Reviewerly/exHarmony huggingface dataset:
Statistical Relational Learning for Collaborative Filtering a State-of-the-Art Review

People nowadays base their behavior by making choices through word of mouth, media, public opinion, surveys, etc. One of the most prominent techniques of recommender systems is Collaborative filtering (CF), which utilizes the known preferences of several users to develop recommendation for other users. CF can introduce limitations like new-item problem, new-user problem or data sparsity, which can be mitigated by employing Statistical Relational Learning (SRLs). This review chapter presents a comprehensive scientific survey from the basic and traditional techniques to the-state-of-the-art of SRL algorithms implemented for collaborative filtering issues. Authors provide a comprehensive review of SRL for CF tasks and demonstrate strong evidence that SRL can be successfully implemented in the recommender systems domain. Finally, the chapter is concluded with a summarization of the key issues that SRLs tackle in the collaborative filtering area and suggest further open issues in order to advance in this field of research.
Market Basket Analysis

Knowledge discovery is the effort to find information from data. In contemporary terms, it is the application of tools (from statistics and from artificial intelligence) to extract interesting patterns from data stored in large databases. Here interesting means non-trivial, implicit, previously unknown, and easily understood and described knowledge that can be used (actionable). One of the original applications of data mining to generate interesting knowledge for business was market basket analysis (Agrawal et al. in IEEE Transactions on Knowledge and Data Engineering 5(6): 914–925, 1995).
Handwritten character recognition based on CNN

research-article Handwritten character recognition based on CNN Share on Authors: Shengxuan Ji Fujian University of Technology, China Fujian University of Technology, ChinaSearch about this author , Yihe Zeng Crofton House School, Canada Crofton House School, CanadaSearch about this author Authors Info & Claims AIAM2021: 2021 3rd International Conference on Artificial Intelligence and Advanced ManufactureOctober 2021 Pages 2874–2880https://doi.org/10.1145/3495018.3501199Online:14 March 2022Publication History 0citation0DownloadsMetricsTotal Citations0Total Downloads0Last 12 Months0Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Legaltech in Public Administration: Prospects and Challenges

The information revolution affects every aspect of our life, such as communication, banking, learning and teaching, entertainment, socializing, and even government and the administration. In some fields the potential of such revolution is already apparent, as new kinds of value is created - suffice it to mention the new forms of communication and marketing, the emergence of new business models in the financial industry, the so-called fintechs, and the spread of social networks. Likewise, information technology creates opportunities and disrupts even the legal industry, with the emergence of legaltech or lawtech. Despite such growing attention, in the field of public administration, the potential uses of new technologies have been for a long time only discussed in theory, and limited implementation has been recorded (in most of the cases, in an experimental or pilot phase). As Part 2 will show, such technologies in the best-case scenario were tested at an experimental level, and only in a few countries some small steps were taken to implement them. A rapid shift, nonetheless, took place with the imposed lockdowns due to the Covid-19 pandemic. Such pandemic will possibly leave its mark not only in history books and medicine textbooks, but also in the field of administration and public law, as it spurred the implementation of the technologies of the information revolution era.
Two-stream Graph Attention Convolutional for Video Action Recognition

Graph convolution network can efficiently obtain the spatial features in the task of human action recognition based on skeleton information, and HSGAC network incorporating graph attention convolution mechanism could obtain more abstract spatial features. However, HSGAC does not consider the spatial dynamic importance of joints. In this paper, we propose a two-stream graph attention convolutional network(Two-Stream GAC), which aims to fuse the static and dynamic feature relationships between joints. Two-Stream GAC first preprocesses the original human pose information dynamically, and use two HSGAC models to obtain the static feature relationship of the original pose information and the dynamic feature relationship of the preprocessed pose information respectively. The two network models are fused by weighted sum. The experimental results on the RDT and NTU-RGB+D datasets show that the method achieves better accuracy of action recognition.
Research in the Era of Sensing Technologies and Wearables

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Entanglement-Enhanced Physical-Layer Classifier Using Supervised Machine Learning

We introduce physical-layer classifiers enhanced by multipartite entanglement learned through a supervised support-vector machine. The required entangled states are practical and give error probability advantage over classical schemes even in presence of loss.
Poirot: A Web Inspector for Designers

