Reviewerly/exHarmony huggingface dataset:
Intelligent recognition of semantic relationships based on antonymy

Since computing semantic similarity tends to simulate the thinking process of humans, semantic dissimilarity must play a part in this process. In this paper, we present a new approach for semantic similarity measuring by taking consideration of dissimilarity into the process of computation. Specifically, the proposed measures explore the potential antonymy in the hierarchical structure of WordNet to represent the dissimilarity between concepts and then combine the dissimilarity with the results of existing methods to achieve semantic similarity results. The relation between parameters and the correlation value is discussed in detail. The proposed model is then applied to different text granularity levels to validate the correctness on similarity measurement. Experimental results show that the proposed approach not only achieves high correlation value against human ratings but also has effective improvement to existing path-distance based methods on the word similarity level, in the meanwhile effectively correct existing sentence similarity method in some cases in Microsoft Research Paraphrase Corpus and SemEval-2014 date set.
SRVB cryptosystem: another attempt to revive Knapsack-based public-key encryption schemes

Public-key cryptography is a ubiquitous buildingblock of modern telecommunication technology. Among the most historically important, the knapsack-based encryption schemes, from the early years of public-key cryptography, performed particularly well in computational resources (time and memory), and mathematical and algorithmic simplicity. Although effective cryptanalyses readily curtailed their widespread adoption to several different attempts, the possibility of actual usage of knapsack-based asymmetric encryption schemes remains unsettled. This paper aims to present a novel construction that offers consistent security improvements on knapsack-based cryptography. We propose two improvements upon the original knapsack cryptosystem that address the most important types of attacks: the Diophantine approximationsbased attacks and the lattice problems oracle attacks. The proposed defences demonstrably preclude the types of attacks mentioned above, thus contributing to revive knapsack schemes or settle the matter negatively. Finally, we present the http://t3infosecurity.com/nepsecNep.Sec, a contest that is offering a prize for breaking our proposed cryptosystem.
Exploring the Design Space of Badge Based Input

In this paper, we explore input with wearables that can be attached and detached at will from any of our regular clothes. These wearables do not cause any permanent effect on our clothing and are suitable to be worn anywhere, thus making them very similar to badges we wear. To explore this idea of non-permanent badge input, we studied various methods to fasten objects to our clothing and organise them in the form of a design space. We leverage this synthesis, along with literature and existing products to present possible interaction gestures these badge-based wearables can enable.
Power System State Estimation Based on RWUKF Algorithm

A robust weighted unscented Kalman filter(RWUKF) algorithm is proposed to solve the problem that the unscented Kalman filter(UKF) algorithm is easily affected by system noises and gross errors in power system state estimation. By modifying Sage-Husa noise statistic estimator, the stability of noise statistics is improved and the robustness of the algorithm is guaranteed. In addition, the estimation index based on Markov distance is introduced, and the measurement noise error matrix is modified by adaptive weighting factor, which reduces the influence of gross error on filtering accuracy. Simulation results of IEEE 30-bus standard system show that the proposed RWUKF algorithm is better than the traditional UKF algorithm.
Enhanced Password-Based Authentication Mechanism in Cloud Computing with Extended Honey Encryption (XHE): A Case Study on Diabetes Dataset

The recent advancement on cloud technologies promises a cost-effective, scalable and easier maintenance data solution for individuals, government agencies and corporations. However, existing cloud security solutions that exclusively depend on conventional password-based authentication mechanism cannot productively defence to ongoing password guessing and cracking attacks. Recent HashCat attack would brute be able to compel any hashed eight-characters-length secret key that comprises of any blend of 95 characters in less than 2.5 h. Several trivial approaches such as two-factor authentication, grid-based authentication and biometric authentication mechanisms have been enforced recently as an additional or optional countermeasure of defending password guessing and cracking attacks. These approaches, be that as it may, can be frustrated with an ongoing malware assault that capable of intercepting One-Time Password (OTP) sent to the mobile device. These stolen passwords often do not trigger any alerts and can be subsequently exploited to access other users’ cloud accounts (e.g. 61% of the users are utilizing the single secret key repeatedly to access different online records). To address these problems, this research aimed to implement an eXtended Honey Encryption (XHE) scheme for improving the assurance of conventional password-based authentication mechanism in cloud computing. At the point when the attacker tries to retrieve the patient’s diabetes information by speculating password, rather than dismissing their record access as a customary security defence mechanisms, the proposed XHE outputs an indistinct counterfeit patient’s record that closely resembles the legitimate patients’ diabetes information in light of each off base speculation on legitimate password. Along these lines, the implemented XHE scheme solidifies the multifaceted nature of password speculating and cracking assaults, as assailant cannot distinguish which of his speculated passwords is correct password. Then, a security message will be produced and delivered to alert the network administrator and security responses team. Furthermore, the potential implementation of the proposed XHE scheme can be aimed at improving the password-based authentication system in other networks, including but not limited to Internet of Things (IoT) and mobile computing.
Multi-document Summarization Using Deep Learning

