Reviewerly/exHarmony huggingface dataset:
Context-Awareness in Ensemble Recommender System Framework

Recommender systems that provide recommendations based uniquely on information over users and items may not be very accurate in some situations. Therefore, adding contextual information to recommendations may be a good choice resulting in a system with increased precision. In an early work, we proposed an Ensemble Variational Autoencoders (EnsVAE) framework for recommendation. EnsVAE is adjusted to output interest probabilities by learning the distribution of each item's ratings and attempts to provide diverse novel items that are pertinent to users. In this paper, we propose and investigate a context awareness framework based on the Ensemblist Variational Autoencoders model with integrating the contextual information. The context awareness EnsVAE can easily be inferred from preceding sub-recommenders or applied as a filter to the final output. Test performed on real dataset, using an instance of the proposed framework show clear improvement compared to baseline architectures with similar ends as of this instance.
Conditional Extreme Value Theory for Open Set Video Domain Adaptation

With the advent of media streaming, video action recognition has become progressively important for various applications, yet at the high expense of requiring large-scale data labelling. To overcome the problem of expensive data labelling, domain adaptation techniques have been proposed, which transfer knowledge from fully labelled data (i.e., source domain) to unlabelled data (i.e., target domain). The majority of video domain adaptation algorithms are proposed for closed-set scenarios in which all the classes are shared among the domains. In this work, we propose an open-set video domain adaptation approach to mitigate the domain discrepancy between the source and target data, allowing the target data to contain additional classes that do not belong to the source domain. Different from previous works, which only focus on improving accuracy for shared classes, we aim to jointly enhance the alignment of the shared classes and recognition of unknown samples. Towards this goal, class-conditional extreme value theory is applied to enhance the unknown recognition. Specifically, the entropy values of target samples are modelled as generalised extreme value distributions, which allows separating unknown samples lying in the tail of the distribution. To alleviate the negative transfer issue, weights computed by the distance from the sample entropy to the threshold are leveraged in adversarial learning in the sense that confident source and target samples are aligned, and unconfident samples are pushed away. The proposed method has been thoroughly evaluated on both small-scale and large-scale cross-domain video datasets and achieved the state-of-the-art performance.
A Web-Based Campus Navigation System with Mobile Augmented Reality Intervention

Abstract In this era of information technology, the introduction of new and greatly improved technologies is becoming vital especially with the progressive development of computing power to fulfill current technological needs. Augmented Reality (AR) is a part of the emerging technologies that are gaining great attention and interest with new and better innovations being developed. AR is an immersive technology where it complements real-world objects together with computer-generated objects to give more details and meaning to the objects in the real world. AR has been successfully growing nowadays in many industries, mainly in gaming and entertainment, education and navigation. However, AR has not been standardized and to be widely used in navigation systems even though there are many applications developed for this purpose. This paper proposed a web-based AR-UUM Campus Navigation System using ARToolKit, that is accessible via mobile devices that uses AR to overlay the information as images of the searched location on campus, especially for indoor navigations for lecture halls, tutorial rooms, laboratories, and offices which rarely are covered by the normal maps. Based on the respondents' evaluation, they managed to successfully interact with the AR-UUM Campus Navigation System, however, some improvements are required in terms of the functions expected from the system. Nevertheless, the use of AR in a navigation system shows promising results and has the potential to be widely used due to high interest in augmented reality.
Aviation and the Cybersecurity Threats

The article points out the problem of legal regulation and ensuring cybersecurity in the aviation sector.This issue has acquired particular relevance in connection with the increasing level of development of information technologies in the aviation sector.The author analyses in detail the existing international standards and norms for ensuring aviation cybersecurity, substantiates the implementation of various initiatives and technologies on this matter.The study analyses the existing strategies and plans of international organizations in the aviation area and indicates the opinions of experts on the possibilities of solving the problems of cybercrimes in this area.To achieve the goals set for the study, the author actively uses dialectical, comparative, systemic, formal-logical, logical-normative, and statistical scientific methods.The article points out the need to harmonize national legislation with existing international regulations, and also provides recommendations for improving the level of cybersecurity in Ukraine and the world.The author also concludes that to achieve the goal of ensuring universal cybersecurity in the field of aviation, it is necessary to combine efforts not only by air carriers but also by all actors interested in maintaining the quality and safety of flights, as well as by national governments.
“I see something you don’t see”: A computational analysis of the Digital Services Act and the Digital Markets Act

