Reviewerly/exHarmony huggingface dataset:
Supporting Prostitutes Protection Act through DLT

This paper presents the use case of digitizing health certificates of prostitutes. It is shown that a centralized approach lacks trust in privacy by the prostitutes as well as not sufficient actions to ensure the integrity of digital health certificates. To counter these short comings, it is evaluated if a blockchain/distributed ledger technology approach should be considered. Based on this discussion, an evaluation of Ethereum Hyperledger Fabric and IOTA is presented, stating that IOTA matches the use case requirements the best. Therefore, an IOTA based architecture for digital health certificates of prostitutes is presented and discussed.
Virtual Reality Lab and Animation for Enhanced Remote Learning

Virtual reality is a fascinating technology in which the user is completely immersed in a simulated 3D environment. The three distinct features of VR that set it apart from other types of media are user dynamic control of viewpoint, 3D Stereovision, and the surrounding experience. In this paper virtual reality and animation is used to teach digital electronics lab. Earlier VR was expensive and not easily accessible to consumers. As processors became small, power-efficient and fast, we saw the emergence of consumer VR systems in 2013. The different applications of VR can be seen in the fields of the travel industry, healthcare, shopping and e-commerce, entertainment, education and training, etc. Especially, VR has gathered traction in education and training. To understand the circuitry subjects well, practical implementation of the circuit is very essential. In the current pandemic situation, online teaching has become a norm, be it a theory subject or a lab. Students are getting bored and exhausted because of online teaching which is delivered monotonously throughout the lecture on platforms as Google meet, Zoom, etc. The level of immersion VR or a 360 video provides is unmatched by a 2D video. VR is a transition from this monotonous learning as it provides an immersive fun learning experience. The environment and the equipment are built and the animations are done using blender, an open source software for 3d modeling and creating animations. The animated video is exported as a 360 video and uploaded to YouTube. Integrating animation in teaching laboratory experiments increases student's interest in learning. This animated video can be used to access digital electronics lab remotely.
Meta-Reinforcement Learning for Mastering Multiple Skills and Generalizing across Environments in Text-based Games

Text-based games can be used to develop taskoriented text agents for accomplishing tasks with high-level language instructions, which has potential applications in domains such as human-robot interaction.Given a text instruction, reinforcement learning is commonly used to train agents to complete the intended task owing to its convenience of learning policies automatically.However, because of the large space of combinatorial text actions, learning a policy network that generates an action word by word with reinforcement learning is challenging.Recent research works show that imitation learning provides an effective way of training a generation-based policy network.However, trained agents with imitation learning are hard to master a wide spectrum of task types or skills, and it is also difficult for them to generalize to new environments.In this paper, we propose a meta-reinforcement learning based method to train text agents through learning-to-explore.In particular, the text agent first explores the environment to gather task-specific information and then adapts the execution policy for solving the task with this information.On the publicly available testbed ALFWorld, we conducted a comparison study with imitation learning and show the superiority of our method.
Speculative Scenarios: The Exhibition as a New Space of Thinking

In this paper, curating exhibition is used as a tool to show future scenes from the perspective of speculative design. Speculative scenarios are used to provide a platform for researchers to show and transmit information. Through the tools of design fiction and prototype design, we can deduce, design and foresight the possible development of future scenarios. Audiences conduct nonlinear exploration and interactive experience in the process of exhibition, and get reflection in the speculative scenarios. The purpose of this paper is to improve our ability of constructing futures thinking and design prototypes. This paper will discuss three levels: diegetic prototype, discursive space and social engagement. This paper analyzes the diegetic logic and curatorial methods of speculative scenarios, discusses the role relationship among curators, audiences and works. And explores the audience's behavior changes and speculative feedback in the speculative scenarios. Through the speculative scenarios in the exhibition, this research aims to show the future possibilities of technology and humanity through the speculative scenarios in an exhibition hall, encourage people to imagine freely, and trigger new thinking of the future. The research intuitively shows the theoretical thinking of enterprises and institutions on the future through speculative scenarios, so as to pursue a more desirable future.
Machine Learning to Detect the Toxicity of Web Textual Contents Against Gender-Based Cyberoffenses