Due to the advancement of the Internet nowadays, a lot of people are mostly dependent on the Web to get the required information. As data is increasing exponentially, there is a high chance of duplication of data; it is difficult and tedious for the manual reading of all the documents as well as the rejection of the duplicates and extraction of useful information. One of the solutions to this issue is “Text Summarization,” through which a huge volume of data can be read quickly; but it is very hard to summarize documents manually, thus necessitating the use of an automatic tool to perform this task. Abstractive Text Summarization is one such automated technique of producing a short and accurate summary of a document while preserving essential information and comprehensive meaning. In this paper, Abstractive Text Summarization using Deep Learning with Attention Mechanism has been proposed. The designed framework removes duplicate data and generates new sentences by rephrasing them or adding words originally absent in the source text. Experimental results on the dataset, Amazon Fine Food Review, are evaluated by utilizing performance metrics such as Rouge scores.
An Optimization Modeling Method for Adaptive Systems Based on Goal and Feature Models

Adaptive Socio-Cyber-Physical Systems (SCPSs) need a comprehensive requirements modeling approach to embed social concerns (goals) in their development activities. Since these kinds of systems often involve complicated and dynamic interactions with their environment, they must react to environmental changes using different potential solutions that satisfy social concerns as well as system objectives and qualities. This paper presents an optimization modeling method that monitors an SCPS's environment and qualities to provide design-time and runtime solutions that satisfy required goals of the system and its stakeholders, as well as imposed correctness constraints specified in a feature model. We combine arithmetic functions generated automatically from goal and feature models as an objective function input to an optimization tool (IBM CPLEX) in order to compute, at design time, optimal solutions for common situations. Runtime optimization can also be used for unforeseen situations. An illustrative example is used to assess the feasibility of the method. The results show that optimizing the mathematical functions of goal/feature models together is beneficial in exploring SCPS requirements and detecting weaknesses in common adaptation situations.
Moderate Detection and Removal of Omnipresent Modules in Software Clustering

Software clustering is important in program comprehension, and it is applied to architecture recovery and software visualization to analyze the actual software system structure. The detection of omnipresent modules, programs used by many parts of the system such as utility classes, is a well-known issue in software clustering. Omnipresent modules obscure the system structure. The detection and removal of omnipresent modules have been studied for years. However, traditional approaches based on the number of the caller classes remove too many classes, including important domain model classes. In this study, we introduce a method to detect omnipresent modules by calculating a metric called usage equality; it represents the equality in the usage of a class by its caller classes based on the dedication score, which is a weight calculated for inter-class dependency defined in the literature. The developed method was evaluated and compared with traditional methods using real examples. The results show that our method identified omnipresent modules more moderately than the traditional methods to avoid the essential domain classes.
The importance of professional regulation of geoscientists and their role in a fast-changing world

Abstract Population growth, political and social factors, technological advances, and climate change are driving forces that are shaping a world where access to natural resources is critical. Geoscientists’ contribution to the comprehensive management of natural resources is vital and this calls for the active involvement of skilled, experienced professionals. The authors describe how, in this framework, systems for professional regulation and certification of geoscientists facilitate mobility, interchanging of ideas and knowledge, best ethical practice and protection of the public and environment.
Classification of Human Posture with RGBD Camera: Is Deep Learning Necessary?

We describe an approach to human posture classification using RGBD camera (Kinect V2 sensor) data. We compared deep learning methods for human posture classification versus classical data classification methods. We conducted a user study where participants assumed various postures, including whole body, upper and lower body, as well as body transition motion. Several classical data classification methods, such as support vector machine, random forest, neural network, and Adaboost, were used for posture classification. Results show that the posture classification accuracy for the classical data classification methods is between 75% an 99%. The accuracy of the classical data classification methods is comparable to the accuracy of the long-short-term-memory (LSTM) deep learning method which is between 86% and 99%. Our findings suggest that the use of the classical data classification methods on the RGBD camera data is likely sufficient for posture classification, at least for certain task scenarios without incurring the overhead of deep learning.
Designing of YouTube Access Interface for Institutional Digital Repository

