Reviewerly/exHarmony huggingface dataset:
Sentiment-Enhanced Content-Based System for Online Recommendations and Rating Prediction

The scarcity of dependable product descriptions and limited emotion unmasking capabilities of user-ratings compromise the accuracy of content-based filtering (CBF) systems. This work puts forward a sentiment-enhanced content-based recommender system (SEC-Rec). The model has four modules, namely key feature extraction module, feature sentiment analysis module, recommendation module, and rating prediction module. Key feature extraction module uses hybrid of RAKE and TextRank to uncover key product features. The authors propose a hybridized model HSVADER (Hybrid SVM and VADER) for feature sentiment evaluation. The recommendation module combines sentiment and similarity for robust product ranking strategy. The practical benefits of SEC-Rec are demonstrated using Amazon Camera dataset, and the results are compared to the state of the art. The rating prediction module uses key feature sentiment score to estimate the overall user-rating resolving the multi-criteria decision-making issue. The RMSE value obtained ascertains the effectiveness of the approach compared to recent models.
Spatial Distribution Prediction of Oil and Gas Based on Bayesian Network with Case Study

Effectively predicting the spatial distribution of oil and gas contributes to delineating promising target areas for further exploration. Determining the location of hydrocarbon is a complex and uncertain decision problem. This paper proposes a method for predicting the spatial distribution of oil and gas resource based on Bayesian network. In this method, qualitative dependency relationship between the hydrocarbon occurrence and key geologic factors is obtained using Bayesian network structure learning by integrating the available geoscience information and the current exploration results and then using Bayesian network topology structure to predict the probability of hydrocarbon occurrence in the undiscovered area; finally, the probability map of hydrocarbon-bearing is formed by interpolation method. The proposed method and workflow are further illustrated using an example from the Carboniferous Huanglong Formation (C 2 hl) in the eastern part of the Sichuan Basin in China. The prediction results show that the coincidence rate between the results of 248 known exploration wells and the predicted results reaches 89.5%, and it has been found that the gas fields are basically located in the high value area of the hydrocarbon-bearing probability map. The application results show that the Bayesian network method can effectively predict the spatial distribution of oil and gas resources, thereby reducing exploration risks, optimizing exploration targets, and improving exploration benefits.
Theory of Constructive Semigroups with Apartness – Foundations, Development and Practice

This paper has several purposes. We present through a critical review the results from already published papers on the constructive semigroup theory, and contribute to its further development by giving solutions to open problems. We also draw attention to its possible applications in other (constructive) mathematics disciplines, in computer science, social sciences, economics, etc. Another important goal of this paper is to provide a clear, understandable picture of constructive semigroups with apartness in Bishop’s style both to (classical) algebraists and the ones who apply algebraic knowledge.
Rooting Your Android Device

You may have heard about “rooting” your device. You’ve already probably done it and are enjoying its benefits. In this chapter, we will take a look at what it means to root your device, what benefits you can get out of it, and how to actually root a device. We also take a look at some of the things we normally do on an emulator, but this time on the device proper.
DISTANCE EDUCATION IN UKRAINE: REALITIES OF TODAY

The first attempts to introduce distance education technologies in the education sector were made in the 60s of the twentieth century in Western Europe and the United States. The rapid development of information technology and computerization of the population have led to an increase in demand for new, specific technologies in education, which allows applicants to study at considerable distances from educational institutions. It is the criteria to which the distance learning conforms, and which has been and remains the subject of active scientific discussion both abroad and in our country. Such a system of acquiring knowledge can significantly reduce the cost of education, as distance education is cheaper than traditional forms of higher education, and also reduces transport costs for travel to the place of study and back. Moreover, distance education can be obtained in any rhythm convenient for the applicant, distributing the workload at will. With the correct organization of classes and control of knowledge high efficiency of this method of professional education can be achieved through the use of new software and hardware platforms and electronic courses. On the other hand, critics of distance education say that its quality cannot be compared even with extramural education, and such educational technologies allow only to obtain a diploma with minimal investment of effort and resources without acquiring factual knowledge. The low quality of distance education, according to some experts, can be explained by the lack of quality methods of distance teaching, the average quality of e-courses, as well as the e-learning environments themselves.
Deep Learning Video Action Recognition Method Based on Key Frame Algorithm

