Reviewerly/exHarmony huggingface dataset:
Group attack detection in recommender systems based on triangle dense subgraph mining

Aiming at group shilling attacks in recommender systems, a shilling group detection approach based on triangle dense subgraph mining is proposed. First, the user relation graph is built by mining the relations among users in the rating dataset. Second, the improved triangle dense subgraph mining method and the personalizing PageRank seed expansion algorithm are used to divide candidate shilling groups. Finally, the suspicious degrees of candidate groups are calculated using several group detection indicators and the attack groups are obtained. Experiments indicate that our method has better detection performance on the Amazon and Yelp datasets than the baselines.
An investigation and analysis of methods and applications of skein mining

E-commerce is all about skein-based companies. It is about transactions, primarily the purchase and sale of goods and services on the skein by customers and companies. Skein is one of the main information sources, collecting multiple files stored on various server with size also increases rapidly. E-Commerce keeps the company going, makes it difficult to compete in the dynamic e-business climate. E-commerce has established an affordable and productive way to operate digitally. Skein mining utilizes methods of info scooping to find, retrieve relevant knowledge on the snarl.
Science and Technology in the Modern Agricultural Sector: An Overview

Innovation is an important one in contemporary agricultural sector than ever before. Contemporary farming and agricultural activities work another way than those a few years ago, principally because of progressions in the science and technology, with sensors, devices, equipments, machines, and information technology. This sector, as a whole is facing huge confronts, from increasing costs of inputs, a scarcity of workers, and changing consumers’ preferences for lucidity and sustainability. There is mounting importance from agricultural corporations that solutions are desirable for these confronts. As a consequence recent agricultural sector habitually uses classy technologies like robotics, sensors for measuring temperature and moisture, aerial images, and global positioning system technology. In the last 10 years, application scientific technologies in the agricultural sector has witnessed a increment in the investment worth of 6.7 billion USD endowed in the last five years and attracted 1.9 billion USD in the last one year alone. These modern technologies and accurate modern agricultural and robotic systems permit businesses to be more gainful, competent, safer, and more eco friendly. Further these modern technologies have paying attention around different areas such as interior vertical farming, computerization and robotics, livestock rearing technology, up to date greenhouse farming practices, exactitude agricultural and artificial intelligence, and blockchain. With this backdrop the present paper made an attempt to explain applications of the science and technology in the modern agricultural sector.
Header investigation for spam email forensics using framework of national institute of standards and technology

Today's technology makes communication very easy and can be used anywhere, even a distance of hundreds to thousands of kilometres is not a barrier in communicating. One of the tools or media that is widely used is via email. However, there are many disadvantages that may be obtained from the email, one of which is spamming or mail. The purpose of this research is to know the stages of spamming email analysis through header analysis. The method used in this study is the National Institute of Standards and Technology (NIST) and this method uses 4 stages, namely collection, examination, analysis, and reporting. The results of this study are expected to be able to find out the spam sender's email address, the spam email sender's IP address, and other information needed.
Scientific Computing With Python on High-Performance Heterogeneous Systems

The articles in this special section focus on the modern approach to enabling scientific progress with computers with particular emphasis on high-performance heterogeneous systems.
Epigenetic opportunities for evolutionary computation

Evolutionary computation is a group of biologically inspired algorithms used to solve complex optimization problems. It can be split into evolutionary algorithms, which take inspiration from genetic inheritance, and swarm intelligence algorithms, that take inspiration from cultural inheritance. However, much of the modern evolutionary literature remains relatively unexplored. To understand which evolutionary mechanisms have been considered, and which have been overlooked, this paper breaks down successful bioinspired algorithms under a contemporary biological framework based on the extended evolutionary synthesis, an extension of the classical, genetics focused, modern synthesis. Although the idea of the extended evolutionary synthesis has not been fully accepted in evolutionary theory, it presents many interesting concepts that could provide benefits to evolutionary computation. The analysis shows that Darwinism and the modern synthesis have been incorporated into evolutionary computation but the extended evolutionary synthesis has been broadly ignored beyond: cultural inheritance, incorporated in the sub-set of swarm intelligence algorithms, evolvability, through covariance matrix adaptation evolution strategy (CMA-ES), and multilevel selection, through multilevel selection genetic algorithm (MLSGA). The framework shows a gap in epigenetic inheritance for evolutionary computation, despite being a key building block in modern interpretations of evolution. This leaves a diverse range of biologically inspired mechanisms as low hanging fruit that should be explored further within evolutionary computation and illustrates the potential of epigenetic based approaches through the recent benchmarks in the literature.
Sheltered Instructional Observational Protocol Model in Digital Teaching EFL Students in Ukraine

