Reviewerly/exHarmony huggingface dataset:
High-Quality Paths: Multi-View Attention Neural Network With High-Quality Paths for Top-N Recommendation

Recommender systems are ubiquitous and play an important role in most service-based applications. Since rich semantics lie in interactions between user and item, various models based on Graph Neural Network (GNN) are widely adopted to model the interactions and achieve excellent recommendation performance. Nevertheless, these models mainly learn nodes in the path between user and item separately. The path is also selected randomly, which would bring in low-quality paths and lead to suboptimal performance. To address the above issues, this paper proposes a model named Multi-View Attention neural network with high-quality paths (HP-MA). The path between user and item is viewed as an entirety, and a neural network is designed to distill path semantic embedding. Meanwhile, a multi-view attention model is provided to learn the influences between embedding of the path and user/item. Besides, we provide a method to construct HP-MA between target user and item, which would consider the length and inner details of the path. Moreover, a generalized version of our model (HP-MA*) is proposed to consider several kinds of multi-hop paths, simultaneously. Lastly, extensive experiments are conducted on several public datasets, the results demonstrate that our model can achieve better performance than the state-of-the-art models.
The Application of Computer Network Technology in Intercultural Communication

Abstract In this paper, the improving of the network technology has changed people in daily production and living in the way of thinking, makes the correlation between people is also a new change, brought new impact to People’s Daily communication, and expand the scope of interpersonal communication, so that in the interpersonal communication to two-way or multidirectional interaction, interpersonal communication is also becoming more direct, more quickly. The advent and application of network technology have changed people’s self-behavior to a certain extent, and also promoted the development of intercultural communication in today’s era from a broader and deeper perspective. This paper analyzes and interprets the intercultural communication, the way of intercultural communication and the development of intercultural communication.
Adversarial Adaptive Interpolation for Regularizing Representation Learning and Image Synthesis in Autoencoders

Data interpolation is typically used to explore and understand the latent representation learnt by a deep network. Naive linear interpolation may induce mismatch between the interpolated data and the underlying manifold of the original data. In this paper, we propose an Adversarial Adaptive Interpolation (AdvAI) approach for facilitating representation learning and image synthesis in autoencoders. To determine an interpolation path that stays on the manifold, we incorporate an interpolation correction module, which learns to offset the deviation from the manifold. Further, we perform matching with a prior distribution to control the characteristics of the representation. The data synthesized from random codes along with interpolation-based regularization are in turn used to constrain the representation learning process. In the experiments, the superior performance of the proposed approach demonstrates the effectiveness of AdvAI and associated regularizers in a variety of downstream tasks.
Hybrid Semantic Feature Descriptor and Fuzzy <i>C</i>-Means Clustering for Lung Cancer Detection and Classification

Lung cancer (LC) will decrease the yield, which will have a negative impact on the economy. Therefore, primary and accurate the attack finding is a priority for the agro-dependent state. In several modern technologies for early detection of LC, image processing has become a one of the essential tool so that it cannot only early to find the disease accurately, but also successfully measure it. Various approaches have been developed to detect LC based on background modelling. Most of them focus on temporal information but partially or completely ignore spatial information, making it sensitive to noise. In order to overcome these issues an improved hybrid semantic feature descriptor technique is introduced based on Gray-Level Co-Occurrence Matrix (GLCM), Local Binary Pattern (LBP) and histogram of oriented gradients (HOG) feature extraction algorithms. And also to improve the LC segmentation problems a fuzzy c-means clustering algorithm (FCM) is used. Experiments and comparisons on publically available LIDC-IBRI dataset. To evaluate the proposed feature extraction performance three different classifiers are analysed such as artificial neural networks (ANN), recursive neural network and recurrent neural networks (RNNs).
Theme Identification for RDF Graphs Based on LSTM Neural Reccurent Network

Since the number of Resource Description Framework (RDF) datasets on the web is growing at an exponential rate, an effective mechanism is needed to explore these datasets in order to determine which information is relevant for the user’s own needs. The task is challenging due to the complex and heterogeneous structure of RDF graphs also the user must be familiar with the content of the dataset in terms of resources and properties. To facilitate this exploration, we present a system that provides a thematic view method to encode the nodes’ vector-based representation by using a long short-term memory recurrent neural network. In this paper, we first learn nodes vector-based representation by training the distributed memory model to generate embeddings of nodes. Then we apply clustering criteria in order to identify homogeneous clusters, each one corresponding to a theme. Our experiments on a real dataset show appropriate results.
Sports——Analysis on the Application of Computer Virtual Reality Technology in College Sports Training