In its latest proposals, the Digital Markets Act (DMA) and Digital Services Act (DSA), the European Commission puts forward several new obligations for online intermediaries, especially large online platforms and “gatekeepers.” Both are expected to serve as a blueprint for regulation in the United States, where lawmakers have also been investigating competition on digital platforms and new antitrust laws passed the House Judiciary Committee as of June 11, 2021. This Article investigates whether all stakeholder groups share the same understanding and use of the relevant terms and concepts of the DSA and DMA. Leveraging the power of computational text analysis, we find significant differences in the employment of terms like “gatekeepers,” “self-preferencing,” “collusion,” and others in the position papers of the consultation process that informed the drafting of the two latest Commission proposals. Added to that, sentiment analysis shows that in some cases these differences also come with dissimilar attitudes. While this may not be surprising for new concepts such as gatekeepers or self-preferencing, the same is not true for other terms, like “self-regulatory,” which not only is used differently by stakeholders but is also viewed more favorably by medium and big companies and organizations than by small ones. We conclude by sketching out how different computational text analysis tools, could be combined to provide many helpful insights for both rulemakers and legal scholars.
Conditional Wasserstein Generative Adversarial Networks for Rebalancing Iris Image Datasets

The recent development of deep learning-based generative models has sharply intensified the interest in data synthesis and its applications. Data synthesis takes on an added importance especially for some pattern recognition tasks in which some classes of data are rare and difficult to collect. In an iris dataset, for instance, the minority class samples include images of eyes with glasses, oversized or undersized pupils, misaligned iris locations, and iris occluded or contaminated by eyelids, eyelashes, or lighting reflections. Such class-imbalanced datasets often result in biased classification performance. Generative adversarial networks (GANs) are one of the most promising frameworks that learn to generate synthetic data through a two-player minimax game between a generator and a discriminator. In this paper, we utilized the state-of-the-art conditional Wasserstein generative adversarial network with gradient penalty (CWGAN-GP) for generating the minority class of iris images which saves huge amount of cost of human labors for rare data collection. With our model, the researcher can generate as many iris images of rare cases as they want and it helps to develop any deep learning algorithm whenever large size of dataset is needed.
Secure Multi-party Co-planning of Barge Departures

Container transport requires real-time control and a high degree of cooperation to alleviate disturbances and perform smoothly without unnecessary environmental impact and monetary losses. The involved operators are, however, often reluctant to cooperate as they fear loosing valuable information and autonomy, eventually leading their clients to choose another (cooperating) operator. In this paper we propose a real-time co-planning method, called Secure Departure Learning that in real-time lets several truck operators indicate to a barge operator what departure schedules they prefer without revealing any sensitive information. The method uses Paillier encryption and a learning method inspired by Bayesian Optimization in a model predictive control framework. At frequent time intervals a number of potential barge schedules are communicated to the co-planning truck operators. They evaluate their operation cost for each schedule and communicate it to the barge operator encrypted using several public keys. The barge operator computes the encrypted total cost for each schedules, which hereafter is decrypted by several truck operators. The first action of the schedule that results in the lowest total cost is implemented in a model predictive control fashion. Simulated experiments on a realistic, Dutch transport network illustrate that Secure Departure Learning is a good alternative for replacing the current method in practice, where barge departures are scheduled ahead of time and only mode-decisions can be updated in real time. Secure Departure Learning offers a new perspective on cooperation at the operational level in freight transport where co-planning and information protection can go hand in hand.
A Survey of Deep Reinforcement Learning in Recommender Systems: A Systematic Review and Future Directions

In light of the emergence of deep reinforcement learning (DRL) in recommender systems research and several fruitful results in recent years, this survey aims to provide a timely and comprehensive overview of the recent trends of deep reinforcement learning in recommender systems. We start with the motivation of applying DRL in recommender systems. Then, we provide a taxonomy of current DRL-based recommender systems and a summary of existing methods. We discuss emerging topics and open issues, and provide our perspective on advancing the domain. This survey serves as introductory material for readers from academia and industry into the topic and identifies notable opportunities for further research.
Application of a Fuzzy Inference System for Optimization of an Amplifier Design

Simulation programs are widely used in the design of analog electronic circuits to analyze their behavior and to predict the response of a circuit to variations in the circuit components. A fuzzy inference system (FIS) in combination with these simulation tools can be applied to identify both the main and interaction effects of circuit parameters on the response variables, which can help to optimize them. This paper describes an application of fuzzy inference systems to modeling the behavior of analog electronic circuits for further optimization. First, a Monte Carlo analysis, generated from the tolerances of the circuit components, is performed. Once the Monte Carlo results are obtained for each of the response variables, the fuzzy inference systems are generated and then optimized using a particle swarm optimization (PSO) algorithm. These fuzzy inference systems are used to determine the influence of the circuit components on the response variables and to select them to optimize the amplifier design. The methodology proposed in this study can be used as the basis for optimizing the design of similar analog electronic circuits.
Graph Powered Machine Learning in Smart Sensor Networks