YouTube is a video-sharing website. It allows users to upload, view, rate, share, add to favorites, report, comment on videos, and subscribe to other users and offers a wide range of user-oriented and education-related videos. Digital resource is growing and digital libraries will include digital materials that subsist outside the physical and administrative bounds of any one digital library. Even though digital libraries include all traditional processes, these have to be revised and enhanced to accommodate the differences between digital media and traditional media. This paper describes a system that can describe and manage digital resources in libraries.
Fine-Grained Channel Pruning for Deep Residual Neural Networks

Pruning residual neural networks is a challenging task due to the constraints induced by cross layer connections. Many existing approaches assign channels connected by skip-connections to the same group and prune them simultaneously, limiting the pruning ratio on those troublesome filters. Instead, we propose a Fine-grained Channel Pruning (FCP) method that allows any channels to be pruned independently. To avoid the misalignment problem between convolution and skip connection, we always keep the residual addition operations alive. Thus we can obtain a novel efficient residual architecture by removing any unimportant channels without the alignment constraint. Besides classification, We further apply FCP on residual models for image super-resolution, which is a low-level vision task. Extensive experimental results show that FCP can achieve better performance than other state-of-the-art methods in terms of parameter and computation cost. Notably, on CIFAR-10, FCP reduces more than 78% FLOPs on ResNet-56 with no accuracy drop. Moreover, it achieves more than 48% FLOPs reduction on MSRResNet with negligible performance degradation.
EasierPath: An Open-Source Tool for Human-in-the-Loop Deep Learning of Renal Pathology

Considerable morphological phenotyping studies in nephrology have emerged in the past few years, aiming to discover hidden regularities between clinical and imaging phenotypes. Such studies have been largely enabled by deep learning based image analysis to extract sparsely located targeting objects (e.g., glomeruli) on high-resolution whole slide images (WSI). However, such methods need to be trained using labor-intensive high-quality annotations, ideally labeled by pathologists. Inspired by the recent “human-in-the-loop” strategy, we developed EasierPath, an open-source tool to integrate human physicians and deep learning algorithms for efficient large-scale pathological image quantification as a loop. Using EasierPath, physicians are able to (1) optimize the recall and precision of deep learning object detection outcomes adaptively, (2) seamlessly support deep learning outcomes refining using either our EasierPath or prevalent ImageScope software without changing physician’s user habit, and (3) manage and phenotype each object with user-defined classes. As a user case of EasierPath, we present the procedure of curating large-scale glomeruli in an efficient human-in-the-loop fashion (with two loops). From the experiments, the EasierPath saved 57% of the annotation efforts to curate 8,833 glomeruli during the second loop. Meanwhile, the average precision of glomerular detection was leveraged from 0.504 to 0.620. The EasierPath software has been released as open-source to enable the large-scale glomerular prototyping. The code can be found in https://github.com/yuankaihuo/EasierPath.
Collaborative Application for Rapid Design of Paintings in Vector Format

Online cooperative or collaborative work have become common nowadays, because this helps to do any job without require meet together physically in one place. On the other hand, due to out current reality, inherent to many health conditions, traffic difficulties, and social insecurity; virtual or distance education is increasingly being promoted. In that sense, the main goal of this research work is develop a web based collaborative tool for rapid designing paints. This could be useful in many different areas, like as replacing the classical whiteboard, or express ideas to a workgroup, or even to design any realistic picture. This application allows to work together with a group of around ten connected users to collaborate virtually designing drawings in real time. The interaction experience depends on the internet bandwidth of all the users. For the implementation, many open source popular frameworks and programming utilities have been used. The presented results demonstrate the high scalability and versatility of our system being capable of managing hundreds of objects in real time.
IoT and Computer Vision-Based Electronic Voting System

Focusing on complete transparency with maximum security, a novel type of advanced electronic voting system is introduced in this paper. Identification and verification of voters are assured by microchip embedded national identity (NID) card and biometric fingerprint technology, which is unique for every single voter. Also, with the help of live image processing technology, this system becomes more secure and effective. As voting is an individual opinion among multiple, so the second influence is unacceptable. So while voting if multiple faces detected by the camera module of the voting machine, automatically the vote will not be counted. Viola–Jones algorithm for face detection and local binary pattern histogram (LBPH) algorithm for face recognition has begun the image preparing innovation increasingly exact and faster. Four connected machines work together to accumulate each successful vote in this system. To reduce corrupted situation and to recapture the faith of mass people on the election, this inexpensive and effective system can play a vital role.
A Systematic Literature Review of Smart Contract Applications

