existing classifiers. We can see that the classifier with the lowest standard deviation was the K-NN classifier with K = 7 (0.0385), but the Acc was less than 79% for the following classes: left falcate fin (75.83%), right hooked fin (73.33%), and left hooked fin (78.33%). The K-NN classifier for K = 9 yielded similar Acc values, with 75.85% for the left falcate fin class and 74.16% for the right hooked fin class. The opposite result is shown for the same classifier in experiment 2 (without image segmentation), where a larger standard deviation was obtaining for each of the Acc values. The Acc values obtained were below 79% for the following classes: right falcate fin (72.68%), left falcate fin (72.32%) and left hooked fin (66.27%).
Performance processing time of the proposed method
Tables 4 and 5 respectively show the results for all six classes of fins with and without segmentation, including the average performance in terms of the processing time (PT) given in seconds with the Jetson Tegra TK1mobile platform compact GPU hardware and the Intel i7 CPU laptop computer.
Table 4. Runtimes for different images with and without segmentation (proposed classifier).
Table 5. Runtimes for the different images with and without segmentation (proposed classifier).
The results in Tables 4 and 5 show that the best processing time of 0.9 seconds was obtained for the left and right triangular fin classes, particularly for the images with segmented ROIs.
Photo-id systems have been developed for humpback whales, bottlenose dolphins, manta rays, etc. Currently, however, there is no system dedicated to the photo-id of the blue whale fin contours, which is more reliable than identification using the skin pigmentation in the blue whale since the latter can be affected by skin flaking caused by dietary changes, temperature, and the salinity of the sea. For this reason, the results obtained by the PhotoId-Whale classifier were compared with those obtained with the method in [49], which performs the photo-id of the humpback whale, the sperm whale and the pilot whale. The method presented in [50] uses a chain code, which consists of obtaining a border pattern for each interval when there is a change in the direction of the border. This method was implemented for the identification of the fin shape of dolphins and whales, although the runtime results were not shown due to the nature of their algorithm, which is slow and consumes a large amount of computing resources [51]. The reason for the high computational costs of the chain code is the greater image quality due to the better resolution of images obtained today compared with the images taken with professional cameras 10 years ago. As a result of this technological advance, clearer details can be seen in the images of the edges and contours of the dorsal fin of the blue whale. In this work, we carried out the search and identification of the blue whale in dorsal fin images from a blue whale dorsal fin database through the Jetson Tegra TK1, which meets the requirements of low energy consumption and can simultaneously compare newly acquired images of the dorsal fin taken from the right and left flanks to those in the different classes described earlier in the document.
The results obtained for Experiment #1 showed that the triangular fin class had the highest Acc at 93% while the left falcate fin class had the lowest Acc at 79%. In the Se test, the best performance was obtained for the right triangular fin class with 86%, while the lowest was obtained for the left falcate fin class at 40%. Finally, for the Sp test, the best performance was obtained for the left Falcate fin class at 98%, and the lowest was obtained for the right falcate fin class at 85%.
