Reviewerly/exHarmony huggingface dataset:
IPAPS: An improved public auditing protocol for secure data storage in cloud‐based medical storage system

Abstract Cloud‐based medical storage system is becoming popular. Cloud server is curious about the private information of stored cases, and the integrity of outsourced data has become increasingly concerned. Numerous public auditing protocols for the outsourced data into the cloud server were proposed. Unfortunately, in the existing protocols, some of them may neglect security to improve efficiency. Recently, Li et al. proposed an efficient privacy‐preserving public auditing protocol for cloud‐based medical storage system (EPPAP), which improved the communication efficiency by storing some of data owner's data in third part auditors, however we find that the protocol is vulnerable to collusion attack and replace attack. In this paper, we analyze the security attacks of the existing protocol, and further propose an improved public auditing protocol for secure data storage in cloud‐based medical storage system (IPAPS). In addition, we give the formal security proof and analyze the performance of our proposed protocol. The security proof shows our protocol takes into account integrity, collusion attack and replace attack at the same time. The simulation experiments in our performance analysis show that the proposed protocol is practical.
The Teacher's Role in Increasing Student Motivation in Learning Mathematics for Fourth Grade Students at SD N Gotakan

The teacher plays an important role in fostering student motivation to learn by always actively encouraging and encouraging students in the class during the teaching and learning process or learning takes place. This study aims to describe the teacher's role in increasing students' motivation to learn mathematics and the supporting and inhibiting factors of teachers in increasing learning motivation in students' mathematics learning. This study uses a qualitative method with a survey approach. The object of research is the role of the teacher in increasing learning motivation in learning mathematics for grade IV students. Data collection techniques through observation, interviews, and documentation. Data analysis techniques use the Miles and Huberman models which include data reduction, data presentation, and drawing conclusions. The results of the study show that the teacher has carried out his role well. The role played by the teacher in increasing students' motivation to learn mathematics is the role of the teacher as a source of learning, the role of the teacher as a facilitator, and the role of the teacher as a motivator. Factors supporting the teacher's role in increasing motivation to learn mathematics include infrastructure, student awareness, and support from school members and parents. Factors inhibiting the teacher's role in increasing learning motivation in learning mathematics include the behavior of students who lack discipline and student motivation that has not grown.
ALMEGA-VIR: face video retrieval system

The limitations of Content Based Video Retrieval (CBVR), such as large pools of video data, selection of features, limited processing capacity, and content-related issues, can be overcome by Deep Belief Neural Networks (DBNs). The search engine does the processing, and the results are effectively returned to the users. The deep learning model also has some comparable challenges to be solved at various levels. Typically, the selection of frames is one of the most essential tasks for a face retrieval system which is efficiently accomplished by using deep learning models. Also, the performance of recognition models highly depends on the selection of frames that include both low-level and high-level features from the given video sequence. This paper proposes ALPHA-TO-OMEGA (ALMEGA-VIR) Visual Information Retrieval scheme that analyzes the contents of the video file for a particular face and retrieves the user's relevant videos. VIR aims to solve the challenge of locating relevant pictures and videos based on a query. VIR can be based on text-based search, content-based search, and Mixed based search. VIR here focuses on content-based search approach. The YouTube Celebrities (YTC) dataset, the Youtube Face (YTF) dataset, and Honda / UCSD dataset are used in this work. The used dataset addresses issues such as illumination changes, occlusion, rotation, and scale. We propose an ALMEGA-VIR framework for visual novelty face identification for specific investigation and evaluation. The classification accuracy of the proposed ALMEGA-VIR model shows that 4% is more effective than traditional classifier algorithms such as SVM, Naïve Bayesian classifier, MLP and Random Forest classifier. A comparative study is also implemented with other existing works to check the superiority of the proposed method.
The flow of scientific books as a source for scientific library collection development: The problems of completeness and accessibility

Purpose. The authors analyze the trends in the national flow of book publications in science for the period 2012–2021 and its impact upon scientific library collections. Sources and methods . The study is based on the statistical data of large information systems, i.e. the Russian Book Chamber and scientific electronic library eLibrary.ru), and State Public Scientific and Technological Library of the Russian Academy Siberian Branch (RAS SB SPSTL). The data of the Higher School of Economics characterize scientific focuses of Russian researchers. Findings and conclusions . The data on the flow of scientific book publications were analyzed to confirm that after 2014, the volume of science publications has been continuously decreasing. It is especially true for academic book publishing. The trend for a disproportion in the discipline structure of scientific book flow, and for scattering information on scientific book publications, is revealed. It is due to the lack of coordination between scientific content creators and institutions intended to acquire complete data on scientific document flow. Science information support and knowledge generation require special infrastructure necessarily embracing scientific and academic libraries.
Exploration of Talent Cultivation and Curriculum Construction for the Five Year Unified System