Abstract This paper not only analyzes the application feature and necessity through the practical application program of computer virtual reality technique in domestic universities and colleges, but also explains the important significance of this advanced technology to college sports training through a mass of survey data. It indicates that this technique is practical and effective in college sports training, which can perfect the results and quality of college sports training, perfect the physical quality of students, enhance the attractiveness of sports to students and facilitate the healthy and sustainable development of college sports [1].
A recommendation algorithm for sparse data via implicit relationship

The sparseness of user-to-item rating is one of the main factors affecting the quality of a recommender system. To address the sparsity problem, several recommendation techniques have been proposed. They add auxiliary information to the recommender system to improve the accuracy of rating prediction. We observe that there are more implicit relationships besides the explicit connection between users and the items to be predicted. If these rich implicit relations are introduced into the recommendation model, the sparsity of the original data can be greatly reduced. Therefore, we propose a new recommendation algorithm via implicit relationship, which is applied to the classic movie recommendation problem. This model extracts the implicit relationship from the movie's cast data to make movie recommendations, so as to better understand the movie. Different from the existing context-based methods, our method extracts more diverse preference information of users and integrates the implicit relationship between users and other entities. This paper takes a step forward in this direction, and studies how to use rich implicit relations to alleviate the sparsity of recommendation and further improve the recommendation methods. We conduct a series of experiments on real datasets which show that our method sianificantly outperforms state-of-the-art methods.
Runtime Verification of Ethereum Smart Contracts Based on MSVL

Ethereum has become the most widely used underlying platform of blockchain smart contracts in the world. Compared with traditional software, the security problem of Ethereum smart contracts is more prominent due to its characteristics. Being a lightweight formal verification technique, runtime verification is pursued to check whether one monitored program execution obeys a desired property. In this paper, we propose a runtime verification method for smart contract security based on modeling, simulation and verification language (MSVL). First, a smart contract program is modeled by MSVL and a translator SOL2M is developed to convert a Solidity program to MSVL program. Then Propositional Projection Temporal Logic (PPTL) formulas are used to describe the security properties of smart contracts. Finally, runtime verification is used to verify whether the modeling program conforms to the given security properties. As a case study, a voting smart contract is employed to show the efficiency and effectiveness of the proposed approach.
The State of the art tools and techniques for remote digital forensic investigations

Remote forensic techniques are widely searched in these days for its response capability in providing a way to perform evaluation of potentially compromised devices without physically accessing them. Discreet internal investigations are possible in remote forensics without the knowledge of the system owner. The need for eliminating the cost and time delays involved in delivering digital forensic capabilities to remote locations all around the world is profoundly increasing. This paper analyses various methodologies currently in force for remote forensic investigation. Different state of the art software and hardware tools and techniques are compared for performing different stages of investigation. Comparison tables presented for the understanding of merits, demerits, challenges and opportunities involved in those techniques. The overall intent of this paper is to perform comparative analysis based on qualitative outputs observed from memory, timeline and live forensics imaging on an incident that can simplify the process of finding out more appropriate technique under varying circumstances for effective remote forensic investigation.
Hierarchical Convolutional Neural Network for Infrared Image Super-Resolution

Due to the high price of thermal imaging sensors, methods for high quality upscaling of infrared images, acquired from low-resolution inexpensive IR-cameras become in high demand. One of the very promising branches of such kinds of methods is base on super-resolution (SR) techniques that exploit convolutional neural networks (CNN), which are developed rapidly for the last decade. During the review of existing solutions, we found that most of the super-resolution neural networks are intended for the upscaling of images in the visible spectrum band. Among them, the BCLSR network has proven to be one of the best solutions that ensure a very high quality of image upscaling. Thus, we selected this network for further investigation in the current paper. Namely, in this research, we trained and tested the BCLSR network for upscaling of far-infrared (FIR) images for the first time. Moreover, inspired by the BCLSR architecture, we proposed our own neural network, which defers from the BCLSR by the absence of recursive and recurrent layers that were replaced by series-connected Residual- and parallel-connected Inception-like blocks correspondingly. During the tests, we found that the suggested modifications permit to increase the network inference speed almost twice and even improve the quality of upscaling by 0,063 dB compared to the basic BCLSR implementation. Networks were trained and tested using the CVC-14 dataset that contains FIR images acquired at the night. We used data augmentation with random dividing dataset images onto 100×100 pixel patches and with subsequent application random brightness, contrast, and mirroring to the obtained patches. The training procedure was performed in a single cycle with single increase and decrease of the learning rate and used the same parameters for the proposed and the BCLSR networks. We employed the Adam optimizer for the training of both networks. Nevertheless, the proposed model has more parameters (2,7 М) compared to the BCLSR (0,6 М), both of the networks can be considered as the small ones, and thus can be used in applications for conventional personal computers, as well as in embedded solutions. The direction of the further research can be focused on the improvements of the proposed network architecture by introducing new types of layers as well as on the modifying of hyperparameters of the used layers. The quality of the upscaling can be increased also by using other loss functions and by the change of learning rate-varying strategies.
Computational Statistics of Data Science for Secured Software Engineering

