Reviewerly/exHarmony huggingface dataset:
Search UI with fill-in-the-blank for clarifying purpose of information exploration and its evaluation

The ability to retrieve needed information from the various types of information available on the Internet is becoming increasingly important. Although a query recommendation is a common search aid, we believe that users need to be able to create their own keywords when attempting to select a proper query. In this paper, we investigate how presenting a search UI when entering search keywords during a web search applying a fill-in-the-blank template (i.e., the Search UI with Fill-in-the-Blank), which reads "I want to know [BLANK] of [BLANK]," affects the ability to create a search query. As a result, the questionnaire evaluation and a user log analysis showed that although the Search UI with Fill-in-the-Blank is burdensome in terms of clarifying information requests of the users, it is effective for allowing the users to create specific queries.
Paillier Cryptosystem Based ChainNode for Secure Electronic Voting

Blockchain is a distributed and decentralized ledger of transactions that are linked together cryptographically leading to immutability and tamper-resistance, thereby ensuring the integrity of data. Due to the ability of blockchain to guarantee the integrity of data, it has found wide-range adoption in electronic voting (e-voting) systems in recent years, this is in a bid to prevent manipulation of votes. However, due to the distributed nature of the blockchain, opportunities arise for privacy intrusion of the data being secured. The translation of this privacy flaw in blockchain to e-voting systems is the possibility of violation of the privacy of the electorates. Consequently, in a bid to achieve integrity and privacy of votes in e-voting, this study presents the use of an open-source blockchain system, coupled with a privacy-oriented cryptosystem known as the Paillier cryptosystem, towards addressing the privacy concerns of the blockchain. The performance of the system was evaluated and a transaction throughput of 1424 tps was obtained for ten thousand simulated ballot transactions. Further evaluation was carried out on the system, by increasing the number of system transactions. This showed that the mining time of the blockchain increased by an average factor of 0.18 s for every thousand increases in the number of transactions. Also, the response time of the system to a range of user actions was evaluated over an increasing number of voters. Results obtained showed that the response time of the system for vote casting operations increased by an average of 0.33 min per thousand voters while for vote tallying there was an increase in response time by an average of 0.848 min per thousand voters. The scientific value of this study is the development of an integrity and privacy-preserving e-voting system consisting of an open-source nodechain coupled with a privacy-oriented cryptosystem known as the Paillier cryptosystem following the security requirements of e-voting systems. The proposed system addresses the issue of integrity in e-voting while still maintaining the privacy of the electorates.
Hadoop Small Image Processing Technology Based on Big Data Processing and Its Application Effect in Face Feature Extraction and Face Recognition System Design

From standard software to crucial applications, face recognition (FR) is always at the core of many unique advances over the past 20 years. Big data is a rapidly growing collection of data. It is important in many applications such as medical, academic, and industries, and it refers to vast data sources that are hard to analyze, store, and interpret for subsequent procedures. Hadoop is an open-source large data processing platform that stores and analyses data in scalable computer clusters. FR technology is built on Hadoop processing to boost the effectiveness of recognition using MapReduce computing. This paper presents a novel adaptive fine-tuned AdaBoost (AFTA) algorithm to enhance the FR in the Hadoop processing. Collected face data sets are employed in preprocessing stage to normalize and enhance the superiority through the median filter (MF) and contour-based image enhancement (CIE), correspondingly. To handle vast quantities of the data, boosted k-means clustering (BKC) approach is used over the Hadoop servers for the MapReduce process. The binary partition tree (BPT) approach is employed in the segmentation stage to split the data into many subgroups. To lessen the dimension of the data, we use the Gabor filter. To select the consistent features, threshold-based binary particle swarm optimization (T-BPSO) is applied. Then, our proposed technique is utilized in the FR process. Finally, the performance metrics of the proposed technique are examined and compared with existing techniques to accomplish our research with the greatest effectiveness. For accuracy, precision, recall, and F-score, we attained the proposed (AFTA) values of 99.67, 97.13, 95.21, and 94.11, respectively. The outcomes are depicted in graphical representation by employing the MATLAB tool.
Bridging the Gap of AutoGraph Between Academia and Industry: Analyzing AutoGraph Challenge at KDD Cup 2020