Introduced the design elements of a five-year consistent talent training plan for the electronics major, provided training objectives and professional abilities, constructed a curriculum system for connecting vocational and secondary schools, and demonstrated in detail the requirements for knowledge and skills points in the electrical and electronic technology course. Designed the selection principles and assessment plan for vocational and secondary school students to be promoted to vocational colleges.
Study on the Classroom Questions in Senior High School English Reading Class based on Classroom Observation

Classroom questioning is not only the topic of dialogue between teachers and students in classroom teaching, but also the medium of communication determines the degree of students' thinking and whether and how much students' learning takes place (Liu Ruimin, 2020). Based on classroom observation and corpus translation, this paper makes an in-depth analysis of classroom questioning by case teachers in the process of classroom teaching, and points out some deficiencies in the process and design of classroom questioning. In addition, the types of classroom questions are not diverse enough, the hierarchy is low, and the language used in classroom feedback is monotonous and lacking. There are no classroom problems, such as the context of classroom problems.
Digital-Analog Co-Design of the Harrow-Hassidim-Lloyd Algorithm

The Harrow-Hassidim-Lloyd quantum algorithm was proposed to solve linear systems of equations $A\stackrel{\ensuremath{\rightarrow}}{x}=\stackrel{\ensuremath{\rightarrow}}{b}$ and it is the core of various applications. However, there is no explicit quantum circuit for the subroutine that maps the inverse of the problem matrix $A$ into an ancillary qubit. This makes implementation in current quantum devices challenging, forcing us to use hybrid approaches. Here, we propose a systematic method to implement this subroutine, which can be adapted to other functions $f(A)$ of matrix $A$, we present a co-designed quantum processor that reduces the depth of the algorithm, and we introduce its digital-analog implementation. The depth of our proposal scales with precision $ϵ$ as $\mathcal{O}({ϵ}^{\ensuremath{-}1})$, which is bounded by the number of samples allowed for a certain experiment. The co-design of the Harrow-Hassidim-Lloyd algorithm leads to a ``kitelike'' architecture, which allows us to reduce the number of required swap gates. Finally, merging a co-design quantum processor architecture with a digital-analog implementation contributes to the reduction of noise sources during the experimental realization of the algorithm.
Invited Paper: Disaggregating Applications Using Uniservices

The current method for building software infrastructure in disaggregated data centers involves creating new virtual machine monitors or operating system kernels that make the underlying hardware appear as a group of logical servers. Although this approach is effective for maintaining backward compatibility, we suggest that it would be more beneficial to invest in redesigning the applications themselves to be disaggregated along physical boundaries. We propose uniservices, a new programming paradigm that utilizes the actor model and is highly specialized for a single type of hardware resource for disaggregated architecture. Applications would be built from these uniservices, communicating over fast interconnects and a shared log. This approach simplifies operating systems while making better use of available hardware resources.
Feature difference-aware graph neural network for telecommunication fraud detection

With the continuous escalation of telecommunication fraud modes, telecommunication fraud is becoming more and more concealed and disguised. Existing Graph Neural Networks (GNNs)-based fraud detection methods directly aggregate the neighbor features of target nodes as their own updated features, which preserves the commonality of neighbor features but ignores the differences with target nodes. This makes it difficult to effectively distinguish fraudulent users from normal users. To address this issue, a new model named Feature Difference-aware Graph Neural Network (FDAGNN) is proposed for detecting telecommunication fraud. FDAGNN first calculates the feature differences between target nodes and their neighbors, then adopts GAT method to aggregate these feature differences, and finally uses GRU approach to fuse the original features of target nodes and the aggregated feature differences as the updated features of target nodes. Extensive experiments on two real-world telecom datasets demonstrate that FDAGNN outperforms seven baseline methods in the majority of metrics, with a maximum improvement of about 5%.
Invited Paper: Common Public Knowledge for Enhancing Machine Learning Data Sets

In this study, we show the advantages of incorporating multi-source knowledge from publicly available sources, such as ChatGPT and Wikipedia, into existing datasets to enhance the performance of machine learning models for routine tasks, such as classification. specifically, we propose the utilization of supplementary data from external sources and demonstrate the utility of widely accessible knowledge in the context of the Forest Cover Type Prediction task launched by the Roosevelt National Forest of Northern Colorado. Additionally, we exhibit an improvement in classification accuracy for the Isolated Letter Speech Recognition dataset when incorporating information on regional accents in the prediction of spoken English letter names.
A Unified Neuro-Fuzzy Framework to Assess the User Credibility on Twitter

