Computer Vision and Machine Learning for Human Rights Video Analysis: Case Studies, Possibilities, Concerns, and Limitations

2018 ◽  
Vol 43 (04) ◽  
pp. 1188-1209 ◽  
Author(s):  
Jay D. Aronson
Keyword(s):  
Machine Learning ◽  
Computer Vision ◽  
Human Rights ◽  
Playing Field ◽  
Human Rights Organizations ◽  
Efficiency And Effectiveness ◽  
Computer Scientists ◽  
Human Rights Advocacy ◽  
Working Together ◽  
Cover Up

Citizen video and other publicly available footage can provide evidence of human rights violations and war crimes. The ubiquity of visual data, however, may overwhelm those faced with preserving and analyzing it. This article examines how machine learning and computer vision can be used to make sense of large volumes of video in advocacy and accountability contexts. These technologies can enhance the efficiency and effectiveness of human rights advocacy and accountability efforts, but only if human rights organizations can access the technologies themselves and learn how to use them to promote human rights. As such, computer scientists and software developers working with the human rights community must understand the context in which their products are used and act in solidarity with practitioners. By working together, practitioners and scientists can level the playing field between the human rights community and the entities that perpetrate, tolerate, or seek to cover up violations.

scholarly journals Advocating Abortion Rights in Northern Ireland

2016 ◽  
Vol 25 (6) ◽  
pp. 716-740 ◽  
Author(s):  
Catherine O’Rourke
Keyword(s):  
Human Rights ◽  
Northern Ireland ◽  
International Human Rights Law ◽  
International Human Rights ◽  
Optional Protocol ◽  
Human Rights Organizations ◽  
The Family ◽  
Human Rights Advocacy ◽  
The Subject ◽  
International Human

It is frequently claimed that the Convention on the Elimination of All Forms of Discrimination against Women (CEDAW) is more significant for the cultural, rather than legal, work that it does in reframing locally contested gender issues as the subject of international human rights. While this argument is well developed in respect of violence against women, CEDAW’s cultural traction is less clear in respect of women’s right to access safe and legal abortion. This article examines the request made jointly by Alliance for Choice, the Family Planning Association Northern Ireland and the Northern Ireland Women’s European Platform to the CEDAW Committee to request an inquiry under the CEDAW Optional Protocol into access to abortion in the jurisdiction. The study found that the CEDAW framework was useful in underpinning alliances between diverse pro-choice organizations but less effective in securing the support of ‘mainstream’ human rights organizations in the jurisdiction. The article argues that the local cultural possibilities of CEDAW must be understood as embedded within both the broader structural gendered limitations of international human rights law and persistent regressive gendered sub-themes within mainstream human rights advocacy.

The Unintended Consequences of the Goldstone Commission of Inquiry on Human Rights Organizations in Israel

2019 ◽  
Vol 30 (3) ◽  
pp. 877-901
Author(s):  
Hala Khoury-Bisharat
Keyword(s):  
Human Rights ◽  
Unintended Consequences ◽  
International Human Rights ◽  
Human Rights Organizations ◽  
Legal Literature ◽  
Human Rights Advocacy ◽  
Commissions Of Inquiry ◽  
International Human ◽  
Fact Finding

Abstract Scholarly writings on internationally constituted commissions of inquiry (COIs), as outlined in the introduction to this symposium, give inadequate attention to the effects that they might have on local disputes that these bodies are often created to address. The United Nations Fact-Finding Mission on the Gaza Conflict (2009), popularly known as the Goldstone Commission, had unintended and unforeseen consequences at the domestic level. Specifically, the Commission caused a severe backlash against human rights organizations in Israel (IsHROs). This article analyses the backlash against the Commission and the effect of that backlash on human rights organizations and human rights advocacy in Israel and the Occupied Palestinian Territory in the first few years after the release of the Goldstone report. This case study reveals how a government can use a COI intervention in an ongoing conflict to deflect criticism against it and to delegitimize local human rights organizations and, as a result, to intensify enemy–friend dynamics within a conflict. The findings of this case study thus challenge the assumption of much of the socio-legal literature that the interaction of international human rights institutions with domestic actors leads to positive human rights change. But the case study also adds a new dimension to the academic and policy literature that has been critical of the international human rights enterprise in recent years. Despite delegitimization campaigns, international funding has increased for many IsHROs, and, eventually, some groups have become even more visible and have enjoyed, internationally, a higher reputation and greater credibility. The Commission’s experience thus demonstrates that the establishment of COIs in deeply divided conflict societies can have negative, as well as positive, implications on human rights.

scholarly journals Machine Learning Computer Vision Applications for Spatial AI Object Recognition in Orange County, California