In order to solve the problem of action recognition in short video and capture the key information of video, this paper first proposes a KGAF-means method for key frame extraction. The KGAF-means method is based on the clustering principle and combines the K-means algorithm with the artificial fish swarm algorithm to realize the key frame sequence extraction. Based on the extracted key frame sequence, the RGB image and the optical flow image are separately extracted by the improved dual-stream variable convolution network. Then, using the cascading method, the image feature vector and the optical flow feature vector are fused to obtain the fused feature vector for action recognition. The selected data set is the Charades data set. The experimental results show that the mAP value of the method is 22.9 on the public dataset Charades. And the results show that the proposed method has better robustness than other network models and improves the short video action recognition effect.
Multi-party Semi-quantum Secret Sharing Scheme Based on Bell States

Based on the semi-quantum theory, we propose an easy-to-implement multi-party semi-quantum secret sharing scheme using Bell states, which reduces the construction cost of quantum secret sharing network and completes the multi-party secret sharing. The quantum Alice uses Bell states as the initial resource and has the abilities of performing Bell basis measurement and storing qubits, the participant Bobi has limited quantum capabilities. The secret sharing network based on semi-quantum theory completes the secret sharing between the secret distributor and multiple participants. Security analysis shows that the scheme can resist internal attacks and external entanglement attacks and is safe and feasible for current technologies.
Digital laser speckle technique for generating deflection, slope, and curvature contours of bent plates

A digital method is proposed whereby spatial speckles created when an optically rough surface is illuminated by a diffuse coherent laser beam are used to generate slope contour fringes. This is done by photographing the speckles contained in a parallel plane in front of the plate before and after deformation. The resulting photographs are then fast Fourier transformed twice and numerically compared with the digital speckle correlation software called computer-aided speckle interferometry to produce slope contours of the plate. The slope contour data are then stored in a computational array. The curvature contours are generated by numerical differentiation of the slope data; similarly, the deflection contours are generated by numerical integration of the slope data. The curvature data can then be further used in conjunction with plate theory to obtain the bending and twisting moments the plates experience as well as the stress distribution in the surface of the plate. The method is simple and straight forward. A comparison with several other techniques is presented.
Futuristic media: A temporal reflection and eternal platform capitalism

Digital media is not a remedy for excessive consumption and environmental damage; the seemingly reduced impacts on the environment by the pandemic are on the ground of accelerating other modes of consumption, resource exploitation and capitalist investments. In this article, I would take China for a case to reveal the ephemeral ecology and the perpetual consumption on digital platforms in a singular future.
Challenges of Novice Teachers and Strategies to Cope at Secondary Level

The main purpose of this research study was to find out challenges of novice teachers affecting their performance and suggest measures to control their attrition. Population of the study comprised all novice teachers of public sector girls secondary schools of Sahiwal, Okara and Pakpattan districts. Sample of the study consisted on 206 novice teachers randomly selected from 45 schools out of population. A self-developed questionnaire consisted on Five - point Likert scale was used to collect data, which was organized, tabulated, analyzed and interpreted by using mean score, frequency score, percentage, standard deviation and t-value. It was found that lack of conducive school environment, non-supportive head teachers attitude, lack of pedagogical skills and students disruptive behavior create hindrances for novice teachers to perform their duties in better ways. It was recommended that provision of head teachers supportive attitude and conducive school environment prove helpful in novice teachers job retention.
Get-Rich Quick scheme: Malaysian current legal development

Purpose In Malaysia, Get-Rich-Quick scheme (GRQS) is one of the financial fraud activities prohibited under Malaysian law. The common facet of such schemes involves plans that promise unrealistic rates of returns, and this new scheme continues to proliferate every year as the list of illegal investment companies and websites are growing. Indeed, GRQS will remain proliferating as long as there are people who are easily lured by the promise that wealth can be generated with little skill, effort or time. This paper aims to explain the phenomenon of GRQS in light of the existing laws in Malaysia. This paper also highlights the current development of Australian law pertaining to GRQS for comparative purpose. Design/methodology/approach This paper mainly relies on statutes as its primary sources of information. As such, this paper analyses the scope and provisions of the relevant laws that regulate GRQS and compare the existing GRQS provisions that are equivalent with Australian law. Findings Malaysia has comprehensive laws to combat GRQS activities. However, these laws are far from perfection, and only with immediate amendments, GRQS problems can be resolved more effectively. One of the weaknesses of current Malaysian laws to tackle GRQS is the lack of more stringent punishment against the operators of GRQS as well as the participants of the scheme. A comparison with equivalent GRQS law in Australia demonstrates that Australian laws provide a wide range of punishment to the operators and prohibits participation in GRQS. More importantly, Australia regards the offense as a strict liability offense where the mens rea or guilty mind of the perpetrators is exempted. Indeed, numerous proceedings have been instituted in the Australian Court against the operators and participants of GRQS. Originality/value This paper analyses the scope of relevant laws in Malaysia to combat GRQS and examines the strengths and weaknesses of these laws. This paper also compares Malaysian law with equivalent GRQS-related laws available in Australia. This paper further suggests that Malaysia should regulate sterner punishment for operators and participants of the scheme and that the offense is categorized under a strict liability offense where the mens rea or guilty mind of the offender is exempted.
QR Code Detection with Faster-RCNN Based on FPN

