Reviewerly/exHarmony huggingface dataset:
Causal versus Marginal Shapley Values for Robotic Lever Manipulation Controlled using Deep Reinforcement Learning

We investigate the effect of including application knowledge about a robotic system states’ causal relations when generating explanations of deep neural network policies. To this end, we compare two methods from explainable artificial intelligence, KernelSHAP, and causal SHAP, on a deep neural network trained using deep reinforcement learning on the task of controlling a lever using a robotic manipulator. A primary disadvantage of KernelSHAP is that its explanations represent only the features’ direct effects on a model’s output, not considering the indirect effects a feature can have on the output by affecting other features. Causal SHAP uses a partial causal ordering to alter KernelSHAP’s sampling procedure to incorporate these indirect effects. This partial causal ordering defines the causal relations between the features, and we specify this using application knowledge about the lever control task. We show that enabling an explanation method to account for indirect effects and incorporating some application knowledge can lead to explanations that better agree with human intuition. This is especially favorable for a real-world robotics task, where there is considerable causality at play, and in addition, the required application knowledge is often handily available.
Digital skills mentoring for online teaching and evaluation in the Industrial Engineering Faculty of the University of Málaga

Abstract COVID-19 pandemic has forced the usual face-to-face educative activities to change to e-learning processes. When the faculty has the necessary knowledge on digital skills this obligatory step becomes easier. Videoconferences, as synchronous communication tool and Learning Management Systems (LSM) allow to carry out the e-learning actuations. The University of Málaga has required to adapt its educative activities to e-learning processes due to the pandemic. To facilitate this change, a digital skill mentoring actuation has been implemented in all faculties of the University of Málaga. The digital skill actuations have been related with evaluation processes and with communication systems to development the teaching classes. In this work, the experience in the Faculty of Industrial Engineering of the University of Malaga, is exposed. The results obtained show that the previous LSM used by the University of Malaga facilitated the change to the e-learning process in the Faculty of Industrial Engineering.
Talent as a Personality Resource of People with Schizotypal Personality

Talent is understood as a person’s aptitude for creativity. Talent is a personality resource that supports effective adaptation of a person in different situations. In the research talent was measured based on expert assessment. Guest experts, an art expert and a painter, provided their expert opinion on a 1 to 10 scale. Originality of thinking was taken as a psychological criterion of the analysis. It was determined using a classical pathopsychological test. Guilford’s SI theory was the methodological basis of the research. The following methods were used: pathopsychological test, Torrance figurative subtests, incomplete sentences, analysis of verbal and graphic production of the participants, Buss-Perry Aggression Questionnaire, Spielberg’s State-Trait Anxiety Inventory. People with schizotypal personality were the participants of the research. The purpose of the research was to study manifestations of talent as a personality resource in people with schizotypal personality. Apart from diagnostics, the participants underwent psychological rehabilitation program developed by the authors. Thus, talent as a personality resource was studied based on expert opinion, psychological diagnostics as well as during psychological rehabilitation. The work was also an attempt to describe creativity in interdisciplinary terms. The empirical research has showed that talent and schizotypal personality are interconnected. Talent acts as a personality resource during psychological rehabilitation of such people. The authors of the research have developed, tested and implemented the program of psychological rehabilitation for people with schizotypal personality. In each case it is customized into an individual program of psychological rehabilitation.
Privacy in Blockchain

Privacy in blockchains is rather complicated as it contradicts with some highly praised properties of blockchain such as immutability. Immutability is considered a cornerstone of blockchains' security and, therefore, an indisputable property according to which transactional blockchain data cannot be edited nor deleted. However, blockchain's immutability is being called into question lately in the light of the new erasing requirements imposed by the GDPR's "Right to be Forgotten (RtbF)" provision. Given that the RtbF compels data stored in blockchains to be editable so as restricted content redactions, modifications or deletions to be carried out when requested, blockchains' alignment with the regulation is indeed challenging, if not unfeasible. Towards resolving this discrepancy, in this Chapter we first discuss the privacy challenges faced by blockchain technology, and we then explore blockchain's contradiction with the RtbF erasing provisions of the GDPR. In this respect, we provide a comprehensive review on the state-of-the-art approaches, technical methods and workarounds and advanced cryptographic techniques that have been put forward to resolve this contradiction, and we discuss their potentials and limitations when applied at large to either permissioned or permissionless blockchains.
On the Linear Complexity of Feedforward Clock-Controlled Sequence

