Reviewerly/exHarmony huggingface dataset:
Boosting Algorithm and Meta-Heuristic Based on Genetic Algorithms for Textual Plagiarism Detection

Day after day, the plagiarism cases increase and become a crucial problem in the modern world, caused by the quantity of textual information available in the web and the development of communication means such as email service. This paper deals on the unveiling of two plagiarism detection systems: Firstly boosting system based on machine learning algorithm (decision tree C4.5 and K nearest neighbour) composed on three steps (text pre-processing, first detection, and second detection). Secondly using genetic algorithm based on an initial population generated from the dataset used a fitness function fixed and the reproduction rules (selection, crossover, and mutation). For their experimentation, the authors have used the benchmark pan 09 and a set of validation measures (precision, recall, f-measure, FNR, FPR, and entropy) with a variation in configuration of each system; They have compared their results with the performance of other approaches found in literature; Finally, the visualisation service was developed that provides a graphical vision of the results using two methods (3D cub and a cobweb) with the possibility to have a detailed and global view using the functionality of zooming and rotation. The authors' aims are to improve the quality of plagiarism detection systems and preservation of copyright.
Performance evaluation of breast cancer classification using traditional and modern techniques: A comparative study

In recent years, the incidence of breast cancer has increased year by year, posing a great threat to women's health. There are two types of breast cancer, non-invasive and invasive, and most patients get invasive breast cancer, which is fatal. Therefore, it's meaningful and necessary for breast cancer diagnosis at early stage. Breast lumps are the most common manifestations and they are detectable on different formats of medical images. However, traditional human-based diagnosis and image analysis place high demands on doctors’ professional skills and energy. Fortunately, with the development of artificial intelligence technology, computer-aided diagnosis (CAD) is gradually replacing the traditional mammogram diagnosis and analysis. In this study, we developed a non-invasive CAD system on the digital mammograms from the Minimammographic dataset by using discrete wavelet transform (DWT) and higher order gradient (HoG) image decomposition methods for features extraction, and principle component analysis (PCA) for dimensionality reduction. Then we compared the classification effects of traditional machine learning algorithms and modern deep learning based algorithms on the decomposited images. The traditional supported vector machine (SVM) achieved an accuracy of 79.66% and the modern convolutional neuron network (CNN) achieved an accuracy of 83.05%. They showed a slight difference. Considering the computational cost of the CAD system, the traditional SVM seems to be more suitable. In addition, we also tried to use a breast lump index (BLI) to quantify the patients’ diagnosis results.
The Padding of Vein Image Features and Hardware Designs in M-Health Environments

This chapter describes the timing diagrams of padding features and hardware designs of segmentation, controllers, and filters. Further, the authors have described that the hardware design concept of segmentation task can be performed online in a distributed cloud computing m-health environment. The segmentation phase uses two Gaussian filter functions with different sizes of filter masks and standard deviation with a threshold value to make a distinction between veins image patterns and the corresponding backgrounds in the cloud IoT-based m-health environment. In order to design the hardware architecture of the median filter, the superior moving window architecture is used by researchers to accommodate a larger size median filter in the cloud IoT-based m-health environment.
3D Medical Images Compression

Medical images are digital representations of the body. Medical imaging technology has improved tremendously in the past few decades. The amount of diagnostic data produced in a medical image is vast and as a result could create problems when sending the medical data through a network. To overcome this, there is a great need for the compression of medical images for communication and storage purposes. This chapter contains an introduction to compression types, an overview of medical image modalities, and a survey on coding techniques that deal with 3D medical image compression.
Deep Clustering

Clustering is an unsupervised technique used in various application, namely machine learning, image segmentation, social network analysis, health analytics, and financial analysis. It is a task of grouping similar objects together and dissimilar objects in different group. The quality of the cluster relies on two factors: distance metrics and data representation. Deep learning is a new field of machine learning research that has been introduced to move machine learning closer to artificial intelligence. Learning using deep network provides multiple layers of representation that helps to understand images, sound, and text. In this chapter, the need for deep network in clustering, various architecture, and algorithms for unsupervised learning is discussed.
ECOPOLYTECHNIC: A SUSTAINABLE AND FLEXIBLE E-LEARNING SYSTEM FOR AGILE SMART LEARNING SCENARIOS

