Reviewerly/exHarmony huggingface dataset:
Rhetorical portfolio for the Bachelor of Law Education in Dagestan

The subject of this research is the currently relevant question of the formation of rhetorical portfolio for the Bachelor of Law Education in the Dagestan State University (discipline 44.03.01). This portfolio is highly demanded due to the need for improving the quality of professional competencies of students during the spread of COVID-19 pandemic, which allows the student to share educational information with the group remotely. The term &amp;ldquo;Rhetorical Portfolio&amp;rdquo; suggests which course it should applied in. It may perform the function of a legal document. The scientific novelty of this research lies in detailed description of the topic; offer of a possible structure and approximate thematic content of the rhetorical portfolio; advise to include in the rhetorical portfolio student's achievements, various tasks and reference materials, legal documentation, flaws in the educational process, and recommendations that would allow the future pedagogue of law to succeed in their activity. Rhetorical portfolio is a new technology developed on the basis of extensive experience in teaching eloquence to the humanities students; what needs to be implemented in the educational process and effectively used in modern educational system. Therefore, the material is prepared using the method of observation over educational process, various surveys, and analysis of rhetorical classes. The author underlines the importance of considering local peculiarities in communication to ensure the success of any constructive rhetorical speech in multinational Dagestan. This would also contribute to the development of skills of peer review, critical re-evaluation of the content of the material, and reflection. This document, which consists of three parts, is one-off nature and has variations, should turn into a collective rhetorical portfolio in order to activate the educational process. This is the reflection of an alternative point of view to demonstrate a true picture of personal and collective academic performance.
A study on the limitations of university distance education on COVID- 19 situation and improvement measures for the Post-COVID-19 Era - From the perspective of an adult learner-friendly university -

The outbreak of COVID-19 has been a major crisis factor in the various aspects of education, but it can be evaluated as meaningful in that it achieved changes in the educational operating system that had not been achieved before in a short period of time. In order for meaningful changes to be made from the perspective of innovation in the fields where distance education is being conducted at universities responding to COVID-19, it is necessary to check whether efforts for the changes are to be friendly to adult learners based on andragogy. For this, this study has examined the current status of online education at universities after COVID-19, focusing on the related studies, and derived issues for the improvement of universities’ constitutions as adult learner-friendly ones. Based on this, in preparation for the post-COVID-19, the following six measures were suggested for the revitalization of distance education to improve universities’ constitution as adult learner-friendly ones. First, insufficient quality of lectures and the need to strengthen professors’ capabilities such as teaching methods; second, reinforcement of interactions between instructors and learners and between learners; third, the need to increase learners’ ability for self-directed learning; fourth, preparation of the systematic learning design, evaluation, effective feedback and management measure; fifth, expanding the use of two-way platforms and advancement of functions; sixth, the flexibility of academic operation for activation of distance education. In particular, the sixth item, the expansion of andragogy-based academic operation flexibility, is expected that the activation of distance education will create a context and environment that can result in the improvement of universities’ constitution as adult learner-friendly ones from the perspective of university innovation.
Privacy‐preserving and efficient user matching based on attribute encryption in mobile social networks

Summary With the rapid popularity of social networking platforms, users can be matched when sharing their profiles. However, there is a risk of leakage of sensitive user information during the user matching process, which leads to the lack of user privacy protection. In this paper, we propose a privacy protection scheme based on the encryption of hidden attributes during user matching in mobile social networks, which uses linear secret sharing scheme (LSSS) as the access structure based on ciphertext policy attribute‐based encryption (CP‐ABE), and the match server can perform friend recommendation by completing bi‐directional attribute matching determination without disclosing user attribute information. In addition, the use of selective keywords protects the privacy of requesters and publishers in selecting keywords and selecting plaintext attacks. The scheme reduces the encryption and decryption overhead for users by dividing encryption into a preparation phase and an online phase and shifting most of the decryption overhead from the requester to the match server. The experimental results show that the scheme ensures user privacy while effectively reducing communication overhead.
Prototyping Assistive Systems for Manual Assembly in Real Production Environments: Challenges and Lessons Learned

In current production environments, industry 4.0 or smart production plays an essential role. The trend towards more and more individualization and an increasing number of product variants place high demands on people working in manufacturing. Assembly workers must achieve increasingly shorter cycle times and constantly learn how to assemble new products. The support from assistive systems (e.g. digital work instructions, pick-by-light, automatic component recognition for quality control) is necessary in these environments. But many systems and prototypes presented in this field work perfectly in lab environments, however, they may fall short when deployed in a real production environment. We developed a comprehensive and flexible assistance system for manufacturing and identified design and technical challenges, which may only appear when working at the shop floor and. In this paper, we present our experiences and discuss our key challenges and lessons learned.
Multi-task Feature Learning for Social Recommendation