For Experiment #2, the best Acc was obtained for the right triangular fin class at 95.36%, and the lowest was obtained for the left hooked dorsal fin class at 66.27%. In terms of Se, the best performance was obtained for the right triangular fin class at 95.23%, and the lowest was obtained for the left hooked fin class at 24.92%. Finally, for Sp, the best performance was obtained for the left triangular fin class at 95.98%, and the lowest was obtained for the right falcate fin class at 74.62%. Across both experiments, a PT of 0.9 seconds was obtained for the identification of the dorsal fin of the blue whale, far outperforming other photographic identification methods such as Europhlukes (EC EuroPhlukes Initiative, University of Leiden, The Netherlands), which had a PT of more than 90 seconds. Unlike previous methods, the PhotoId-Whale determines and catalogs the blue whale dorsal fin type on a mobile platform, which provides independence for monitoring the blue whales off the Mexican coast. The results obtained in this study demonstrates that the reduction of the data of the characteristic vectors obtained from SIFT in conjunction with the implemented statistical estimator provides accurate and timely results for the identification of the dorsal fin of the blue whale compared to the computational costs shown in the works of [34,35]. In addition, it was found that this method is portable since it was developed in the field through mobile technologies, offering superior performance unlike other development boards, as shown in [51]. One of the advantages of PhotoId-Whale is that the results are obtained using images acquired with standard cameras that do not contain infrared light, irrespective of the incident light, the movement of objects, the time of day or the presence of background objects, which decreases the observation and monitoring costs. The proposed methodology is simple and uses basic algorithms such as SIFT, mathematical expectations, standard deviations, and characteristic vector thresholding, unlike more complex systems that require high calculation costs, such as 13S [31]. The lower computational costs of PhotoId-Whale make the field identification and classification of blue whale dorsal fin images in field research possible, and it is available to scientists and users interested in participating in conservation and ecology activities. Technological developments of this type allow interoperability, maximizing the use and management of information in an accessible and simple way, and improving the user experience that was previously developed using a manual process. The interoperability of this development, the decreased computational costs, and the optimization of the SIFT algorithm allow for the method to be feasibly installed on smartphones, tablets and notebooks. The results show that the best processing time of 0.9 seconds was obtained for the left and right triangular fin classes, particularly for the images with segmented ROIs.
By acquiring characteristic vectors from SIFT, the number of samples required to obtain the threshold by means of the variance, the mean and the standard deviation is reduced. The results were obtained for segmented and unsegmented ROIs from the images of the dorsal fin of the blue whale by means of two comparative classifiers with the best performance obtained based on the proposed statistical estimation method for sample reduction. The experimental results reveal that the proposed method achieves better performance in terms of the Acc and computational costs (time) in most cases compared to the existing results. We can also conclude that the proposed algorithm is easy to operate in terms of the preprocessing of the images obtained in the blue whale habitat and that the implemented application is feasible on mobile devices, despite their battery and memory limitations.
The results were also validated by plotting the ROC curves and obtaining the AUC values of the curves, which showed that the automatic photographic identification system provides positive results and, therefore, supports the photographic identification of the blue whale in its natural habitat. Finally, in this work, the optimization of the SIFT algorithm is presented to reduce the redundant data. The results validate that the said optimization obtains relevant results focused on the energy consumption, processing time, and preserving the result-processing time relationship. This improvement can be validated on devices with limited resources such as tablets or mobile platforms. The method provides a conservation and ecology tool that may be used by scientists and the general public.
We thank all anonymous reviewers for their help during the work.
1. Caldwell D., Notes on the Spotted Dolphin, Stenella plagiodon, and the first record of the common dolphin, Delphinus Delphis, in the Gulf of Mexico, J Mammal, 36; 3: 467–470, 1955, https://www.jstor.org/stable/1375705.
2. Bigg M, An assessment of killer whale (Orcinus orca) stocks off Vancouver Island, British Columbia, Rep Int Whal Comm,36: 655–666, 1982. https://eurekamag.com/research/020/652/020652536.php.
3. Hammond P, Estimating the size of naturally marked whale populations using capture-recapture techniques, Rep Int Whal Comm,8:253–282, 1986.
4. Shane S, Wells R, Würsig B, Ecology, behavior and social organization of the bottlenose dolphin: A review. Mar. Mamm. Sci., 2;1:34–63, 2006,
5. Würsig B, Jefferson T, Methods of photo-identification for small cetaceans, Rep Int Whal Comm, Special Issue, 12;43–52, 1990.
6. Mann J, Connor R, Tyack P, Whitehead H, Cetacean societies: Field studies of dolphins and whales, J Biogeogr, 28; 6: 814–815, 2001, https://www.jstor.org/stable/827459.
7. Calambokidis J, Steiger G, Straley J, Herman L, Cerchio S, Salden D, et al., Movements and population structure of humpback whales in the North Pacific, Mar. Mamm. Sci., 17;4:769–794, 2006, https://doi.org/10.1111/j.1748-7692.2001.tb01298.x.