The rapid change of technological and human-machine co-evolutionary processes due to smart working and smart industries, present several issues that are also influencing the world of e-learning systems.Nowadays is quite hard to distinguish between learning spaces/systems/organizations and working organizations especially when smart working and tele-monitoring are becoming widespread.In this work we analyze smart learning concepts that are leading to a new emerging scenario in which the distinction between learning and working activities is dynamic and in evolution.In particular we present an integrated smart learning model called EcoPolytechnic which aims to redesigns the organization model of a Polytechnic by introducing several concepts of agile learning and project-based learning.The model is built upon the notion of smart learning cell that replaces in a flexible and sustainable way the notion of set of courses and can be used in a better way in smart learning scenarios.The result is a complete and easy to be configured smart learning organization system for which a comparative analysis is carried out.The benefits in terms of innovation and adaptive learning performance are given by using agile learning analysis.
On the Pixel Expansion of Visual Cryptography Scheme

In this paper, we first follow Ateniese et al.'s work that provides upper bounds of the pixel expansion of visual cryptography schemes(VCSs) for more kinds of graph access structures, in which we require that a subset of parties can determine the secret if they contain an edge of the graph G. The constructive upper bounds are derived by the graph decomposition technique. Then we generalize Ateniese et al.'s method of comparing the optimal pixel expansion of VCSs with two different access structures.
Language learnability analysis of Hindi: a comparison with ideal and constrained learning approaches

Native language acquisition is one of the first tasks undertaken by the human brain in the infant stage of life. The linguist community has always been interested in finding the method adopted by the human brain to acquire the native language. Word segmentation is one of the most important tasks in language acquisition. Statistical learning has been proposed to be one of the earliest strategies that an infant can adapt to segment a lot of different words. The language learnability theories are supposed to be universal in nature and work on all the languages. In this work, we have analyzed the learnability of the most popular Indian language, Hindi, based on ideal (universal) and constrained Bayesian learner models. We have analyzed the learnability of the language using unigram and bigram approaches by considering word, syllables, and phonemes as the smallest unit of the language. We demonstrate that Bayesian inference is indeed a viable crosslinguistic strategy and can be used on Hindi as well.
A novel priority-based resource scheduling mechanism for containerised clouds

With the growing adaption of the containerisation idea, cloud environments are being stuffed with the containers to gain the business, technical and user benefits of both the cloud idea and the containerisation paradigm. Due to the lightweight nature of containers, a cloud centre comprises millions or even billions of containers. This rapid growth of containers in cloud poses several operational and management challenges. The other key problem is to know how optimally allocate different containers for all kinds of incoming tasks on a timely basis. This paper has been prepared based on priority-based resource scheduling approach to fulfil the varying resource requests for business workloads by continuously monitoring the Docker container performance, health condition, and security. Further on, when there is a stream of new tasks waiting for resources, there is also a challenge of how resources can be optimally identified and scheduled in order to fulfil task requests.
Supplementary Material for "A Machine Learning Attack on Illegal Trading"

This supplementary document contains information regarding the run times of the NN DTW and ARMA(1,1) illegal insider trading detection models. Details regarding the implementation of the ARMA(1,1), Gaussian Mixture Model (GMM), One-Class Support Vector Machine (OCSVM) and Isolation Forrest (iForest) models which are used as anomaly detection benchmarks in the paper ``A Machine Learning Attack on Illegal Trading" are also provided.
Runtime analysis of population-based evolutionary algorithms

tutorial Runtime analysis of population-based evolutionary algorithms Authors: Per Kristian Lehre University of Birmingham, UK University of Birmingham, UKView Profile , Pietro S. Oliveto University of Sheffield, UK University of Sheffield, UKView Profile Authors Info & Claims GECCO '21: Proceedings of the Genetic and Evolutionary Computation Conference CompanionJuly 2021 Pages 856–880https://doi.org/10.1145/3449726.3461423Published:08 July 2021Publication History 0citation66DownloadsMetricsTotal Citations0Total Downloads66Last 12 Months22Last 6 weeks1 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
UX in Industry: An ‘Unconference’ w/ the Practitioners

UX roles in industry come in various flavors. From job titles, roles and responsibilities to organizational structure, companies define user experience practitioners in varying ways. The purpose of this alternative session is to give students a diverse look at UX in Industry. Seattle and the Pacific Northwest are home to several major technology companies such as Amazon, Microsoft, Google, and Intel. This alt session will utilize the unconference format. This will allow facilitators to lead small group discussions based on topics posed by the session attendees. The group of facilitators will consist of a diverse collective of UX researchers and designers from various companies with varying degrees of formal education. Conference attendees will be asked to submit questions via Twitter and at the beginning of the session. Following introductions, the session attendees will be broken into 5 smaller groups. The industry practitioners will serve as discussion facilitators leading the conversation around the submitted topics. It is the intent that they will be able to discussed topics such as what “UX looks like” at their respective companies and the best approach a student can take to prepare now to be successful at their company, but ultimately the topics will come from the attendees. The five panelists have several years of industry experience and have worked for multiple companies; therefore, they can bring perspective from a myriad of experiences.
Social Media and Professional Learning Networks