The purpose of the recommender system is to recommend personalized products or information for users. It is widely used in many scenarios to deal with information overload problems to improve user experience. As an existing popular recommendation method, collaborative filtering usually suffers from data sparsity and cold start problems. Therefore, researchers usually make use of side information, such as contexts or item attributes, to solve the problem and improve the performance of the recommender systems. In this paper, we consider social relationship and knowledge graph as side information, and propose a multi-task feature learning model, Social-MKR, which consists of recommendation module and knowledge graph embedding (KGE) module. In recommendation module, we build the social network among users based on the user-item interactions, and conduct the GCN model to obtain the specific user's neighborhood representation, which can be used as the input of the recommendation module. Like MKR, the KGE module is used to assist recommendation module by a cross&compression unit, which can learn high-order hidden features between items and entities. Extensive experiments on real-world datasets (e.g., movie,book and news) demonstrate that Social-MKR outperforms several state-of-the-art methods.
A Checking Method of Architecture Engineering Kernel States for Large-scale and Complex Information System

To facilitate architecture development of largescale and complex systems, we have proposed an architecture engineering methodology and defined seven kernels of architecture engineering (i.e., opportunity, Stakeholder, Need, Architecture, Team, Work and Way-of-Working) which must be considered during the process of developing an architecture. Each kernel has five or six different states that can indicate the progress and health of architecture development. To further improve practicability of the defined seven kernels and their 36 states in architecture development, a reference guide is suggested based on our engineering experience in practice, which contains more than 100 items helping to check kernel states and move them forward. Using the reference guide, we conducted an application of architecture development of a complex business information system. Results show that the proposed guide can be effectively used to help architecture engineers to determine and push on the state of architecture development. Architecture development can be proceeded clearly, timely and smoothly. Moreover, all the team members can work well together.
An Iterative Approach Based Reversible Data Hiding with Weight Update for Dual Stego Images

The paper proposes an iterative approach based reversible data hiding with weight update used for dual stego images. Proposed method initially estimates the error between the adjacent pixels from which the embedding strength \(\propto \) is estimated. Proposed method then estimates a weight using the error and the data to be hidden. This weight is updated iteratively by using the subsequent data. Further, to improve the security, the random binary sequence is mixed with the weight. To acquire the two stego images, the weight is entrenched on the original image pixels. In the extraction phase, the data is extracted in a reverse order of embedding, where the inverse weight updation is performed, from which the data is extracted. By averaging the two stego images, the original image can be restored. According to the results of the experimental study, the proposed scheme for embedding data on dual stego images outperforms recent data hiding algorithms in terms of embedding rate and visual efficiency.
Are Theme Songs Usable for Anime Retrieval?

Japanese anime is well known as a multimedia-related popular culture but its retrieval techniques have not been fully developed. In this paper, we made an attempt of similarity-based retrieval of anime works using their theme songs. We hypothesized that similar anime works have similar theme songs because the atmosphere of anime works may be reflected in their theme songs. Under this hypothesis, we measured the audio-based and lyrics-based similarity among theme songs to search for anime works similar to a given query work. Experimental results show that the anime retrieval with audio-based and lyrics-based theme song similarity succeeded with average accuracy of 63% and 66%, respectively. This accuracy is higher than random selection, even though it is lower than an upper-bound accuracy based on manually prepared summary texts.
Legal Analysis and Victim Identification of Credit Card Fraud Cases

To justify that the bank is the real victim of the credit card fraud cases, this study, from the perspective of the attribute of money, makes it clear that money still has the attribute of "possession is ownership" in the field of property crimes.The money deposited by depositors in the bank is immediately occupied and owned by the bank, and what the thief violates is actually the bank's property.However, when a fraud case occurs, the bank immediately compensates for its own losses by eliminating the same amount of claims of depositors, and the identity of the bank's victim is therefore covered up.Theoretically speaking, the legal background of the disputes of credit card fraud cases is the issue of the effectiveness of the debtor's payment to the quasi-possessor of creditor's rights in civil law.Under the influence of this theory, we can observe and interpret the common defense reasons in the disputes of credit card fraud cases, in order to benefit the judicial practice.
Road intersections vehicle tracking based on multi-layer graphs and multi-angle

