Crime in the Age of the Smart Machine: A Zuboffian Approach to Computers and Crime

This analysis ruminates on the quintessential qualities that underpin the relationship between computers and crime by drawing from the foundational work of Shoshana Zuboff, a scholar whose work has to date been largely ignored in the study of crime. From this perspective, computers are best described as “informating” machines that require “intellective skills” in both licit and illicit forms of work. The first part of this analysis describes the role of such skills in the commission of computer-related crimes and considers factors that affect the degree to which such skills are necessary for perpetration. The second part considers how a Zuboffian approach can inform examinations of other subjects that have historically been considered important for criminological inquiries, including learning and subculture, the emotional experience of crime, and perceptions held by offenders and victims

Machine learning approach in the development of building occupant personas

The user persona is a communication tool for designers to generate a mental model that describes the archetype of users. Developing building occupant personas is proven to be an effective method for human-centered smart building design, which considers occupant comfort, behavior, and energy consumption. Optimization of building energy consumption also requires a deep understanding of occupants' preferences and behaviors. The current approaches to developing building occupant personas face a major obstruction of manual data processing and analysis. In this study, we propose and evaluate a machine learning-based semi-automated approach to generate building occupant personas. We investigate the 2015 Residential Energy Consumption Dataset with five machine learning techniques - Linear Discriminant Analysis, K Nearest Neighbors, Decision Tree (Random Forest), Support Vector Machine, and AdaBoost classifier - for the prediction of 16 occupant characteristics, such as age, education, and, thermal comfort. The models achieve an average accuracy of 61% and accuracy over 90% for attributes including the number of occupants in the household, their age group, and preferred usage of heating or cooling equipment. The results of the study show the feasibility of using machine learning techniques for the development of building occupant persona to minimize human effort.Comment: 12 pages, 4 figure

Inferring Social-Demographics of Travellers based on Smart Card Data

[EN] With the wide application of the smart card technology in public transit system, traveller’s daily travel behaviours can be possibly obtained. This study devotes to investigating the pattern of individual mobility patterns and its relationship with social-demographics. We first extract travel features from the raw smart card data, including spatial, temporal and travel mode features, which capture the travel variability of travellers. Then, travel features are fed to various supervised machine learning models to predict individual’s demographic attributes, such as age group, gender, income level and car ownership. Finally, a case study based on London’s Oyster Card data is presented and results show it is a promisingZhang, Y.; Cheng, T. (2018). Inferring Social-Demographics of Travellers based on Smart Card Data. En 2nd International Conference on Advanced Reserach Methods and Analytics (CARMA 2018). Editorial Universitat Politècnica de València. 55-62. https://doi.org/10.4995/CARMA2018.2018.8310OCS556

An AI-based Intelligent System for Healthcare Analysis Using Ridge–Adaline Stochastic Gradient Descent Classifier

Recent technological advancements in information and communication technologies introduced smart ways of handling various aspects of life. Smart devices and applications are now an integral part of our daily life; however, the use of smart devices also introduced various physical and psychological health issues in modern societies. One of the most common health care issues prevalent among almost all age groups is diabetes mellitus. This work aims to propose an Artificial Intelligence (AI) – based intelligent system for earlier prediction of the disease using Ridge Adaline Stochastic Gradient Descent Classifier (RASGD). The proposed scheme RASGD improves the regularization of the classification model by using weight decay methods, namely Least Absolute Shrinkage and Selection Operator(LASSO) and Ridge Regression methods. To minimize the cost function of the classifier, the RASGD adopts an unconstrained optimization model. Further, to increase the convergence speed of the classifier, the Adaline Stochastic Gradient Descent classifier is integrated with Ridge Regression. Finally, to validate the effectiveness of the intelligent system, the results of the proposed scheme have been compared with state-of-art machine learning algorithms such as Support Vector Machine and Logistic Regression methods. The RASGD intelligent system attains an accuracy of 92%, which is better than the other selected classifiers

Toward the autism motor signature : gesture patterns during smart tablet gameplay identify children with autism

Autism is a developmental disorder evident from infancy. Yet, its clinical identification requires expert diagnostic training. New evidence indicates disruption to motor timing and integration may underpin the disorder, providing a potential new computational marker for its early identification. In this study, we employed smart tablet computers with touch-sensitive screens and embedded inertial movement sensors to record the movement kinematics and gesture forces made by 37 children 3-6 years old with autism and 45 age- and gender-matched children developing typically. Machine learning analysis of the children’s motor patterns identified autism with up to 93% accuracy. Analysis revealed these patterns consisted of greater forces at contact and with a different distribution of forces within a gesture, and gesture kinematics were faster and larger, with more distal use of space. These data support the notion disruption to movement is core feature of autism, and demonstrate autism can be computationally assessed by fun, smart device gameplay

Deep Learning for Computer Vision in Smart Cities

[EN] The Digital Age has caused a rapid shift from traditional industry to an economy mainly based upon information technology. According to recent studies, 74 zettabytes (ZB) of data have been generated, captured and replicated in the world in 2021, with video accounting for 82% of internet traffic. This figure has been amplified due to the coronavirus pandemic, and it is expected to keep increasing, reaching 149 ZB by 2024. Processing this impressive amount of information is one of the main scientific challenges of our time. Against this backdrop, Machine Learning (ML) and two related paradigms have emerged: big data and deep learning. These disciplines take advantage of mathematical optimization methods, bioinspiration and modern Graphics Processing Units (GPUs) to manage large datasets efficiently and effectively. Cities from around the world have adapted the previous methods to make use of the newly available data, promoting themselves as “smart”. Apart from aiming to integrate innovative technologies in their daily operation, Smart Cities (SCs) aim to attract new residents and external investors. Some of the key motivations of the Horizon projects and NextGenerationEU funds are precisely to make cities more digital, greener, healthier and robust. Artificial Intelligence (AI) can greatly contribute to the achievement of those objectives. Several lines of action have been identified in SCs, such as: smart mobility, smart environment, smart people, smart living and smart economy. This dissertation focuses on vision applications of deep learning within the scope of SCs. Theoretical and practical research gaps are identified and suitable solutions are proposed. As a result, the state of the art has been pushed forward and new use cases have been successfully implemented. A novel solution is proposed for each of the identified lines of action. Two models have been designed and evaluated with special attention to efficiency and scalability, and a third model has been created and tested focusing on accuracy within a high-resource environment. Moreover, two novel methods have been developed: a method for automatising crucial healthcare challenges, making early diagnosis an option; and another method for automatic unbiased cadastral categorization

A systematic review of ethical challenges and opportunities of addressing domestic violence with AI-technologies and online tools

Domestic violence remains a pressing complex social problem of people of any gender, age, socio-economic status, and ethno-cultural background, an issue that worsened worldwide during the COVID-19 pandemic. Digital, online, or artificial intelligence-based smart technological services, applications, and tools provide novel approaches in addressing domestic violence, including intimate partner violence. This systematic literature review analyses the ethical challenges and opportunities these (protective) digital and smart technologies provide to the stakeholders involved. Our results highlight that the public health and societal issue are the leading narratives of domestic violence, which is predominantly interpreted as gender-based violence. The review highlights an emerging trend of the role of machine learning- and artificial intelligence-based approaches in identifying and preventing domestic violence. However, we argue that little recommendation is available to professionals about how to use these approaches in a responsible way, and that the smartness of high-tech technologies is often challenged by basic-level technologies from perpetrators, creating an imbalance that also limits an impactful development of a comprehensive socio-technical regime that serves the safety and resilience of families in their communal setting