A generic representation of sensor network data can be done by inherent graph structure within IoT sensor networks. We can develop a standardized graph-based framework and graphical features to support different prediction tasks from additional IoT network sensor data. In that case, we can offer IoT apps a tool for boosting prediction tasks. Graphic approaches in several IoT applications have been successful. This chapter presents the basic concepts and reasons behind a graphics learning project. It shows some basic principles to understand how graph models can be useful tools to produce better outcomes at the end of machine learning. The chapter covers the framework based on Graphical Features with some deep learning approaches such as GCN (Graph Convolutional Network) Approach, Deep Graph Convolutional Neural Network (DGCNN) and Window-Based Approach with Graphical Features. The work will also show that graphical-based features based on a smart sensor network can perform better than non-graph based features.
Research on Intelligent Compaction Technology of Subgrade Based on Regression Analysis

A remote monitoring system with the intelligent compaction index CMV as the core is designed and developed to address the shortcomings of traditional subgrade compaction quality evaluation methods. Based on the actual project, the correlation between the CMV and conventional compaction indexes of compaction degree K and dynamic resilient modulus E is investigated by applying the one-dimensional linear regression equation for three types of subgrade fillers, clayey gravel, pulverized gravel, and soil-rock mixed fill, and the scheme of fitting CMV to the mean value of conventional indexes is adopted, which is compared with the scheme of fitting CMV to the single point of conventional indexes in the existing specification. The test results show that the correlation between the CMV and conventional indexes of clayey gravel and pulverized gravel is much stronger than that of soil-rock mixed subgrades, and the correlation coefficient can be significantly improved by fitting CMV to the mean of conventional indexes compared with single-point fitting, which can be considered as a new method for intelligent rolling correlation verification.
Monitoring the Adoption of SPI-Related Best Practices. An Experience Report

Software Process Improvement requires significant effort related not only to the identification of relevant issues and providing an adequate response to them, but also to the implementation and adoption of the changes. Best practices provide recommendations to software teams on how to address the identified objectives in practice, based on aggregated experience and knowledge. In the paper, we present the GEANT experience and observations from the process of adopting thebest practices, and present the setting we have been using.
Noisy Localization Annotation Refinement for Object Detection

Well annotated dataset is crucial to the training of object detectors. However, the production of finely annotated datasets for object detection tasks is extremely labor-intensive, therefore, cloud sourcing is often used to create datasets, which leads to these datasets tending to contain incorrect annotations such as inaccurate localization bounding boxes. In this study, we highlight a problem of object detection with noisy bounding box annotations and show that these noisy annotations are harmful to the performance of deep neural networks. To solve this problem, we further propose a framework to allow the network to modify the noisy datasets by alternating refinement. The experimental results demonstrate that our proposed framework can significantly alleviate the influences of noise on model performance.
Metaphorical cards as innovative educational technology for students’ social intelligence developing

This article aims to identify the features of using metaphorical cards for the development of students’ social skills studying in university. The methods of research included testing and observation methods that allow to identify the levels of formation of students’ social intelligence structural components effectively; to determine the features of changes in the perception and control of their own and others' emotional and behavioral reactions, as well as the level of development of understanding of emotional expression. The study shows that the technology of using metaphorical cards is one of the leading ways of emotional-psychological, pedagogical and socio-moral influence on the development of structural components of students’ social intelligence. The effectiveness of working with metaphorical cards in developing communicative skills has been shown to be due to the mechanisms involved in different forms of working with metaphor. It is considered that metaphorical cards allow projecting both negative emotional reactions arising in communication onto images, reducing the intensity of their experience, and projecting social images, acting as moral guidelines, onto real situations, using them as models of behavior. It has been found that working with this educational technology helps to develop students' skills in understanding and managing their feelings, emotions and experiences. Practical relevance: The data obtained in this paper can be used in the study of problems related to educational psychology, personality psychology, social psychology and pedagogy.&#x0D; &#x0D; Keywords: educational technology, social skills, student youth.
Improving Neural Network Based on Seagull Optimization Algorithm for Controlling DC Motor