Graph structured data is ubiquitous in daily life and scientific areas and has attracted increasing attention. Graph Neural Networks (GNNs) have been proved to be effective in modeling graph structured data and many variants of GNN architectures have been proposed. However, much human effort is often needed to tune the architecture depending on different datasets. Researchers naturally adopt Automated Machine Learning on Graph Learning, aiming to reduce human effort and achieve generally top-performing GNNs, but their methods focus more on the architecture search. To understand GNN practitioners' automated solutions, we organized AutoGraph Challenge at KDD Cup 2020, emphasizing automated graph neural networks for node classification. We received top solutions, especially from industrial technology companies like Meituan, Alibaba, and Twitter, which are already open sourced on GitHub. After detailed comparisons with solutions from academia, we quantify the gaps between academia and industry on modeling scope, effectiveness, and efficiency, and show that (1) academic AutoML for Graph solutions focus on GNN architecture search while industrial solutions, especially the winning ones in the KDD Cup, tend to obtain an overall solution (2) with only neural architecture search, academic solutions achieve on average 97.3% accuracy of industrial solutions (3) academic solutions are cheap to obtain with several GPU hours while industrial solutions take a few months' labors. Academic solutions also contain much fewer parameters.
A Method for Calculating Diffuse Solar Radiation Using Data From Various Information Sources

The applicability of two models (empirical formulas) for diffuse solar radiation estimation on tilted surfaces is considered. Models developed by the authors for the monthly diffuse solar radiation determination from the Meteonorm database, the Scientific and Applied Reference Book on the Climate of the USSR and the World Radiation Data Center are presented. The possibility of developing an empirical linear dependence and an empirical cubic dependence for determining the share of the diffuse component of solar radiation depending on the coefficient of transparency of the atmosphere to calculate the diffuse component according to the global solar radiation at any point in Kyrgyzstan and at different elevations of the country is considered.
An enumeration of distinct and non-isomorphic functional quasi-order relations

The problem of enumerating the number of distinct functional quasi-orders and the number of non-isomorphic functional quasi-orders that can be formed from a finite point set is considered. We establish the link between functional quasi-orders, functional digraphs, and functional Alexandroff topologies. As a result, we provide the generating functions associated to orbit-equivalent and orbit-isomorphic functional digraphs.
A method to recognize spams based on Dynamic-LSTM

Nowadays, people are increasingly inseparable from electronic communication tools. Email is one of the important means of communication, but the existence of spams seriously affects the users' usage. This paper focuses on the spam classification problem in a practical context. Real email messages are collected and the classification is performed using the Dynamic_LSTM model. By comparing with algorithms of traditional machine learning as well as ordinary RNN, it is shown that the accuracy of Dynamic_LSTM is increased by 8%.In addition, it is not affected by the max-feature. The experimental results show that the Dynamic_LSTM model performs better at the classification accuracy.
Computation intelligent new approach for image steganography calculations

Image steganography is a mean to conceal the existence of secret information in a non-secret audio, which is referred to as cover media. Different from cryptography, the steganography focuses on hiding the secrecy of the information rather than obfuscating it. Additionally, the complex nature of the cover media makes it harder for the attacker to expose the hidden information. In this paper, we propose a new method to hide secret image in an audio file. The proposed method for images steganography is based on embedding the confidential information in an audible part of the sound files. Additionally, for the purpose of increasing the difficulty for the intruder to gain access to the secret information, the technique of random sample location was utilized to initiate the process of hiding the secret information. Experimental results proved that our proposed method is effective and efficient due to the imperceptibility aspect of the human hearing of the resultant audio file, which is referred to as stego file. The performance of the proposed method was tested using two common fidelity measurements, Peak-Signal-to-Noise-Ratio (PSNR) and Mean-Square-Error (MSE).
Experimental Approach for the Detection of Defects Employing High-Resolution Digital Image Correlation