This chapter examines social media as a form of professional development. It sheds light on social media platforms that support collaboration and reflection among educators. The International Society for Teachers in Education (ISTE) continues to stress the importance of teachers possessing skills and behaviors of digital age professionals. This is necessary as educators become co-learners with their students and colleagues around the world. Social networks, such as Twitter and Google+ communities, provide opportunities to move up the Substitution Augmentation Modification Redefinition Model developed by Dr. Ruben Puentedura, or offer a method of seeing how computer technology might impact teaching and learning, as well as professional learning for teachers.
The Word-Image-Virtual Body

In the present knowledge and innovation society, including creative industries, DIY practices, and citizen science, it is not enough to produce solely a product; it is necessary to sell a story about its groundbreaking appearance and function, which means that the product is well-marketed only at the moment when it is sold in a package together with a story written, performed, or shot about it. This turns into the world of arts; sciences, technologies, and business goes hand in hand with the contemporary storytelling, which is becoming distinctively performative and conceptual; rather than making a stable “textwork,” it directs to textual service, which is integrated in the trendy service economy. Textual services generate novel forms of expanded narrative, shaped by means of new media textuality and novel narrative practices. One of the most attractive modes of today's textual service is a story as application.
A personal best at Computing4Change in 2018

SC'19 is maintaining a blog with special articles, features, and updates on upcoming deadlines or events, to help attendees get oriented and excited about the show. The following article is taken from there; please head to the SC'19 blog site for the latest happenings.
Dark and Deep Webs-Liberty or Abuse

While the Dark Web is the safest internet platform, it is also the most dangerous platform at the same time. While users can stay secure and almost totally anonymously, they can also be exploited by other users, hackers, cyber-criminals, and even foreign governments. The purpose of this article is to explore and discuss the tremendous benefits of anonymous networks while comparing them to the hazards and risks that are also found on those platforms. In order to open this dark portal and contribute to the discussion of cyber and politics, a comparative analysis of the dark and deep web to the commonly familiar surface web (World Wide Web) is made, aiming to find and describe both the advantages and disadvantages of the platforms.
RESEARCH OF MOTIVATION OF FUTURE ENGINEERS OF COMPUTER PROFESSIONAL PEDAGOGES

The article analyzes the scientific literature of domestic and foreign scientists, on the basis of which the approaches to the concept of motivation (motivation as a combination of factors and motives, motivation as a process and mechanism of activity, motivation as a set of all determinants of behavior) and a motive (an internal position of the student; driving force of the student). On the basis of the analysis of leading editions and own understanding of the concept of motivation, in the context of the scientific article, the concept of motivation will be understood as a set of factors and motives. The analysis of the educational activities of future engineers-teachers of the computer profile has shown that low motivation is one of the reasons for the lack of interest of those who are taught in the educational process while studying pedagogical disciplines. On the basis of the questionnaire on the methodology «The motivation of students studying at higher educational institution», the prevailing external and pragmatic motives for future engineers-teachers of the computer profile were established. In the course of student interviewing, it has been established that the regress of motives from the domestic ones in the process of entering the institution of higher education and the actual ones arose as a result of the fact that 61% of students disappointed in choosing their future profession. According to the results of interviews, the respondents found a low level of motivation in the process of studying humanitarian disciplines, including the pedagogical cycle. The survey shows that 64% of students do not see themselves as teachers, 73% study pedagogical disciplines because they are included in the curriculum and are mandatory. On the other hand, the information society creates demand for skilled workers in the IT industry, whose training can only be provided by highly skilled engineers-teachers of the computer profile provided that they have the appropriate pedagogical training. Recommendations on formation of positive motivation of students are developed.
An Integrated AI Medical Diagnosis Application Based on Deep Learning

