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Towards a systems-based framework for understanding the diffusion of technology: A case study of a modest technological innovation in the multi-agency context of policing
Technological innovation in policing is being given greater emphasis. In public discourse about technology and policing, there is often a focus on large-scale projects that are known to fail, sometimes at significant cost. The implementation of smaller innovations are often overlooked. This thesis examines practice of innovation and adoption in the context of multi-agency working.
The literature review in this thesis reveals that little is known about contexts where decision making does not rest with the police and exposes potential limitations in the use of diffusion and adoption frameworks/models. The research question is: In the context of multi-agency diffusion and adoption of a technology to enhance policing, can systems thinking techniques enhance, or even replace, existing frameworks and models?
This empirical research study looks at the adoption of a relatively simple technology that scans identification documents. However, the decision to adopt and implement an ID scanner takes place within a complex setting. Tracking an adoption decision requires understanding of the various actors and their roles. The research includes 48 semi-structured interviews with police officers, premises owners and managers and other stakeholders involved in the decision to adopt an ID scanner. Their perceptions of the history leading to an adoption decision, their own role and that of other key actors is examined.
Initial analysis takes place using spray diagrams and further analysis is made through the lenses of existing diffusion and adoption frameworks/models. Subsequently systems thinking techniques are deployed and the additional insights they provide are highlighted. This research finds that systems thinking can extend understanding of multi-agency diffusion and adoption decisions when compared with solely utilising existing frameworks/models. Finally, the research proposes a systems-based framework for collaborative diffusion and adoption analysis
Multidisciplinary perspectives on Artificial Intelligence and the law
This open access book presents an interdisciplinary, multi-authored, edited collection of chapters on Artificial Intelligence (‘AI’) and the Law. AI technology has come to play a central role in the modern data economy. Through a combination of increased computing power, the growing availability of data and the advancement of algorithms, AI has now become an umbrella term for some of the most transformational technological breakthroughs of this age. The importance of AI stems from both the opportunities that it offers and the challenges that it entails. While AI applications hold the promise of economic growth and efficiency gains, they also create significant risks and uncertainty. The potential and perils of AI have thus come to dominate modern discussions of technology and ethics – and although AI was initially allowed to largely develop without guidelines or rules, few would deny that the law is set to play a fundamental role in shaping the future of AI. As the debate over AI is far from over, the need for rigorous analysis has never been greater. This book thus brings together contributors from different fields and backgrounds to explore how the law might provide answers to some of the most pressing questions raised by AI. An outcome of the Católica Research Centre for the Future of Law and its interdisciplinary working group on Law and Artificial Intelligence, it includes contributions by leading scholars in the fields of technology, ethics and the law.info:eu-repo/semantics/publishedVersio
Redefining Disproportionate Arrest Rates: An Exploratory Quasi-Experiment that Reassesses the Role of Skin Tone
The New York Times reported that Black Lives Matter was the third most-read subject of 2020. These articles brought to the forefront the question of disparity in arrest rates for darker-skinned people. Questioning arrest disparity is understandable because virtually everything known about disproportionate arrest rates has been a guess, and virtually all prior research on disproportionate arrest rates is questionable because of improper benchmarking (the denominator effect). Current research has highlighted the need to switch from demographic data to skin tone data and start over on disproportionate arrest rate research; therefore, this study explored the relationship between skin tone and disproportionate arrest rates. This study also sought to determine which of the three theories surrounding disproportionate arrests is most predictive of disproportionate rates. The current theories are that disproportionate arrests increase as skin tone gets darker (stereotype threat theory), disproportionate rates are different for Black and Brown people (self-categorization theory), or disproportionate rates apply equally across all darker skin colors (social dominance theory). This study used a quantitative exploratory quasi-experimental design using linear spline regression to analyze arrest rates in Alachua County, Florida, before and after the county’s mandate to reduce arrests as much as possible during the COVID-19 pandemic to protect the prison population. The study was exploratory as no previous study has used skin tone analysis to examine arrest disparity. The findings of this study redefines the understanding of the existence and nature of disparities in arrest rates and offer a solid foundation for additional studies about the relationship between disproportionate arrest rates and skin color
Natural and Technological Hazards in Urban Areas
Natural hazard events and technological accidents are separate causes of environmental impacts. Natural hazards are physical phenomena active in geological times, whereas technological hazards result from actions or facilities created by humans. In our time, combined natural and man-made hazards have been induced. Overpopulation and urban development in areas prone to natural hazards increase the impact of natural disasters worldwide. Additionally, urban areas are frequently characterized by intense industrial activity and rapid, poorly planned growth that threatens the environment and degrades the quality of life. Therefore, proper urban planning is crucial to minimize fatalities and reduce the environmental and economic impacts that accompany both natural and technological hazardous events
SportsMOT: A Large Multi-Object Tracking Dataset in Multiple Sports Scenes
Multi-object tracking in sports scenes plays a critical role in gathering
players statistics, supporting further analysis, such as automatic tactical
analysis. Yet existing MOT benchmarks cast little attention on the domain,
limiting its development. In this work, we present a new large-scale
multi-object tracking dataset in diverse sports scenes, coined as
\emph{SportsMOT}, where all players on the court are supposed to be tracked. It
consists of 240 video sequences, over 150K frames (almost 15\times MOT17) and
over 1.6M bounding boxes (3\times MOT17) collected from 3 sports categories,
including basketball, volleyball and football. Our dataset is characterized
with two key properties: 1) fast and variable-speed motion and 2) similar yet
distinguishable appearance. We expect SportsMOT to encourage the MOT trackers
to promote in both motion-based association and appearance-based association.
