Reviewerly/exHarmony huggingface dataset:
Research on Load Balancing of Cloud Storage Server Based on Fully Connected Group

Abstract The scheduling problem of online cloud storage traffic is a hot topic in current network research. Aiming at the scheduling problem of file upload requests in online cloud storage systems, the existing schemes rarely meet the requirement of providing users with different bandwidth guarantees. One of the purposes of using multiple volume servers is to let multiple volume servers share the system load evenly and avoid the system performance bottleneck. Once the load is aggregated and one of the multiple volume servers is overloaded, the volume server will become the system bottleneck again, which will greatly affect the overall performance of the system. On the basis of analyzing the existing scheduling algorithms of server clusters, this paper proposes a dormant scheduling algorithm of fully connected groups, which can automatically equip servers with I D, redirect tasks according to the actual processing capacity, and ensure load balance by unlimited increase. The experimental results show that the load balancing strategy among multi-volume servers ensures the overall reading and writing performance of the system, and effectively improves the I/O throughput and I/O times per second.
Toward Mobile Application of Cyber Argumentation with Social Networking

Argumentation is one of the basic activities people perform in our society. People often spend a lot of time arguing about social, cultural, economic, financial, political, and other issues. Existing cyber-argumentation platforms facilitate large-scale discussions among participants and play an important role in the analysis of collective opinions and collective decision-making. However, they do not provide social networking capabilities and are not designed to be used in social networking environments. With the advent of android and iOS mobile operating systems, people are gravitated towards the mobile applications than mobile websites. So, it is highly desirable to create a mobile application of cyber argumentation with social networking by integrating cyber argumentation and social networking. In this paper, we present a prototype of an innovative mobile application of cyber argumentation with social networking and introduced multiple analytical capabilities of argumentation with social networking. It will provide intelligent analytical argumentation tools, such as argumentation with social networking, diversity enhanced connection recommendation, opinion mining, group identification, analysis of group relationships, polarization analysis and so on.
Tendermint Performance with Large Transactions: The Healthcare System Scenario

Different implementations of the Blockchain technologies are getting momentum. Besides the initial financial ones, the most popular are those in healthcare, logistics, smart energy and right management. Most of the proposed applications are built on top of Ethereum and Hyperledger platform. The attractiveness for implementation of Blockchain technologies in healthcare is the main reason for conducting in-depth analysis and determining the feasibility of the healthcare system with different Blockchain solutions. This paper presents the performance analysis of the Tendermint network on an emulated environment, with traffic characteristics similar to those that are circulating in the healthcare system. The patient coverage analysis provides better understanding of scalability and limitations of the Tendermint protocol and its capabilities for implementation in a healthcare system.
Union model performance improving for text data classification

The text data union model is considered in this paper. The current classification system is based on the stacking principles. The idea is that the right models combination will improve the classification quality. The model takes into account both a statistical approach (including models such as: entropy characteristics of symbolic diversity model, TF-IDF metric, substrings model) and the linguistic approach (which is based on prototype theory). The main characteristics of the considered models are presented. The union model use cases for literature and patent texts classification are shown. It should be noted that not every model is capable to classify different text data types. The linguistic models only applicable with the TF-IDF metric. The current classification system structure is considered. The categorization quality of the system was evaluated using precision, recall, and F-measure. The text data classification results based on separate and union models are presented. The overall efficiency of the union model is on average 10-15% higher by the F-measure compared to single models. In order to improve the classification quality, weight coefficients were used. The best combination of weight coefficients for literature and patents text (they are different for each text type) is shown. Possibilities for improving the text data classification system performance are presented (including the union and single models). The corresponding parallelization results of the proposed models are shown.
An approach for Storage Optimized Cloud using AES Mechanism

Cloud computing (CC) is the next-gen of IT enterprise architecture. Unlike conventional solutions, CC transfers application software and databases to vast data centres, where file and service storage might not be completely reliable. This one-of-a-kind feature, on the other hand, introduces a slew of new security issues that are not well known. Cloud services are distributed across all servers, users, and individuals. As a result, ensuring file confidentiality is difficult for the cloud provider. As a result, an attacker can easily access, misuse, and kill data in its original form. Entrusting cloud is useless in the occurrence of a compromise at any expense. For file information archiving in the cloud, a solution that is “practically infeasible to strike” is needed. This paper uses AES cryptosystem. If the cloud-stored data in encrypted form, it would effectively solve various security issues.
Energy Efficient Multistandard Decompressor ASIP

