Reviewerly/exHarmony huggingface dataset:
Design and Implementation of a Modified AES Cryptography with Fast Key Generation Technique

Cryptography is an emerging topic that has gained momentum since data explosion. With the advent of data it has become more important to secure the data from external interference. Various algorithms has been deployed with a penultimate goal of securing data of which Advanced Encryption Standard (AES) is one. The paper provides a substantial modification over Advanced Encryption Standard, with the aim of security and easy implementation focusing on the Symmetric Key Cryptosystem and AES algorithm. The performance was compared using different parameters such as data block, block size, encryption/decryption speed, run-time, and compile time. The main reason for this experiment was to exploit the effectiveness of the newly modified AES over the actual AES using the real-life application. A new key implementation process, the right shift key technique, and the elimination of the Mix-Column process were introduced. This paper provides calculations based on the AES-128 bit version, but the proposed algorithm can work on other versions as well. The goal was to achieve fast execution time and less memory usage while keeping the security hard to breach. The result depicts that our method achieved 88% throughput and better net execution time. This is a substantial improvement in terms of efficiency from the traditional AES. Our implementation with detailed documentation is available at GitHub <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
Privacy-Preserving Learning of Human Activity Predictors in Smart Environments

The daily activities performed by a disabled or elderly person can be monitored by a smart environment, and the acquired data can be used to learn a predictive model of user behavior. To speed up the learning, several researchers designed collaborative learning systems that use data from multiple users. However, disclosing the daily activities of an elderly or disabled user raises privacy concerns.In this paper, we use state-of-the-art deep neural network-based techniques to learn predictive human activity models in the local, centralized, and federated learning settings. A novel aspect of our work is that we carefully track the temporal evolution of the data available to the learner and the data shared by the user. In contrast to previous work where users shared all their data with the centralized learner, we consider users that aim to preserve their privacy. Thus, they choose between approaches in order to achieve their goals of predictive accuracy while minimizing the shared data. To help users make decisions before disclosing any data, we use machine learning to predict the degree to which a user would benefit from collaborative learning. We validate our approaches on real-world data.
Topic Modeling for Assessment of Text Complexity in Russian Textbooks

This article explores the problem of assessing the complexity of Russian educational texts. We present a quantitative and qualitative investigation of topics in the textbooks of different grades by applying several topic models. The corpus compiled for the current study comprises a collection of textbooks used in middle and high schools of the Russian Federation. The corpus consists of two sets of texts derived from textbooks on Social science by L.N. Bogolyubov and A.F. Nikitin. For our analysis, we employ Latent Dirichlet Allocation (LDA) and Additive regularization of topic models (ARTM). In this paper, we use the grades of educational texts as indicators of text complexity. To identify quantitative measures of text complexity, we analyze the correlation of book grades with topic properties. We also analyze a correlation of a topic's properties with the growth of its proportion in a book of a specific complexity level. The key finding of our research is the identification of statistically significant correlations of educational texts complexity with the revealed topic features, i.e., topic coherence and distance between topic words in a semantic space. This study can be beneficial for two communities: textbook writers and their consumers, i.e., teachers and schoolchildren.
Measurements for Visual Function, Including Gaze, and Electrooculography (EOG)

Humans obtain information from their five senses, which include vision. In recent decades, numerous displays have been developed as a result of technological advancements, and such apparatuses can be used in daily life. The development of evaluation indicators for gaze data is a very important research topic in the hygiene field. In this chapter, we outline the types of eye movements, such as the saccade, smooth pursuit eye movement, vergence eye movement, optokinetic nystagmus, vestibulo-ocular reflex, and rotational eye movement. Thereafter, we introduce several methods for measuring eye movements, including the magnetic search coil method, pupil center corneal reflection method, limbus tracking meth-od, image analysis method, and electrooculography. Based on the eye movements measured by noncontact devices, indices are developed to evaluate the severity of visually induced motion sickness.
Attention Relational Graph Convolution Networks for Relation Prediction in Knowledge Graphs

