An Evaluation Schema for the Ethical Use of Autonomous Robotic Systems in Security Applications

We propose a multi-step evaluation schema designed to help procurement agencies and others to examine the ethical dimensions of autonomous systems to be applied in the security sector, including autonomous weapons systems

NEET in Essex: A Review of the Evidence

This report reviews the published research evidence on the factors and processes that lead some young people into becoming ?Not in Employment, Education or Training? (NEET), and the policy interventions that are deemed to prevent this. It also includes a previously conducted Latent Class Analysis (LCA) of the 2009 Essex NEET cohort, which is analysed alongside the more general published evidence. The literature reviewed was generated from wide rage of bibliographic search engines, academics, policy makers and practitioners working in this field The review will contribute towards the development of more effective policy interventions, and provide an initial foundation for the development of a possible multi-method research project. A primary research project will be able to provide more robust inferences on the causes and processes of becoming NEET and on the interventions designed to prevent this. This will enable Essex County Council to better target and implement effective policy interventions, ultimately reducing the social and economic costs of youth unemployment in Essex

Combining computer game-based behavioural experiments with high-density EEG and infrared gaze tracking

Rigorous, quantitative examination of therapeutic techniques anecdotally reported to have been successful in people with autism who lack communicative speech will help guide basic science toward a more complete characterisation of the cognitive profile in this underserved subpopulation, and show the extent to which theories and results developed with the high-functioning subpopulation may apply. This study examines a novel therapy, the "Rapid Prompting Method" (RPM). RPM is a parent-developed communicative and educational therapy for persons with autism who do not speak or who have difficulty using speech communicatively.The technique aims to develop a means of interactive learning by pointing amongst multiple-choice options presented at different locations in space, with the aid of sensory "prompts" which evoke a response without cueing any specific response option. The prompts are meant to draw and to maintain attention to the communicative task–making the communicative and educational content coincident with the most physically salient, attention-capturing stimulus – and to extinguish the sensory–motor preoccupations with which the prompts compete.ideo-recorded RPM sessions with nine autistic children ages 8–14years who lacked functional communicative speech were coded for behaviours of interest

Deep Learning Techniques for Music Generation -- A Survey

This paper is a survey and an analysis of different ways of using deep learning (deep artificial neural networks) to generate musical content. We propose a methodology based on five dimensions for our analysis: Objective - What musical content is to be generated? Examples are: melody, polyphony, accompaniment or counterpoint. - For what destination and for what use? To be performed by a human(s) (in the case of a musical score), or by a machine (in the case of an audio file). Representation - What are the concepts to be manipulated? Examples are: waveform, spectrogram, note, chord, meter and beat. - What format is to be used? Examples are: MIDI, piano roll or text. - How will the representation be encoded? Examples are: scalar, one-hot or many-hot. Architecture - What type(s) of deep neural network is (are) to be used? Examples are: feedforward network, recurrent network, autoencoder or generative adversarial networks. Challenge - What are the limitations and open challenges? Examples are: variability, interactivity and creativity. Strategy - How do we model and control the process of generation? Examples are: single-step feedforward, iterative feedforward, sampling or input manipulation. For each dimension, we conduct a comparative analysis of various models and techniques and we propose some tentative multidimensional typology. This typology is bottom-up, based on the analysis of many existing deep-learning based systems for music generation selected from the relevant literature. These systems are described and are used to exemplify the various choices of objective, representation, architecture, challenge and strategy. The last section includes some discussion and some prospects.Comment: 209 pages. This paper is a simplified version of the book: J.-P. Briot, G. Hadjeres and F.-D. Pachet, Deep Learning Techniques for Music Generation, Computational Synthesis and Creative Systems, Springer, 201