The chapter focuses on exploring the work done for applying data science for software engineering, focusing on secured software systems development. With requirements management being the first stage of the life cycle, all the approaches that can help security mindset right at the beginning are explored. By exploring the work done in this area, various key themes of security and its data sources are explored, which will mark the setup of base for advanced exploration of the better approaches to make software systems mature. Based on the assessments of some of the work done in this area, possible prospects are explored. This exploration also helps to emphasize the key challenges that are causing trouble for the software development community. The work also explores the possible collaboration across machine learning, deep learning, and natural language processing approaches. The work helps to throw light on critical dimensions of software development where security plays a key role.
A Scoring System For Multiplayer Game Base On Blockchain Technology

An essential part of game development, especially in the multiplayer genre, is the scoring system. Data assurance on each device and data security is a significant issue in developing the assessment system. In this paper, our scoring system creates with blockchain-based data sharing for multiplayer games. We implemented the data design on the Unity game engine using the ethereum framework. The test results show that each device can connect to the network and can display the player's update score via the leaderboard. Some ethereum variables that affect the score data transaction process include gas prices, gas limits, and the gas used. The transaction speed test results show that the faster the gas limit value, the faster the transaction validation process will make the data transaction process faster. In this study, the gas limit value 500000 is the most optimal to get player score data transactions. While the average cost incurred for each data delivery score is 0.00996434 ETH.
EDUCATION SPACE IN MACRO-REGIONS OF THE RUSSIAN FEDERATION: STATE AND DEVELOPMENT PROSPECTS

The article defines characteristics of the state at all levels of education — preschool, school, primary, secondary and tertiary education in regions of Russia with the purpose of determining imbalances of these levels and the possibility of creating a proportional to a unified educational space on the territory of the Russia. The basis for creating such a space is federal projects that are aimed at implementing the national project «Education» and in the future will contribute to the further formation of a competitive national economy of a new generation — knowledge-intensive.&#x0D; The main research method was a comparative analysis of specific indicators, and the information base was the data of the annual state statistical reports for the regions of the Russia.&#x0D; The identified irregularity and disproportionate development of the levels of education allow to formulate the strategic goals, performance standards, and plans of specific measures to increase proportionality and reduce the uneven development of the educational environment in the regions of Russia.
A Cluster-based Solution to Achieve Fairness in Federated Learning

Recently, privacy has become a major social issue since personal data is collected and analyzed from different IoT devices. To prevent the disclosure of private information, and violation of data protection rules, Federated Learning caters to this need. It provides a way to train a global model without exposing raw personal data. One of the most popular tools in this paradigm is Federated Averaging, where a few selected devices are forwarded to a global model, and the gradients thus obtained are averaged at the server. However, this aggregated global model faces the problem of dissimilar performance over clients due to unbalanced data and non-Independent and identically distributed data. In this paper, we proposed a novel framework called cluster-based Federated Averaging to achieve a fair global model by organizing the devices into groups and selecting clients from each group equally. In this way, the accuracy of the minority group could be improved significantly at the low expense of the majority group. To follow the federated learning's instinct of privacy protection, we adapt the training weights as the features to divide the users ensure the clients' training data does not leave their devices. We applied our framework on three popular datasets in machine learning: MNIST, Fashion MNIST, and Cifar-10. The experiments demonstrated that our framework could train a fair shared model effectively and efficiently.
Imitation model for keystroke dynamics base on state context representation

Abstract This paper describes different methods of keystroke dynamics. As part of a study on the development of methods for the dynamic analysis of keystroke dynamics, a method is proposed for representing the process of typing on the keyboard in the form of a successive change of states. Based on states it is proposed to define certain sequences called the state context as feature of a biometric image. Based on this representation, an imitation model of keystroke dynamics was developed, which allows replacing the physical source of biometric data for the purpose of testing and debugging the identification system of keystroke dynamics
A New Paradigm of Cloud Brokerage

