Reviewerly/exHarmony huggingface dataset:
Non-interactive Zero Knowledge Proof Based Access Control in Information-Centric Internet of Things

With the development of communication technology represented by 5G, the core business model of Internet of Things (IoT) has undergone great changes. The traditional host-centric network can no longer meet the needs of the IoT for throughput, privacy protection and interrupt tolerance. IC-IoT, the combination of ICN (Information Centric Networking) and IoT was put forward, which could provide scalable content distribution by using caching-router, multi-party communication, and the decoupling between senders and receivers. However, this paradigm still faces two major problems. First, the access control relationship established between publishers and subscribers requires additional maintenance of complex data structure and authentication process. Second, unencrypted named-data objects (NDO) lead to potential risks of privacy protection. To address those challenges, this paper proposes an algorithm called ZK-CP-ABE as an encryption means for distributed content distribution. Based on CP-ABE, it introduces the non-interactive zero knowledge proof protocol into the CP-ABE’s secret-key existence proof to ensure user privacy and reduce invalid bandwidth consumption. On this basis, a system called DPS-IoT is proposed, which uses Hyperledger Fabric based blockchain system to store access policies and evidence for ZKP to prevent them from being tampered with. In addition, we use smart contract to implement ZK-CP-ABE based access control, so as to improve the robustness and throughput of the system. Finally, by comparing with the existing related works, it is proved that the method and system proposed in this paper have greater advantages in utilization of transmission bandwidth, and better performance in system throughput.
A Novel One-Shot Object Detection via Multifeature Auxiliary Information

With the advantage of using only a limited number of samples, few-shot learning has been developed rapidly in recent years. It is mostly applied in the object classification or detection of a small number of samples which is typically less than ten. However, there is not much research related to few-shot detection, especially one-shot detection. In this paper, the multifeature information-assisted one-shot detection method is proposed to improve the accuracy of one-shot object detection. Specifically, two auxiliary modules are applied to the detection algorithm: Semantic Feature Module (SFM) and Detail Feature Module (DFM), which, respectively, extract semantic feature information and detailed feature information of samples in the support set. Then these two kinds of information are then calculated with the feature image extracted from the query image to obtain the corresponding auxiliary information that is used to complete one-shot detection. Thanks to the two auxiliary modules, which can retain more semantic and detailed information of samples in the support set, the proposed method can enhance the utilization rate of sample feature information and improve object detection accuracy by 2.97% compared to the benchmark method.
Improved Accuracy of Animal Skin Pattern retrieval with CNN Model and Distance Metrics

This study describes the retrieval of animal skins with very diverse shapes and textures. With so many variations in each animal skin pattern, an appropriate and supported CNN model is needed as well as an appropriate distance matrix method to support retrieval performance. This research was conducted on 6 types of animal skin images. In experimenting to obtain this animal skin image, 4 CNN models were used, namely EfficientnetB7, Inception V3, MobilenetV2, and Resnet50 V2, as well as 2 distance metrics methods, namely Euclidean and Manhattan. Based on the experiment, the average with 2 measuring distances is more than 90%. The CNN model has the highest retrieval accuracy in Inception V3 with an average of 97.2% MobilenetV2 with an average of 92.9%, Resnet50 V2 with an average of 95.5%, and EfficientNetB7 with an average of 96%. In the skin animal image retrieval accuracy which is more than 70%, the highest number on CNN model Inception V2 is 41 images and EfficientNetb7 is 40 images with Manhattan Distance. Some animal skin image patterns have a retrieval accuracy of up to 100% using 4 CNN models, but some patterns have an accuracy below 50%, so this is part of the continuation of research on animal skin patterns. Keywords-- Manhattan, Euclidean, CNN, EfficientnetB7, InceptionV3, Mobilenet V2, Resnet50 V2
Performance Evaluation of Tourism Human Resource Management Based on Fuzzy Data Mining

Tourism human resources performance management is an important means of enterprise core competitiveness; the assessment results is good or bad will directly affect the staff's work enthusiasm and attitude; the use of tourism human resources, configuration, and performance carries on the scientific, reasonable, and effective comprehensive evaluation and can fully stimulate the potential of employees. To continuously improve the work efficiency, quality, and level of staff, which has certain application value and practical significance for the long-term and healthy development of tourism industry, this article analyzes the current situation of tourism human resource performance management, introduces the data mining technology and fuzzy data mining technology, and then gives the fuzzy data mining algorithm and detailed fuzzy data mining steps, through the fuzzy data mining technology to evaluate the performance of tourism human resource management. It also analyzes the key factors and existing problems of human resource management in tourism industry and puts forward innovative thinking countermeasures for the performance management of human resources in the tourism industry.
Research on Expert Guidance System of Simulation Intelligent Training in Thermal Power Plant

