Reviewerly/exHarmony huggingface dataset:
SIR: Similar Image Retrieval for Product Search in E-Commerce

We present a similar image retrieval (SIR) platform that is used to quickly discover visually similar products in a catalog of millions. Given the size, diversity, and dynamism of our catalog, product search poses many challenges. It can be addressed by building supervised models to tagging product images with labels representing themes and later retrieving them by labels. This approach suffices for common and perennial themes like "white shirt" or "lifestyle image of TV". It does not work for new themes such as "e-cigarettes", hard-to-define ones such as "image with a promotional badge", or the ones with short relevance span such as "Halloween costumes". SIR is ideal for such cases because it allows us to search by an example, not a pre-defined theme. We describe the steps - embedding computation, encoding, and indexing - that power the approximate nearest neighbor search back-end. We also highlight two applications of SIR. The first one is related to the detection of products with various types of potentially objectionable themes. This application is run with a sense of urgency, hence the typical time frame to train and bootstrap a model is not permitted. Also, these themes are often short-lived based on current trends, hence spending resources to build a lasting model is not justified. The second application is a variant item detection system where SIR helps discover visual variants that are hard to find through text search. We analyze the performance of SIR in the context of these applications.
RETRACTED: Research and Implementation of The Fault Information System FOR Relay Protection Based on Cloud Computing Technology

Abstract With the continuous development of smart grid technology, it is of great engineering application value and urgent practical demand to collect and manage more intelligently the operation and fault information of relay protection in 110kV and below substation. With the introduction of “cloud computing” technology, a cloud platform of fault information with good expansibility, high stability, loose coupling, strong reusability and convenient maintenance could be built up, which is the core of the system construction. In order to make full use of the existing data network channels and network resources in 110kV substation, a fault information system of relay protection, with its sub-station modeling and communication management, is presented based on “cloud computing” technology to realize a more efficient and convenient relay protection exchange between 110kV substation and the dispatching control center.
Using Big Data Analysis to Explore the Causes of Anxiety and Depression in Medical Students’ Sleep Quality

Abstract There are many medical courses, heavy tasks and heavy academic pressure, so medical students are often faced with higher learning and life pressure. In addition, medical students with anxiety and depression tend to reflect their poor sleep quality. In order to clarify the relationship between sleep quality, anxiety and depression of medical students, this paper first introduces the data and methods used in this study. And then uses computer technology to analyze the results of this investigation, and finally studies the correlation between anxiety and depression of medical students on their sleep quality.
MHASiam: Mixed High-Order Attention Siamese Network for Real-Time Visual Tracking

Fully Convolutional Siamese network (SiamFC) has demonstrated high performance in the visual tracking field, but the learned CNN features are redundant and not discriminative to separate the object from the background. To address the above problem, this paper presents a Mixed High-order Attention Siamese network (MHASiam) for real-time object tracking. We first proposes a High-order Attention (HA) module that is integrated into the Siamese network to select the features in the channel domain. Especially, a high-order attention module is followed by the last layer of the network, and this benefit to obtain the higher-order representation for improving the discriminate ability of the model. Then, a First-order Attention (FA) module is introduced to further enhance the richness of attention knowledge, which is combined with the HA module in a parallel manner. Finally, the GOT10k data set is employed to train our Mixed High-order Attention Siamese network (MHASiam) to improve the target representation ability. Experimental results show that the proposed algorithm improves the accuracy by 9.4% and the success rate by 4.8% compared with the SiamFC tracker.
Not so long, but very rich: a history of Russian crypto standardization

In this note we briefly present the history of standardization in cryptography area in the Russian Federation. We begin by introducing the first standard GOST 2817-89 which origins is USSR. Then we speak about the main properties of modern basic crypto mechanisms standardized in Russia. We introduce a bunch of more complicated standards based on these basic mechanisms. Then we discuss current situation in Russian crypto standardization and topics to consider in the future.
Construction and Application of Metal Equipment Supervision Management Information System

Abstract Thermal power plant contains a large number of metal equipment. The data of construction, overhaul, maintenance and operation of metal equipment can fully reflect the safety status of the equipment. Aiming at a large number of metal supervision data of thermal power plants in Inner Mongolia, according to the requirements of national and industrial standards, a metal supervision and management information system including various data of metal equipment design, manufacturing, installation, use, inspection, transformation and repair, life assessment and other stages is established to realize the whole life cycle management of supervision equipment in thermal power plants. At the same time, make full use of the big data retrieval function of the system, realize the big data comparison of the same type of equipment or the same type of fault, provide sufficient information for the technical supervision and failure analysis personnel, and further improve the technical supervision and technical service quality.
Score Prediction Model for Sentiment Classification Using Machine Learning Algorithms