Abstract Recently, the tasks based on knowledge graph (KG), such as question answering, information retrieval, are more and more widely used. However, due to the incompleteness of the relations (links) between entities, it is very important to study the relations prediction based on KG to complete the missing relations between entities. In recent years, graph convolution networks (GCNs) have been a new method to solve the reasoning of knowledge graph. However, the existing knowledge graph relation prediction model based on GCNs fails to consider the importance between nodes. In order to obtain more abundant relations information between entities (nodes), inspired by the graph attention network (GAT), we propose an attention weighted relational graph convolutional network (denoted as AWR-GCN), which is used as an encoder of the encoder-decoder model for relationship prediction, and the decoder is a linear model. Compared with the advanced methods in the commonly used relational prediction data sets, our model has a certain performance improvement and reached advanced level.
Dance Teaching Activities in the Context of Informatization

Abstract In the context of the rapid development of the Internet in the information age, the deepening of resource sharing concept and the enhancement of information reform awareness in the field of higher education have played a catalytic role in the development of MOOC and the rise of SPOC. This paper mainly studies the design of dance teaching activities under the background of informatization. This paper makes full preparations for the model construction and teaching design by using the preliminary analysis, and at the same time synthesizes the curriculum foundation of many subjects involved in the teaching of physical dance, and chooses the professional course of physical dance as the medium for the next teaching experiment. In the teaching effect verification based on summative evaluation, 40 students majoring in dance performance in an art college were selected as experimental objects, and SPSS22.0 software was used to test homogeneity within and between the experimental group and the control group, respectively. It is feasible to apply the MOOC+SPOC based hybrid teaching mode to the external conditions, internal motivation, emotion and other support of sports dance teaching in universities.
Kernel alignment unsupervised discriminative dimensionality reduction

The performance of most existing adaptive graph learning methods, which adjust data similarity matrix according to the data representation, depends on the hypotheses that the data representation is a good indicator of the underlying data structure. However, this hypothesis is not always applicable when dealing with high dimensional data. In this paper, we propose a novel kernel alignment unsupervised discriminative dimensionality reduction (KaUDDR) algorithm. By integrating adaptive graph learning and feature learning into a joint learning framework, graph construction and dimensionality reduction are conducted simultaneously to guarantee the optimality of graph for feature learning in the proposed algorithm. Data kernel and similarity indicator kernel are defined by learned graph and the projected data in a low-dimensional subspace, a compact and discriminative data representation in the projected subspace is obtained by means of kernel alignment to explore the consistency between the projected data kernel and similarity indicator kernel. Experimental results on dimensionality reduction as well as clustering show that our method consistently outperforms the related unsupervised dimensionality reduction algorithm.
A Practical Quantum Network Coding Protocol Based on Non-Maximally Entangled State

In many earlier works, perfect quantum state transmission over the butterfly network can be achieved via quantum network coding protocols with the assist of maximally entangled states. However, in actual quantum networks, a maximally entangled state as auxiliary resource is hard to be obtained or easily turned into a non-maximally entangled state subject to all kinds of environmental noises. Therefore, we propose a more practical quantum network coding scheme with the assist of non-maximally entangled states. In this paper, a practical quantum network coding protocol over grail network is proposed, in which the non-maximally entangled resource is assisted and even the desired quantum state can be perfectly transmitted. The achievable rate region, security and practicability of the proposed protocol are discussed and analyzed. This practical quantum network coding protocol proposed over the grail network can be regarded as a useful attempt to help move the theory of quantum network coding towards practicability.
Topic-enhanced knowledge-aware retrieval model for diverse relevance estimation

Relevance measures the relation between query and document which contains several different dimensions, e.g., semantic similarity, topical relatedness, cognitive relevance (the relations in the aspect of knowledge), usefulness, timeliness, utility and so on. However, existing retrieval models mainly focus on semantic similarity and cognitive relevance while ignore other possible dimensions to model relevance. Topical relatedness, as an important dimension to measure relevance, is not well studied in existing neural information retrieval. In this paper, we propose a Topic Enhanced Knowledge-aware retrieval Model (TEKM) that jointly learns semantic similarity, knowledge relevance and topical relatedness to estimate relevance between query and document. We first construct a neural topic model to learn topical information and generate topic embeddings of a query. Then we combine the topic embeddings with a knowledge-aware retrieval model to estimate different dimensions of relevance. Specifically, we exploit kernel pooling to soft match topic embeddings with word and entity in a unified embedding space to generate fine-grained topical relatedness. The whole model is trained in an end-to-end manner. Experiments on a large-scale publicly available benchmark dataset show that TEKM outperforms existing retrieval models. Further analysis also shows how topic relatedness is modeled to improve traditional retrieval model with semantic similarity and knowledge relevance.
Comparative Analysis of Generative Adversarial Networks and their Variants