Internet serves as a knowledge resource medium for people on all walks-of-life. Especially children below 16 years are the current era’s technology practitioners, who often refer the Web resources for their thirst on knowledge. The other side of the Internet also paves a medium for anonymity, which opens the channels for portraying women and children as sexual victims. Cyberbullying and cyberstalking are the most reported cases against girl children but the preventive measures except criminal action against victimization of girl children are yet to shape up. As a proactive measure, this paper proposes to design a machine learning model that detects the probability of toxicity in the textual contents displayed in Web sites and alarm the children so that the crimes may get averted. The model has two components namely text extraction and text classification. Long short-term memory (LSTM) algorithm based on the recurrent neural network (RNN), linear support vector classifier and the prediction model based on logistic regression are the primary machine learning algorithms that implement text recognition and text classification. The performance of the model proposed here achieves 90% accuracy due to the presence of multiclass texts in the data.
Designing Feature Space for Educational Analytics Tasks Based on Data from Electronic Information Educational Environment

The article is devoted to the construction of features aimed for solving the problem of identifying students in a risk group. Early identification of such students will allow the university leadership to take necessary actions. This makes it possible to better retain the student population. Feature Engineering techniques were used to design these features. All stages of data preparation are briefly considered: data sampling, data preprocessing and data transformation. Scaling and aggregation techniques were used to transform the data. The selection of data was chosen from databases and data storages of the university’s electronic information and educational environment. The basic data that make up the attribute space are grouped into two categories: evaluations and data on the activity of students during the period of studying the discipline. Students’ activity is characterized by several attributes that are difficult to compare. Therefore, the technique of aggregation of attributes based on correlation analysis was used. The training was conducted in the Python programming language. The dataset is compiled from historical data - 2019 graduates. Using the support vector machine, the coefficients of the linear multiple regression equation are obtained. The main result of the work is to present the students’ activity as an integral characteristic that can be used for comparative purposes. It can be used to analyze students’ results in the educational process of studying the discipline. #CSOC1120.
Exploring Human-to-Human Telepresence and the Use of Vibro-Tactile Commands to Guide Human Streamers

Human-to-human telepresence is rising to mainstream use, and there is opportunity to provide rich experiences through novel interactions. While previous systems are geared towards situations where two users are previously acquainted, or provide channels for verbal communication, our work focuses on situations where audio is not desirable or available, by incorporating vibro-tactile commands into a telepresence setup. We present results from a lab-based study regarding a human-to-human telepresence system which enables one person to remotely control another through these vibro-tactile cues. We conducted a study with 8 participants to solicit their feedback when acting as a Streamer, 8 additional participants to solicit feedback as a Viewer, and 30 bystanders, through surveys and debriefing sessions. While our participants generally found the application favorable, we did find mixed feelings towards vibro-tactile devices, and much room for improvement for the whole interaction. We discuss the implications of our findings and provide design guidelines for future telepresence developers.
Underwater Image Enhancement with a Deep Residual Framework

This paper focuses on framework developed with the goal to enhance the quality of underwater images using machine learning models for the Underwater Image enhancement system. It also covers the various technologies and language used in the development process using Python programming language. The developed system provides two major functionality such as feature to provide input as image or video and returns enhanced image or video depending upon user input type with focus on more image quality, sharpness, colour correctness etc.
Teaching Reinforcement Learning Agents with Adaptive Instructional Systems

Traditionally, adaptive instructional systems (AISs) are built to instruct human students. However, they are not the only students that might benefit from an AIS. The field of reinforcement learning (RL), a subfield of machine learning, studies the instruction of synthetic students called agents, by means of various algorithms. In this paper, we advocate the use of an AIS as a conceptual framework to design and teach RL agents. We form our argument by deconstructing what it means to build and use an AIS for a human student, and discuss how the various concepts and relationships may apply to RL agents. We illustrate our findings by means of examples from the reinforcement learning literature and show a domain implementation of an AIS for RL agents.
Circle detection method based on contour extraction and rule verification

Product appearance visual inspection is a research hot spot in the field of industrial machine vision technology. In the manufacturing process of Flexible Integrated Circuit Substrate (FICS), the defects of circle hole on FICS surface affect the electrical connection performance between chip and substrate, so detecting the defects of circle hole on FICS surface is an important content of visual inspection. However, the surface of FICS presents local high texture characteristics and the circle hole may have edge defects, which make it difficult to accurately detect the circle hole. To solve this problem, this paper proposes a circle detection method based on contour extraction and rule verification. Firstly, a contour extraction operator is used to extract candidate circular contours from FICS surface images. Secondly, the closed contours are divided into two categories according to the roundness of the contours, where the circle hole verification method based on the Helmholtz principle is used to verify large roundness contours, the iterative least squares method is used to verify small roundness contours. Finally, parameters of the resulted circular holes are calculated to determine whether they are defective circular holes or not. Compared with classical circle detection algorithms such as random circle detection and ED circle detection, the proposed method performs better results in terms of accuracy, precision and speed. The average accuracy and recall of the proposed method are 100%, and the average detection speed is 1.6ms on the test dataset.
Research on Creativity and Personality Convergence Education to Enhance University Students&amp;apos; Future Competencies