With the tremendous proliferation of social media in day-to-day life, social content has become one of the potential information sources. Ensuring social trust is crucial to averting the negative impact on viral marketing, expertise retrieval, and recommendation systems, leading to ascertainment of credibility of social media users. A unified framework is proposed for evaluation of the user credibility through analysis of three fundamental credibility-related factors from social media and e-commerce sites, namely, probability of being a promoter, a spam bot, and/or a spammer. The factors are estimated using deep learning baseline model and/or further analysed using Fuzzy Inference System for making a decision on Tweeter credibility. The framework is demonstrated on Twitter and Amazon benchmark datasets for evaluating its efficacy in identifying credible users. The proposed system outperformed the baseline model and state-of-the-art techniques providing an accuracy of 97% in detecting the credible user.
Ontological knowledge inferring approach: Introducing Directed Collocations (DC) and Joined Directed Collocations (JDC)

The growing need of utilizing unstructured knowledge embedded in open-domain natural language text into machine-processable forms requires the induction of hardly extracted structured knowledge into knowledge bases which makes the Semantic Web vision a reality. In this context, ontologies, and ontological knowledge (triples) plays a vital role. This research introduces two novel concepts named Directed Collocation (DC) and Joined Directed Collocation (JDC) along with a methodical application of them to infer new ontological knowledge. Introduced Quality-Threshold-Value (QTV) parameter improves the quality of the inferred ontological knowledge. Having set a moderate value (3) for QTV, this approach inferred 95,491 new ontological knowledge from 43,100 triples of open domain Sri Lankan English news corpus. Indeed, the outcome was approximately doubled in size as the source corpus. Some inferred ontological knowledge was identical with the original corpus content, which evidences the accuracy of this approach. The remaining were validated using inter-rater agreement method (high reliability) and out of which around 56% were estimated as effective. The inferred outcome which is in the triple format may use in any knowledge base. The proposed approach is domain independent. Thus, helps to construct/extend ontologies for any domain with the help of less or no human specialists.
Optimal trajectory planning algorithm for autonomous flight of multiple UAVs in small areas

The development of science and technology requires UAV to improve the accuracy of path planning to better apply in the military field and serve the people. The research proposes to use the social spider algorithm to optimize the ant colony algorithm, and jointly build an IACA to deal with the optimal selection problem of UAV path planning. Firstly, the swarm spider algorithm is used to make a reasonable division and planning of the UAV’s flight field. Secondly, the AC is used to adjust and control the UAV’s state and path. Then, the IACA is formed to carry out performance simulation and comparison experiments on the optimal path planning of the UAV to verify the superiority of the research algorithm. The results show that the maximum number of iterations of the original AC and the IACA is 100, but the IACA under the route planning optimization reaches the convergence state in 32 generations; Moreover, when the number of iterations is about 20 generations, there will be a stable fitness value, which saves time for the experiment to find the optimal path. In the simulation experiment, it is assumed that three UAVs will form a formation to conduct the experiment, and the multiple UAVs will be subject to global track planning and repeated rolling time domain track planning. The autonomous operation time of multiple UAVs through the assembly point is (5.30 s, 5.79 s, 9.29 s). The distance between UAVs during flight is predicted. It is found that the nearest distance is 2.3309 m near t= 6.65 s, which is in line with the safety distance standard. Under the improved algorithm, the speed in all directions is also relatively gentle. All the above results show that the improved algorithm can effectively improve the iteration speed and save time.
Invited Paper: Enhancing Privacy in Federated Learning via Early Exit

In this paper, we investigate the interplay between early exit mechanisms in deep neural networks and privacy preservation in the context of federated learning. Our primary objective is to assess how early exits impact privacy during the learning and inference phases. Through experiments, we demonstrate that models equipped with early exits perceivably boost privacy against membership inference attacks. Our findings suggest that the inclusion of early exits in neural models can serve as a valuable tool in mitigating privacy risks while, at the same time, retaining their original advantages of fast inference.
Drug abusers characteristics on the online community