Generative Adversarial Networks (GAN) [1] is a generative modeling approach with a potential to learn high dimensional, complex real data distribution. In particular, they don't depend on any assumptions about the conveyance and can produce real-like examples from inert space in a simple manner. This powerful property drives GAN [1] to be applied to different applications, for example, picture blend, picture quality altering, picture interpretation, space variation and other scholarly fields. While great outcomes have been approved by visual assessment, a few quantitative rules have developed as of late. In this paper, we aim to discuss the operations and objective functions of variants of GAN [1] but do not comprehend GAN [1] deeply or who wish to view GAN from various perspectives. In addition, we present the comparison of evaluation of the images generated from variants of GAN like DCGAN, FCC-GAN and more.
A Study on the Issues Related to Building a Library Information System Based on Deep Learning

High-performance information systems are essential for libraries to carry out their work efficiently. Recently, a digital library environment has been created that allows electronic resources such as e-books, e-journal, and the Web to be linked to SNS, smartphones and tablet PCs. The environment available anytime and anywhere has increased the use of digital libraries, and the amount and scope of information search has also increased or expanded dramatically. These changes call for improved accuracy in the search for internal or external information in the library information system. This study designs library information retrieval system from the perspective of QA system, and builds a model based on text data. Through this, finally, we present a study comparing performance with various algorithms based on perplexity.
A Tourist Tracking Model by Tourist Bureau

Abstract Tour guides are the critical services for the Travel Bureau. Especially for the services, they provide to tourists. So, a realtime system is needed to be able to interact between the two parties, especially regarding service records in the field and information processing for the Bureau. Based on ScienceDirect library, unfortunately, until there was no reporting system facilitate both parties and recorded in realtime. This study discusses software development solutions that receive various field notes by workers and can be directly viewed online for employers. This research is in the form of a reporting model, which is a solution for the Travel Bureau to have data and be stored electronically on the server. The system was built using a platform that is on a smartphone, so the tour guide reporting system becomes a solution for the Travel Bureau and the Tour Guide to document the trips carried out.
Design and implementation of EtherCAT Master based on Loongson

With the gradual development and competition among enterprises, the products of motion control system are diversified and the performance difference is bigger. The technology of motion control system is monopolized by a few foreign professional manufacturers. The development of motion controller in China is relatively late, and the number with independent research and development ability is still in the minority. Compared with foreign countries, some developed countries still restrict the export of high-end products to China. This paper will use Loongson 3A3000 processor to realize open motion controller. Loongson series processor is a general domestic processor developed by Institute of computing technology, Chinese Academy of Sciences. It uses MIPS instruction system. At present, Loongson is gradually catching up with and surpassing the world advanced level. The communication module of motion controller uses EtherCAT real-time Ethernet, which has the characteristics of high efficiency, flexible topology and short refresh cycle. At present, there are few cases of open motion controller based on domestic CPU Loongson, which provides a typical case for independent innovation in China.
SOcial and Cultural IntegrAtion with PersonaLIZEd Interfaces (SOCIALIZE)

This is the first edition of the SOcial and Cultural IntegrAtion with PersonaLIZEd Interfaces (SOCIALIZE) workshop. The main goal is to bring together all those interested in the development of interactive techniques that may contribute to foster the social and cultural inclusion of a broad range of users. More specifically, we intend to attract research that takes into account the interaction peculiarities typical of different realities, with a focus on disadvantaged and at-risk categories (e.g., refugees and migrants) and vulnerable groups (e.g., children, elderly, autistic and disabled people). Among others, we are also interested in human-robot interaction techniques aimed at the development of social robots, that is, autonomous robots that interact with people by engaging in social-affective behaviors, abilities, and rules related to their collaborative role.
Website Recommendation with Side Information Aided Variational Autoencoder