8. Grellier K, Hammond P, Wilson B, Sanders-Reed C, Thompson P, Use of photo-identification data to quantify mother-calf association patterns in bottlenose dolphins. Can J Zool, 81: 1421–1427, 2003, https://doi.org/10.1139/z03-132.
9. Parsons K, Procedural Guideline No. 4–5 Using photographical identification techniques for assessing bottlenose dolphin (Tursiops truncatus) abundance and behaviour, In Marine Monitoring Handbook. Ed. Jon Davies 2001, Joint Nature Conservation Committee, 2005.
10. Bertulli C., Rasmussen M, Rosso M, An assessment of the natural marking patterns used for photo-identification of common minke whales and white-beaked dolphins in Icelandic waters, J Mar Biol Assoc U K, 96; 4: 807–819, 2016, https://doi.org/10.1017/S0025315415000284.
11. McCoy E, Burce R, David D, Aca E, Hardy J, Labaja J, et al., Long-Term Photo-Identification Reveals the Population Dynamics and Strong Site Fidelity of Adult Whale Sharks to the Coastal Waters of Donsol, Philippines, Front Mar Sci, 5;271:1–11, 2018, https://doi.org/10.3389/fmars.2018.00271.
12. Molloy S, Davis R, Van Etten E, Incorporating Field Studies into Species Distribution and Climate Change Modelling: A Case Study of the Koomal Trichosurus vulpecula hypoleucus (Phalangeridae), PLoS ONE, 11; 4: 1–18, pmid:27104611
13. Armstrong A, Armstrong A, Bennett M, Richardson A, Townsend K, Dudgeon C, Photographic identification and citizen science combine to reveal long distance movements of individual reef manta rays Mobula alfredi along Australia's east coast, Mar Biodivers Rec, 12;14;1–7, 2019, https://doi.org/10.1186/s41200-019-0173-6.
14. Lu D, Weng Q, A survey of image classification methods and techniques for improving classification performance, Int J Remote Sens, 28; 5: 823–870, 2007,
15. Damm PE, Grand JB, Barnett SW, Variation in detection among passive infrared triggered-cameras used in wildlife research, Proc Annu Conf Southeast Assoc Fish Wildl Agen, 2010, 125–130.
16. Meek PD, Ballard G, Fleming PJS, The pitfalls of wildlife camera trapping as a survey tool in Australia, Aust. Mammal, 37; 2015: 13–22,
17. Swann DE, Hass CC, Dalton DC, Wolf SA, Infrared-triggered cameras for detecting wildlife: an evaluation and review, Wildl. Soc. Bull., 2004;32:357–365, https://doi.org/10.2193/0091-7648(2004)32[357:ICFDWA]2.0.CO;2
18. Buehler P, Carroll B, Bhatia A, Gupta V, Lee D E, An automated program to find animals and crop photographs for individual recognition, Ecol Inform, 50; 2019: 191–196, https://doi.org/10.1016/j.ecoinf.2019.02.003.
19. Kelly MJ, Computer-aided photograph matching in studies using individual identification: an example from Serengeti cheetahs, J. Mammal, 82;2001:440–449, https://doi.org/10.1644/1545-1542(2001)082<0440:CAPMIS>2.0.CO;2.
20. Ardovini A, Cinque L, Sangineto E, Identifying elephant photos by multi-curve matching. Pattern Recogn, 41; 2008: 1867–1877. https://doi.org/10.1016/j.patcog.2007. 11.010.
21. Raj A, Choudhary P, Suman P, Identification of tigers through their pugmark using pattern recognition, 3rd Int Conf on Comp for Sustainable Global Development (INDIACom), New Delhi, 2016, 335–337. Available: https://ieeexplore.ieee.org/document/7724282?section=abstract.
22. Gamble L, Ravela S, McGarigal K, Multi-scale features for identifying individuals in large biological databases: an application of pattern recognition technology to the marbled salamander Ambystoma opacum, J. Appl. Ecol. 45; 2008: 170–180, https://doi.org/10.1111/j.1365-2664.2007.01368.x.