With the explosive boom of social media content over the Internet in the recent few years, users now present their views on nearly something in the discussion. There are many microblogging web sites like Twitter, Facebook, LinkedIn, Tumbler, etc. Twitter has grown to be a very famous verbal information exchange tool amongst the users and is one of the most open and simplest platforms to share their sentiments on extraordinary topics. In this context, we acquire the tweets from twitter data and preprocess the tweet that removes irrelevant inappropriate phrases such as name, symbols, etc. It additionally compares every tweet to a database of positive, negative, and impartial words. This paper presents an enhanced score prediction model for sentiment analysis using machine learning as a real-time data processing system for evaluating the public sentiments based on the data of social media Twitter.
A Cooperative Block-variant Monitoring Mechanism Based on Spectral Clustering for Internet of Things

Abstract There exist two main defects in traditional monitoring systems: rigid monitoring intensities and fixed task roles, which induce low monitoring efficiencies and large power consumptions. To alleviate these two problems, this paper proposes a cooperative block-variant monitoring mechanism for Internet of Things. This mechanism divides the whole monitoring area into several blocks with different monitoring intensities according to the spatial distribution of monitoring terminals based on spectral clustering. The monitoring intensities, including monitoring densities and frequencies, are decided by the status of monitoring objects in different blocks and the values of pre-setting thresholds. By adjusting the monitoring densities and frequencies in real time, this method makes the monitoring focus on the most critical blocks, which improves the monitoring efficiencies and reduces the overall consumption of system. In addition, adequate switching of the task roles of nodes balances their workloads, and therefore extends the overall life of the monitoring systems. A large number of experiments have been carried out, and the results show that this collaborative monitoring mechanism achieves good performance.
Research on earthquake damage prediction of stone structure in southern Fujian based on fuzzy comprehensive evaluation

Abstract Based on fuzzy mathematics method, earthquake damage prediction of stone structure houses in Southern Fujian is studied. The subsets of earthquake damage affecting factors applicable to stone structures and their fuzzy relation with earthquake damage levels are established. Furthermore, the earthquake damage prediction model of stone structure houses in southern Fujian is developed based on fuzzy comprehensive evaluation method. Taking the two-storey stone structure house as an example, the model is applied to predict and evaluate the damage state of the two-storey stone structure house after earthquake damage. The results show that, according to the fuzzy comprehensive prediction and evaluation model, the stone structure house suffers serious damage degree in the earthquake damage, the evaluation grade of earthquake damage is C, and the evaluation result is consistent with the actual damage state. The model of earthquake damage prediction is clear and easy to calculate, which provides a theoretical basis for earthquake damage prediction and reinforcement and maintenance of stone structure houses in southern Fujian.
Supersingular Isogeny-based Cryptography: A Survey

With the advent of quantum computers that showed the viability of Shor's Algorithm to factor integers, it became apparent that asymmetric cryptographic algorithms might soon become insecure. Since then, a large number of new algorithms that are conjectured to be quantum-secure have been proposed, many of which come with non-negligible trade-offs compared to current cryptosystems. Because of this, both research and standardization attempts are an ongoing effort.
An infinitary axiomatization of dynamic topological logic

Abstract Dynamic topological logic ($\textsf{DTL}$) is a multi-modal logic that was introduced for reasoning about dynamic topological systems, i.e. structures of the form $\langle{\mathfrak{X}, f}\rangle $, where $\mathfrak{X}$ is a topological space and $f$ is a continuous function on it. The problem of finding a complete and natural axiomatization for this logic in the original tri-modal language has been open for more than one decade. In this paper, we give a natural axiomatization of $\textsf{DTL}$ and prove its strong completeness with respect to the class of all dynamic topological systems. Our proof system is infinitary in the sense that it contains an infinitary derivation rule with countably many premises and one conclusion. It should be remarked that $\textsf{DTL}$ is semantically non-compact, so no finitary proof system for this logic could be strongly complete. Moreover, we provide an infinitary axiomatic system for the logic ${\textsf{DTL}}_{\mathcal{A}}$, i.e. the $\textsf{DTL}$ of Alexandrov spaces, and show that it is strongly complete with respect to the class of all dynamical systems based on Alexandrov spaces.
Relation-Enhanced Multi-Graph Attention Network for Recommendation

