Reviewerly/exHarmony huggingface dataset:
Detecting incentivized review groups with co-review graph

Online reviews play a crucial role in the ecosystem of nowadays business (especially e-commerce platforms), and have become the primary source of consumer opinions. To manipulate consumers’ opinions, some sellers of e-commerce platforms outsource opinion spamming with incentives (e.g., free products) in exchange for incentivized reviews. As incentives, by nature, are likely to drive more biased reviews or even fake reviews. Despite e-commerce platforms such as Amazon have taken initiatives to squash the incentivized review practice, sellers turn to various social networking platforms (e.g., Facebook) to outsource the incentivized reviews. The aggregation of sellers who request incentivized reviews and reviewers who seek incentives forms incentivized review groups. In this paper, we focus on the incentivized review groups in e-commerce platforms. We perform the data collections from various social networking platforms, including Facebook, WeChat, and Douban. A measurement study of incentivized review groups is conducted with regards to group members, group activities, and products. To identify the incentivized review groups, we propose a new detection approach based on co-review graphs. Specifically, we employ the community detection method to find the suspicious communities from co-review graphs. We also build a “gold standard” dataset from the data we collected, which contains the information of reviewers who belong to incentivized review groups. We utilize the “gold standard” dataset to evaluate the effectiveness of our detection approach.
Tripartite quantum-memory-assisted entropic uncertainty relations for multiple measurements

Quantum uncertainty relations are typically analyzed for a pair of incompatible observables, however, the concept per se naturally extends to situations of more than two observables. In this work, we obtain tripartite quantum-memory-assisted entropic uncertainty relations for multiple measurements and show that the lower bounds of these relations have three terms that depend on the complementarity of the observables, the conditional von-Neumann entropies, the Holevo quantities, and the mutual information. The saturation of these inequalities is analyzed.
Motion blur invariant for estimating motion parameters of medical ultrasound images

High-quality medical ultrasound imaging is definitely concerning motion blur, while medical image analysis requires motionless and accurate data acquired by sonographers. The main idea of this paper is to establish some motion blur invariant in both frequency and moment domain to estimate the motion parameters of ultrasound images. We propose a discrete model of point spread function of motion blur convolution based on the Dirac delta function to simplify the analysis of motion invariant in frequency and moment domain. This model paves the way for estimating the motion angle and length in terms of the proposed invariant features. In this research, the performance of the proposed schemes is compared with other state-of-the-art existing methods of image deblurring. The experimental study performs using fetal phantom images and clinical fetal ultrasound images as well as breast scans. Moreover, to validate the accuracy of the proposed experimental framework, we apply two image quality assessment methods as no-reference and full-reference to show the robustness of the proposed algorithms compared to the well-known approaches.
Blended Learning - The Efficiency of Video Resources and YouTube in the Modern Dental Education

The media-enhanced teaching techniques became popular in the last years because they stimulate the students' engagement and motivation. The educational movies uploaded on YouTube became nowadays a highly used resource for learning in the academic environment. We conducted an opinion survey among the students at the Faculty of Dental Medicine, “Grigore T. Popa” University of Medicine and Pharmacy, Romania, about their agreement and interest in using educational movies as didactic tools, compared with the traditional teaching methods, and about the features they prefer in such materials. The study group included 170 students with average age 20.93 ± 2.751. Most students (91.2%) use didactic movies in their practical training; 77.6% think that the didactic movies from YouTube contain correct scientific information. 74.6% of the students like to learn using didactic movies, 39.7% of them appreciate the oral speech accompanied by PowerPoint Presentations and 46.6% like the oral speech accompanied by written explanations, with no significant differences between genders and age groups. The most popular features of the educational movies are: human narrator (94.7%), who speaks in Romanian (82.4%). The narrator’s speech must be accompanied by animated schemes with explanations (90.6%) and drawings (93.5%). The movie must have a clear structure, evolving from basic to complex (85.9%). The educational movies uploaded on YouTube are an adequate source of information for the dental students; the professionals in universities must survey better the scientific quality of such materials.
AN EFFICIENT KEY EXCHANGE SCHEME USING SANTILLI’S ISOFIELDS SECOND-KIND FOR SECURE COMMUNICATION

Adv. Math. Sci. J. Vol. 10, No. 2 Open Access AN EFFICIENT KEY EXCHANGE SCHEME USING SANTILLI’S ISOFIELDS SECOND-KIND FOR SECURE COMMUNICATION Mamta S. Dani, A.J. Meshram, C. Meshram, and N. M. Wazalwar DOI: https://doi.org/10.37418/amsj.10.2.39 Full Text Abstract Abstract We intend to bring out a unique method for constructing key exchange scheme (KES) using … page AMSJ10-N02-39 Read More »
An Intelligent Analytics Approach to Minimize Complexity in Ambiguous Software Requirements