Nowadays, the QR code is often used in many popular fields, such as payment and social networking. Therefore, it is particularly important to quickly and accurately detect the position of QR code in real complex scenes. Traditional QR code detection methods mainly use hand-engineered features for detection. However, the QR code photos we take may be blurred due to pixel, distance, and other problems, and may even produce some rotations and deformations because of the complex scenes. Under such circumstances, the traditional QR code detection methods may not be so applicable. Faster-RCNN was originally used for multiple object detection, but we adjusted it slightly and applied it to the detection of QR code. At the same time, we made a small dataset under complex scenes for training Faster-RCNN networks. However, in complex scenes, the size of the QR code vary greatly due to the distance of shooting, so we add an FPN module to the Faster-RCNN to improve the detection performance for small and multi-scale QR code. Experimental results show that our method has achieved good performances in the detection of QR code in complex scenes.
DeepPhish: Automated Phishing Detection Using Recurrent Neural Network

Phishing attacks are one among the foremost common and least defended safety threats these days. We have got an inclination to gift associate technique that uses tongue method techniques to analyze text and see tangential statements that are indicative of phishing assaults. Our approach is novel compared to the previous paintings as a result of it makes a specialty of the seasoning language matter content contained among the assault, taking part in linguistics analysis of the matter content to come back across malicious reason. a novel strategy dependent on RNN. This implies it as a versatile and quick acting proactive discovery framework that does not require full substance investigation.
Benchmarking for Metaheuristic Black-Box Optimization: Perspectives and Open Challenges

Research on new optimization algorithms is often funded based on the motivation that such algorithms might improve the capabilities to deal with real-world and industrially relevant optimization challenges. Besides a huge variety of different evolutionary and metaheuristic optimization algorithms, also a large number of test problems and benchmark suites have been developed and used for comparative assessments of algorithms, in the context of global, continuous, and black-box optimization. For many of the commonly used synthetic benchmark problems or artificial fitness landscapes, there are however, no methods available, to relate the resulting algorithm performance assessments to technologically relevant real-world optimization problems, or vice versa. Also, from a theoretical perspective, many of the commonly used benchmark problems and approaches have little to no generalization value. Based on a mini-review of publications with critical comments, advice, and new approaches, this communication aims to give a constructive perspective on several open challenges and prospective research directions related to systematic and generalizable benchmarking for black-box optimization.
Component based comparative analysis of each module in image captioning

Image captioning is a task to generate a new caption using the training data of the image and caption. Since existing deep learning is a black-box model, it is crucial to analyze the influence on each module for understanding the model. In this paper, we analyze the impact of the five modules and do a comparative analysis according to three losses and two optimizations using two datasets. From extensive experiments, the best component of each module has been identified as an improved method.
Improved PSO for Task Scheduling in Cloud Computing

An improved Particle Swarm Optimization (PSO) is used for performing task scheduling in cloud computing with the aim of distributing uniform load on each Virtual Machine (VM) in a datacenter. It is achieved using an objective function which tries to enhance the candidate solutions iteratively and thus finds an optimal mapping of task set to VM set. Experimental results have shown that the improved PSO performs better than the original PSO by maintaining the consistency in scheduling.
Detection of rare earth element anomalies in Esfordi phosphate deposit of Central Iran, using geostatistical-fractal simulation

This study is aimed to determine geochemical anomalies of rare earth elements (REEs) and provides a concentration distribution map for the Esfordi phosphate deposit (EPD), Bafq metallogenic province (BMP), Central Iran. With an average grade of 5519 ppm for REEs, the EPD is one of the prominent deposits of the region. In this research, sequential Gaussian simulation (SGS) and concentration-area (C-A) fractal modeling are used to determine concentration anomalies and provide a ∑REEs concentration map based on surface data. The log-ratio matrix is used to investigate the mineralization processes, determining the relationships between the anomalies and the rock units, and validating the results of SGS fractal modeling. The results are indicating that the main anomaly has strong correlation with the apatite-iron, and in particular, with the apatite mineralization, which can be considered as an exploration guide. The results also confirm the efficiency of simultaneous application of fractal modeling and SGS simulation.
Multiview feature fusion optimization method for image retrieval based on matrix correlation