Contracting is an essential factor in doing business. A smart contract, which is a new phrase in recent years and related to the blockchain has been applied in many kind of industry’s applications. These include financial organizations and insurance corporations who currently using smart contracts. A smart contract focuses on many specific processes such as solving financial fraud, electronic voting, combined blockchain and Internet of Things (IoT). Due to the high requirements of data quality, smart contract becomes increasingly important. However, since this technique has emerged only within recent years, how smart contract works is still being figured out. Therefore, this qualitative research paper provides a systematic literature review of what a smart contract is, based on data from relevant materials and case studies and illustrates different smart contracts applications.
Auxiliary Decision Technology and Application of Power Grid Fault Disposal Based on Knowledge Understanding of Fault Preplan

Combined with the characteristics of power grid fault disposal, auxiliary decision-making technology and implementation architecture of power grid fault disposal that based on knowledge understanding of fault preplan are proposed. Natural language processing technology is used to structurally extract key information of the fault disposal preplan. On this basis, a fault disposal knowledge graph is established. The method can realize the intelligent decision-making and disposal of faults by online retrieving, matching knowledge graph information of real-time faults, automatically extracting key information of fault disposal, and automatic analysis and control navigation of disposal task. The application example shows that the proposed method can realize information extraction, system mapping, knowledge retrieval and construction of fault disposal platform based on fault disposal preplan. This method can effectively reduce the fault disposal pressure and improve fault disposal efficiency and intelligent level.
Asymptotic enumeration of lonesum matrices

We provide bivariate asymptotics for the poly-Bernoulli numbers, a combinatorial array that enumerates lonesum matrices, using the methods of Analytic Combinatorics in Several Variables (ACSV). For the diagonal asymptotic (i.e., for the special case of square lonesum matrices) we present an alternative proof based on Parseval's identity. In addition, we provide an application in Algebraic Statistics on the asymptotic ML-degree of the bivariate multinomial missing data problem, and strengthen an existing result on asymptotic enumeration of permutations having a specified excedance set.
Global and Local Attention Embedding Network for Few-Shot Fine-Grained Image Classification

Few-shot fine-grained image recognition aims to classify fine-grained images with limited training samples. Nowadays exist in a majority of few-shot fine-grained image classification methods the following problems: local information loss and ignoring pivotal parts. To solve the above problems, this paper proposes a new embedding module, called GLAE. The author designs a hierarchical structure and combines the first-order and second-order information to reduce the local information loss. Besides, this paper proposes an attention mechanism to obtain the vital parts by the attention mask. On the StanfordCars dataset, GLAE achieves an accuracy of 91.18% which is the best result in the field of few-shot fine-grained image recognition.
Debiasing Learning to Rank Models with Generative Adversarial Networks

Unbiased learning to rank aims to generate optimal orders for candidates utilizing noisy click-through data. To deal with such problem, most models treat the biased click labels as combined supervision of relevance and propensity, which pay little attention to the uncertainty of implicit user feedback. We propose a semi-supervised framework to address this issue, namely ULTRGAN (Unbiased Learning To Rank with Generative Adversarial Networks). The unified framework regards the task as semi-supervised learning with missing labels, and employs adversarial training to debias click-through datasets. In ULTRGAN, the generator samples potential negative examples combined with true positive examples for the discriminator. Meanwhile, the discriminator challenges the generator for better performances. We further incorporate pairwise debiasing to generate unbiased labels diffusing from the discriminator to the generator. Experimental results over both synthetic and real-world datasets show the effectiveness and robustness of ULTRGAN.
The Effect of Mobile-Learning Models on Students’ Learning Outcomes of Research Methodology Courses at the Cosmetology and Beauty Department

This study identified learning outcomes for students using Android-based mobile learning models as compared to student learning outcomes using conventional learning models. It determined how much influence the Android-based mobile learning model has on student learning outcomes at the Makeup and Cosmetology Department Research Methodology course. This study used a quasi-experimental design method with a non-equivalent control group design. The population was makeup and beauty students who took research methodology courses in the July-December 2019 semester, totaling 94 students consisting of 3 classes. The sampling technique used in this study was cluster sampling from 3 classes; two classes were taken randomly. From the two classes, there was an experimental group for 30 students and as a control group for 31 students. The experimental class applied the Android-based mobile learning model of learning media, and the control class applied the conventional learning model. Research data collection used tests and documentation. The data collected were pretest and posttest data. The techniques of data analysis used quantitative descriptive. The hypothesis testing method used was the t-test analysis. The results showed that two crucial points. First, the calculation of scores on student learning outcomes has an increase in classes that applied an Android-based learning model. Second, the t-test results showed this study succeeded in applying the Android-based learning model to affect learning outcomes and effectively improve student learning outcomes in research methodology courses in the Department of Make-Up and Cosmetology.