As a research field of stream ciphers, the pursuit of a balance of security and practicality is the focus. The conditions for security usually have to satisfy at least high period and high linear complexity. Because the feedforward clock-controlled structure can provide quite a high period and utility, many sequence ciphers are constructed based on this structure. However, the past study of its linear complexity only works when the controlled sequence is an m-sequence. Using the theory of matrix over the ring and block matrix in this paper, we construct a more helpful method. It can estimate the lower bound of the linear complexity of the feedforward clock-controlled sequence. Even the controlled sequence has great linear complexity.
Varying user agency and interaction opportunities in a home mobile augmented virtuality story

New opportunities for immersive storytelling experiences have arrived through the technology in mobile phones, including the ability to overlay or register digital content on a user’s real world surroundings, to greater immerse the user in the world of the story. This raises questions around the methods and freedom to interact with the digital elements, that will lead to a more immersive and engaging experience. To investigate these areas the Augmented Virtuality (AV) mobile phone application Home Story was developed for iOS devices. It allows a user to move and interact with objects in a virtual environment displayed on their phone, by physically moving in the real world, completing particular actions to progress a story. A mixed methods study with Home Story either guided participants to the next interaction, or offered them increased agency to choose what object to interact with next. Virtual objects could also be interacted with in one of three ways; imagining the interaction, an embodied interaction using the user’s free hand, or a virtual interaction performed on the phone’s touchscreen. Similar levels of immersion were recorded across both study conditions suggesting both can be effective, though highlighting different issues in each case. The embodied free hand interactions proved particularly memorable, though further work is required to improve their implementation, arising from their novelty and lack of familiarity.
Attentive Contrast Learning Network for Fine-Grained Classification

Fine-grained visual classification is challenging due to subtle differences between sub-categories. Current popular methods usually leverage a single image and are designed by two main perspectives: feature representation learning and discriminative parts localization, while a few methods utilize pairwise images as input. However, it is difficult to learn representations discriminatively both across the images and across the categories, as well as to guarantee for accurate location of discriminative parts. In this paper, different from the existing methods, we argue to solve these difficulties from the perspective of contrastive learning and propose a novel Attentive Contrast Learning Network (ACLN). The network aims to attract the representation of positive pairs, which are from the same category, and repulse the representation of negative pairs, which are from different categories. A contrastive learning module, equipped with two contrastive losses, is proposed to achieve this. Specifically, the attention maps, generated by the attention generator, are bounded with the original CNN feature as positive pair, while the attention maps of different images form the negative pairs. Besides, the final classification results are obtained by a synergic learning module, utilizing both the original feature and the attention maps. Comprehensive experiments are conducted on four benchmark datasets, on which our ACLN outperforms all the existing SOTA approaches. For reproducible scientific research https://github.com/mpskex/AttentiveContrastiveLearningNetwork.
Comparative Usability Evaluation of Novice and Expert Gojek Users

Gojek is one of the biggest online transportations in Indonesia which has been downloaded more than 142 million times. Even though it has been used by a wide number of users, the application still needs to be improved which can be seen from users’ complaints and feedback. This research aimed to analyze the comparison between novice and expert users of the Gojek application through usability evaluation using the Think Aloud Protocol (TAP) and System Usability Scale (SUS) Questionnaire. The TA protocol was chosen to give authors insight into the participants’ way of thinking when using the application. Meanwhile, the SUS can quantify the effectiveness, efficiency, and user satisfaction. Five usability criteria were being tested in this research include learnability, efficiency, memorability, error, and user satisfaction. The results showed that the SUS score for the Gojek application from expert users is 69.68 which is above average and higher compared to the score from novice users which is 64.38. The minimum SUS score for a good application is 68, meaning that the Gojek application is usable enough for expert users but not for novice users. In conclusion, Gojek needs to make the application more user-friendly for novice users.
Stock issues and the structure of argumentative discussions: An integrative analysis

We develop a method for analyzing argumentative discussions centered around the notion of ‘stock issues’, i.e., the field-dependent standard issues addressed by the participants in such discussions. The method yields an overview of the structure and content of complex argumentative discussions with multiple participants, including the activated stock issues and the ‘depth’ of the argumentation advanced per each stock issue. We start from the assumption that any given discussion context requires a set of stock issues to be addressed by the participants through their argumentation, tied together by a decision rule regulating the weight of each stock issue on the matter at hand. The building blocks of our method and the results of its application are illustrated through an example. We discuss several extensions and problems, concluding with directions for further research.
An Analytical Review of Load Balancing in Heterogeneous Networks

