Reviewerly/exHarmony huggingface dataset:
Theoretical Validation of Data Warehouse Requirements Metrics Based on Agent Goal Decision Information Model Using Zuse’s Framework

Requirement’s data models’ quality is one of the main data models that influence quality of information stored in data warehouse to take major decisions in organization. Thus, it becomes significant for an organization to maintain and assure the information quality of data warehouse. Very few proposals were seen in the literature for assuring quality of requirements data model of DW. However, no theoretical validation of DW requirements metrics using Zuse’s framework was seen in the literature. Hence, in this paper, theoretical validation of requirements traceability metrics (based on agent goal decision information model) is done by applying Zuse’s framework. Results indicate that all traceability metrics are valid and correct. Thus, requirements traceability metrics can be used for assuring quality of DW requirements model.
Person Re-Identification Method Based on the Construction of Graph Convolutional Network with Attribute Feature

To fully pay attention to identity-sensitive feature information and utilize the correlations of inter-attributes and attributes-body parts, this paper proposes a person re-identification (re-ID) method based on the construction of graph convolutional network (GCN) with crucial attribute feature and body parts. First, it establishes the multiscale context-aware network (MSCAN) using dilated convolution with different expansion ratios, which can learn multiscale context information and obtain diversified global features. Subsequently, the human parsing model is utilized to extract the body part features. According to the attribute importance degree, the paper constructs low-dimensional GCN integrating the vital attributes and body parts of person descriptions to obtain discriminative local features. Finally, based on attribute prediction, it reduces the range of the images to be matched with discriminating possible objects from query images, thereby simplifying retrieval process. The experimental results demonstrate that the novel designed method can effectively improve person re-ID performance and achieve competitive evaluation results on typical public testing datasets.
Implementation of Filters and Rules on a Gateway to Mitigate Cyberattacks Originated by Email

This work proposes a methodology for the implementation of a set of rules and filters applied on a Gateway to mitigate the main cyberattacks originated through emails, such as: malware, spam, phishing, and includes information leakage. Through a comparative analysis of the tools that face these cyberattacks, ”Proxmox Email Gateway” is selected to be implemented in two test scenarios, the first without applying the methodology, and the second with its application, in which several Controlled cyberattacks of each of those previously defined. According to data obtained from the test scenarios, it was found that the application of this set of rules reduces cyberattacks by 38.75%, and with the statistical chi-square test with a confidence level of 95% it is shown that the set of filters and elaborate rules applied on a Gateway if it reduces the percentage of the amount of cyberattacks originated through e-mail.
Blockchain-Based Privacy-Preserving Public Auditing for Group Shared Data

Cloud storage has been widely used to team work or cooperation development.Data owners set up groups, generating and uploading their data to cloud storage, while other users in the groups download and make use of it, which is called group data sharing.As all kinds of cloud service, data group sharing also suffers from hardware/software failures and human errors.Provable Data Possession (PDP) schemes are proposed to check the integrity of data stored in cloud without downloading.However, there are still some unmet needs lying in auditing group shared data.Researchers propose four issues necessary for a secure group shared data auditing: public verification, identity privacy, collusion attack resistance and traceability.However, none of the published work has succeeded in achieving all of these properties so far.In this paper, we propose a novel blockchain-based ring signature PDP scheme for group shared data, with an instance deployed on a cloud server.We design a linkable ring signature method called Linkable Homomorphic Authenticable Ring Signature (LHARS) to implement public anonymous auditing for group data.We also build smart contracts to resist collusion attack in group auditing.The security analysis and performance evaluation prove that our scheme is both secure and efficient.
C-Face: Using Compare Face on Face Hallucination for Low-Resolution Face Recognition

Face hallucination is a task of generating high-resolution (HR) face images from low-resolution (LR) inputs, which is a subfield of the general image super-resolution. However, most of the previous methods only consider the visual effect, ignoring how to maintain the identity of the face. In this work, we propose a novel face hallucination model, called C-Face network, which can generate HR images with high visual quality while preserving the identity information. A face recognition network is used to extract the identity features in the training process. In order to make the reconstructed face images keep the identity information to a great extent, a novel metric, i.e., C-Face loss, is proposed. We also propose a new training algorithm to deal with the convergence problem. Moreover, since our work mainly focuses on the recognition accuracy of the output, we integrate face recognition into the face hallucination process which ensures that the model can be used in real scenarios. Extensive experiments on two large scale face datasets demonstrate that our C-Face network has the best performance compared with other state-of-the-art methods.
A novel Internet of Things and federated learning-based privacy protection in blockchain technology