23. Arzoumanian Z, Holmberg J, Norman B, An astronomical pattern-matching algorithm for computer-aided identification of whale sharks Rhincodon typus, J. Appl. Ecol., 42; 2005 999–1011, https://doi.org/10.1111/j.1365-2664.2005.01117.x.
24. Van Tienhoven A M, Den Hartog JE, Reijns RA, Peddemors VM, A computer aided program for pattern-matching of natural marks on the spotted ragged tooth shark Carcharias Taurus, J. Appl. Ecol., 2007; 44: 273–280, https://doi.org/10.1111/j.1365-2664.2006.01273.x.
25. Sherley RB, Burghardt T, Barham PJ, Campbell N, Cuthill IC, Spotting the difference: towards fully-automated population monitoring of African penguins Spheniscus demersus, Endang Species Res 11; 2010: 101–111,
26. Town C, Marshall A, Sethasathien N, Manta Matcher: automated photographic identification of manta rays using keypoint features, Ecology and Evolution, 2013, 3(7):1902–1914 pmid:23919138
27. Adams JD, Speakman T, Zolman E, Schwacke LH, Automating image matching, cataloging, and analysis for photo-identification research, Aquat. Mamm. 32; 2006: 374–384. https://doi.org/10.1578/AM.32.2.2006.374.
28. Gope C, Kehtarnavaz N, Hillman G, Würsig B, An affine invariant curve matching method for photo-identification of marine mammals, Pattern Recogn. 38; 2005: 125–132, https://doi.org/10.1016/j.patcog.2004.06.005.
29. Bolger DT, Morrison TA, Vance B, Lee D, Farid H, A computer-assisted system for photographic mark–recapture analysis, Methods Ecol. Evol. 3;2012: 813–822, https://doi.org/10.1111/j.2041-210X.2012.00212.x.
30. Moya Ó, Mansilla PL, Madrazo S, Igual JM, Rotger A, Romano A, et al, APHIS: a new software for photo-matching in ecological studies, Ecol. Inform., 27;2015: 64–70, https://doi.org/10.1016/j.ecoinf.2015.03.003.
31. Hartog J, Reijns R, Interactive Individual Identification System (I3S), Boston, Reijns Free Software Foundation Inc., (2007).
32. Suriyamongkol T., Mali I., Feasibility of Using Computer-Assisted Software for Recognizing Individual Rio Grande Cooter (Pseudemys gorzugi), Copeia, 2018, 106;4, 646–651,
33. Gendron D, Ugalde de la Cruz A., A new classification method to simplify blue whale photo-identification technique, J. Cetacean Res. Manag., 2012, 12;1,79–84, Available: https://www.researchgate.net/publication/256518774_A_new_classification_method_to_simplify_blue_whale_photo-identification_technique.
34. Chater A, Lasfar A, New approach to the identification of the easy expression recognition system by robust techniques (SIFT, PCA-SIFT, ASIFT and SURF), TELKOMNIKA, 2020, 18;2, 695–704,
35. Qu X, Soheilian B, Habets E, Paparoditis N, Evaluation of SIFT and surf for vision based localization, In: Halounova L., Schindler K., Limpouch A., Pajdla T., Šafář V., Mayer H., Elberink O, Mallet C, Rottensteiner F, Brédif M, Skaloud J, Stilla U, editors. WG III/4 – 3D Scene Analysis programming, The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences; 2016, 12–19 july, Prague, Czech Republic, XXIII ISPRS Congress, Commission III; 2016. p. 685–692.
36. Zhao F, Fan J, Liu H, Optimal-selection-based suppressed fuzzy c-means clustering algorithm with self-tuning non-local spatial information for image segmentation, Expert Syst Appl, 41; 2014: 4083–4093, https://doi.org/10.1016/j.eswa.2014.01.003.