Socio-Cognitive and Affective Computing

Social cognition focuses on how people process, store, and apply information about other people and social situations. It focuses on the role that cognitive processes play in social interactions. On the other hand, the term cognitive computing is generally used to refer to new hardware and/or software that mimics the functioning of the human brain and helps to improve human decision-making. In this sense, it is a type of computing with the goal of discovering more accurate models of how the human brain/mind senses, reasons, and responds to stimuli. Socio-Cognitive Computing should be understood as a set of theoretical interdisciplinary frameworks, methodologies, methods and hardware/software tools to model how the human brain mediates social interactions. In addition, Affective Computing is the study and development of systems and devices that can recognize, interpret, process, and simulate human affects, a fundamental aspect of socio-cognitive neuroscience. It is an interdisciplinary field spanning computer science, electrical engineering, psychology, and cognitive science. Physiological Computing is a category of technology in which electrophysiological data recorded directly from human activity are used to interface with a computing device. This technology becomes even more relevant when computing can be integrated pervasively in everyday life environments. Thus, Socio-Cognitive and Affective Computing systems should be able to adapt their behavior according to the Physiological Computing paradigm. This book integrates proposals from researchers who use signals from the brain and/or body to infer people's intentions and psychological state in smart computing systems. The design of this kind of systems combines knowledge and methods of ubiquitous and pervasive computing, as well as physiological data measurement and processing, with those of socio-cognitive and affective computing

Real estate stock selection and attribute preferences

The majority of studies that explore property portfolio construction and management strategies utilise highly aggregated ex-post data, but stock selection is known to be a significant determinant of portfolio performance. Thus, here we look at stock selection, focusing on the choices faced by investors, necessitating the collection and analysis of primary data, carried out utilising conjoint analysis. This represents a new step in property research, with the data collection undertaken using a simulation exercise. This enables fund managers to make hypothetical purchase decisions, viewing properties comprising a realistic bundle of attributes and making complex contemporaneous trade-offs between attributes, subject to their stated market and economic forecasts and sector specialism. In total 51 fund managers were surveyed, producing 918 purchase decisions for analysis, with additional data collected regarding fund and personal characteristics. The results reveal that ‘fixed’ property characteristics (location and obsolescence) are dominant in the decision-making process, over and above ‘manageable’ tenant and lease characteristics which can be explicitly included within models of probabilities of income variation. This reveals investors are making ex-ante risk judgements and are considering post acquisition risk management strategies. The study also reveals that behavioural factors affect acquisition decisions

Data Science and Knowledge Discovery

Data Science (DS) is gaining significant importance in the decision process due to a mix of various areas, including Computer Science, Machine Learning, Math and Statistics, domain/business knowledge, software development, and traditional research. In the business field, DS's application allows using scientific methods, processes, algorithms, and systems to extract knowledge and insights from structured and unstructured data to support the decision process. After collecting the data, it is crucial to discover the knowledge. In this step, Knowledge Discovery (KD) tasks are used to create knowledge from structured and unstructured sources (e.g., text, data, and images). The output needs to be in a readable and interpretable format. It must represent knowledge in a manner that facilitates inferencing. KD is applied in several areas, such as education, health, accounting, energy, and public administration. This book includes fourteen excellent articles which discuss this trending topic and present innovative solutions to show the importance of Data Science and Knowledge Discovery to researchers, managers, industry, society, and other communities. The chapters address several topics like Data mining, Deep Learning, Data Visualization and Analytics, Semantic data, Geospatial and Spatio-Temporal Data, Data Augmentation and Text Mining

The engines of gravity-driven movement on passive margins: quantifying the relative contribution of spreading vs. gravity sliding mechanisms

Movement of gravity-driven systems on passive margins is fuelled by the loss of gravitational potential energy. Two end-member modes (gravity spreading and gravity gliding) are defined by whether the potential energy loss is due to deformation and movement towards the base of the system (spreading), or by movement parallel to the base of the system (gliding); most natural systems consist of a mixture of the two processes. Hitherto, use of these concepts has been limited or equivocal due to lack of a quantitative measure. In some cases, characterisation of gliding vs. spreading systems based on secondary attributes has resulted in controversy, because there is a lack of consensus as to which of these are truly diagnostic. This paper presents a new, simple quantitative method based on vector analysis, providing a numerical measure of the relative contribution of spreading vs. gliding. The method is applied to synthetic examples, where deformation can be tracked, and to natural examples where a valid palinspastic reconstruction is available. The results confirm that most natural examples exhibit mixed-mode behaviour, and that some have been mischaracterized; much of the Angola margin is dominated by spreading. The method can also provide an estimate of the absolute amount of gravitational potential energy released in the movement, and the energy contribution made by gliding vs. spreading. Determining the dominant process has implications for predicting the development of seafloor topography and stratal architecture