Knowledge graph captures structured information and relations between a set of entities. Researchers always introduce knowledge graph (KG) into recommender systems for more accurate and explainable recommendation. Recently, many researchers deploy Graph Neural Network (GNN) with knowledge graph in recommender systems. However, they do not consider proper aggregation and ignore the layer limitation of the GNN. To tackle these issues, we propose a novel recommendation framework, named Relation-Enhanced Multiple Graph Attention Network (REMAN for short), which models the heterogeneous and high-order relationships among entities in recommendation. Firstly, we encode user behaviors and item knowledge as a unified relational graph. Then we utilize a relation-specific attention aggregator to aggregate the embeddings of the heterogeneous neighbors. Thirdly, we propose a relation-enhanced user graph in order to make up for the limitations of the GNN layer in recommendation. Finally, we make prediction based on the embeddings we learned in graphs. Extensive experiments on three benchmark datasets demonstrate that our framework significantly outperforms strong recommender methods.
Evaluating the Impact of Semantic Gaps on Estimating the Similarity using Arabic Wordnet

Semantic similarity measureKnowledge-based approach is wield used in various NLP applications.For example, to evaluate the semantic similarity between words, the semantic evidence in lexical ontologies (wordnets) is commonly used.The success of the English WordNet (EnWN) in this domain has inspired the creation of several wordnets in different languages, including the Arabic WordNet (ArWN).The English synsets have been extended to Arabic synsets through translation, which have introduced semantic gaps in ArWN structure.Therefore, compared to EnWN, ArWN has limited coverage in terms of lexical and semantic knowledge.This paper explores to what degree the richness of the wordnets' semantic structure influences the semantic evidence that can be used in wordnet-based applications, in particular the effect of filling the semantic gaps in ArWN.The paper studies the performance of applying English-based and Arabic-based similarity measures over ArWN.A set of experiments was performed by applying six path-based semantic similarity measures over Arabic benchmark dataset to investigate the usability and efficacy of the enriched structure of ArWN.The Performance measures, Person Correlation and Mean Square Error, are computed against and compared to human judgment benchmark.The obtained results demonstrate that the semantic similarity between words can be significantly improved when filling the semantic gaps.In addition, the experiment findings show that Arabic-based measures competitively perform well compared to the English-based measures.Further, ArWN enhanced structure is also available for public.
A New Fast Twin Support Vector Regression

Twin support vector regression (TSVR) was proposed recently as a novel regression algorithm that determines a pair of -insensitive up- and down-bound functions by solving two related SVM-type problems, each of which is smaller than that in a classical SVR. However, it lack of complexity control and only implements empirical risk minimization principle. This paper proposed modified twin support vector regression that implements structural risk minimization principle by introducing the regularization term based on TSVR. The optimization problems are solved by successive overrelaxation technique. Experimental results on several datasets show good generalization performance and decreases computation time.
CSRF Detection Based on Graph Data Mining

CSRF is a popular web security vulnerability. To detect this type of vulnerability, we need to analyze the relationship between HTTP request parameters and state transitions on the server side. However, the existing detection methods are not suitable for detecting this vulnerability. In this paper, we propose a method to detect CSRF by capturing the execution traces of web application and importing them into graph database. Then we use data mining to find HTTP requests that could cause the state transitions on the server side, and infer the type of all the parameters contained in the request parameter, so as to detect the CSRF vulnerability. Experiments show that this method is feasible.
Influence of Term Familiarity in Readability of Spanish e-Government Web Information

It is well known that linguistic features of a written text affect its readability, understanding readability as the ease with which a reader can understand the text. This paper is focused on the analysing of the influence of some linguistic features on the readability of current Spanish e-Government websites. Specifically, the “familiarity” of the terms on web pages, as well as the “frequency” of these terms are studied, among others. Firstly, this research has analysed a corpus extracted from the current information websites of the Spanish eGovernment and its simplified counterparts. Then, using machine learning methods, a supervised model is built on the influence of different term familiarity lists on text readability in the corpus. Different term lists have been tested and it has been concluded that the differences between them have a great impact on their performance. An accuracy of 81% has been achieved with a combination of frequency lists. As a conclusion, term lists and the frequencies of the terms allow to determine to a high degree the difficulty of understanding the text.
Persuasive Design for Improving Battery Swap Service Systems of Electric Scooters