Many applications make extensive use of various forms of compression techniques for storing and communicating data. As decompression is highly regular and repetitive, it is a suitable candidate for acceleration. Examples are offloading (de)compression to a dedicated circuit on a heterogeneous System-on-Chip, or attaching FPGAs or ASICs directly to storage so they can perform these tasks on-the-fly and transparently to the application. ASIC or FPGA implementations will usually result in higher energy-efficiency compared to CPUs. Various ASIC and FPGA accelerators have been developed, but they typically target a single algorithm. However, supporting different compression algorithms could be desirable in many situations. For example, the Apache Parquet file format popular in Big Data analytics supports using different compression standards, even between blocks in a single file. This calls for a more flexible software based co-processor approach. To this end, we propose a compiler-supported Application-Specific Instruction-set Processor (ASIP) design that is able to decompress a range of lossless compression standard without FPGA reconfiguration. We perform a case study of searching a compressed database dump of the entire English Wikipedia.
Transdisciplinary approach to Augmented reality Digital heritage Mobile applications

This paper follows the development and collaboration behind the creation of an Augmented reality (AR) mobile application. Sponsored by the Nupur arts organization and commissioned by the Arts Council England, we were asked to create a digital heritage experience telling the story of Gujarati migrants from East Africa and India during the 70s and 80s in Leicester, United Kingdom. This paper examines how being in a transdisciplinary team aided in the successful creation of 3D scanned models, which were later imported into an AR environment. Finally, this paper concludes with an analysis of the cultural implication of mobile applications such as this one and the social and communal impact they might have.
Industrial Time Series Data Cleaning Using Generative LSTM and Adaptive Confidence Interval

In this paper, a method of industrial time series data cleaning using generative LSTM model and adaptive confidence interval is proposed. Firstly, the generative LSTM model is used to predict the probability distribution of industrial time series data, and then the confidence boundary of data anomalies is calculated according to the adaptive confidence interval method, so as to judge whether the data is abnormal or not. This paper uses network open data to complete data cleaning experiment, and the precision rate and recall rate of abnormal data detection are above 96%. The comparison with other anomaly detection algorithms also proves the effectiveness of the proposed algorithm in industrial time series data cleaning.
Features of Professional Wellbeing of Teachers

The phenomenon of professional wellbeing is included in the context of research on psychological wellbeing. The researchers point out the importance of the professional and labor sphere of human activity for ensuring the general state of subjective wellbeing. At the same time, the ability to maintain a state of subjective professional wellbeing is the main condition for the successful work of a teacher. The purpose of this study was to study the characteristics of the experience of professional wellbeing of teachers working in educational organizations of different levels. The study involved 65 teachers engaged in professional activities in preschool educational institutions and secondary schools (M = 31.3; SD = 1.2; 28% men). Methods were used: the psychological wellbeing of K. Riff in the adaptation of N. N. Lepeshinsky, the professional demand for the personality of E. V. Kharitonova, B. A. Yasko, as well as statistical methods (descriptive statistics, Mann Whitney U test, Spearman rank correlation coefficient). The differences between the experience of psychological wellbeing and the professional demand for teachers engaged in professional activities in educational institutions of different levels were established. The interrelationships of psychological well-being and professional demand of the individual in the group of teachers were also found. The prospects for further development of this topic are the expansion of the level of educational institutions, the study of factors that affect the level of psychological wellbeing of teachers and determine the level of professional demand for an individual, the study of the factor structure of professional wellbeing of teachers.
Digital Transformation of Russian Vocational Education Based on a Systematic Approach

The article discusses some aspects of the digital transformation of vocational education in Russia based on a systematic approach. In the context of the formation of a new technological order and the transition to the digital economy, the requirements for the quality, content, infrastructure of vocational education, the organization of the educational process, and the management mechanisms in the system of vocational education have changed significantly. Each educational institution will undergo digital transformation sooner or later, so the choice of direction and development strategy is relevant for each of them. Considering that the development and implementation of the program (concept) of digital transformation in each educational institution is complex, the authors propose a systematic approach to solving this issue. The article substantiates the optimal variant of the practical application of the system approach—the functional system method. The functional system is based on a focused useful result, which is an integral, decisive component and has a central organizing influence on all stages of system formation. The authors formulate a focused useful result that underlies the digital transformation of vocational education and on the basis of which the formation of a new system of vocational education should take place.
A Novel Algorithm of Pre-Cooperative Route Planning for Multiple AUVs Based on Level Set Method