2021 ◽  
Author(s):  
Kostas Alexandridis
Keyword(s):  
Machine Learning ◽  
Computer Vision ◽  
Object Recognition ◽  
Spatial Data ◽  
Field Data ◽  
Orange County ◽  
Data Systems ◽  
Neural Network Learning ◽  
Stop Sign ◽  
Efficiency And Effectiveness

We provide an integrated and systematic automation approach to spatial object recognition and positional detection using AI machine learning and computer vision algorithms for Orange County, California. We describe a comprehensive methodology for multi-sensor, high-resolution field data acquisition, along with post-field processing and pre-analysis processing tasks. We developed a series of algorithmic formulations and workflows that integrate convolutional deep neural network learning with detected object positioning estimation in 360\textdegree~equirectancular photosphere imagery. We provide examples of application processing more than 800 thousand cardinal directions in photosphere images across two areas in Orange County, and present detection results for stop-sign and fire hydrant object recognition. We discuss the efficiency and effectiveness of our approach, along with broader inferences related to the performance and implications of this approach for future technological innovations, including automation of spatial data and public asset inventories, and near real-time AI field data systems.

scholarly journals Machine Learning Computer Vision Applications for Spatial AI Object Recognition in Orange County, California

2021 ◽  
Author(s):  
Kostas Alexandridis
Keyword(s):  
Machine Learning ◽  
Computer Vision ◽  
Object Recognition ◽  
Spatial Data ◽  
Field Data ◽  
Orange County ◽  
Data Systems ◽  
Neural Network Learning ◽  
Stop Sign ◽  
Efficiency And Effectiveness

We provide an integrated and systematic automation approach to spatial object recognition and positional detection using AI machine learning and computer vision algorithms for Orange County, California. We describe a comprehensive methodology for multi-sensor, high-resolution field data acquisition, along with post-field processing and pre-analysis processing tasks. We developed a series of algorithmic formulations and workflows that integrate convolutional deep neural network learning with detected object positioning estimation in 360\textdegree~equirectancular photosphere imagery. We provide examples of application processing more than 800 thousand cardinal directions in photosphere images across two areas in Orange County, and present detection results for stop-sign and fire hydrant object recognition. We discuss the efficiency and effectiveness of our approach, along with broader inferences related to the performance and implications of this approach for future technological innovations, including automation of spatial data and public asset inventories, and near real-time AI field data systems.

scholarly journals Harnessing Human Rights to the Olympic Games: Human Rights Watch and the 1993 ‘Stop Beijing’ Campaign

2016 ◽  
Vol 53 (2) ◽  
pp. 415-438 ◽  
Author(s):  
Barbara Keys
Keyword(s):  
Human Rights ◽  
Cold War ◽  
Olympic Games ◽  
Moral Legitimacy ◽  
Post Cold War ◽  
Playing Field ◽  
Human Rights Organizations ◽  
Moral Claims ◽  
The Cold War ◽  
The Olympic Games

In 1993 Human Rights Watch, one of the two most influential human rights organizations in the world, launched a major campaign to derail Beijing's bid to host the 2000 Olympic Games. This article situates this highly publicized campaign in the context of Sino–US relations, the end of the Cold War, and the ‘victory’ of human rights as a global moral lingua franca. It argues that Human Rights Watch's decision to oppose Beijing's bid stemmed from its new post-Cold War focus on China combined with the organization's search for new ways to secure media attention and the funding that flowed from publicity. The campaign most likely swayed the International Olympic Committee's close vote in favor of Sydney. It also brought Human Rights Watch a windfall of favorable publicity among new audiences. The article argues that the campaign irrevocably inserted broad-based human rights considerations into the Olympic Games, decisively moving moral claims-making around the Olympics beyond the playing field. It also linked Human Rights Watch's moral legitimacy to US power in problematic ways and triggered a powerful anti-US backlash in China.

scholarly journals Machine Learning Computer Vision Applications for Spatial AI Object Recognition in Orange County, California

2021 ◽  
Author(s):  
Kostas Alexandridis
Keyword(s):  
Machine Learning ◽  
Computer Vision ◽  
Object Recognition ◽  
Spatial Data ◽  
Field Data ◽  
Orange County ◽  
Data Systems ◽  
Neural Network Learning ◽  
Stop Sign ◽  
Efficiency And Effectiveness

We provide an integrated and systematic automation approach to spatial object recognition and positional detection using AI machine learning and computer vision algorithms for Orange County, California. We describe a comprehensive methodology for multi-sensor, high-resolution field data acquisition, along with post-field processing and pre-analysis processing tasks. We developed a series of algorithmic formulations and workflows that integrate convolutional deep neural network learning with detected object positioning estimation in 360 degree equirectancular photosphere imagery. We provide examples of application processing more than 800 thousand cardinal directions in photosphere images across two areas in Orange County, and present detection results for stop-sign and fire hydrant object recognition. We discuss the efficiency and effectiveness of our approach, along with broader inferences related to the performance and implications of this approach for future technological innovations, including automation of spatial data and public asset inventories, and near real-time AI field data systems.