This study proposes some of the elements of competencies that university students must have for coping with life in a society of the future. Furthermore, it proposes a particular teaching method to effectively strengthen these future competencies. In order to carry out this research, a review of previous studies, as well as an analysis of pertinent literature, were conducted in order to ascertain the competencies in question. In addition, three Delphi surveys were conducted by 10 experts in each major field involved. The results showed that future university students would require the following competencies: creativity and personality competency (problem solving ability, curiosity, a challenging spirit, cognitive flexibility, responsibility, leadership, etc.), a common good competency (common good sensitivity, inclusiveness, empathy and communication ability, etc.), and convergence competency (humanities literacy, engineering literacy, aesthetic literacy, etc.), and digital technology application competency (digital literacy, big data utilization, computing thinking, etc.). The results of this study can be used to develop a plan for the advancement of university education-one that meets the various needs of students living in any given future society. Moreover, it can also provide some ideas for the development of educational content by applying certain future competencies and future teaching methods.
FUNCTIONAL ACTION BIOLOGICAL IMAGE DATA SCREENING METHOD TO REDUCE SPORTS INJURY

ABSTRACT Introduction: The Functional Movement Test (FMS) is an evaluation method for the basic movement patterns of the human body that is designed by Gray Cook. Objective: This paper explores the application value of functional action test (FMS) biological image data in the risk assessment of sports injuries of Chinese rugby players. Methods: Taking the active national football team and provincial football players as the object, the standard FMS test is used to collect the data to determine the best deadline for the total FMS score. Results: The area under the ROC curve (AUC) of the overall athletes, men and women was significantly different from the assumption of AUC=0.5, which were 0.780 (P=0.000), 0.877 (P=0.001), 0.7130 (P=0.013); The best cutoff points corresponding to the total score of FMS are 13.5 points, 15.5 points, and 13.5 points, respectively. The chi-square test showed that the prevalence of the positive group (the total FMS score was less than the corresponding cutoff point) was significantly higher than the negative group (the total FMS score was greater than the corresponding cutoff point) (P&lt;0.01). The OR values of the total athlete, male and female FMS total score positive groups were 25.85 (95%CI: 3.34∼200.23), 25.00 (95%CI: 2.36∼264.80), 14.22 (95%CI: 1.76∼114.92). Conclusions: Among Chinese rugby players, the total score of FMS has a strong correlation with non-contact sports injuries. The score is 13.5 for women and 15.5 for men. Level of evidence II; Therapeutic studies - investigation of treatment results.
Plan Explanations that Exploit a Cognitive Spatial Model

Ideally, people who navigate together in a complex indoor space share a mental model that facilitates explanation.This paper reports on a robot control system whose cognitive world model is based on spatial affordances that generalize over its perceptual data.Given a target, the control system formulates multiple plans, each with a model-relevant metric, and selects among them.As a result, it can provide readily understandable natural language about the robot's intentions and confidence, and generate diverse, contrastive explanations that reference the acquired spatial model.Empirical results in large, complex environments demonstrate the robot's ability to provide humanfriendly explanations in natural language.
The Use of Digital Technology Platforms in the Context of Cybersecurity in the Industrial Sector

Abstract This study discusses the use of digital technology platforms (DTPs) in the context of cyber-security in the industrial sector, with a focus on digital industry (industrial) platforms (DIPs). A definition of DTPs is presented, including the author's interpretation, as well as the scope of DTP application in the industrial sector, which includes, in particular, European Digital Platforms (EDPs) and Polish Digital Platforms (PDPs), such as non-ferrous metals PDP or intelligent transport systems PDP. This is followed by a section covering the theoretical basis of the study that highlights the key challenges and risks associated with the use of DTPs as well as the methods for their neutralization in the form of specific concepts and systems that can be employed in the industrial sector. The subsequent section of the study is based on results of the author's own survey which collected information from a total of 120 companies operating in Poland, which were granted subsidies under the Operational Program Innovative Economy for investments involving the implementation and development of DTPs. The survey was carried out using a questionnaire developed by the author, which consisted of 23 questions. In this respect, as shown by the author's own studies, of greatest relevance are hardware failures and Internet outage events. Most importantly, concerns about such risks are some of the major factors underlying the negative attitudes of management staff of industrial companies toward DTPs, and therefore, it is so important to ensure that any such risks can be effectively addressed. They can be avoided through the use of certain concepts and systems such as STOE or CVSS. A typical company may know the model of DTPs in the context of challenges in the field of cybersecurity through this study; in particular, it can improve IT security.
Effectiveness of Reference Models for Knowledge Organization Systems