With the rapid development of artificial intelligence, in order to solve the problems of many questions from trainees, poor efficiency of teachers in answering questions and high work intensity in the process of thermal power plant simulation practice, this paper proposes an intelligent training expert guidance system for thermal power plant simulation, and this paper uses Visual C++ software programming to implement an intelligent training expert guidance system with voice interaction and video presentation functions, using the speech development package Microsoft Speech SDK, in-depth study of the SAPI it provides, design and implementation of speech recognition function and speech synthesis function. The XML file is used to define the types of questions and the corresponding Q&amp;A test statements for keyword extraction; the video presentation function is realized with the help of Windows Media Player. The test results show that, based on the successful speech recognition, the system can simulate the way of dialogue between teachers and learners and answer learners’ questions in real time. The system has an open function, users can add questions independently, expand the database in real time, jump out of the traditional limitations of space, time and geography, and increase the interest of learning.
Towards a digital service to help the elaboration, implementation and follow-up of study regulations at the University of Geneva - a hands-on experiment

Writing study regulations for academic study programs and automatically implementing those regulations is a difficult task that involves a variety of actors and requires at each step careful compliance to the constraints defined in the regulations. This paper describes: (1) the innovation process, taking place through a hands-on experiment, that lead the R&amp;D Unit of the University of Geneva to provide a proposal for a digital service targeting the above purpose; (2) the actual design of such a digital service, providing various functionalities: (a) the elaboration of study regulations; (b) the elaboration of the corresponding study plan; (c) the actual implementation of the study plan through the information system. The digital service relies on two main ideas: (1) all study regulations and study plans are built from common atomic elements, that we call building blocks; (2) ensuring compliance to various constraints is achieved through a reasoning engine capturing the constraints defined over an ontology of the study regulations domain. Each year, for a given University, several study regulations, with various constraints and structure are defined or updated. They all need to be carefully crafted and implemented. The work presented in this paper has the potential to alleviate and improve this task for the various actors involved (students, program directors, lawyers, scientific committee members, study advisor, information systems managers, students’ office).
On the Diversified Practice of Teachers’ Roles with the Goal of Stimulating Students’ Internal Motivation

Teachers are the core of the school, and it is the foundation of every educational system.Parents worldwide are eager and anxious to find the "Best" teacher for their children.Over time, the concept of the 'best teacher is vague.It is often measured from various aspects, such as the learning outcomes and development of autonomous learning skills of the students.However, as students enter post-secondary and higher Education, their expectations from the teachers become more heightened.A single characteristic of the teacher might not be enough to take care of students' needs academically and psychologically.Then, based on literature and case studies in similar areas of the study, this short review article argues that on the diversified practice of teachers' roles to stimulate students' internal motivation.The discussions and findings are based on different theoretical interpretations of teachers' comprehensive multi-role literacy; when teachers face different students with diversified tasks, including the time outside of the classroom, and flexibly change their roles, providing emotional Scaffolding for the students.The essay concludes by highlighting the trade-offs between roles to provide optimal guidance for the students to motivate their internal learning motivation.
Multiple clues inspired banding detector in high dynamic range videos

In this paper, a method for detecting banding artefacts in high-dynamic-range (HDR) videos is proposed. The proposed method is the first banding detector designed for HDR videos. To effectively detect banding artefacts, a multi-scale banding detection strategy based on multiple visual clues is proposed. Specifically, in each scale, the visually perceptual effects of banding artefacts are measured sequentially by luminance, edge strength, and edge length clues. All these clues are exploited by considering the properties of the human vision system and HDR videos. Experimental results show that our method can effectively detect banding artefacts and provide banding visibility maps that highly match the human visual perception.
Power Apps

Power Apps allow citizen developers to build mobile-friendly apps quickly and without the need to implement traditional pro-code. While this low-code approach makes it easy to create custom apps quickly, a citizen developer might still need some support from you to connect the data from a web service and surface that data in a Power App.
On Generalizing Static Node Embedding to Dynamic Settings