Purpose Be that as it may, BC is computationally costly, has restricted versatility and brings about critical transmission capacity upward and postpones, those seems not to be fit with Internet of Things (IoT) setting. A lightweight scalable blockchain (LSB) which is improved toward IoT necessities is suggested by the authors and investigates LSB within brilliant house setup like an agent model to enable more extensive IoT apps. Less asset gadgets inside brilliant house advantage via any unified chief which lays out common units for correspondence also cycles generally approaching and active solicitations. Design/methodology/approach Federated learning and blockchain (BC) have drawn in huge consideration due to the unchanging property and the relevant safety measure and protection benefits. FL and IoT safety measures’ difficulties can be conquered possibly by BC. Findings LSB accomplishes fragmentation through shaping any overlaid web with more asset gadgets mutually deal with a public BC and federated learning which assures complete protection also security. Originality/value This overlaid is coordinated as without error bunches and reduces extra efforts, also batch leader will be with answer to handle commonly known BCs. LSB joins some of advancements which also includes computations related to lesser weighing agreement, optimal belief also throughput regulatory body.
A Multimodal Fusion Online Music Education System for Universities

In the context of Internet technology, the integration of information technology and education is a powerful supplement to the traditional teaching model of higher education. Online learning has become the new development direction of the education industry in the network era. To address the problems of serious difficulty in completing online teaching tasks, difficulty in monitoring teaching effects, and fragmentation of course resources in universities, a multimodal music knowledge graph is constructed. A personalized learning strategy based on users' interest is proposed through the mining of online education data, and a music online education system has been developed on this basis. To improve the recommendation accuracy of the model, an embedding propagation knowledge graph recommendation method based on decay factors is proposed. The model considers the changes in the strength of user interest during the intra- and interlayer propagation of the knowledge graph interest map and focuses on higher-order user potential interest representations for enhancing the semantic relevance of multihop entities. The experimental results show that the proposed model brings a good prediction effect on several benchmark evaluation metrics and outperforms other comparative algorithms regarding recommendation accuracy.
Student views on the use of mobile technologies in libraries: the example of Balıkesir University

This study aims to emphasize the importance of mobile technologies in terms of library services, and to examine the perceptions of university students regarding their access to library and information services via mobile phones, taking into account the use of mobile phones. In the study, data were obtained through a questionnaire using the descriptive method. The data were collected from university students using the library of Balıkesir University. A sample of 410 students was selected within the scope of the study. The results demonstrated that the participants' views on accessing library services using mobile technology were mostly positive and they were particularly willing in terms of accessing remote services, electronic resources, and audio-visual materials via mobile technologies. According to the findings, it is recommended that libraries make their services compatible with mobile systems.
The role of universities in regional development

This article examines the legal and organizational issues of the role of universities in the development of regions. The purpose of the study is to determine the models of cooperation between universities in the development of regions. Methodology. In the course of the study, logical and formal-legal methods of analysis of individual regulatory legal acts of the Republic of Kazakhstan were used. The legislation of the Republic of Kazakhstan regulating the issues of strategic planning, ownership of universities as legal entities, missions of universities and achievement of the stated goals, the organizational and legal form of universities, corporate governance bodies, requirements for members of the board of directors of universities were studied. Along with this, this study is based on international publications in the fi eld of higher education management, the place of development strategy in achieving sustainable development goals. Originality / value of research. We built our research on the analysis of the best universities in Kazakhstan according to the results of the IQAA-2020 ratings, since it was these universities that showed good practice of interaction with the regions. Along with this, the strengths and weaknesses of such interaction were identified to determine the further development path. Findings. The research has shown that many Kazakhstani universities do not quite clearly understand the role of universities in the development of regions and the performance of a social function. The lack of a clear understanding of the function assigned to the university by the society leads to the fact that universities do not see key priorities in such cooperation. The study made it possible to draw some conclusions and develop recommendations that are important for continuing eff orts in this direction. Universities should view this interaction as an important, strategic direction for their work and build on established practices.
An Optimized Path-Planning Concept for a Micro-Mouse Robot for Autonomous Applications in Terrained Environments