An inconsistent and ambiguous Software Requirement Specification (SRS) document results in an erroneous/failed software project. Hence, it is a serious challenge to handle and process complex and ambiguous requirements. Most of the literature work focuses on detection and resolution of ambiguity in software requirements. Also, there is no standardized way to write unambiguous and consistent requirements. The goal of this research was to generate an ambiguity-less SRS document. This paper presents a new approach to write ambiguity-less requirements. Furthermore, we design a framework for Natural Language (NL) to Controlled Natural Language (CNL) (such as Semantic Business Vocabulary and Rules (SBVR)) transition and develop a prototype. The prototype also generates Resource Description Framework (RDF) representation. The SBVR has a shared meaning concept that minimizes ambiguity, and RDF representation is supported by query language such as SPARQL Protocol and RDF Query Language (SPARQL). The proposed approach can help software engineers to translate NL requirements into a format that is understandable by all stakeholders and also is machine processable. The results of our prototype are encouraging, exhibiting the efficient performance of our developed prototype in terms of usability and correctness.
Nimble: Efficiently Compiling Dynamic Neural Networks for Model Inference

Modern deep neural networks increasingly make use of features such as dynamic control flow, data structures and dynamic tensor shapes. Existing deep learning systems focus on optimizing and executing static neural networks which assume a pre-determined model architecture and input data shapes--assumptions which are violated by dynamic neural networks. Therefore, executing dynamic models with deep learning systems is currently both inflexible and sub-optimal, if not impossible. Optimizing dynamic neural networks is more challenging than static neural networks; optimizations must consider all possible execution paths and tensor shapes. This paper proposes Nimble, a high-performance and flexible system to optimize, compile, and execute dynamic neural networks on multiple platforms. Nimble handles model dynamism by introducing a dynamic type system, a set of dynamism-oriented optimizations, and a light-weight virtual machine runtime. Our evaluation demonstrates that Nimble outperforms state-of-the-art deep learning frameworks and runtime systems for dynamic neural networks by up to 20x on hardware platforms including Intel CPUs, ARM CPUs, and Nvidia GPUs.
The weight distribution of irreducible cyclic codes associated with decomposable generalized Paley graphs

We use known characterizations of generalized Paley graphs which are Cartesian decomposable to explicitly compute the spectra of the corresponding associated irreducible cyclic codes.As applications, we give reduction formulas for the number of rational points in Artin-Schreier curves defined over extension fields and to the computation of Gaussian periods.
ANALYSIS ON CLARITY AND CORRECTNESS OF GOOGLE TRANSLATE IN TRANSLATING AN INDONESIAN ARTICLE INTO ENGLISH

The objective of this study is to analyze the aspects of clarity and correctness in Google Translate’s ability in translating an Indonesian article from English into Indonesian. This research refers to qualitative research. Data used in this research is a published Indonesian article which is translated into English by using Google Translate. Based on the analysis, the researcher concludes that Google Translate is a machine translator, but there is always going to be potentially less clarity and correctness at the end of the translation product such as in Indonesian articles into English. Because English grammar is a complicated thing to be learned, people perhaps cannot expect more that machine translator understands every aspect of the way human beings communicate with each other. That is why the answer about the clarity and the correctness of Google Translate is that it still has a way to go before it can consistently, clearly, and correctly translate the language without errors. In the clarity aspect, there is still no clarity in English translation by Google Translate, even it translated the language word-for-word. In the correctness aspect, it refers to the mechanical rule in writing which is related to grammar, punctuation, and spelling. Some examples of non-correctness are related to grammar and punctuation errors. Machine translators have come a long way in a short amount of time, but some features still lack good translation such as in aspects of grammar and punctuation.
Effectiveness of Security Incident Event Management (SIEM) System for Cyber Security Situation Awareness