Recommender systems had been proposed to help people to find the interested items, such as recommending products to a buyer; identifying movies or music that a user will find interest, etc. However, the existing recommendation approaches mainly focus on capturing user-item interaction patterns for prediction, and ignore the user's side information such as visit frequency and duration. In this paper, we study the side information aided website recommendation problem that using the browsing history of a set of users and their side information to predict the websites that will be of interest to a certain user. We propose a novel recommendation approach called SI-VAE that incorporates side information with the variational autoencoders (VAEs) model for top-k recommendation. The proposed method takes both user-website interaction information and side information as input, and adopts an encoder/decoder model to generate user's interested websites from partial observations. The model of SI-VAE is implemented as a neural network, and trained with a multinomial likelihood objective function to form the ranking of user-website interaction probabilities. We conduct extensive experiments on two real-world datasets, which show that the proposed model outperforms the baselines in a number of performance metrics in website recommendation.
Enabling Containerized, Parametric and Distributed Database Deployment and Benchmarking as a Service

Containerized environments introduce a set of performance challenges that require extensive measurements and benchmarking to identify and model application behavior regarding a variety of parameters. Databases present extra challenges given their extensive need for synchronization and orchestration of a benchmark run, especially in microservice-oriented technologies (such as container platforms) and dynamic business models such as DBaaS. In this work we describe the adaptation of our open source, baseline load injection as a service tool, Flexibench, in order to enable the automated, parametric launching and measurement of containerized and distributed databases as a service. Adaptation and synchronization needs are described for ensuring test sequence and applied through a case study on MySQL. Therefore a performance engineer can directly test selected configuration and performance of a database in a given target workload with simple REST invocations. Experimentation starts from adapting the official MySQL docker images as well as OLTP Bench Client ones and investigates scenarios such as parameter sweep experiments and co-allocation scenarios where multiple DB instances are sharing physical nodes, as expected in the DBaaS paradigm.
Information System Audit for Mobile Device Security Assessment

The competency to use mobile devices for work-related tasks gives advantages to the company productiveness and expedites business processes. Thus Bring Your Own Device (BYOD) setting emerge to enable work flexibility and technological compatibility. For management, employees’ productivity is important, but they could not jeopardise the security of information and data stored in the corporate network. Securing data and network becomes more complex tasks as it deals with foreign devices, i.e., devices that do not belong to the organisation. With much research focused on pre-implementation and the technical aspects of mobile device usage, post-implementation advancement is receiving less attention. IS audit as one of the post-implementation mechanisms provides performance evaluation of existing IS assets, business operations and process implementation, thus helping management formulating the best strategies in optimising IS practices. This paper discusses the feasibility of IS audit in assessing mobile device security by exploring the risks and vulnerabilities of mobile devices for organisational IS security as well as the perception of Information system management in mobile device security. By analysing related literature, authors pointed out how the references used in the current IS audit research address the mobile device security. This work serves a significant foundation in the future development in mobile device audit.
Application of Different Methods to Determine Urease Activity in Enzyme Engineering Experiment and Production

The determination of urease activity is one of the important projects in 《Enzyme engineering experiment》 and production. In the paper, the urease activity of soybean was determined by pH increment method, titration method and Nash reagent method, and the three methods were compared. The results showed that the urease activity of the same sample measured by pH increment method, titration method and Nash reagent method was not equal in value and could not be used mutually, and the urease activity of the same sample measured by Nash reagent method was the highest. The same experimental project adopts a variety of methods to improve students ’ interest in learning, improve students’ data analysis ability, and provide a better way to cultivate applied and innovative talents.
Learning Bayesian networks using A* search with ancestral constraints

When using a Bayesian network to model a practical problem, weak prior knowledge projected as ancestral constraints is necessary. However, it is difficult to directly utilize these non-decomposable constraints using search strategies based on the decomposable score. In this study, we attempt to solve this problem by conducting an implicate path-space search graph and driving the A* algorithm, which is used to obtain the globally optimal solution satisfying the given constraints. We use a maximum covering principle to provide useful pruning rules based on these constraints in the new framework. Moreover, we improve the simple heuristic and the static k-cycle conflict heuristic to adapt to ancestral constraints. We theoretically prove that the new heuristic functions remain admissible and consistent. Our experiments demonstrate that the proposed framework with the new heuristic functions significantly reduces the space complexity of A* search compared with state-of-the-art frameworks, such as Bayesian network graphs and equivalent class trees, when integrating ancestral constraints.