The design, manufacture, and implementation of a micro-mouse robot controlled by a microcontroller are discussed in this study.Micro mice is a mobile robot that has been developed & is being done programmed to go thro' a maze like structure by learning the way.The goal of the research was designing & building a small path following mechanism to help with the construction of a moving object tracking system.The design and construction of the micro-mouse was the first stage of the project.To give a load torque, electric motors have been incorporated.The electronics were tackled next, and were controlled by the Micro-mouse microcontroller in order to transmit and receive positioning information in order to enable tracking.The communication is carried out via the RF Transmitter and Receiver module.The majority of the torquerelated development work has been accomplished.The project was a success in general, and all of the design concepts were proven.Further changes / tweaks are suggested to perfect the tracking of the process & a realistic future expansion to the work that could be for developing it to become a perfect model from the practical implementation standpoint.
Traceability Transformed: Generating more Accurate Links with Pre-Trained BERT Models

Software traceability establishes and leverages associations between diverse development artifacts. Researchers have proposed the use of deep learning trace models to link natural language artifacts, such as requirements and issue descriptions, to source code; however, their effectiveness has been restricted by availability of labeled data and efficiency at runtime. In this study, we propose a novel framework called Trace BERT (T-BERT) to generate trace links between source code and natural language artifacts. To address data sparsity, we leverage a three-step training strategy to enable trace models to transfer knowledge from a closely related Software Engineering challenge, which has a rich dataset, to produce trace links with much higher accuracy than has previously been achieved. We then apply the T-BERT framework to recover links between issues and commits in Open Source Projects. We comparatively evaluated accuracy and efficiency of three BERT architectures. Results show that a Single-BERT architecture generated the most accurate links, while a Siamese-BERT architecture produced comparable results with significantly less execution time. Furthermore, by learning and transferring knowledge, all three models in the framework outperform classical IR trace models. On the three evaluated real-word OSS projects, the best T-BERT stably outperformed the VSM model with average improvements of 60.31% measured using Mean Average Precision (MAP). RNN severely underperformed on these projects due to insufficient training data, while T-BERT overcame this problem by using pretrained language models and transfer learning.
Hybrid neural networks for noise reductions of integrated navigation complexes

The necessity of integrated navigation complexes (INC) construction is substantiated. It is proposed to include in the complex the following inertial systems: inertial, satellite and visual. It helps to increase the accuracy of determining the coordinates of unmanned aerial vehicles. It is shown that in unfavorable cases, namely the suppression of external noise of the satellite navigation system, an increase in the errors of the inertial navigation system (INS), including through the use of accelerometers and gyroscopes manufactured using MEMS technology, the presence of bad weather conditions, which complicates the work of the visual navigation system. In order to ensure the operation of the navigation complex, it is necessary to ensure the suppression of interference (noise). To improve the accuracy of the INS, which is part of the INC, it is proposed to use the procedure for extracting noise from the raw signal of the INS, its prediction using neural networks and its suppression. To solve this problem, two approaches are proposed, the first of which is based on the use of a multi-row GMDH algorithm and single-layer networks with sigm_piecewise neurons, and the second is on the use of hybrid recurrent neural networks, when neural networks were used, which included long-term and short-term memory (LSTM) and Gated Recurrent Units (GRU). Various types of noise, that are inherent in video images in visual navigation systems are considered: Gaussian noise, salt and pepper noise, Poisson noise, fractional noise, blind noise. Particular attention is paid to blind noise. To improve the accuracy of the visual navigation system, it is proposed to use hybrid convolutional neural networks.
A joint image compression and encryption algorithm based on compression sensing and bit-plane embedding

Abstract In medical, financial and other fields, the direct transmission of digital images in public channels is vulnerable to attacks and information leakage. In order to solve such problems, this paper proposes a new one-dimensional chaotic map. The information hiding visual security algorithm is based on the chaotic sequence diffusion image and bit plane decomposition technology, which greatly improves the visual security of the embedded image, and its structure and vision maintain a high degree of similarity with the carrier image. Firstly, a new one-dimensional chaotic map is proposed. The generated key stream can improve the randomness of the algorithm. Secondly, we optimize the measurement matrix to reduce the minimum coherence between the matrices and improve the reconstruction performance. Then we obtain the intermediate state cipher image through compression and encryption, and then embed the intermediate state cipher image by decomposing the carrier image bit plane, and finally obtain a visual security image. The experimental results show that, compared with the existing related literature, the algorithm has better reconstruction performance, security performance and operational efficiency.
Research on Key Technologies of Real Scene 3D Cloud Service Platform for Digital Twin Cities