The evaluation of the structural integrity of components concerns different fields of engineering. The detection of defects is essential, especially in materials that could present discontinuities associated with the manufacturing processes, or after being mechanically loaded. The present work investigates the potential of full-field optical techniques to detect defects. These techniques could present an interesting alternative to specific techniques such as traditional ultrasound inspection or Laser Scanning Vibrometry (SLDV), which is very sensitive even at high frequencies. Specifically, 3D Digital Image Correlation (3D-DIC) is explored to detect inner defects. This full-field optical technique is based on measuring displacement and recent advances in camera resolution allows a high sensitivity. In this work, it is analysed different cantilever-beam specimens with different internal discontinuities. These specimens are made using additive manufacturing in order to control shape and size of discontinuities. Mode shapes are explored with 3D-DIC employing high-resolution cameras and a subsampling procedure to capture images. This procedure presents a high special resolution, unlike other approaches. Results are compared with those obtained by SLDV and simulations. This preliminary analysis shows the potential of full filed optical techniques in the determination of behaviours that indicates the presence of a defect.
A Stigmergy-Based Differential Evolution

Metaheuristic algorithms are techniques that have been successfully applied to solve complex optimization problems in engineering and science. Many metaheuristic approaches, such as Differential Evolution (DE), use the best individual found so far from the whole population to guide the search process. Although this approach has advantages in the algorithm’s exploitation process, it is not completely in agreement with the swarms found in nature, where communication among individuals is not centralized. This paper proposes the use of stigmergy as an inspiration to modify the original DE operators to simulate a decentralized information exchange, thus avoiding the application of a global best. The Stigmergy-based DE (SDE) approach was tested on a set of benchmark problems to compare its performance with DE. Even though the execution times of DE and SDE are very similar, our proposal has a slight advantage in most of the functions and can converge in fewer iterations in some cases, but its main feature is the capability to maintain a good convergence behavior as the dimensionality grows, so it can be a good alternative to solve complex problems.
Face Recognition through Integrated Model

The goal of this paper is to create a computerized participation framework that uses face recognition. When a user snaps a picture of a person, our program uses picture recognition to pull for associated data in a database. The pre-owned picture recognition calculation required to be invariant to changes in brightness and view point because a client of the program can capture a photo in more favorable settings. Because the calculation had to run on the portable, a lightweight photo acknowledgment calculation was required. Two or three of these invariants can be grouped together as a component vector, which can be used to recognize a picture in particular. A best match can be found by calculating the distance between the vectors of an obscure picture and realized information base photos. Android is versatile and provides a wide range of devices for app development. This allowed us to accelerate the development of our exhibition hall guide application in a short period of time. A big number of Android's potential has been considered, evaluated, and used. For the application dataset, SQL is used. Without cooperating with the tables, additions, deletions, and modifications to the information in the framework can be made directly through the intended GUI. The framework allows for a variety of data visualizations.
Sentimental Analysis for E-Commerce Website

Opinion mining is another term for sentiment analysis, which is used to categorize views about a product. People currently value online shopping since it allows them to save time and effort while purchasing goods. Online shopping has increased significantly over the years, and the input of the users has become more critical in determining their interests and actions. To construct the web application, it demonstrates case the real-time sentimental analysis done on the customer reviews on the items accessible in the market and allows the user to provide an interactive experience and offers a clear judgment about the product based on the customer reviews.
Teaching Mode of Japanese Intensive Reading in Colleges and Universities Based on Cloud Computing Intelligent Platform

In order to improve the effect of Japanese teaching in colleges and universities, this study uses the variance of the bias error of the data channel of the cloud transmission platform to represent the intensity of the bias error between the data channels of the cloud transmission platform. Moreover, this study uses the perfect reconstruction filter algorithm to compensate for the frequency-dependent mismatch error of the broadband signal, obtains the value at the ideal sampling point from the sampling sequence estimation, and then corrects the delay deviation. In addition, this study constructs the overall structure of the Japanese intensive reading teaching system in colleges and universities based on the network cloud platform, proposes a method for solving the correction filter, and conducts simulation verification. Finally, this study constructs a teaching system for Japanese intensive reading in colleges and universities based on the network cloud platform. Through experimental research, it is verified that the teaching mode of Japanese intensive reading in colleges and universities based on the cloud computing intelligent platform has certain feasibility.
Image Restoration using Nonlocal Regularized Variational Model with Spatially Adapted Regularization Parameter