We benchmark several state-of-the-art trackers and reveal the key challenge of
SportsMOT lies in object association. To alleviate the issue, we further
propose a new multi-object tracking framework, termed as \emph{MixSort},
introducing a MixFormer-like structure as an auxiliary association model to
prevailing tracking-by-detection trackers. By integrating the customized
appearance-based association with the original motion-based association,
MixSort achieves state-of-the-art performance on SportsMOT and MOT17. Based on
MixSort, we give an in-depth analysis and provide some profound insights into
SportsMOT. The dataset and code will be available at
https://deeperaction.github.io/datasets/sportsmot.html
Neural Architecture Search for Image Segmentation and Classification
Deep learning (DL) is a class of machine learning algorithms that relies on deep neural networks (DNNs) for computations. Unlike traditional machine learning algorithms, DL can learn from raw data directly and effectively. Hence, DL has been successfully applied to tackle many real-world problems. When applying DL to a given problem, the primary task is designing the optimum DNN. This task relies heavily on human expertise, is time-consuming, and requires many trial-and-error experiments.
This thesis aims to automate the laborious task of designing the optimum DNN by exploring the neural architecture search (NAS) approach. Here, we propose two new NAS algorithms for two real-world problems: pedestrian lane detection for assistive navigation and hyperspectral image segmentation for biosecurity scanning. Additionally, we also introduce a new dataset-agnostic predictor of neural network performance, which can be used to speed-up NAS algorithms that require the evaluation of candidate DNNs
Detecting anomalies from liquid transfer videos in automated laboratory setting
In this work, we address the problem of detecting anomalies in a certain laboratory automation setting. At first, we collect video images of liquid transfer in automated laboratory experiments. We mimic the real-world challenges of developing an anomaly detection model by considering two points. First, the size of the collected dataset is set to be relatively small compared to large-scale video datasets. Second, the dataset has a class imbalance problem where the majority of the collected videos are from abnormal events. Consequently, the existing learning-based video anomaly detection methods do not perform well. To this end, we develop a practical human-engineered feature extraction method to detect anomalies from the liquid transfer video images. Our simple yet effective method outperforms state-of-the-art anomaly detection methods with a notable margin. In particular, the proposed method provides 19% and 76% average improvement in AUC and Equal Error Rate, respectively. Our method also quantifies the anomalies and provides significant benefits for deployment in the real-world experimental setting
Monitoring Dangerous Goods on Roads Using Computer Vision
The transportation of dangerous goods on roads poses significant risks, as accidents involving hazardous materials can be deadly and devastating, especially in densely populated areas and confined spaces like tunnels. In the event of an accident, fast emergency response is crucial. Vehicles carrying dangerous goods must be marked with orange hazmat plates, one attached to the front and another to the back of the vehicle. This establishes a universal way to recognize these trucks within ADR member states. This thesis aims to determine if it is possible to use computer vision to automatically monitor dangerous goods in an efficient manner based on the visual information provided by the hazmat plates.
This research began with a review of relevant literature and background information. Then, a dataset was created for training deep learning-based object detectors to identify and locate trucks and hazmat plates in images. A method for classifying trucks as carrying dangerous goods was also developed. Finally, the effectiveness of the method was tested on a set of videos of highway traffic that were collected manually.
The proposed approach for dangerous goods detection and monitoring in this thesis proved to be highly accurate. The method was evaluated using a set of highway traffic videos with varying levels of complexity, including scenarios where false positives could occur. The proposed method was able to identify all trucks carrying dangerous goods and avoid false positives. The method also proved to be highly efficient, as it could process 26 frames per second on a modern edge device
Unveiling the frontiers of deep learning: innovations shaping diverse domains
Deep learning (DL) enables the development of computer models that are
capable of learning, visualizing, optimizing, refining, and predicting data. In
recent years, DL has been applied in a range of fields, including audio-visual
data processing, agriculture, transportation prediction, natural language,
biomedicine, disaster management, bioinformatics, drug design, genomics, face
recognition, and ecology. To explore the current state of deep learning, it is
necessary to investigate the latest developments and applications of deep
learning in these disciplines. However, the literature is lacking in exploring
the applications of deep learning in all potential sectors. This paper thus
extensively investigates the potential applications of deep learning across all
major fields of study as well as the associated benefits and challenges. As
evidenced in the literature, DL exhibits accuracy in prediction and analysis,
makes it a powerful computational tool, and has the ability to articulate
itself and optimize, making it effective in processing data with no prior
training. Given its independence from training data, deep learning necessitates
massive amounts of data for effective analysis and processing, much like data
volume. To handle the challenge of compiling huge amounts of medical,
scientific, healthcare, and environmental data for use in deep learning, gated
architectures like LSTMs and GRUs can be utilized. For multimodal learning,
shared neurons in the neural network for all activities and specialized neurons
for particular tasks are necessary.Comment: 64 pages, 3 figures, 3 table
AI: Limits and Prospects of Artificial Intelligence
The emergence of artificial intelligence has triggered enthusiasm and promise of boundless opportunities as much as uncertainty about its limits. The contributions to this volume explore the limits of AI, describe the necessary conditions for its functionality, reveal its attendant technical and social problems, and present some existing and potential solutions. At the same time, the contributors highlight the societal and attending economic hopes and fears, utopias and dystopias that are associated with the current and future development of artificial intelligence
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