Temporal graph embedding has been widely studied thanks to its superiority in tasks such as prediction and recommendation. Despite the advances in algorithms and novel frameworks such as deep learning, there has been relatively little work on systematically studying the properties of temporal network models and their cornerstones, the graph time-series representations that are used in these approaches. This paper aims to fill this gap by introducing a general framework that extends an arbitrary existing static embedding approach to handle dynamic tasks, and conducting a systematic study of seven base static embedding methods and six temporal network models. Our framework generalizes static node embeddings derived from the time-series representation of stream data to the dynamic setting by modeling the temporal dependencies with classic models such as the reachability graph. While previous works on dynamic modeling and embedding have focused on representing a stream of timestamped edges using a time-series of graphs based on a specific time-scale (\eg, 1 month), we introduce the notion of an ε-graph time-series that uses a fixed number of edges for each graph, and show its superiority in practical settings over the standard solution. From the 42 methods that our framework subsumes, we find that leveraging the new ε-graph time-series representation and capturing temporal dependencies with the proposed reachability or summary graph tend to perform well. Furthermore, the new dynamic embedding methods based on our framework perform comparably and on average better than the state-of-the-art embedding methods designed specifically for temporal graphs in link prediction tasks.
Target Professions Based Approach for Individual Learning Pathway Creation

University education is becoming more student-centered and personalized in recent years, whereas connections between professional skills and learning outcomes are recognized vaguely. The article proposes an approach to create relations between skills that students acquire in a higher education process and those required in various careers. The approach makes it possible to trace consistency between course material, educational programs, and professions. Thus, the implementation of skills and interdisciplinary connections allows to build the most relevant individual learning tracks for a specific career and provides students with valuable recommendations. The proposed approach is implemented in ITMO University’s (Russia) web service “Educational Program Maker,” initially designed for education process management. The system ensures the development of educational program elements, provides a platform for describing relations between educational entities, and enables users to choose a curriculum for a specific profession.
Diversified Query Generation Guided by Knowledge Graph

Relevant articles recommendation plays an important role in online news platforms. Directly displaying recalled articles by a search engine lacks a deep understanding of the article contents. Generating clickable queries, on the other hand, summarizes an article in various aspects, which can be henceforth utilized to better connect relevant articles. Most existing approaches for generating article queries, however, do not consider the diversity of queries or whether they are appealing enough, which are essential for boosting user experience and platform drainage. To this end, we propose a Knowledge-Enhanced Diversified QuerY Generator (KEDY), which leverages an external knowledge graph (KG) as guidance. We diversify the query generation with the information of semantic neighbors of the entities in articles. We further constrain the diversification process with entity popularity knowledge to build appealing queries that users may be more interested in. The information within KG is propagated towards more popular entities with popularity-guided graph attention. We collect a news-query dataset from the search logs of a real-world search engine. Extensive experiments demonstrate our proposed KEDY can generate more diversified and insightful related queries than several strong baselines.
Trends in the development of technological education and advanced vocational training of students in the context of technological education

The purpose of this article is to study the trends in the development of technological education and advanced vocational training of students in the context of technological education. Based on the retrospective analysis, the authors identified the most significant problems arising in the process of reforming technological education. The problems are as follows: problems related to determining the result of technological education; problems related to the content; problems related to the facilities; problems related to the human resource, which correlates with the survey of students studying to be teachers in the technological field, to determine the most important aspects of the organization of advanced professional training of schoolchildren. The methodological basis of the research consists of general scientific cognition methods, including analysis and synthesis (as a general methodological approach), comparison, generalization, and methods of systemic, complex, logical, structural, comparative, statistical analysis.&#x0D; &#x0D; &#x0D; Keywords: advanced training, technological education; vocational education; technology; school education
An Initial Analysis of E-Procurement Search Behaviour