When the cloud computing industry started its commercial acceleration a few years back, a new player entered the computing supply chain - the cloud broker. With the transformation of core business models through hybrid cloud and multi-cloud environments being witnessed across industries today, next generation cloud brokerage is poised to disrupt cloud adoption. In this paper, we propose a cloud broker service framework that will shield consumers from the vagaries of multiple clouds. To achieve this insulation, cloud brokers will disguise themselves as end-providers by creating and operating a virtual data center construct that we call Meta Cloud, which is assembled on top of participating supplier clouds. The Meta Cloud will bring simplification and unified dependability by creating converged determinism on top of non-uniform and nondeterministic provider clouds. Hand in hand with Meta Clouds, is the concept of Meta Services that realize intelligent frameworks to enable creation of value-added multi-cloud services. Based on observations from other industries, Meta Cloud brokerage can trigger a dramatic upswing in multi-cloud adoption. This paper contributes to the technical intrinsic of this impending evolution. We identify the research questions that need to be answered to construct Meta Cloud brokerage. We design the architecture of the proposed Meta Cloud broker and model the Meta Cloud and Meta Service constructs to advance classical cloud brokerage to next-generation brokerage. We also build experimental results to demonstrate the validity of the our design.
The development of learning media based on Yu-Gi-Oh Physics Smart Card (YOPSA) to increase learning interest of student on the static fluid material

Abstract The aims of this study were to produce learning media based on Yu Gi Oh Physics Smart Card (YOPSA) in order to increase students’ interest learning in static fluid material of grade X Vocational High School of 1 Semarang as well as to find out the enhancement of the interest in learning physics of the students who take the part in YOPSA learning media. This study was a Research and Development (R&amp;D) with a 4-D model (define, design, develop, and disseminate). The results of this research showed that the developed product of Yu Gi Oh Physics-based Smart Card (YOPSA) learning media was suitable to according to the assessment by the expert lecturers and physics, and the average results from students score is 3.77 (very good). Otherwise, the Yu Gi Oh Physics Smart Card (YOPSA) learning media was able to increase students’ interest in learning physics with gain value 0.40 (medium).
A Film and TV News Digest Generation method Based on HanLP

Recent years have seen a rapid development of film and television industry, Concurrent with this increase has been a steady rise in user demands on the quality of video content on Internet. This paper mainly introduces HanLP natural language processing package and TextRank algorithm, explains its operation principle in detail by comparing PageRank and TextRank, so as to further study the automatic abstract generation technology of entertainment film and television news. Based on the natural language processing toolkit HanLP, this paper improves and strengthens the abstract generation technique and realizes the core sentence generation. We conduct comprehensive experimental studies. The experimental results show that this method used in this study is of higher quality and more readable than state-of-the-art methods. This technology can also effectively analyze important sentences in film and television news, generate readable abstracts, provide convenience for readers to extract the core fragments of the text, and has certain application value.
Quantifying Assimilate-Contrast Effects in Online Rating Systems

Online rating system serves as an indispensable building block for many web applications such as Amazon, TripAdvior and Yelp. It enables production quality estimation via aggregate ratings (a.k.a. wisdom of the crowd) as well as product recommendation via inferring user preference from ratings, etc. Previous studies showed that due to assimilate-contrast effects, historical ratings can significantly distort user's ratings, leading to low accuracy of product quality estimation and recommendation. To understand assimilate-contrast effects, an "accurate'' model is still missing as previous models do not capture important factors like rating recency, selection bias, etc. Furthermore, an analytical framework to characterize product estimation accuracy under assimilate-contrast effects is also missing. This paper aims to fill in this gap. We propose a mathematical model to quantify the aforementioned important factors on assimilate-contrast effects. Our model attains a good balance between model complexity and model accuracy, such that it is neat enough for us to develop an analytical framework to study assimilate-contrast effects. Based on our model, we derive sufficient conditions, under which the product estimation and collective opinion converges to the "ground-truth''. These conditions reveal important insights on how the aforementioned factors influence the convergence and guide the online rating system operator to design appropriate rating aggregation rules and rating displaying strategies. To demonstrate the versatility of our model, we apply to rating prediction tasks and product recommendation tasks. Experiment results on four public datasets show that our model can improve the rating prediction and and recommendation accuracy over previous models significantly.