A heterogeneous network is a system of technological devices with different interconnected operating systems and protocols. Load balancing is the procedure of allocating incoming traffic on the network amongst a cluster of servers. Heterogeneous networks are the topic of interest for many researchers in the IT field because this type of network is deployed all over the world, as users have a growing demand to stay connected to the Internet. At the same time, an increasing number of users are utilising multimedia services that require intensive bandwidth to adapt to real-time interactivity. As there are millions of simultaneous digital connections, the use of only a single node to process requests from numerous users is not a good practice. This is because the network will suffer from slow data transmission and congestion due to the increased processing power and bandwidth required to handle all requests. Consequently, load balancing has become an essential concern in heterogeneous networks to ensure reliability and efficiency. In this paper, we present an analytical review of several load-balancing mechanisms together with a comparative analysis. The results reveal that, although the proposed load balancing mechanisms by several researchers possess extensive benefits, the drawback of overload and congestion issues remains a major challenge to be considered. A concrete review is important to develop a good technique for load balancing that can support future heterogeneous networks.
The Technology of Tracking System Integrated Big Data based on Tracer Study to Improve Social Networking of Graduates in The Era of Society 5.0

Currently, the development of digital education resource management continues to increase. In the era of Society 5.0, social networking skills are needed. However, this ability is relatively not maximally owned by graduates. This research aims to (1) develop a tracking system technology; (2) testing the attractiveness level of tracking system technology; and (3) testing the effectiveness of tracking system technology integrated with big data based on tracer studies. The method used in this research is R&#x0026;D. The stages are carried out starting from literature study, product design, product design, product building, product testing, and analysis of results. This research was centered at state universities in East Java. The results of this study include: (1) the tracking system technology integrated with big data based on the tracer study that was developed has a high level of compatibility with an average score of 95; (2) the average percentage of the attractiveness of refined products is 88&#x0025; (design aspect), 96&#x0025; (display aspect), 90&#x0025; (content aspect), and 92&#x0025; (workflow aspect); and (3) the percentage level of feasibility of the developed product is 90&#x0025; (user interface aspect), 86&#x0025; (target aspect), 86&#x0025; (usefulness aspect), and 88&#x0025; (user ease aspect).
Knowledge Management Applied in the Comparative Study of the IRETE Intrusion Methodology to Access to the Computer Systems

The threats in cyberspace materialize big digital security threats for any organization. New computer incidents are permanently reported, visualizing the advanced technical skills of cybercriminals and the cybersecurity professionals' response limitations. Most expensive digital security products are based on the treatment of known threats and are vulnerable to new threats known as zero-day attacks. Likewise, the human factor continues to be one of the main weaknesses when deploying IT security strategies and policies. In this way the tests or computer penetration tests are one of the most appropriate techniques to know and establish digital security mechanisms according to each organization. Therefore, the present work analyzes international standards to carry out computer vulnerability tests and proposes a methodology of ethical hacking under the postulates of gratuity and resources availability. Suggestions on the scalability of cybersecurity strategies are presented at the end of the document, considering that each organization is different and requires adaptability in the use of the available infrastructure to manage known and unknown digital risks. IRETE's research is based on the management that must be carried out in the creation, distribution and appropriation of knowledge, through the methodology that guides the assurance of information through the PenTESTING phases. Likewise, it proposes a process that integrally integrates the collection of information from any operating system, indicating that IRETE generates information more simply and accurately from the inspection, track, examination, testing and exfiltration phases. IRETE presents a complete methodology to approach the knowledge management of a vulnerable system, which allows an in-depth review of the factors that affect the handling of information.
SC-VDM: A Lightweight Smart Contract Vulnerability Detection Model