Aiming at the problem of tracking uncertain vehicle motion flow at intersections, a method based on multi-level graphs and multi-angle is proposed. Firstly, each motion flow is assigned to different layers with diverse neighborhoods by using the constructed multi-layer graphs. Secondly, all multi-layer graphics under multi-angle views are mapped to the selected main view. Finally, the vehicle tracking is realized by solving the shortest path of the mapping main view motion flow. Through the experiment and analysis of vehicle tracking at intersections, the results show that this method can effectively predict vehicle trajectory for uncertain vehicle flow at intersections, and the misjudgment rate between tracking effect and the real situation on the ground is less than 6%, which can provide a new method for vehicle tracking in intelligent transportation.
Advancement on Security Applications of Private Intersection Sum Protocol

Secure computation protocols combine inputs from involved parties to generate an output while keeping their inputs private. Private Set Intersection (PSI) is a secure computation protocol that allows two parties, who each hold a set of items, to learn the intersection of their sets without revealing anything else about the items. Private Intersection Sum (PIS) extends PSI when the two parties want to learn the cardinality of the intersection, as well as the sum of the associated integer values for each identifier in the intersection, but nothing more. Finally, Private Join and Compute (PJC) is a scalable extension of PIS protocol to help organizations work together with confidential data sets. The extensions proposed in this paper include: (a) extending PJC protocol to additional data columns and applying columnar aggregation based on supported homomorphic operations, (b) exploring Ring Learning with Errors (RLWE) homomorphic encryption schemes to apply arithmetic operations such as sum and sum of squares, (c) ensuring stronger security using mutual authentication of communicating parties using certificates, and (d) developing a Website to operationalize such a service offering. We applied our results to develop a Proof-of-Concept solution called JingBing, a voter list validation service that allows different states to register, acquire secure communication modules, install it, and then conduct authenticated peer-to-peer communication. We conclude our paper with directions for future research to make such a solution scalable for practical real-life scenarios.
User Interface Improvement in English Kids Talk Application using The Heuristic Evaluation Method

Heuristic evaluation is an informal method used to analyze the presentation of an interface design to several evaluators. Heuristic evaluation is done by conducting several interface tests and analyzing the interpretation of the interface. English Kids Talk application is an English learning application for elementary school children. Based on the test results on the initial version of the English Kids Talk Application, user interface improvements are essential. English Kids Talk (Ekids) application development of the Ekids application still does not pay attention to UX. However, User Experience improvements are also necessary, minimizing disruption to the comfort and loyalty of users English Kids Talk application. In this paper, we propose prototype recommendations with a low evaluation value that improving User Experience. A prototype recommendation will be generated that suits the user's needs. The result is usability testing and an interaction cost between the initial version of the English Kids Talk Application and the new version. The result shows higher interaction costs from the new English Kids Talk application than the old one. Usability testing of the new English Kids Talk application is better than the old one.
Eliciting Smartphone App Requirements for Helping Senior People: A Questionnaire Approach

The world’s unprecedented growth of the senior demographic is bringing key age-related issues to the forefront. Issues such as memory loss, vision impairment, hearing loss, loss of mobility (or loss of dexterity in the hands), and speech loss prevent seniors from interacting with others. We believe that a lot can be done to improve seniors’ lives, but current smartphone apps are often not usable or adaptable enough to address these issues. Specifically, current app development practices do not seem to adequately consider seniors’ needs and possibly lack a process that includes asking appropriate questions, building relevant scenarios, or coming up with essential requirements. We observe that omitting questions and asking irrelevant or incorrect questions in the requirements elicitation phase needs to be avoided. We propose a Goals, Questions, Scenarios, Requirements (GQSR) Elicitation framework as well as an expanded questionnaire approach for requirements elicitation. We develop improved guidelines and rules for questions as part of the question/interview process. Through asking important questions and applying the GQSR framework, we developed the smartphone app, HOPE (Helping Our People Easily), with the intention of helping seniors in their daily lives and alleviating problems such as aphasia. The GQSR framework defines nonfunctional requirements (NFRs), FRs, questions, and scenarios producing questionnaires for the purposes of interviewing seniors. We achieve traceability between the GQSR Requirements Engineering framework and the HOPE smartphone app. HOPE, a real mobile app targeting seniors, is used to illustrate and evaluate our framework.
Generalized Funnelling: Ensemble Learning and Heterogeneous Document Embeddings for Cross-Lingual Text Classification