Multiview learning is an important method and widely used for feature fusion in the fields of image process or big data analysis. Determining how to integrate compatible and complementary information from multiple views is a crucial and challenging task. We present a multiview feature fusion optimization method for image retrieval based on matrix correlation. This method first extracts four view features (Gist, histogram of color, pyramid histogram of oriented gradients, and multitrend structure descriptor) from the image. Then these features are, respectively, converted to different graph Laplacian matrices through local embedding. Third, a multiview feature alternating optimization process is constructed using matrix correlation statistics that adaptively combines the different view feature maps to a unified, low-dimensional embedding. Finally, the fusion feature is used for image retrieval experiments. Various experimental results show that the proposed algorithm is an effective method.
MM-Stega: Multi-modal Steganography Based on Text-Image Matching

This paper proposes a multi-modal steganography (MM-Stega) scheme based on text-image matching. Currently, most steganographic methods embed secret information into a cover by modifying its content. However, the distortion of the cover caused by the modification may be detected by steganalysis methods. Other steganographic methods hide secret information by generating covers, but the imperceptibility of this kind of steganographic methods is limited by the quality of the generated covers. Our method is different from these steganographic methods in two aspects. First, our method utilizes multi-modal covers, i.e., texts and images, while most steganographic methods use single-modal covers. Second, our method embeds secret information in the relevance between the texts and images without modifying or generating a cover, thus our method has strong resistance to steganalysis. Our method is based on a text-image matching model which can measure the similarity between a text and an image. The text-image matching model utilizes a visual semantic embedding (VSE) model, which can project texts and images into a common subspace. After choosing a text from the text database randomly, several images relevant to the text are selected with the text-image matching model on the basis of the secret information that needs to be hidden. Experimental results and analysis prove that our method has adjustable hiding capacity and desirable security.
Sync or Fork: Node-Level Synchronization Analysis of Blockchain

As the cornerstone of blockchain, block synchronization plays a vital role in maintaining the security. Without full blockchain synchronization, unexpected forks will emerge and thus providing a breeding ground for various malicious attacks. The state-of-the-art works mainly study the relationship between the propagation time and blockchain security at the systematic level, neglecting the fine-grained impact of peering nodes in blockchain networks. To conduct a node-level synchronization analysis, we take advantage of the large deviation theory and game theory to study the pull-based propagation from a microscopic perspective. We examine the blockchain synchronization in a bidirectional manner via investigating the impact of full nodes as responders and that of partial nodes as requesters. Based on that, we further reveal the most efficient path to speed up synchronization from full nodes and design the best synchronization request scheme based on the concept of correlated equilibrium for partial nodes. Extensive experimental results demonstrate the effectiveness of our analysis.
A general formulation based on algebraic spinors for the quantum computation

In this work, we explore the structure of Clifford algebras and the representations of the algebraic spinors in quantum information theory. Initially, we present a general formulation through elements of minimal left ideals in tensor products of Clifford algebras. Posteriorly, we perform some applications in quantum computation: qubits, entangled states, quantum gates, representations of the braid group, quantum teleportation, Majorana operators and supersymmetry. Finally, we discuss advantages compared to standard Hilbert space formulation.
ONTMAT1: ontology matching using a reasoner and property restriction

This paper presents an ontology matching approach called ONTMAT1, which aims to automatically provide correspondence relations among both fundamentally heterogeneous ontologies through diverse similarity calculations between their entities. First, the problem of searching for the most pertinent, coherent and significant alignments is addressed. Next, a strategy that ensures flexibility and scalability of the application through a combination of different matchers is suggested. Moreover, a reasoner and WordNet dictionary have been used to resolve usual matching problems encountered with synonyms among other things. The approach has been simulated and tested online under the Ontology Alignment Evaluation Initiative (OAEI) 2019 conference task. The obtained results are detailed here with remarks about the findings.
Bio-Inspired Metaheuristics

In real life, problems becoming more complicated, among them NP-Hard problems. To resolve them, two families of methods exist, exact and approximate methods. When exact methods provide the optimal solution in an unacceptable amount of time, the approximate ones provide good solutions in a reasonable amount of time. Approximate methods are two kinds, heuristics and metaheuristics. The first ones are problem specific, while metaheuristics are independent from problems. A broad number of metaheuristics are inspired from nature, specially from biology. These bio-inspired metaheuristics are easy to implement and provide interesting results. This paper aims to provide a comprehensive survey of bio-inspired metaheuristics, their classification, principals, algorithms, their application domains, and a comparison between them.