Cyber-attacks have always targeted information communication technology systems of various organizations.Intruders and hackers have within their reach, very powerful tools through which they capable to bypassthe existing network security so as to deliver a payload that might have a severe impact on the wholeorganization. Therefore, it has become essential for organizations to develop mechanisms through whichthey can detect a possible cyber threat and then respond accordingly. By establishing cybersecurity situationawareness, organizations will understand what is happening and then respond effectively. The presentstudy evaluated the effectiveness of the Security Incident Event Management (SIEM) system for CyberSecurity Situation Awareness. A Hierarchical Network Security Situation Assessment Model (referred toHNSSAM) which joins Security Incident Event Management (SIEM) system evidence theory fusion ruleswith classified quantitative risk assessment method was applied. Data processing was initially designed soas to collect security data from various sensors. Mechanisms for data verification were then adopted so as toestablish whether SIEM was effective in successfully detecting any form of cyber-attack. Results show thatSIEM tools may be applied by security analysts to gain visibility into the security threats attacking the ITsystems of an organization and then respond appropriately.
Color-coded computer-generated Moiré profilometry with real-time 3D measurement and synchronous monitoring video collection

A color-coded computer-generated Moiré profilometry (CGMP) with real-time 3D measurement and synchronous monitoring video collection is proposed. In this method, a sinusoidal grating and the direct current (DC) component of it are encoded in the blue and red channels of a color composite grating, respectively. This color composite grating is projected onto the measured object, and a corresponding color deformed pattern is sampled by an RGB camera. A monochrome sinusoidal deformed pattern and a flat image are demodulated from the color deformed pattern by color separation. The flat image reflects the DC component of the monochrome sinusoidal deformed pattern and the monitoring image in real time. The DC component of the monochrome sinusoidal deformed pattern is efficiently figured out by multiplying the flat image with an introduced coefficient, even though the DC spectrum and the first-order spectrum of the monochrome sinusoidal deformed pattern overlap. By establishing a color-coded CGMP, the 3D shape of the measured object is reconstructed successfully. Due to its single-shot feature, the 3D information and synchronizing monitoring video are sampled in real time. Some static objects and moving objects are measured to verify the feasibility, validity, and accuracy of the proposed method.
Seepage Time Soft Sensor Model of Nonwoven Fabric Based on the Extreme Learning Machine Integrating Monte Carlo

Nonwoven fiber materials are materials with multifunctional purposes, and are widely used to make masks for preventing the new Coronavirus Disease 2019. Because of the complexity and particularity of their structure, it becomes difficult to model the penetration and flow characteristics of liquid in nonwoven fiber materials. In this paper, a novel seepage time soft sensor model of nonwoven fabric, based on Monte Carlo (MC), integrating extreme learning machine (ELM) (MCELM) is proposed. The Monte Carlo method is used to expand data samples. Then, an ELM method is used to establish the prediction model of the dyeing time of the nonwoven fiber material overlaps with the porous medium, as well as the insertion degree and height of the different quantity of hides. Compared with the back propagation (BP) neural network and radial basis function (RBF) neural network, the results show that the prediction model based on the MCELM method has significant power in terms of accuracy and prediction speed, which is conducive to the precise and rapid manufacture of nonwoven fiber materials in practical applications between liquid seepage characteristics and structural characteristics of porous media. Furthermore, the relationship between the proposed models has certain value for predicting the behavior and use of nonwoven fiber materials with different structural characteristics and related research processes.
Improved Algorithms for Edge Colouring in the W-Streaming Model

In the W-streaming model, an algorithm is given O(n polylog n) space and must process a large graph of up to O(n2) edges. In this short note we give two algorithms for edge colouring under the W-streaming model. For edge colouring in W-streaming, a colour for every edge must be determined by the time all the edges are streamed. Our first algorithm uses Δ + o(Δ) colours in O(n log2 n) space when the edges arrive according to a uniformly random permutation. The second algorithm uses (1 + o(1))Δ2/s colours in Õ(ns) space when edges arrival adversarially.
Implementation of Convolutional Neural Network in the classification of red blood cells have affected of malaria

Malaria is a disease caused by plasmodium which attacks red blood cells. Diagnosis of malaria can be made by examining the patient's red blood cells using a microscope. Convolutional Neural Network (CNN) is a deep learning method that is growing rapidly. CNN is often used in image classification. The CNN process usually requires considerable resources. This is one of the weaknesses of CNN. In this study, the CNN architecture used in the classification of red blood cell images is LeNet-5 and DRNet. The data used is a segmented image of red blood cells and is secondary data. Before conducting the data training, data pre-processing and data augmentation from the dataset was carried out. The number of layers of the LeNet-5 and DRNet models were 4 and 7. The test accuracy of the LeNet-5 and DrNet models was 95% and 97.3%, respectively. From the test results, it was found that the LeNet-5 model was more suitable in terms of red blood cell classification. By using the LeNet-5 architecture, the resources used to perform classification can be reduced compared to previous studies where the accuracy obtained is also the same because the number of layers is less, which is only 4 layers
Improving Students’ Academic Achievement Using the ScEd-ALS Android-Based