In view of the existing multiple AUVs cooperative route planning algorithm does not consider the impact of current and route conflict on route planning, the robustness is not good and the quality of route is not high, this paper proposes a new hybrid level set algorithm based on the optimal fusion of total energy consumption and sailing time. The new algorithm considers the constraints of energy consumption and route conflict, and achieves a better fusion of low exposure risk and low total energy consumption. In addition, in order to improve the efficiency of cooperative route planning, the level set method is used in the new algorithm. The new algorithm is simulated and verified in the three-dimensional simulated current environment and the three-dimensional real current environment. The simulation results show that the new algorithm is feasible and effective.
Fast Secret Computation Using Secret Sharing for n&lt;2k-1 Secure Against Malicious Adversaries

In general, the (k,n)-threshold secret sharing scheme cannot be used to perform an information-theoretically secure secret computation for n<2k-1. Therefore, our research team has been focusing on conditions under which secret computation can be performed securely for n<2k-1, and proposed a secret computation method that is safe against malicious adversaries. However, this method is inefficient because it performs all operations as secret computations. In this paper, we divide the arithmetic operations into precomputations and secret computations and show that we can achieve faster secret computation than the conventional method by concentrating the operations that require communication in precomputation.
UserRBPM: User Retweet Behavior Prediction with Graph Representation Learning

Social and information networks such as Facebook, Twitter, and Weibo have become the main social platforms for the public to share and exchange information, where we can easily access friends’ activities and are in turn be influenced by them. Consequently, the analysis and modeling of user retweet behavior prediction have important application value in such aspects as information dissemination, public opinion monitoring, and product recommendation. Most of the existing solutions for user retweeting behavior prediction are usually based on network topology maps of information dissemination or design various hand-crafted rules to extract user-specific and network-specific features. However, these methods are very complex or heavily dependent on the knowledge of domain experts. Inspired by the successful use of neural networks in representation learning, we design a framework UserRBPM to explore potential driving factors and predictable signals in user retweet behavior. We use the graph embedding technology to extract the structural attributes of the ego-network, consider the drivers of social influence from the spatial and temporal levels, and use the graph convolutional network and the graph attention mechanism to learn its potential social representation and predictive signals. Experimental results show that our proposed UserRBPM framework can significantly improve prediction performance and express social influence better than traditional feature engineering-based approaches.
Deep Explanatory Polytomous Item-Response Model for Predicting Idiosyncratic Affective Ratings

Towards explainable affective computing (XAC), researchers have invested considerable effort into post hoc approaches and reverse engineering to seek explanations for deep learning models. However, alternative, intrinsic approaches that aim to build inherently interpretable models by restricting their complexity are yet to be widely explored. In this study, we integrate an explanatory polytomous item response model that provides a well-established psychological interpretation for ordinal scales with deep neural networks to realize high prediction performance and good result interpretability. We conducted an experiment on a growing task (i.e., predicting the idiosyncratic perception of emotional faces of an individual); as expected theoretically, the topmost parameters of our model demonstrated strong correlations with those of the corresponding ordinal item response model: r = 0.928 to 1.00. Our proposed intrinsic approach can used as a complementary framework for post-hoc methods in XAC to coach and support human social interactions.
Observer determination of the make, model, and year of questioned vehicles

Often, criminal acts involving a vehicle are caught on digital video surveillance systems. While potentially useful for an investigation, the recording conditions are typically less than optimal for the extraction of key information for the identification of the perpetrator, such as a license plate. Providing the make, model and year of a questioned vehicle is a common request for examiners, to narrow the field of potential suspect vehicles. This study seeks to compare the performance of a nonpeer-reviewed make, model, and year determination between two separate groups, specifically, trained forensic image examiners and nontrained individuals. Results show that even with varied image capture conditions and quality, the trained forensic image examiners more correctly and completely identified the test group of questioned vehicles make, model, and year.
Alliance-Aware Service Composition with Efficient Matching Search