Based on the background of real 3D China Construction, this paper develops real scene 3D cloud service platform for digital Twin Cities to solve the difficulties of Urban fine governance, deeply analyzes the key problems of digital twin cities, gives advanced solutions combined with the development of new generation information technology, including high precision urban 3D data acquisition and preprocessing, integration and storage of urban 3D reality data, information fusion of urban dynamic data and 3D reality, intelligent classification and positioning of urban management objects, demonstrates its feasibility, applies it to the construction of future communities, and points out the innovation of the scheme including fusion innovation of city perception data and 3D real city scene based on new generation artificial intelligence, Semantic automatic recognition method of small objects in 3D scene based on urban real scene, and hybrid model simulation method for urban application and management.
Neural Architecture Search using Progressive Evolution

Vanilla neural architecture search using evolutionary algorithms (EA) involves evaluating each architecture by training it from scratch, which is extremely time-consuming. This can be reduced by using a supernet to estimate the fitness of every architecture in the search space due to its weight sharing nature. However, the estimated fitness is very noisy due to the co-adaptation of the operations in the supernet. In this work, we propose a method called pEvoNAS wherein the whole neural architecture search space is progressively reduced to smaller search space regions with good architectures. This is achieved by using a trained supernet for architecture evaluation during the architecture search using genetic algorithm to find search space regions with good architectures. Upon reaching the final reduced search space, the supernet is then used to search for the best architecture in that search space using evolution. The search is also enhanced by using weight inheritance wherein the supernet for the smaller search space inherits its weights from previous trained supernet for the bigger search space. Exerimentally, pEvoNAS gives better results on CIFAR-10 and CIFAR-100 while using significantly less computational resources as compared to previous EA-based methods. The code for our paper can be found in https://github.com/nightstorm0909/pEvoNAS
Internet of Things (IoT) and Artificial Intelligence for Smart Communications

In the growing technological world with an exponential increase in the number of applications, people are connected with things. It enhances the power of serving the demand of the customers with satisfaction and interconnectivity. This is possible only by the use of a variety of sensors connected to the ubiquitous networks supporting mass media communications over the internet. The number of machines and devices connected to the internet has increased and communication between them happens with involvement and integration of Hi-Tech and advanced technologies such as the Internet of Every Thing, CONASENSE and Human Bond Communications. After all, it’s a mind-control game that changes according to the requirements of data, speed, cooperation, content and cost. The smart communication between things, people to people (P2P), machine to machine (M2M), device to device (D2D), device to machine (D2M) and people to machine (P2M) over the internet adds value to the concept of virtualisation. At the same time, Artificial Intelligence (AI) and machine learning algorithms are useful to analyse the data and make correct decisions. In future generation networks, the speed and data rates along with latency in connected things are of utmost importance and the purpose could be served by 6G (5G+IoT+Cloud Computing). Also, the sensors are the heart of smart devices involved in IoE with four pillars: people, things, data and process. The integration of IoT along with AI will be the most precious tool to evaluate complex problems; at the same time, it helps to reduce the redundant data used in mass communication with increased accuracy. This shift in technological evolutions will hold more responsibilities for productivity from person to machine to organisation. IoT has functions of controlling and smart communications among virtually connected heterogeneous devices possess the problem of interconnectivity, privacy, trust and security.
Geometrical Analysis of White Blood Cells by Using Image Segmentation Algorithm

Abstract: The technique behind image analysis is based on machine learning. In order for the software to recognize specific irregularities, it doesn't matter what field of medicine (or other industry) we're thinking about. The way AI learns differs dramatically from how humans learn. It makes use of enormous image databases that the software must interpret and analyze. In the present work, geometrical features of White Blood Cells such Area, Perimeter, Orientation, and Equivalent Diameter are calculated by using Image Segmentation approach.
Implementation of software random number generator based on ShangMi algorithm

Random numbers are the security foundation in the field of cryptography and information security, providing the most basic security guarantee for cryptography and security protocols. The quality of random numbers depends to some extent on the randomness of the data generated by the random number generator. For devices such as mobile smart terminals, high-quality true random numbers cannot be generated because a true random number generators cannot be embedded. However, these usage scenarios have certain requirements on the quality of random numbers. A random number scheme, that is, generating higher-quality random numbers without increasing hardware investment. In this paper, a software random number generator based on the Shangmi algorithm is implemented for the situation where there is no true random number generator. The existing hardware or components are used as the entropy source, and the random information generated by the entropy source is collected as the initial data, and processed through the Shangmi algorithm. Generate a random sequence, and the final generated random number meets the Shangmi detection standard.