AI medical diagnosis has become an easy and effective approach method by which patients could get their health reports faster. This paper introduced an integrated AI medical diagnosis application with an image recognition system, chat system, and user interface. With the powerful help of the Convolutional Neural Network (CNN), an accurate image diagnosis system for breast cancer is ready to use. The chat system provides a bond between AI and patients through communication, using the Q&A sub-system based on Knowledge Graph (KG) and the casual chat sub-system based on Natural Language Process (NLP). Health condition query and common converse are satisfied respectively by Q&A sub-system and casual chat sub-system. Depending on the user’s intentional probability of medical problems, the casual chat system will interfere if the user’s intention is not matched in Knowledge Graph. Besides the deep learning part to process patient’s data, an easy-to-use user interface is also designed to guide people getting intended information, using Flask framework to offer API and jQuery to send a request. Our accuracy achieves 0.88 in breast cancer detection, and the result of GradCAM indicates the CNN network has a high performance on such a task. The practical experiment shows that the patient’s intention can proceed well in the chat system and that application can provide people advantageous feedback according to their health condition.
The Impact of Awareness of Password Management of Digital Banking Services on Customer’s Adoption in India

Password management is a highly decisive component while adopting digital banking services. Password composition strategies facilitate the users to construct a safe, secure and strong password which is difficult to crack and misuse your confidential information. There should be an institutional structure to enhance the awareness level of password management of users to minimize the probability of cyber attack. This study has focused on the analysis of impact of awareness of password management of digital banking services on customer adoption in India. To analyze the applicability and reliability of scale, factor analysis has been administered before the use of stepwise method (forward selection) of multiple regression. Primary data on 5 point Likert scale has collected from the Delhi region with the help of questionnaires through a self administered approach. Stratified random sampling technique was used and total 432 useful schedules were considered for analysis of data using SPSS version 23. The results of the study show that there is a positive impact of awareness of password management of digital banking services on customer’s adoption in India.
Double-Joint Motion Planning Method in Park based on High-Definition Map

In order to solve the turning problem in narrow space, Double-Joint Method is proposed in this paper. The spiral has a continuous curvature, but rarely considers road boundary and shape constraints. On the other hand, High-Definition map provides prior information of road for motion planning. Double-Joint Method utilizes road boundary and curvature of map to determine the target area and a series of pseudo target points of motion planning, generates a series of path respectively, and then design the cost function for selection. Above all, path generation employs two joints for shape determination of cubic spiral by designing the objective function and combining road structure information. The method adds a joint near turning to allow the path more flexible without increasing the degree of the curve. VTD-ROS simulation platform is built to verify. Theoretical and simulation results display proposed method can guide the vehicle turning right in narrow space to obtain a larger turning radius through lane borrow without loss of smoothness. And it is feasible to sacrifice the accuracy of reaching target points to suit special scenario.
Mathematical Optimization by Using Particle Swarm Optimization, Genetic Algorithm, and Differential Evolution and Its Similarities

To solve the problems of optimization, various methods are provided in different domain. Evolutionary computing (EC) is one of the methods to solve these problems. Mostly used EC techniques are available like Particle Swarm Optimization (PSO), Genetic Algorithm (GA) and Differential Evolution (DE). These techniques have different working structure but the inner working structure is same. Different names and formulae are given for different task but ultimately all do the same. Here we tried to find out the similarities among these techniques and give the working structure in each step. All the steps are provided with proper example and code written in MATLAB, for better understanding. Here we started our discussion with introduction about optimization and solution to optimization problems by PSO, GA and DE. Finally, we have given brief comparison of these.
Variability realization in UML/SysML models

Motivated by experiences from different industrial settings, the tutorial reveals the increasing need for guidance and decision support on how to handle variants and variability in SysML and UML models. While a substantial amount of variability realization approaches has already been discussed on the level of source code, there is little guidance for practitioners on the model level. With this, there is major uncertainty in dealing with concurrent changes and parallel modeling of similar system variants
Handling Traceability in Graph Fusion for a Trustworthy Framework

We previously developped InSyTo, a framework for soft and semantic information fusion and management relying on the conceptual graph formalism and ontologies. This framework was used in many projects. However, if the framework was well received by end-users, they highlighted an urgent need for traceability within the soft information fusion process. In this paper we propose an approach to provide traceability built-in capabilities to InSyTo. The approach relies on the use of conceptual graphs in order to express the history of each elementary piece of information as a lineage graph. The lineage graph contains all the historical information concerning the initial sources of each elementary information item, as well as the fusion operations that were applied on them. The main advantage of this new development is the ability of having a trustworthy framework and thus let the end-users know what, why and how somethings happens during the information process.