Service bundling within enterprises and service cooperation outside enterprises are quite common on cloud. The correlations between services called Alliance Relation (AR) have great impact on the system QoS of service composition. Existing research have not fully and systematically considered the types of AR. Meanwhile, many works suffer from the low searching efficiency of finding the optimal matching AR for composite services. In this paper, we propose a novel approach called Q-ARIGraph-NSGA3, where we establish a multi-granularity optimization model on quotient space and an efficient matching search method in which a partition mechanism is applied to accelerate the generation of the graph. We expand the alliance relation types in service composition with service granulation and AR granulation employed concurrently. Extensive experiments are conducted on a real-world web service dataset, which demonstrate that our approach outperforms the state-of-the-art approaches in terms of effectiveness and efficiency.
An efficient method in quantum radar cross section predictions of 3D complex targets

In this paper, to further promote the theory of quantum radar cross section (QRCS) to realistic complex objects, an efficient analysis method for QRCS is proposed. GPU acceleration is applied to prominently improve the efficiency of the proposed method. QRCSs of the three-dimensional (3D) complex targets are simulated, which shows great efficiency improvement.
Towards a Universal Data Training Set for Coded-Diffraction Image Reconstruction and No-Hidden-Layer Neural Networks

The stability of an inverse-problem solver depends on algorithm computational complexity. We demonstrate generalizable image reconstruction with the simplest of hybrid machine vision systems: fixed, linear optical preprocessors combined with no-hidden-layer,”small-brain” neural networks.
Synergistic combination of ontologies and Pareto method

Today, a lot of data is actively appearing. New methods and programs for data analysis are emerging as a result. One of the most interesting problems is the problem to find the most optimal solution between two proposed.
CNTK Communication Optimization Based on Parameter Server

In the initial CNTK framework, the data communication is carried out through asynchronous point-to-point aggregation, which is supported by the Multiverso framework, that is, through all the worker nodes to transfer gradients to each other, and the worker nodes do not need to maintain consistency when transferring data. The main idea of optimization is to change the asynchronous transmission mode provided by the Multiverso framework to the parameter server in the source code of the CNTK framework, so as to achieve the goal of data unified transmission to the parameter server, so as to improve the efficiency of communication.
A secure encryption method of communication channel based on cloud computing technology

The traditional channel encryption method is interfered by noise signal, which leads to long encryption time and poor security. This paper proposes a communication channel security encryption method based on cloud computing technology. In order to obtain the minimum benefits of the same channel and orthogonal channel in the frequency conversion receiver, the dual input and dual output model is constructed; the influence of time domain and frequency domain on the transmission signal is analyzed, and the nonlinear transmission characteristics of the channel signal are obtained. Through cloud computing technology, the channel transmission characteristics are divided into dynamic parameters and static parameters to realize the key distribution of communication channel under cloud computing technology; the balance factor is used to provide the key for the sorted generators to realize the secure encryption of communication channel. The results show that the average error probability of the proposed method is about 0.275, and the signal-to-noise ratio is between 115 and 118 SNR.
Joint Extraction Methods for Semantic Retrieval in Chinese Judicial Cases

The Supreme People’s Court of China has implemented the compulsory retrieval system for similar cases, making similar case retrieval the focus of research. Over 110 million judgment documents have been published on China Judgements Online, which provides massive data support. Recent approaches for semantic search are to embed queries and documents into semantic space and compute the similarity between them for ranking. These methods only represent the overall meaning of queries and documents, but learn a lot of redundant information from long text. In order to capture the case facts precisely from judgment documents, we propose a joint extraction model to build knowledge graphs for judicial cases. Experiments on recent judgment documents demonstrated the effectiveness of this knowledge graph construction process. The proposed case retrieval method can improve the efficiency for judicial trials, promote judicial justice, prevent the abuse of discretion, and unify the scale of adjudication.
User Experience Design Analysis Using Lean UX Method

Bizmeth is an Android-based application that was created to make it easier for users to build the widest possible business network quickly and easily. Based on the results of observations, it was found that there was a negative response from users related to the experience when using the application both through application comments on Google Playstore and through admin, a decrease in the number of users with the number of installed users as many as 34 and 19 uninstall users, and it was found 6 features that are not yet according to what user needs. In applications, generally, there is an interface that makes it easy for users so that they can be used as needed. Based on these findings, the researcher conducted a study to design the UI/UX of the bizmetch application to meet user needs. The method used is Lean UX. The design process occurs 2 times iteration, in the first iteration it produces 71% positive comments and 29% negative comments, in the second iteration it produces 100 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> positive comments indicating that the user has received the application interface and the new system flow. The results of this research are in the form of a prototype design of the application that can be used by developers to improve the user experience of the application.