This chapter intends to study reference models for the development of knowledge organization systems (KOS) to evaluate their effectiveness and their modeling capacity through a comparison of requirements. The tools for the development of technological knowledge management systems that will be compared are Reference Model for an Open Archival Information System (OAIS), Modular Requirements for Records Systems (MoReq), and The Digital Library Reference Model. Through a comparative analysis of these instruments, it is proposed to evaluate and compare their main requirements. The planning of technological systems based on these standards/requirements brings guarantees of a correct use of classifications, thesauri, ontologies, among other types of KOS. They also promote their configuration in technological systems and regarding the business processes in which these technological systems are used.
Report on supporting and understanding of conversational dialogues workshop (SUD 2021) at WSDM 2021

This report describes the workshop on Supporting and Understanding of (multi-party) conversational Dialogues (SUD) organized as a part of the Web Search and Data Mining conference (WSDM) 2021. The aim of SUD workshop was to encourage researchers to investigate automated methods to analyze and understand conversations. We also discuss the release of a dataset that would be useful in IR research on conversations. The dataset was constructed to support the data challenge in SUD workshop and its precursor event - the Retrieval from Conversational Dialogues (RCD) track at the Forum of Information Retrieval and Evaluation (FIRE) 2020.
Continual Learning with Neuron Activation Importance

Continual learning is a concept of online learning with multiple sequential tasks. One of the critical barriers of continual learning is that a network should learn a new task keeping the knowledge of old tasks without access to any data of the old tasks. We propose a neuron activation importance-based regularization method for stable continual learning regardless of the order of tasks. We conduct comprehensive experiments on existing benchmark data sets to evaluate not just the stability and plasticity of our method with improved classification accuracy also the robustness of the performance along the changes of task order.
On safety assurance case for deep learning based image classification in highly automated driving

Assessing the overall accuracy of deep learning classifier is not a sufficient criterion to argue for safety of classification based functions in highly automated driving. The causes of deviation from the intended functionality must also be rigorously assessed. In context of functions related to image classification, one of the causes can be the failure to take into account during implementation the classifier's vulnerability to misclassification due to high similarity between the target classes. In this paper, we emphasize that while developing the safety assurance case for such functions, the argumentation over the appropriate implementation of the functionality must also address the vulnerability to misclassification due to class similarities. Using the traffic sign classification function as our case study, we propose to aid the development of its argumentation by: (a) conducting a systematic investigation of the similarity between the target classes, (b) assigning a corresponding classifier vulnerability rating to every possible misclassification, and (c) ensuring that the claims against the misclassifications that induce higher risk (scored on the basis of vulnerability and severity) are supported with more compelling sub-goals and evidences as compared to the claims against misclassifications that induce lower risk.
Exploring Micro-architectural Side-Channel Leakages through Statistical Testing

Micro-architectural side-channel leakage received a lot of attention due to their high impact on software security on complex out-of-order processors. These are extremely specialised threat models and can be only realised in practise with high precision measurement code, triggering micro-architectural behavior that leaks information. In this paper, we present a tool to support the inexperienced user to verify his code for side-channel leakage. We combine two very useful tools- statistical testing and hardware performance monitors to bridge this gap between the understanding of the general purpose users and the most precise speculative execution attacks. We first show that these event counters are more powerful than observing timing variabilities on an executable. We extend Dudect, where the raw hardware events are collected over the target executable, and leakage detection tests are incorporated on the statistics of observed events following the principles of non-specific t-tests. Finally, we show the applicability of our tool on the most popular speculative micro-architectural and data-sampling attack models.
Extensive Software Fault Prediction: An Ensemble based comparison

In recent studies, fault prediction is used to envisage the software's quality via conventional machine learning techniques. There are numerous problems that distress the efficiency of the software like redundant data, irrelevance features and missing data. It is observed from the literature that performance of the machine learning methods varies from data-set to data-set. In this paper, authors examines the performance of the model get affected after applying several data processing techniques on different machine learning classifiers. Various performance measures like accuracy, RMSE and ROC (AUC) are considered for predicting the quality of software.