To better understand the issues designers face as they interact with developers and use developer tools to create websites, we conducted a formative investigation consisting of interviews, a survey, and an analysis of professional design documents. Based on insights gained from these efforts, we developed Poirot, a web inspection tool for designers that enables them to make style edits to websites using a familiar graphical interface. We compared Poirot to Chrome DevTools in a lab study with 16 design professionals. We observed common problems designers experience when using Chrome DevTools and found that when using Poirot, designers were more successful in accomplishing typical design tasks (97–63%). In addition, we found that Poirot had a significantly lower perceived cognitive load and was overwhelmingly preferred by the designers in our study.
CrawlPhish: Large-Scale Analysis of Client-Side Cloaking Techniques in Phishing

Phishing websites with advanced evasion techniques are a critical threat to Internet users because they delay detection by current antiphishing systems. We present CrawlPhish, a framework for automatically detecting and categorizing the client-side (e.g., JavaScript) evasion used by phishing websites.
Classical Tracking for Quantum Trajectories

Quantum state estimation, based on the numerical integration of stochastic master equations (SMEs), provides estimates for the evolution of quantum systems subject to continuous weak measurements. The approach is similar to classical state estimation methods in that the ‘quantum trajectories’ produced by solving the SME are conditioned on continuous classical measurement signals. In this paper, we explore the use of classical state estimation for a candidate quantum system, one based on an experimentally realisable system: a material object undergoing continuous feedback cooling in an optical trap. In particular, we demonstrate that classical tracking methods based on particle filters can be used to track quantum states, and are particularly useful for higher temperature regimes where quantum state estimation becomes computationally demanding.
A Multimodal Direct Gaze Interface for Wheelchairs and Teleoperated Robots

Gaze-based interfaces are especially useful for people with disabilities involving the upper limbs or hands. Typically, users select from a number of options (e.g. letters or commands) displayed on a screen by gazing at the desired option. However, in some applications, e.g. gaze-based driving, it may be dangerous to direct gaze away from the environment towards a separate display. In addition, a purely gaze based interface can present a high cognitive load to users, as gaze is not normally used for selection and/or control, but rather for other purposes, such as information gathering. To address these issues, this paper presents a cost-effective multi-modal system for gaze based driving which combines appearance-based gaze estimates derived from webcam images with push button inputs that trigger command execution. This system uses an intuitive "direct interface", where users determine the direction of motion by gazing in the corresponding direction in the environment. We have implemented the system for both wheelchair control and robotic teleoperation. The use of our system should provide substantial benefits for patients with severe motor disabilities, such as ALS, by providing them with a more natural and affordable method of wheelchair control. We compare the performance of our system to the more conventional and common "indirect" system where gaze is used to select commands from a separate display, showing that our system enables faster and more efficient navigation.
Product Line Engineering Comes to the Industrial Mainstream

ABSTRACT Product line engineering (PLE) is a systems engineering discipline to engineer a portfolio of related products in an efficient manner, taking full and ongoing advantage of the products’ similarities while respecting and managing their differences. Managing a portfolio as a variable single entity, as opposed to multiple separate products, brings enormous efficiencies in production and maintenance. This paper shows that PLE has now matured into a repeatable, industrial‐strength engineering discipline. We define and explore the concepts central to modern product line engineering and illustrate how it applies in two of the most challenging systems engineering domains: aerospace and defense, and automotive.
Developing a Common Global Framework for Marine Data Management

The paradigm shift in marine research moving from the traditional discipline based methodology to a multidisciplinary, ecosystem level approach is being driven by changes in both the policies for the management and exploitation of the ocean, and the scientific method itself. The availability of large volumes of good quality data is fundamental to this increasingly holistic approach to ocean research but there are significant barriers to its re-use. The Ocean Data Interoperability Platform (ODIP) project has been funded in parallel by the European Commission, National Science Foundation in the USA and the Australian Government to promote the development of a common framework for marine data management that leverages the existing marine e-infrastructures which have been created in response to the need for greater sharing of marine data at a regional level.
Analysis of intelligent test paper generation method for online examination based on UML and particle swarm optimisation