Funnelling ( Fun ) is a recently proposed method for cross-lingual text classification (CLTC) based on a two-tier learning ensemble for heterogeneous transfer learning (HTL). In this ensemble method, 1st-tier classifiers, each working on a different and language-dependent feature space, return a vector of calibrated posterior probabilities (with one dimension for each class) for each document, and the final classification decision is taken by a meta-classifier that uses this vector as its input. The meta-classifier can thus exploit class-class correlations, and this (among other things) gives Fun an edge over CLTC systems in which these correlations cannot be brought to bear. In this article, we describe Generalized Funnelling ( gFun ), a generalization of Fun consisting of an HTL architecture in which 1st-tier components can be arbitrary view-generating functions , i.e., language-dependent functions that each produce a language-independent representation (“view”) of the (monolingual) document. We describe an instance of gFun in which the meta-classifier receives as input a vector of calibrated posterior probabilities (as in Fun ) aggregated to other embedded representations that embody other types of correlations, such as word-class correlations (as encoded by Word-Class Embeddings ), word-word correlations (as encoded by Multilingual Unsupervised or Supervised Embeddings ), and word-context correlations (as encoded by multilingual BERT ). We show that this instance of gFun substantially improves over Fun and over state-of-the-art baselines by reporting experimental results obtained on two large, standard datasets for multilingual multilabel text classification. Our code that implements gFun is publicly available.
What Are the Core Elements Necessary for Effective Digital Asset and Media Management Practice?

With the digital assets increase, Digital Asset Management (DAM) has become more crucial than ever before.DAM increases the efficiency of managing contents and helps maximize the value of content.This research through two case studies discovers the core elements necessary for effective DAM practice, including BBC Redux and The World Digital Library.Findings show that in selecting a DAM system, managing metadata and understanding user needs are the most necessary elements for effective DAM practice.In addition, this research highlights that in the process of DAM practice, every element interacts with the others.People still have to look at the DAM from a comprehensive perspective of a DAM ecosystem.
Transparency/accountability and human capital development as software in Nigerias quest for economic development 2004 - 2020

Nigeria is endowed with rich human and material resources yet continues to struggle in the quagmire of underdevelopment.This paper explored the place of human capital development and transparency/accountability in economic development.Correlation and regression analyses were used on the secondary data collected, tested for unit root and autocorrelation, over a period of 13 years.Transparency/accountability proxies had positive but insignificant relationship with economic development in Nigeria within the period.Curiously but not surprising, human capital development had a positive though insignificant relationship with economic development in Nigeria for the 13 years.It was recommended that the Nigerian government openly reports dealings and allows public scrutiny of the petroleum industry transactions and increase the amount budgeted, while tackling corruption in budget implementations.
Improved Collaborative Filtering Integrated with Diverse Optimization Strategies

In this paper, an Improved Collaborative Filtering Integrated with Diverse Optimization Strategies (ICFIDOS) is proposed to improve the performance of recommendation system. First, data sparsity problem of collaborative filtering was relieved via Slope One algorithm. Then, Hybrid Shilling Attacks Detection Framework is utilized to help recommendation system to defend the traditional and modern Shilling Attacks, and purify the polluted data. After that, cold start problem of initial trusted degree was resolved via combination with the influence of new users and existed users. Furthermore, similarity computation of Item Preference is improved. Finally, every step of our ICFIDOS algorithm was clarified carefully. In experiment, the efficiency of ICFIDOS is validated and has better performance compared to classical UBCF and K_TUBCF algorithm.
Data Completeness Impact on Deep Learning Based Explainable Recommender Systems

Recommendation systems become an important part in helping users find the most fit items in many domains, such as healthcare, transportation, agriculture, media, and e-commerce. The development of an explainable recommendation system was claimed to add more value to improve user satisfaction. Various studies have been conducted regarding the model to solve real world issues. Yet, the impact of data quality in building an explainable recommendation system is still scarce. In this paper, we investigate whether the completeness of data used in building explainable recommendation system will impact the performance of the recommender systems and quality of the explanation. We use the Yelp and MovieLens dataset and train a deep learning explainable recommendation system model, Co-Attentive Multi-task Learning (CAML), on various amounts of data by reducing the data records using algorithm technique to achieve Missing Completely at Random (MCAR) and Missing at Random (MAR), and by eliminating selected features on each dataset. Finally, we evaluated the outcomes based on Root Mean Square Error (RMSE) for rating evaluation and Bilingual Evaluation Understudy (BLEU) & Recall-Oriented Understudy for Gisting Evaluation (ROUGE) for explanation evaluation. Our experiments conclude with Pearson Correlation Coefficient between completeness of data with the evaluation result in each of the experiment. We found out that different types of data reduction and dataset impact differently to the level of the performance of the ratings and explanation.