Automatic battery swap stations have been recently set widely in Taiwan while the system operators have found significant differences among the battery utilization rates of the stations. To reduce the battery idle time in the less visited stations, this study looks for effective persuasive design strategies that persuade users to choose the less visited stations. After useful persuasive strategies were collected from the related literature, eighteen feasible design strategies were proposed by considering the problem characteristics. A questionnaire survey was conducted to estimate the persuasion effects of the eighteen design strategies by using the storyboard method. The persuasive design strategies with higher persuasion effect are identified and recommended for reducing the gap of battery utilization rates. Furthermore, by using the statistical analysis like ANOVA to analyze the persuasion effect with respect to demographical variables such as gender and age, the results could help choose effective persuasive strategies for different target customer groups.
Combination of Fuzzy C-Means and Simple Additive Weighting Using Partition Coefficient Index

In the selection of palm oil tree seedlings, it is necessary to consider the determinants of alternatives and criteria to produce an optimal choice of seedlings corresponds to land suitability. At the moment, the selection process is still not using any determining factors. This study aims to show the best cluster ranks to solve the problem of seedling selection. This research was conducted with the Decision Support System of palm oil tree seedling selection by proposing the Fuzzy C-Means method and Simple Additive Weighting using Partition Coefficient Index. The Fuzzy C Means method is used for data grouping whereas the Partition Coefficient Index is used to find the best cluster selection and Simple Additive Weighting is used for ranking cluster data selection. Based on the results of the research, it is known that the method can be applied to the Decision Support System of palm oil tree seedling selection by producing more objective and precise decisions. The sensitivity test then proves that the proposed method, Fuzzy C- Means method and Simple Additive Weighting using Partition Coefficient Index, in term of consistency to the effect of changing criteria is better, affected only 1 time out of 6 tests, compared to the method of Fuzzy C Means and Simple Additive Weighting using Xie Beni Index that affected by changing criteria in 3 times out of 6 tests.
Multi-level Prediction with Graphical Model for Human Pose Estimation

More and more complex Deep Neural Networks (DNNs) are designed for the improvement of human pose estimation task. However, it is still hard to handle the inherent ambiguities due to diversity of postures and occlusions. And it is difficult to meet the requirements for the high accuracy of human pose estimation in practical applications. In this paper, reasoning-based multi-level predictions with graphical model for single person human pose estimation is proposed to obtain the accurate location of body joints. Specifically, a multi-level prediction using cascaded network is designed with recursive prediction according to three different levels from easy to hard joints. At each stage, multi-scale fusion and channel-wise feature enhancement are employed for stronger contextual information to improve capacity of feature extraction. Heatmaps with rich spatial and semantic information are refined by explicitly constructing graphical model to learn the structure information for inference, which can implement the interactions between joints. The proposed method is evaluated on LSP dataset. The experiments show that it can achieve highly accurate results and outperform state-of-the-art methods.
PROBLEM-BASED MODULE VALIDATION FOR COMPLEX PROCEDURE TEXT (VALIDASI MODUL BERBASIS MASALAH UNTUK TEKS PROSEDUR KOMPLEKS)

This research was conducted to overcome the limitations of teaching materials in complex text learning procedures. Students needed teaching materials in the form of learning modules as one source of learning. The data analysis technique was descriptive statistics to get the percentage of validity, practicality, and effectiveness of the learning module. The resulting data was displayed in the form of tables and histograms. The results of the study explained that the problem-based complex procedure text learning module is valid in terms of content. Language, presentation, and graphics with a percentage of 94.92% with a very valid category of problem-based text learning procedures in complex procedures. Based on the results of the study, it is concluded that the problem-based complex procedure text learning module developed is valid. This module can be used to overcome the limitations of teaching materials in complex text learning procedures.
Interpretable Machine Learning with Bitonic Generalized Additive Models and Automatic Feature Construction

In many machine learning applications, interpretable models are necessary for the sake of trust or for further understanding the patterns in the data. In particular, scientists often want models that elucidate knowledge and therefore may lead to new discoveries. Currently, Generalized Additive Models (GAM) are gaining interest in other application domains because of their ability to fit the data well while at the same time being intelligible. Moreover, prior domain-specific knowledge is often valuable to guide the learning. In this work, extensions and generalizations of GAM are proposed to incorporate prior knowledge during the learning phase. Specifically, the fitting method for GAM is modified so that it can fit the data with bitonic functions. In physics for instance, the most discriminative variables often present specific distributions with respect to the target variable, especially peaking (i.e. bitonic) distributions. An algorithm is also described to build automatically bitonic high-level features to be used in the GAM terms. Experiments on three physics datasets are used to validate these ideas in conjunction with physics scientists.