This article presents a direct current (DC) motor control approach using a hybrid Seagull Optimization Algorithm (SOA) and Neural Network (NN) method. SOA method is a nature-inspired algorithm. DC motor speed control is very important to maintain the stability of motor operation. The SOA method is an algorithm that duplicates the life of the seagull in nature. Neural network algorithms will be improved using the SOA method. The neural network used in this study is a feed-forward neural network (FFNN). This research will focus on controlling DC motor speed. The efficacy of the proposed method is compared with the Proportional Integral Derivative (PID) method, the Feed Forward Neural Network (FFNN), and the Cascade Forward Backpropagation Neural Network (CFBNN). From the results of the study, the proposed control method has good capabilities compared to standard neural methods, namely FFNN and CFBNN. Integral Time Absolute Error and Square Error (ITAE and ITSE) values from the proposed method are on average of 0.96% and 0.2% better than the FFNN and CFBNN methods.
Adaptive Tracking Method for Non-Cooperative Continuously Thrusting Spacecraft

Performance of the traditional Kalman filter and its variants can seriously degrade when they are used to track a non-cooperative continuously thrusting spacecraft. To overcome this shortcoming, an adaptive tracking method for relative state estimation of a non-cooperative target is proposed based on the interacting multiple model (IMM) algorithm. First, built upon a current statistical jerk (CSJerk) model, a robust CSJerk filtering (RCSJF) algorithm is developed, which can address the issue of low estimation accuracy and instability of traditional approaches at the moments when the spacecraft starts and ends thrusting. Second, the developed RCSJF algorithm is further used to form the model set of the IMM by incorporating different maximum jerk values, based on which an adaptive tracking method is presented that can track a non-cooperative target with different maneuvering levels. Simulation results show that the proposed method can effectively track the target across all thrusts levels under the conditions considered, and the convergence performance of the proposed method is improved in comparison to the CSJerk-based extended Kalman filter, especially at the start and end time of the maneuver.
Critical is not political

Data literacy is slowly becoming a more prominent feature of contemporary society. Some argue that people need to obtain new competencies to mitigate and engage with the multiplicity of ways in which they are affected by data. Literacy as such is positioned as a pathway towards empowerment, enable people to make informed choices about their data environment and increasing their ability to actively participate in the discussion that determines the socio-technical systems that will impact their lives. In this article, I will argue that we need to account for the externalities that emerge from the mere act of centring data in a literacy approach and unpack the assumptions that underpin the concept. To advance the argument that data literacy needs to be (re)politicize, both in terms of the perceived competencies need in a data society and the 'neutrality of the practice in itself. To ensure that the audience will have more thoughtful and actionable pathways forward data literacy should learn from other disciplines that have a more thorough analysis of dismantling power structures, engaging with inequality and encouraging political participation.
Dataset for Identification of Homophobia and Transophobia in Multilingual YouTube Comments

The increased proliferation of abusive content on social media platforms has a negative impact on online users. The dread, dislike, discomfort, or mistrust of lesbian, gay, transgender or bisexual persons is defined as homophobia/transphobia. Homophobic/transphobic speech is a type of offensive language that may be summarized as hate speech directed toward LGBT+ people, and it has been a growing concern in recent years. Online homophobia/transphobia is a severe societal problem that can make online platforms poisonous and unwelcome to LGBT+ people while also attempting to eliminate equality, diversity, and inclusion. We provide a new hierarchical taxonomy for online homophobia and transphobia, as well as an expert-labelled dataset that will allow homophobic/transphobic content to be automatically identified. We educated annotators and supplied them with comprehensive annotation rules because this is a sensitive issue, and we previously discovered that untrained crowdsourcing annotators struggle with diagnosing homophobia due to cultural and other prejudices. The dataset comprises 15,141 annotated multilingual comments. This paper describes the process of building the dataset, qualitative analysis of data, and inter-annotator agreement. In addition, we create baseline models for the dataset. To the best of our knowledge, our dataset is the first such dataset created. Warning: This paper contains explicit statements of homophobia, transphobia, stereotypes which may be distressing to some readers.
Lackadaisical quantum walks on 2D grids with multiple marked vertices

Lackadaisical quantum walk (LQW) is a quantum analog of a classical lazy walk, where each vertex has a self-loop of weight $l$. For a regular $\sqrt{N}\times\sqrt{N}$ 2D grid LQW can find a single marked vertex with $O(1)$ probability in $O(\sqrt{N\log N})$ steps using $l = d/N$, where $d$ is the degree of the vertices of the grid. For multiple marked vertices, however, $l = d/N$ is not optimal as the success probability decreases with the increase of the number of marked vertices. In this paper, we numerically study search by LQW for different types of 2D grids -- triangular, rectangular and honeycomb -- with multiple marked vertices. We show that in all cases the weight $l = m\cdot d/N$, where $m$ is the number of marked vertices, still leads to $O(1)$ success probability.