Ubiquitous learning has recently gained increasing popularity in the education sector, which significantly increased the efficiency of individual and collaborative learning.The aim of this study is to develop an Android version of science education-adaptive learning system (ScEd-ALS) as a science learning medium to accommodate various learning styles.The method used in this research is a mixedmethod consisting of a qualitative method, which is a developmental method that provides illustrations concerning the process of creating the developed media, and a quantitative method, which utilizes quasi-experiments to determine the effectiveness of the developed media application.The study comprised 114 eighthgrade junior high school students and three integrated science teachers in South Tangerang.The final product of the study is an Android-based science learning medium that conforms to the visual, aural, reading/writing, and kinesthetic student learning styles.Hence, this Android-based ScEd-ALS was found to be feasible and useful, as assessed by five experts (two media experts and three science education material experts).Nonetheless, there were significant differences in the learning outcomes between the experimental class using the Android version of ScEd-ALS compared to the control class.The effectiveness of using ScEd-ALS in this study was measured based on the percentage of students' mastery level.ScEd-ALS Android based on visual and kinesthetic learning styles are the most effective media compared to aural and read/write.
Convolutional Neural Networks for Web Documents Classification

Web page classification is an important task in the fields of information retrieving and information filtering. Text classification is a well researched topic and recent state-of-the-art results involve deep learning algorithms. However, few works use text classification deep learning algorithms for web page classification. This work describes an experimental study on classifying web pages using convolutional neural networks (CNNs). An in-house multi-label multi-language dataset is used. We apply text classification CNNs on the textual information extracted from the body, the title and the meta description tag of web pages. Overall, our CNN text classification model achieves good results on web documents classification and one can conclude that it is suited to be part of an automatic web documents classification system.
Security of Sensitive Data on Android Smartphones Using Cloud Storage with Reference to Gravitational Search Algorithm

The focus of this paper is to secure sensitive data on smartphones using cloud infrastructure.Nevertheless, the biggest challenge is data security.This work, therefore, addresses the issues of security of user data by using cloud storage with the development of Solo App v2.1 android application which adopted an Advanced Encryption Standard (AES) for maintaining the confidentiality of data.This will enable smartphones data to remain secure either at rest or in transit.Since numerous threats to mobile phones nowadays have made the issue of security of data on smartphones very critical, hence, the need for the development of such application.Three Android devices were used for the experiments to implement the application.The results are presented using time performance metrics that is based on encryption time and decryption time for each device.The results showed that the work can effectively protect user sensitive data with speed using cloud and when compared with existing works, the results are satisfactory.
Prototyping and Usability Evaluation of Road Safety Education Courseware for Primary Schools in Malaysia

&lt;p&gt;The increasing number of road accidents and deaths among children in Malaysia is a concern. One of the main causes stems from lack of knowledge and skills in dealing with the complexity and condition of the road system. Recognizing the significance of road safety among students, road safety education has been introduced to primary schoolsby the Ministry of Education in 2008. Initially it was blended into the Malay language subject with the goal to equip the children withthe knowledge and skills about road safety.However, road safety teaching and learning still uses the conventional methods without the use of specialized tools. This paper introduces ROSE courseware, a specialized learning tool developed to meet the needs of the Malaysian primary school curriculum for road safety education. Its learning environment is a blend of virtual reality, augmented reality and interactive multimedia, which enables children to understand and acquire skills related to road safety through interactive, real-time and immersive learning tool. In order to receive the children’s feedbacks pertaining to their experience in interacting with the ROSE courseware, a usability evaluation was conducted. The results show that the children strongly agreed on the usefulness, information quality and interface quality of the ROSE courseware.&lt;strong&gt;&lt;/strong&gt;&lt;/p&gt;
Factors associated with length of stay in hospital among the elderly patients using count regression models

Factors associated with length of stay in hospital among the elderly patients using count regression models
An Experimental Study of Machine Learning for Phishing Detection

We approach the phishing detection problem as a data science problem with three different sub-models, each designed to handle a specific sub-task: URL classification, webpage classification based on HTML content and logo detection and recognition from the screenshot of a given webpage. The combined results from the sub-models are used as input for an ensemble model designed to do the classification. Based on the analysis performed on the results, one may conclude that ML techniques are suitable to be part of a system designed for automatic phishing detection.
New ant colony optimization algorithm in medical images edge detection

In this paper we aim to use a demicontractive operator in terms of admissible perturbations in an ant colony optimization (ACO) algorithm for edge detection of medical images. Two admissible perturbations are used. Practical results are presented. Comparison with results using different operators are made.