The smart contract technology of blockchain is being applied in many industries, but its security issues have also caused huge economic losses, so it is very important to conduct security audits on them before smart contracts’ deployment. The existing smart contract security audit methods rely heavily on the rules formulated by experts based on their own knowledge and experience, require high hardware resources and the detection procedure is time-consuming. To address these problems mentioned above, we propose a lightweight smart contract vulnerability detection model(SC-VDM) based on Convolutional Neural Networks(CNN), which can automatically detect the vulnerabilities in the smart contract on a lightweight computer without expert knowledge. We first convert the smart contract bytecode into smart contract bytecode grayscale matrix pictures and then use CNN for vulnerability detecting. We test SC-VDM on two datasets which each contain four types of smart contract vulnerabilities. The experimental results show that the accuracy and F1-score can reach more than 81% and 86% on two datasets. It performs best on the Reentrancy vulnerability which had caused The DAO attack in 2016, and the accuracy and F1-score is 89.52% and 93.96%. Moreover, the detection time is greatly shortened than traditional tools, it costs only 0.021 s for each smart contract.
A Word Embedding Topic Model for Robust Inference of Topics and Visualization

Probabilistic topic models for semantic visualization are useful for discovering and visualizing latent topics in document collections. In these models, the inference of topics and visualization is largely based on word co-occurrences within documents. Therefore, when documents in a corpus are short in length, these models may not achieve good results due to the sparsity of word co-occurrences. In this paper, we propose a word embedding topic model (WTM) that is robust to data sparsity when detecting topics and generating visualization of short texts. Extensive experiments conducted on four real-world datasets show that WTM is more effective in dealing with short texts than state-of-the-art models.
Blockchain for International Security an Introduction

International security sits at the point where civilian and military technologies meet—when an innovation yields both benefits for humanity but also the ferocity of modern warfare. The pace of today’s innovation is disrupting all sectors, creating more urgency for practitioners and students of international security and emerging technology to interact—not only on the potential for advances to be misused but also how they can be used to strengthen international security. As such, effective implementation of treaties and controls need to strike a balance in allowing the free flow of trade with nonproliferation objectives while keeping pace with scientific and technological innovations that have the potential to disrupt both. Distributed ledger technology (DLT) is one emerging technology that offers solutions for creating greater efficiencies in tracking nuclear, biological, chemical and dual-use materials in line with national and international reporting obligations.
Using Natural Language Processing to Build Graphical Abstracts to be used in Studies Selection Activity in Secondary Studies

Context: Secondary studies, as Systematic Literature Reviews (SLRs) and Systematic Mappings (SMs), have been providing methodological and structured processes to identify and select research evidence in Computer Science, especially in Software Engineering (SE). One of the main activities of a secondary study process is to read the abstracts to decide on including or excluding studies. This activity is considered costly and time-consuming. In order to speed up the selection activity, some alternatives such as, structured abstracts and graphical abstracts (e.g. Concept Maps – CMs), have been proposed. Objective: This study presents an approach to automatically build CMs using Natural Language Processing (NLP) to support the selection activity of secondary studies. Method: First, we proposed an approach composed by two pipelines: (1) perform the triple extraction of concept-relation-concept based on NLP; and (2) attach the extracted triples in a structure used as a template to scientific studies. Second, we evaluated both pipelines conducting experiments. Results: The preliminary evaluation revealed that CMs extracted are coherent when compared with their source text. Conclusions: NLP can assist the automatic construction of CMs. In addition, the experiment results show that the approach can be useful to support researchers in the selection of studies in the selection activity of secondary studies.
CS:Show – An Interactive Visual Analysis Tool for First-Person Shooter eSports Match Data

Electronic Sports (eSports) is a fast-growing domain within the entertainment sector and becomes economically relevant in terms of a paying audience, merchandise, and major tournaments with highly endowed prize money. First-person shooter (FPS) games represent a dominant discipline. Professional training methodologies such as post-match analyses and tactics discussions are becoming essential in training sessions besides pure mechanical-oriented exercises such as aiming and movement. Furthermore, professional sports coaches are involved in the training of players. In this paper, we are investigating this newly developing profession, specifically, how multimedia systems can be built to support coaches and players in analyzing data of previous matches for preparing for future ones. In the example of Counter-Strike: Global Offensive (CS:GO), we identified a set of six criteria that can be incorporated into tools to support the analysis of FPS matches. We describe user interface functionalities that allow to interactively analyze the highly multivariate data of FPS matches. We show our concepts’ technical feasibility by implementing them within a tool – CS:Show. Within an expert user study, evaluate our concepts with professionals. We conclude that our proposed eSports analysis tool was preferred over analysis functionalities built in in CS:GO. Supported by statistically significant evidence, our participants rated our tool more efficient, more usable, and assigned the tool with higher analytical ability than an average tool for analyzing FPS eSports matches.