This study aims to gain insights into the basic information and behavioral characteristics of the drug abusers and provide references for drug prevention, control, and correctional strategies. First, the python development tool was used to crawl 8494 posts from 1725 users in the forum of “Dynamic Control Bar” in the Baidu Tieba. The data were cleaned and organized. Subsequently, the content of the posts in text was analyzed using a mixture of topic model, sentiment analysis, and relevance analysis. The result of the LDA indicated that the drug abusers were concerned about the living conditions of this population in their home communities, regular checkups and management by government staff, perceived social discrimination and inconvenience of living in a restrained environment, problems they encountered when consulting with each other in terms of regular medical checkups, recollection of how they came to use drugs, as well as emotions of regret. The result of the emotional analysis indicated that this population was emotionally disturbed and had more negative emotional values, but the above values were stable. Internet information dissemination is of great significance to public opinion dissemination that can indicate the real opinions and attitudes of all social strata to drug abusers, especially the discrimination, stigmatization, and labelling of drug abusers by the general public. Disseminating content to drug abusers about their problems can help them start a new life. Furthermore, the government should guide the attitudes and emotions of this population to help them start a new, more positive life.
The application of interactive genetic algorithm in the optimization of medical device product form design

In order to solve the problem of low efficiency in the form design of medical device products and the inability to meet the needs of users, the research took the gastrointestinal machine of medical device as the research object, extracted the form design genes and semantic genes of medical device products, and constructed the form optimization design model of medical device products based on IGA algorithm. The research took the researcher and user data of the medical device design department as the main data, through crossover, mutation. The satisfactory solution is obtained by genetic process such as selection. Satisfactory solutions were obtained through genetic processes such as crossover, mutation and selection. The 12th generation project with the code of 000011 010001 100000 met the optimal solution, and the consensus satisfaction was 0.829. The research and design model can efficiently and automatically provide solutions for the design optimization of medical devices. It is recommended that the design department send out the best solution of the model, and optimize the solution in more detail again to improve the efficiency and accuracy of function realization.
Digitally Mediated Communication of Emojis By Visually Impaired Users: The Case of Sarcastic WhatsApp Messages

In today’s digital age, emojis have come to compensate for the nonverbal cues that speakers use to communicate their attitudes or emotions as they speak, and hence become an essential element of online communication. In the realm of WhatsApp, where written conversations with an immediacy level close to that of spoken are supported, an ironic or sarcastic message is likely to be misunderstood, as it involves decrypting a meaning that often contradicts what is said. The functions of emojis could facilitate the correct interpretation of a message. However, the phenomenon of emojis may not be ideal for visually impaired WhatsApp users. Distinguishing sarcasm from the sincerity tone of a message requires distinct semantic and cognitive processes. This study aims to uncover how visually impaired users (VIU) use emojis on WhatsApp to clarify sarcastic versus literal intent. To meet this aim, two experimental tasks were conducted. Results provide clear evidence that emojis are necessary tools for VIU in online communication. However, their use varies with emojis linked more to literal WhatsApp messages than sarcastic ones and in praise situations than in criticism.
Pengembangan Media Pembelajaran Teka Teki Silang Materi Alat Gerak dan Fungsinya pada Hewan untuk Siswa Kelas V Sekolah Dasar

Learning media is one of the visual aids that students really need in learning activities. This can be proven from the function of the learning media itself, which can facilitate the delivery of material in learning. So that students can easily understand or accept the material presented. Learning media must also be designed or developed creatively and innovatively so that students are more interested. One method that can be developed in designing instructional media for elementary school students is using packaged media using games. Students will be interested in something that is conveyed by using games, but also does not fade the learning values. One game that can support students in learning is a crossword puzzle. Namely a game where the players have to fill in the empty spaces both vertically and horizontally with the letters that form a word based on the instructions given. For example, in class V of elementary school, the subject that is suitable for the development of this learning media is science with the arteries of the locomotor and its function in animals.
Distributed approximate minimal Steiner trees with millions of seed vertices on billion-edge graphs

In this paper, we present a parallel 2-approximation Steiner minimal tree algorithm and its MPI-based distributed implementation. In place of expensive distance computations between all pairs of seed vertices, the solution we employ exploits a cheaper Voronoi cell computation. Our design leverages asynchronous processing and message prioritization to accelerate convergence of distance computations, and harnesses vertex and edge centric processing to offer fast time-to-solution. We demonstrate scalability and performance using real-world graphs with up to 128 billion edges and 512 compute nodes, and show the ability to find Steiner trees with up to one million seed vertices. Using 12 data instances, we present comparison with the state-of-the-art exact solver, SCIP-Jack, and two sequential 2-approximate algorithms. We empirically show that, on average, the total distance of the Steiner tree identified by our solution is 1.1290 times greater than the Steiner minimal tree – well within the theoretical approximation bound of 2.