Digital teaching has become an alternative way to replace the conventional learning process in COVID pandemic time. Educators explore different language achievement strategies to assist students on the way to academic success. One of the approaches the teachers can benefit from significantly is instructional modal – the Sheltered Instructional Observational Protocol (SIOP). This study aims to determine the effectiveness of SIOP instructional strategies in this digital teaching of EFL students. The study was conducted at the undergraduate level for English language learners in the Faculty of Philology, V.O. Sukhomlynskyi National University of Mykolaiv, Ukraine. The researchers conducted the study in the first semester of 2020. The contribution of this study is to evaluate experience of teachers in instructional strategies and to assess their effectiveness in digital teaching. The study analyzes the responses of twenty-five teachers and 63 students to the SIOP-based survey. A descriptive statistical method was used to test the validation. The results of the recearch show that EFL learners are not satisfied with activities to apply language and content knowledge. The results justified that teachers need to establish active online communication with their students. The study presents some recommendations for EFL teachers to improve their professional skills by adopting the specific components of SIOP and mastering their instructional strategies in digital learning. The implementation of the SIOP Model in digital teaching could bring appropriate and meaningful resources for high school teachers as a tool for them to grow professionally in COVID pandemic time.
Mobile Recommendation System Using Mapreduce

In the past few years, there is a huge demand and increase in the usage of Mobile phones, which increased the market, which raised confusion among the people to choose the best phone for them. This research work attempts to help such users, who are confused to buy mobile phones in the present market. When a user wants to buy a mobile phone from this huge market, he/she chooses to buy the best phone. To judge the phone is perfect for them or not, they have to go through a lot of newspapers, magazines, and also reviews given by the users. It takes them a lot of time to read all those reviews and judge. So, through our project, a large number of reviews are analyzed in order to suggest the perfect mobile phone available in the market. The proposed project considers a huge dataset of Amazon, which includes the information about mobiles.
IIAS: An Intelligent Insurance Assessment System through Online Real-time Conversation Analysis

With the development of Chinese medical insurance industry, the amount of claim cases is growing rapidly. Ultimately, more claims necessarily indicate that the insurance company has to spend much time assessing claims and decides how much compensation the claimant should receive, which is a highly professional process that involves many complex operations. Therefore, the insurance assessor's role is essential. However, for the junior assessor often lacking in practical experience, it is not easy to quickly handle such an online procedure. In order to alleviate assessors' cognitive workload, we propose an Intelligent Insurance Assessment System (IIAS) that helps effectively collect claimant information through online real-time conversation analysis. With the assistance of IIAS, the average time cost of the insurance assessment procedure is reduced from 55 minutes to 35 minutes.
Effects of Resampling Techniques on Imbalanced Data Classification: A New Under-resampling Method

We study the performances of various predictive models including decision trees, random forests, neural networks, and linear discriminant analysis on an imbalanced data set of home loan applications. During the process, we propose our undersampling algorithm to cope with the issues created by the imbalance of the data. Our technique is shown to work competitively against popular resampling techniques such as random oversampling, undersampling, synthetic minority oversampling technique (SMOTE), and random oversampling examples (ROSE). We also investigate the relation between the true positive rate, true negative rate, and the imbalance of the data.
Image Compression and Enlargement Algorithms

In some cases, there are problems associated with the compression and enlargement of images. The use of splines is quite effective in some cases. In this paper, a new image compression algorithm is presented. The features of increasing the size of an image when using local polynomial or non-polynomial splines are considered. The main method for enlarging an image is based on the use of splines of the second and third order of approximation. Polynomial and trigonometric splines are considered. To speed up the process of enlarging the image, we used the parallelization techniques
VIRTUAL AND AUGMENTED REALITY TO EXPLOIT STEM-SKILLS-BASED LEARNING FOR ENGINEERING STUDENTS USING THE TEC21 EDUCATIONAL MODEL