In order to overcome the problems of unclear objective function and inaccurate optimal solution in traditional test paper generation methods, an online intelligent test paper generation method based on UML and particle swarm optimisation is proposed. A mathematical model of test paper generation based on UML modelling tool is established, and the objective function of test paper generation is obtained. The improved particle swarm optimisation algorithm is used to solve the objective function of test paper generation. The optimal solution of the objective function is introduced into the question bank. The test questions in the test bank are combined and imported into the online examination system to realise the intelligent test paper formation of online examination. The experimental results show that this method has strong adaptability and can achieve better performance than the traditional intelligent test paper generation method.
Network Architecture Search Method on Hyperparameter Optimization of Convolutional Neural Network: Review

Deep Learning is a widely used method to solve various kinds of problems. One of the deep learning methods that are used for image classification is Convolutional Neural Network. Convolutional Neural Network has a lot of architecture. Those architectures were manually made and had excellent performance. However, due to a large number of cases tested, it results in the architecture has to be adjusted to the case. If this is manually done, then it should be time-consuming. So there should be an automatic approach to search the appropriate architecture. Neural Architecture Search is one method that is used to search for the appropriate architecture. This paper is containing reviews of Neural Architecture Search method for hyperparameter optimization on Convolutional Neural Network which is done by today's researchers.
Balancing diversity and accuracy of the recommendation system based on multi-objective optimization

Recommendation systems are used in every aspect of people's lives in the context of today's Internet boom. However, most of the research on recommendation algorithms focus on improving the accuracy of algorithms, while the diversity and novelty of recommendation quality are more or less ignored. In addition, the accuracy and diversity of recommendation algorithms are usually contradictory. Therefore, the paper proposed a novel multi-objective framework to balance accuracy and diversity. Under the framework, similar products and diverse products are recommended by user clustering. Two objective functions are designed to represent the similarity and diversity of clustering. One objective function is to bring together users with similar preferences. Another objective function is to recommend diverse items to users. The novel multi-objective evolutionary algorithm can return a series of different trade-off solutions in one run. The proposed algorithm is applied on a real-world baseline dataset: MovieLens. Experimental results show that diversity and novelty are improved without sacrificing too much precision
Cloud Security: An Overview and Current Trend

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Kinesthetic Gaming, Cognition, and Learning

Kinesthetic gaming, which is also called full-body gaming, is the rising star of digital gaming trends of recent years. With the integration of commercially available motion tracking systems into game consoles, players are able to control the games with kinesthetic movements without any handheld controllers. This new form of advanced human-computer interaction (HCI) allows players to interact with games in more realistic and natural ways. Because of its novelty, research in kinesthetic gameplay is limited, and experimental research on the effects of kinesthetic gaming on learning is almost non-existent. The purpose of the chapter is to investigate the philosophical foundations of kinesthetic game-playing by emphasizing the role of physical action on learning under the framework of cognitive theories and to discuss possible implications in P-12 education. The author aims to introduce kinesthetic game-playing as a powerful tool for learning and to develop new insights on gaming in P-12 education.
Animal Tracking within a Formation of Drones

In this study, we develop a distributed system that can be used by unmanned aerial vehicles (UAVs) or drones for single-animal tracking in terrestrial settings. The system involves a video object tracking (VOT) solution and a drone formation. The proposed VOT solution is based on the particle filter (PF) with two measurement providers: a colour image segmentation (CIS) approach and a machine learning (ML) technique. They are switched based on the structural similarity (SSIM) index between the initial and the current target appearances to mitigate the limitation of computational resources of civilian drones, and to ensure good tracking performance. At first, the deep learning object detector You Only Look Once version three (YOLOv3) is used as the second measurement provider. The proposed VOT solution has been tested on wildlife footage recorded by drones (and obtained from an animal behaviour group). The tests demonstrate amongst other results that the proposed VOT solution is more efficient when YOLOv3 is replaced by other methods such as boosting and channel and spatial reliability tracking (CSRT). The results suggest the utility of the proposed VOT solution in single-animal tracking with cooperative drones for wildlife preservation.
Empowerment as Development: An Outline of an Analytical Concept for the Study of ICTs in the Global South