37. Hong Y, Qingling D, Daoliang L, Jianping W, An improved K-means clustering algorithm for fish image segmentation, Mathematical and Computer Modelling, 58; 2013: 790–798, https://doi.org/10.1016/j.mcm.2012.12.025.
38. Ester M, Kriegel H P, Sander J, Xu, A density-based algorithm for discovering clusters in large spatial databases with noise, Proceedings of Second International Conference on Knowledge Discovery and Data Mining, Portland, 226–231, (1996). Available: https://www.aaai.org/Papers/KDD/1996/KDD96-037.pdf.
39. Suykens JAK, Vandewalle J, Least Squares Support Vector Machine Classifiers, Neural Process Lett, 9;3: 1999, 293–300, https://doi.org/10.1023/A:1018628609742.
40. Preece J, Citizen Science: New Research Challenges for Human–Computer Interaction, Int J Hum Comput Interact, 32;8: 585–612, 2016,
41. Teacher A, Griffiths D, Hodgson D, Inger R, Smartphones in ecology and evolution: a guide for the app-rehensive, Ecol Evol, 3;16: 5268–5278, 2013, pmid:24455154
42. Donnelly A, Crowe O, Regan E, Begley S, Caffarra A, The role of citizen science in monitoring biodiversity in Ireland, Int J Biometeorol, 58:1237–1249, 2014, pmid:23975107
43. Sheth A, Citizen sensing, social signals, and enriching human experience, IEEE Internet Computing, 13; 4: 87–92, 2009,
44. Jyh-Da W, Hui-Jun C, Chun-Yuan L, Jin Y, Kuan-Yu Y, Embedded-based graphics processing unit cluster platform for multiple sequence alignments, Evo.Bio., 2017, 13;2017, 1–10, https://doi.org/10.1177/117693431772476.
45. NVIDIA, [Internet]: Jetson tk1: Mobile embedded supercomputer takes cuda everywhere, c2020, [cited 2020, April 12], Available from: https://devblogs.nvidia.com/jetson-tk1-mobile-embedded-supercomputer- cuda-everywhere/.
46. Carvajal-Gámez B E, Trejo-Salazar B, Gendron D, Gallegos-Funes F J, Photo-id of blue whale by means of the dorsal fin using clustering algorithms and color local complexity estimation for mobile devices, EURASIP J Image Video Process, 2017;6: 2–13, https://doi.org/10.1186/s13640-016-0153-2.
47. Lowe D G, Distinctive image features from scale-invariant keypoints, Int J Comput Vis, 60; 2: 91–110, https://doi.org/10.1023/B:VISI.0000029664.99615.94.
48. CICIMAR.ipn [Internet], Centro Interdisciplinario de Ciencias Marinas, c 2020, [cited 2020, April 12], Available from: https://www.cicimar.ipn.mx/.
49. Beekmans Bas W P M, Whitehead H, Hele R, Steiner L, Steenbeek A G, Comparison of Two Computer-Assisted Photo-Identification Methods Applied to Sperm Whales (Physeter macrocephalus), Aquat Mamm, 31; 2: 243–247,
50. Weideman H J, Jablons Z M, Holmberg J, Flynn K, Calambokidis J, Tyson R B, et al., Integral Curvature Representation and Matching Algorithms for Identification of Dolphins and Whales, 2017, arXiv preprint arXiv:1708.07785.
51. Sparsh M, A Survey on optimized implementation of deep learning models on the NVIDIA Jetson platform, JSA, 2019, 97;2019, 428–442, https://doi.org/10.1016/j.sysarc.2019.01.011.
Blue whales
Is the Subject Area "Blue whales" applicable to this article?
Is the Subject Area "Personal computers" applicable to this article?
Is the Subject Area "Imaging techniques" applicable to this article?
Is the Subject Area "Computer hardware" applicable to this article?
Is the Subject Area "Conservation science" applicable to this article?
Is the Subject Area "Algorithms" applicable to this article?
Is the Subject Area "Scientists" applicable to this article?
Is the Subject Area "Cameras" applicable to this article?