A neighborhood prior constrained collaborative representation for classification

2020 ◽  
pp. 2050073 ◽  
Author(s):  
Yanting Li ◽  
Junwei Jin ◽  
Liang Zhao ◽  
Huaiguang Wu ◽  
Lijun Sun ◽  
...  
Keyword(s):  
Machine Learning ◽  
Computer Vision ◽  
Pattern Classification ◽  
State Of The Art ◽  
Test Sample ◽  
Experimental Results ◽  
Training Data ◽  
Collaborative Representation ◽  
Efficiency And Effectiveness ◽  
Sample Representation

With the development of machine learning and computer vision, classification technology is becoming increasingly important. Due to the advantage in efficiency and effectiveness, collaborative representation-based classifiers (CRC) have been applied to many practical cognitive fields. In this paper, we propose a new neighborhood prior constrained collaborative representation model for pattern classification. Compared with the naive CRC models which approximate the test sample with all the training data globally, our proposed methods emphasize the guidance of the neighborhood priors in the coding process. Two different kinds of neighbor priors and the models’ weighted extensions are explored from the view of sample representation ability and relationships between the samples. Consequently, the contributions of different samples can be distinguished adaptively and the obtained representations can be more discriminative for the recognition. Experimental results on several popular databases can verify the effectiveness of our proposed methods in comparison with other state-of-the-art classifiers.

scholarly journals Efficient mobilenet architecture as image recognition on mobile and embedded devices

2019 ◽  
Vol 16 (1) ◽  
pp. 389
Author(s):  
Barlian Khasoggi ◽  
Ermatita Ermatita ◽  
Samsuryadi Samsuryadi
Keyword(s):  
Machine Learning ◽  
Computer Vision ◽  
Image Recognition ◽  
Training Model ◽  
Human Interaction ◽  
Training Data ◽  
Embedded Devices ◽  
Moderate Amount ◽  
Computing Power ◽  
Efficiency And Effectiveness

The introduction of a modern image recognition that has millions of parameters and requires a lot of training data as well as high computing power that is hungry for energy consumption so it becomes inefficient in everyday use. Machine Learning has changed the computing paradigm, from complex calculations that require high computational power to environmentally friendly technologies that can efficiently meet daily needs. To get the best training model, many studies use large numbers of datasets. However, the complexity of large datasets requires large devices and requires high computing power. Therefore large computational resources do not have high flexibility towards the tendency of human interaction which prioritizes the efficiency and effectiveness of computer vision. This study uses the Convolutional Neural Networks (CNN) method with MobileNet architecture for image recognition on mobile devices and embedded devices with limited resources with ARM-based CPUs and works with a moderate amount of training data (thousands of labeled images). As a result, the MobileNet v1 architecture on the ms8pro device can classify the caltech101 dataset with an accuracy rate 92.4% and 2.1 Watt power draw. With the level of accuracy and efficiency of the resources used, it is expected that MobileNet's architecture can change the machine learning paradigm so that it has a high degree of flexibility towards the tendency of human interaction that prioritizes the efficiency and effectiveness of computer vision.

Interview with Omar Shakir

2020 ◽  
Vol 49 (4) ◽  
pp. 127-137
Author(s):  
Noura Erakat
Keyword(s):  
Human Rights ◽  
Supreme Court ◽  
Committee Member ◽  
Editorial Committee ◽  
Rutgers University ◽  
Human Rights Advocacy ◽  
University Professor ◽  
The Government ◽  
Israeli Supreme Court

In late November 2019, the Israeli Supreme Court upheld the Ministry of Interior's order to deport Human Rights Watch (HRW) director for Israel and Palestine, Omar Shakir. The court based its decision on a 2017 amendment to Israel's 1952 Entry into Israel Law enabling the government to refuse entry to foreigners who allegedly advocate for the boycott of Israel. The same law was invoked to deny entry to U.S. congresswomen Rashida Tlaib and Ilhan Omar in the summer of 2019. The campaign against Shakir began almost immediately after he was hired by HRW in 2016, and the court's decision marked the culmination of a multi-year battle against the deportation order. In this interview, JPS Editorial Committee member, Rutgers University professor, and author Noura Erakat discusses the details of his case with Shakir in an exchange that also examines the implications of the case for human rights advocacy, in general, and for Palestinians, in particular. The interview was edited for length and clarity.

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