Restoration of blurred images corrupted with impulse noise has attracted increasing attention due to its great potential toward practical applications. To guarantee the noisy image deblurring performance, total variation (TV), one of the most popular regularizers, has been extensively exploited in the literature. This local regularizer is known for its capability of preserving edges and penalizing oscillations but not sharp discontinuities. However, TV regularized variational models commonly suffer from undesirable staircase-like artifacts in homogeneous regions. The reason behind this phenomenon is that TV regularizer favors solutions that are piecewise constant in numerical experiments. To overcome the model-dependent deficiency, we proposed to replace the commonly used local regularizer by its nonlocal extension, i.e., nonlocal total variation (NLTV) regularizer. By taking advantage of the high degree of self-similarity within images, the proposed nonlocal variational model is able to remove both blurring and impulse noise while preserving meaningful image details. To guarantee solution stability and efficiency, the resulting nonsmooth image restoration problem was effectively dealt with using an alternating optimization algorithm. In addition, to further enhance the image quality, a local variance estimator-based automatic method was introduced to calculate the spatially adapted regularization parameters during image restoration. Extensive experiments have demonstrated that the proposed method could achieve superior imaging performance compared with several state-of-the-art image restoration methods.
A Simple Attention Block Embedded in Standard CNN for Image Classification

The revival of convolution neural networks (CNN) has enhanced feature extraction by reducing the manual hand-engineered methods. These CNN proved their versatility in various domains, such as image classification, object detection, and image segmentation (ROI). Variant neural architectures have been proposed over the past decade with increasing depth, width, and channels for precise feature extraction. In images, feature extraction is a crucial step. The CNN will be able to extract the spatial information, but they can't retrieve the spatial orientations of the entities residing in the image. Further, attention to the required features is not seen. These points are considered challenges, and a neural architecture is to be constructed by overhauling these loops. Hence, we propose an attention block which can be easily embedded into standard convolution neural networks and eventually outperforms the plain CNN on CIFAR-10 and the NIH Malaria Data Set. The code is publicly available at: https://github.com/barulalithb/scaled-mean-attention.
Attention based discriminative visual object tracking algorithm

Visual object tracking is one of the most important topics in computer vision, and it is widely used in many industries, such as security and self-driving. However, the existing tracking algorithms do not perform well in some difficult scenarios. One of the most difficult challenges faced by the current trackers is that when there are distractors around the object, the tracker often occurs the problem of tracking drift. To alleviate this problem, we propose a visual attention based tracking algorithm. Experiments on the benchmarks OTB2013, OTB2015 and GOT-10k show that our algorithm can achieve a good tracking performance.
End Users and Industry 4.0 Systems Cyber Resilience from XSS Attacks

The everyday use of the Internet is constantly growing. It makes everyday tasks easier and faster to do, but with great benefits come the risks of cyberattacks. In modern manufacturing systems and under Industry 4.0, Web-based solutions are often used to interface manufacturing process monitoring, management, and supply-chain communication. One of the most common types of cyberattacks is cross-site scripting attacks (XSS). In this research, XSS attacks are analyzed to understand how they work and as a basis for attack simulation. The simulation was done using BeEF XSS framework. For the simulation, a simple HTML web page was created to implement the malicious script. The simulation results were gathered and analyzed to provide the guidelines for the protection of end users and Industry 4.0-like systems from XSS attacks. This research gives solid guidelines to improve the resilience of end users on XSS attacks, which can prevent the further negative impact of such attacks on Industry 4.0 systems.
Predictive uncertainties for multi-task learning network

Depth estimation and semantic segmentation are important for applications such as autonomous driving cars and minimally invasive surgery. However, the imperfect predictions make even networks with best performance difficult to apply to these high-safety-demand domains. Therefore, in this paper, using the variational representation method, we propose two uncertainty losses to enable multi-task learning network to predict the uncertainties respectively for predictions of depth values and semantic labels. The experimental results on NYU-Depth-v2 and SUN-RGBD datasets demonstrate the novelty and effectiveness of our proposed uncertainty losses.
Formation of Social Security Conditions for Students in Higher Education Using a Personality-Oriented Information Platform to Support Extracurricular Activities

Extracurricular activities at the university are the basis for the development of the creative potential of students. With the purposeful influence of teaching staff on the preparation of extracurricular activities, the effectiveness of the university activities can be increased in both quantitative and qualitative indicators. The most important aspect of educational activities is the opportunity for students to apply practically not only the knowledge received on the subjects studied, to raise the cultural level, to develop interest in certain social areas, but also to develop their talents and gain experience in community service. An indispensable factor in the demonstration of activity in the cultural sphere is the presence of social security of students.