The worldwide confinement due to Covid19 has boosted creativity in academia to develop learning activities with role-playing games and multidisciplinary workshops brought to our students' homes.Tecnologico de Monterrey, like most universities in the world, is delivering classes remotely using internally developed and external platforms designed to continue the practices that were done every day in laboratories, facilities, and visits to companies and institutions.This document describes the migration of academic activities, designed under the university's Tec21 Educational Model guidelines, to an internet platform that adopts virtual and augmented reality to deliver learning with the same academic quality as the traditional face-to-face classes.Our training partners in industry, government, and not-for-profit organizations can communicate and access the practice activities on the virtual platforms.The Tecnologico de Monterrey School of Engineering and Design has worked to generate a simulator of an ERP (Enterprise Resource Planning) system based on the educational platform "Tec21 Car Assembler."Using virtual reality, the student can apply the virtual classroom's theoretical learning to practice, guided by an academic figure who collaborates with training partners from companies and institutions associated with Tecnologico de Monterrey to transfer knowledge to students.Through virtual and augmented reality, the students have been able to interact with the scale car assembly through the EON XR platform, where teachers have designed different lessons based on car assemblies and subassemblies, in this case, Jeep.We are developing our own platform for assembling Meccano designs that can be made by students first using second and third simulations until they can be taken to assembly, using augmented reality and finally, virtual reality.
Multi-strategy Knowledge Distillation Based Teacher-Student Framework for Machine Reading Comprehension

The irrelevant information in documents poses a great challenge for machine reading comprehension (MRC). To deal with such a challenge, current MRC models generally fall into two separate parts: evidence extraction and answer prediction, where the former extracts the key evidence corresponding to the question, and the latter predicts the answer based on those sentences. However, such pipeline paradigms tend to accumulate errors, i.e. extracting the incorrect evidence results in predicting the wrong answer. In order to address this problem, we propose a Multi-Strategy Knowledge Distillation based Teacher-Student framework (MSKDTS) for machine reading comprehension. In our approach, we first take evidence and document respectively as the input reference information to build a teacher model and a student model. Then the multi-strategy knowledge distillation method transfers the knowledge from the teacher model to the student model at both feature and prediction level through knowledge distillation approach. Therefore, in the testing phase, the enhanced student model can predict answer similar to the teacher model without being aware of which sentence is the corresponding evidence in the document. Experimental results on the ReCO dataset demonstrate the effectiveness of our approach, and further ablation studies prove the effectiveness of both knowledge distillation strategies.
Substitution of the Fittest: A Novel Approach for Mitigating Disengagement in Coevolutionary Genetic Algorithms

We propose substitution of the fittest (SF), a novel technique designed to counteract the problem of disengagement in two-population competitive coevolutionary genetic algorithms. The approach presented is domain-independent and requires no calibration. In a minimal domain, we perform a controlled evaluation of the ability to maintain engagement and the capacity to discover optimal solutions. Results demonstrate that the solution discovery performance of SF is comparable with other techniques in the literature, while SF also offers benefits including a greater ability to maintain engagement and a much simpler mechanism.
Temporal and Object Quantification Networks

We present Temporal and Object Quantification Networks (TOQ-Nets), a new class of neuro-symbolic networks with a structural bias that enables them to learn to recognize complex relational-temporal events. This is done by including reasoning layers that implement finite-domain quantification over objects and time. The structure allows them to generalize directly to input instances with varying numbers of objects in temporal sequences of varying lengths. We evaluate TOQ-Nets on input domains that require recognizing event-types in terms of complex temporal relational patterns. We demonstrate that TOQ-Nets can generalize from small amounts of data to scenarios containing more objects than were present during training and to temporal warpings of input sequences.
Meta-Fuzzy Items for Fuzzy Association Rules

A large number of systems with a great predictive capacity, such as Deep Learning, are being currently used to solve a wide variety of real problems. However, the models obtained are not easy to understand by scientists, giving rise to the field of eXplainable Artificial Intelligence, which encourage techniques that obtain accurate and understandable models. Fuzzy Association Rules are models that can be understood by themselves, but its interpretability can be improved by representing the same information with fewer and simpler rules. In this work, we propose Meta-Fuzzy Items, which allows to define more generic fuzzy items to represent the same information with fewer rules, and to extend the type of associations that can be represented. Based on this proposal, a new fuzzy data-mining algorithm is presented to extract interesting and interpretable rules from quantitative transactions. The quality of our approach is analyzed using statistical analysis and comparing with a well-known fuzzy data-mining algorithm.
A study of prevention and detection analysis of SQL injection attack

In the contemporary world, Web applications have turned out as a crucial element. Everything became easy, simpler, and can meet its aim speedy with web applications. These applications help to reach consumers, patrons, and stakeholders throughout the world easily. The web applications made it easier to keep a proper communication channel between business consortium and consumers. The web applications store the decisive information in its databases which it receives from users. The web application and database are accessible through the internet which gets affected by all the kinds of security threads, leads to misuse of the important information of the user by attackers. Among those attacks, the SQL Injection attack been placed under the top ten susceptibilities. By this type of attack, the hacker can steal important and confidential information from the database which will affect any business or consumer’s establishments. The paper here provides quintessential information on the functions of Machine learning, Predictive Analytics, and development of the consequential web that precisely calculates and avoids SQLIA with experiential valuation presented in the Receiver operating curve and Confusion matrix.
BézierSeg: Parametric Shape Representation for Fast Object Segmentation in Medical Images