This chapter turns to the concept of “empowerment” as a result of disenchantment with the concept of “development” in the study of information and communication technologies (ICTs) and social change in the global South. It is a certainty that the proliferation of ICTs (mobile phones in particular) has opened up a range of possibilities and new avenues for individuals, aid agencies, and NGOs. However, overviews of communication supposedly for development reveal a field based on economic understandings of development biased toward techno-determinism. Moreover, these understandings lack sufficient critique and do not take larger contextual factors into account. Therefore, it is argued that empowerment is a better concept to draw upon in the critical study of ICTs and social change. However, empowerment is not an easy concept to define, and no analytical outline of the concept has been found in the existing body of literature. Addressing this lack, this chapter will trace the roots of empowerment in community psychology and in feminist and black power movements as well as explore different understandings of the concept from various disciplines. From this overview, the chapter suggests that empowerment should be studied on a) an intersectional level, b) a contextual level, c) an agency level, and d) a technological level. It further argues that these four levels intersect and must be studied in tandem to understand whether processes of empowerment are taking place, and if so, in what ways? The chapter ends by shortly applying these levels to a study involving market women’s use of mobile phones in Kampala.
Insider Threat

This chapter discusses the threat arising from within the organisation, whether from negligence, malice, or exploitation by an external party. The trusted insider is one of the greatest challenges facing organisations today. The analysis considers the balance to be struck between allowing insiders access and privileges to show trust and increase productivity, and securing that access at the cost of good will and with an increased risk of workarounds being found, placing vulnerabilities at the heart of an organisation’s policies and processes. The tactics of social engineering and exploitation of human psychology to compromise or completely bypass technical and procedural security measures are considered, along with the effectiveness of training and difficulties of raising cultural awareness of security on a long term basis in a rapidly changing technological landscape.
Bio-inspired Trajectory Generation for Robotic Manipulators Based on Intrinsic Tau Exponential Guidance Strategy

In this paper, a novel bio-inspired trajectory generation method is proposed based on the intrinsic Tau exponential (Tau-E) guidance strategy. The intrinsic guidance movement (IGM) of the Tau theory is firstly selected as an exponential function, which shows the feasibility to choose a function without physical significance. The Tau-E is derived to generate the trajectories of robotic manipulators and capable of guiding other objects. Tau-E guidance strategy can achieve the benefits of faster computation and fewer parameters with the similar performance in comparison to other similar Tau-based guidance strategies. Moreover, the feasible interval of coupling coefficient is enlarged and can be calculated according to the motion gap by using the proposed Tau-E guidance strategy. The simulation and experimental tests demonstrate the effectiveness of the proposed Tau-E guidance strategy.
Fifth HUMANIZE workshop on Transparency and Explainability in Adaptive Systems through User Modeling Grounded in Psychological Theory: Summary

The fifth HUMANIZE workshop1 on Transparency and Explainability in Adaptive Systems through User Modeling Grounded in Psychological Theory took place in conjunction with the 26th annual meeting of the Intelligent User Interfaces (IUI)2 community in Texas, USA on April 17, 2021. The workshop provided a venue for researchers from different fields to interact by accepting contributions on the intersection of practical data mining methods and theoretical knowledge for personalization. A total of five papers was accepted for this edition of the workshop.
The Application of Computer Technology in English Teaching

For the database of computer English teaching platform, the specific application of computer technology in English teaching is an important way to improve its English teaching ability. Secondly, based on the construction of computer algorithm application system platform, this paper puts forward the basic mode of formulating English teaching objectives. Finally, through data collection, storage, mining and analysis, it provides strong computer data model support for modern English teaching reform, This paper discusses the importance and necessity of improving the quality of innovation and entrepreneurship education by using the computer mathematical analysis and experiment platform, introduces the general modeling theory, comprehensive simulation theory and method, planning theory and method, safety theory and method, and systematically summarizes the operation optimization and control, benefit evaluation and operation mechanism The existing problems are summarized and analyzed.
Academic and Research Libraries' Portals

This chapter presents a synthesis of the literature on academic library portals. Library portals remain a component of the modern academic library. Portal features differ, but typically contain a single authentication process and access to metadata as well as some services. The authors examined 87 papers in their review. This examination revealed six themes including: developing library portals, utilizing assessment strategies, collaborating on portal projects, incorporating personalization and customization features, conducting environmental scans, and creating instructional portals. The authors assigned themes to the papers based on the primary focus of the material. The findings revealed various issues with the development of academic library portals. The content of academic library portals continues to evolve as their importance to the user increases. Ultimately, an examination of the literature illustrated academic libraries' efforts to remain relevant through the availability of new resources and services on their portals.