Procurement or tender search is where suppliers seek opportunities for providing goods, works or services that authorities, organisations and businesses require. Such opportunities are listed as procurement contract notices for which suppliers can submit tenders. Typically, an E-Procurement system is used to help find and carry out one or more of the stages involved in the procurement process (from finding potential opportunities, bidding on such opportunities, to delivering the goods, works or services, i.e. find, win, deliver). Such systems are crucial in enabling suppliers to efficiently search through the available listings of procurement contract notices listed across various public and commercial portals. However, little research has investigated how end-users search for such opportunities. In this paper, we perform a descriptive analysis of the professional search behaviours of suppliers using a bespoke e-procurement system. Our analysis is based on a sub-sample of six months of search log interaction data. First, we provide an overview of the usage patterns of our sample of users before investigating how the behaviour of searchers is influenced by the type of search form used (quick vs advanced), user expertise (new vs experienced), and the domain of the procurement notices (General, Defence, Medical, etc.). Our findings highlight that more experienced searchers appear to be more strategic than less experienced searchers and that searchers behave differently depending on the domain in terms of querying and assessing behaviours. This analysis suggests that e-procurement search engines need to be mindful of the differences across searchers and between domains when designing a system to help support their users.
Design and app development of a virtual fretless Chinese musical instrument

The article presents the design and development of a virtual fretless Chinese stringed instrument App with the Duxianqin as an example, whose performance is expected to be no different from a real instrument. The digital simulation of fretless musical instruments is mainly divided into two parts: the simulation of the continuous pitch processing of the strings, and the simulation of the sound produced by plucking strings. The article returns to the theory of mechanics and wave theory and obtains the quantitative relationship between string frequency and its deformation and elongation. The Duxianqin selected in this article is a fretless instrument, which cannot be completely simulated by relying solely on sound source data. Playing and vocalization require real-time synthesis through pitch processing, which has certain reference significance for the realization of other fretless instruments.
Semantic Image to Image Translation using Machine Learning Algorithms

In semantic image-to-image translation, the goal will be to learn mapping between an input image and the output image. A model of semantic image to image translation problem using Cycle GAN algorithm is proposed. Given a set of paired or unpaired images a transformation is learned to translate the input image into the specified domain. The dataset considered is cityscape dataset. In the cityscape dataset, the semantic images are converted into photographic images. Here a Generative Adversarial Network algorithm called Cycle GAN algorithm with cycle consistency loss is used. The cycle GAN algorithm can be used to transform the semantic image into a photographic or real image. The cycle consistency loss compares the real image and the output image of the second generator and gives the loss functions. In this paper, the model shows that by considering more training time we get the accurate results and the image quality will be improved. The model can be used when images from one domain needs to be converted into another domain inorder to obtain high quality of images.
On Mahler's Classification of Formal Power Series Over a Finite Field

Abstract Let K be a finite field, K ( x ) be the field of rational functions in x over K and <m:math xmlns:m="http://www.w3.org/1998/Math/MathML" display="inline"> <m:mi mathvariant="double-struck">K</m:mi> </m:math> be the field of formal power series over K . We show that under certain conditions integral combinations with algebraic formal power series coefficients of a U 1 -number in <m:math xmlns:m="http://www.w3.org/1998/Math/MathML" display="inline"> <m:mi mathvariant="double-struck">K</m:mi> </m:math> are U m -numbers in <m:math xmlns:m="http://www.w3.org/1998/Math/MathML" display="inline"> <m:mi mathvariant="double-struck">K</m:mi> </m:math> , where m is the degree of the algebraic extension of K ( x ), determined by these algebraic formal power series coefficients.
Qualimetric approach to assessing the quality of support and support of continuing education of teaching staff: managerial aspect

The article analyzes the problem of creating an integral effective system of support and maintenance of continuing education of teachers in the conditions of the municipal system. The modern understanding of the complexity of the treatment of continuing education, its personality-developing nature, creative embodiment, institutional support and step-by-step development are mentioned. The substantiation of the basic theoretical foundation of this study is based on the characteristics of municipal, the specificity of the study on the quality of education and its criteria and indicators. Based on the application of the qualimetric approach, the algorithm of the quality assessment process, its stages – conceptualization, processing, evaluation, etc. is described. The results of the study are presented in the form of evaluation procedures, development of educational and developmental effects, criteria and indicators of productive support for continuing education of teaching staff.
Learning: What Should a Student Assess?

The article compares the educational aspects of student teaching assessment (STS) in traditional and “digital” technologies and considers the possibility of its application for the formation of universal managerial competencies of students instead of disciplinary action on teachers. To gain a better understanding of a problem of student teaching assessment, the author dwells on a conception of a pedagogical design, which is becoming the subject of students’ critical reflection. In the conditions of a total digitalization, the assessment criteria are changing, given that the students position themselves in a new managerial situation. From this perspective, assessment is viewed as a key managerial competence.