Delineating the lesion area is an important task in image-based diagnosis. Pixel-wise classification is a popular approach to segmenting the region of interest. However, at fuzzy boundaries, such methods usually result in glitches, discontinuity or disconnection, inconsistent with the fact that lesions are solid and smooth.To overcome these problems and to provide an efficient, accurate, robust and concise solution that simplifies the whole segmentation pipeline in AI-assisted applications, we propose the BézierSeg model which outputs Bézier curves encompassing the region of interest.Directly modeling the contour with analytic equations ensures that the segmentation is connected and continuous, and that the boundary is smooth. In addition, it offers sub-pixel accuracy. Without loss of precision, the Bézier contour can be resampled and overlaid with images of any resolution. Moreover, clinicians can conveniently adjust the curve's control points to refine the result.Our experiments show that the proposed method runs in real time and achieves accuracy competitive with pixel-wise segmentation models.
Optical computing with multimode fiber nonlinearities

A novel optical computing framework is presented by harnessing spatiotemporal nonlinear effects of multimode fibers for machine learning. With linear and nonlinear interactions of spatial fiber modes, a powerful computation engine is experimentally realized. We demonstrated excellent performance with the present optical scheme for various classification tasks. We demonstrated that spatiotemporal fiber nonlinearities perform as well as digital neural network structures for challenging computational tasks. With better energy efficiency and easy scalability, our method presents a novel path toward powerful optical computation.
25 Years of ISWC: Time Flies When You're Having Fun

Much has changed in the landscape of wearables research since the first International Symposium on Wearable Computers (ISWC) was organized in 1997. The authors, many of whom were active in this community since the beginning, reflect now 25 years later on the role of the conference, emerging research methods, the devices, and ideas that have stood the test of time—such as fitness/health sensors or augmented reality devices—as well as the ones that can be expected still to come, like everyday head-worn displays.
Circumventing Circumvention: An Economic Analysis of the Role of Education and Enforcement

The role of education and enforcement in ensuring compliance with a law or policy has been debated for more than a century now. We reopen this debate in the context of security circumvention by employees, currently a leading cause of information security and privacy breaches. Drawing on prior literature, we develop a microeconomic framework that captures employees’ circumventing behavior in the face of security controls. This allows us to obtain interesting insights that have implications for how an organization should employ anticircumvention measures. First, unless circumvention is rampant, education and enforcement often work better in combination, and not in isolation. Second, there are incentives for an organization to tolerate circumvention to an extent, even when education and enforcement are cheap. Finally, education and enforcement may be strategic complements or substitutes in different parts of the parameter space. When they are complements, if a change in cost parameters compels the organization to increase one, it would also require an increase in the other in lockstep. In contrast, when they are substitutes, an increase in one is associated with a decrease in the other. This paper was accepted by Chris Forman, information systems.
Gender bias in word embeddings of different languages

Word embeddings are useful for various applications, such as sentiment classification (Tang et al., 2014), word translation (Xing, Wang, Liu, a Lin, 2015) and resume parsing (Nasser, Sreejith, a Irshad, 2018). Previous research has determined that word embeddings contain gender bias, which can be problematic in certain applications such as resume parsing. This research has addressed the question whether gender bias is present in word embeddings of different languages. Gender bias has been measured on word embedding of 26 different lan- guages with the help of the Word Embedding Association Test by Caliskan, Bryson, and Narayanan (2017). The results show that most of the tested languages seem to have bias towards male, while a few languages seem to have a bias towards female. This result is in line with previous literature.
Artificial Bee Colony and Genetic Algorithms in Selecting and Combining Web Services for Enhancing QoS

Service-Oriented Architecture (SOA) is coming up an important software technology for developing complex application.  SOA can be dynamically constructed by flexibly composing and combining existing web service components offered by several service providers. Maintaining the Quality of Service in web service composition is a challenging one due to the frequent implementation of new web services into a dynamic business environment.  Nature Inspired Computing (NIC) based algorithms can be used to enhance the selection and composition process of best web services.  In this paper we have combined two famous NIC based algorithms namely Artificial Bee Colony (ABC) algorithm and Genetic Algorithm (GA) for selecting the required web services and Genetic Algorithm for composing randomly selected web services to produce best set of web services.  We have used the existing data set to compare the proposed approach with the other algorithms in terms of different QoS parameters.  The experiment results indicate the efficiency and feasibility of the proposed approach in terms of QoS.