Reviewerly/exHarmony huggingface dataset:
Investigation on the Learning Programming Techniques via Mobile Learning Application

In resolving the difficulties of learning certain concepts of programming, various teaching and learning programming techniques have been proposed such as Trace- Driven, Problem Solving, Game-based, and Visualization particularly help to develop creative thinking and enhance students’ engagement in learning. With the availability of mobile devices technology, numerous m-learning applications for learning programming available via Google Play and Apple Stores as well as those developed from research works. However, it is very hard to find any published evidence that reveal empirical results pertaining to the usage of m-learning apps as one of the most efficient techniques of learning programming. Hence, this study aims to investigate the extent of the available m-learning applications via Google Play and Apple Stores in utilizing various programming techniques to support effective learning of programming. A controlled experiment was conducted on three (3) selected m-learning applications namely Sololearn, Programming Hub, and Mimo. The results reveal that Mimo, which utilizes the Trace-Driven, Visualization and Problem Solving techniques, is the most preferred application. The utilization of this application in programming classes is recommended as it could serve as an alternative learning medium to improve learners’ ability in understanding basic programming concepts efficiently. Besides, this application can be used as a benchmark of the best-utilized learning programming techniques on the future design of related m- learning applications.
Network virtualization tools – analysis and application in higher education

The education of networking professionals requires not only the knowledge of theory but also acquiring practical skills. For this purpose, specialized networking laboratories equipped with the required diverse technology have been built for years. However, building them is lengthy, expensive, and ultimately laboratories have several limitations, for example in terms of flexibility, sustainability, financial and energy demands, etc. In recent years has begun to intensively evolve the trend of virtualization, both networks as well as computers, that is allowing the development and deployment of more flexible types of virtualized lab solutions. Usually, a general computer hardware and virtualization software are used to operate them. We consider, that this trend of virtualization becomes important and decisive for the coming period. The article focuses therefore on a wider analysis and description of available virtualization solutions specifically aimed at the teaching of network-related university subjects and networking education. At the same time, the article offers a look at our way of using these tools in our everyday teaching process and what experience we have gained from their implementation and usage.
Asymptotic number of clusters for species sampling sequences with non-diffuse base measure

We investigate the asymptotic clustering structure of species sampling sequences (ξn)n, for which the base measure has atomic components. We prove a stochastic representation for (ξn)n in terms of a latent exchangeable random partition. Then, we study the asymptotic behaviour of the total number of blocks and of the number of blocks with fixed cardinality in the partition generated by (ξn)n.
Efficient and Large Universe Ciphertext-Policy Attribute-Based Encryption with Black-Box Traceability for eHealth

In the modern medical environment, massive application of electronic health systems has brought us great convenience. At the same time, the large and cumbersome data storage has become easier with the emergence of cloud computing. However, the emergence of shared platforms has also brought many security issues such as data loss and privacy exposures, especially the exposures of sensitive data by illegal users. In this paper we propose an efficient and large universe ciphertext-policy attribute-based encryption (CP-ABE) scheme with black-box traceability for the collaborative eHealth system. It is of a higher efficiency by reducing greatly the cost of encryption and decryption operations. We prove the security of our new scheme under the q-parallel BDHE assumption in the standard model.
An Improved N-gram Distance for Names Matching

N-gram distance (N-DIST) was developed by Kondrak's lately to measure the distance between two strings. It was found that, this distance could be computed by a smart dynamic programming procedure. The N-DIST has played important roles in a wide array of applications due to its representational and computational efficiency. To effect a more sensible, distance measure, the normalized edit distance was proposed. Many algorithms and studies have been dedicated along this line with impressive performances in last years. There is, however, a fundamental problem with the original definition of N-DIST that has remained without improved: its context-free nature. In determining the possible actions, i.e., deletion, insertion, transposition and substitution, consider work was given to the local behaviors of the string question that indeed encompass great amount of useful information concerning its content. In this proposed framework, two operations are developed. The original N-DIST algorithm does not consider the transposition operations and the algorithm has fixed the cost of insertion and deletion operations. In addition, the proposed E-N-DIST algorithm computes the costs of substitution and transposition operations is dependent on 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n+1</sup> - 1 states while the original N-DIST algorithm has been only dependent on 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sup> states. In this paper, the experiments carried out show the E-N-DIST algorithm, which gives a sort of results that are more accurate than the algorithms under discussion.
Implementation of a Sensor Fusion Based Robotic System Architecture for Motion Control using Human-Robot Interaction

In this paper an initial implementation of a sensor fusion based robotic system architecture for motion control is presented. The architecture also consists of a human robot interaction interface to help the robotic system during the application. This is a work in progress and the paper presents the initial efforts covering the architectural implementation of the system that includes the experimental setup and the block diagram of the architecture.
From Trust and Loyalty to Lock-In and Digital Dependence [Editorial]

Presents the introductory editorial for this issue of the publication.
Risk Management in Software Development Projects

Effective risk management contributes to the success of the software development project. The goal of this work was to identify risk management gaps, perspectives, the evolution of the theme and the study trends, in software development projects, using systematic literature review as a method. For the bibliometric analysis, articles referring to the topic were selected in the period from 2010 to 2018. As tools of analysis, Citespace and VOS Viewer software were used, allowing a comparative evaluation between the articles, as well as the analysis of clusters. Beyond content analysis of articles found. Gaps were identified for performance; team involvement; attention to failures; identification of tools for decision-making; and business strategy. In turn, perspectives were determined for research trends, such as the close relationship between business strategy, risk management and new management models. The research can propose new strategies and perspectives for risk management in software development and show their importance to the academic and practical spheres, demonstrating that the themes are complementary and important in the current technological and innovation sector.
Cooperative Dynamic Fuzzy Perimeter Surveillance: Modeling and Fluid-Based Framework

In this article, considering the performance limitations of the sensors used in real missions, the cooperative dynamic fuzzy perimeter surveillance (CDFPS) problem and its fluid-based solution framework are proposed for the first time. First, the two-dimensional concentration diffusion distribution function is used to imitate an irregular dynamic perimeter. Then, according to the onboard sensor resolution ratio, a fuzzification process is applied for the perimeter area. On this basis, the detected fuzzy perimeter area is modeled with virtual obstacles (VOs). Considering an obstacle set composed of VOs, fixed obstacles and agents, we transform the CDFPS problem into a unified obstacle avoidance problem for the first time and a fluid-based path planning method is designed based on restrained interfered fluid dynamical system and Lyapunov guidance vector field. To quantify the surveillance effect, two indexes for the CDFPS problem are proposed: the perimeter neutrality index and the phase angle uniformity index. Around these proposed indexes, the attractive/repulsive effects and the multiagent cooperative mechanism are constructed. Finally, the receding horizon control is introduced to further improve the surveillance effect. Simulations show that the proposed framework can drive agents to persistently track the perimeter and establish relatively stable phase angle offsets between adjacent agents.
Expressivity results for deontic logics of collective agency

Abstract We use a deontic logic of collective agency to study reducibility questions about collective agency and collective obligations. The logic that is at the basis of our study is a multi-modal logic in the tradition of stit (‘sees to it that’) logics of agency. Our full formal language has constants for collective and individual deontic admissibility, modalities for collective and individual agency, and modalities for collective and individual obligations. We classify its twenty-seven sublanguages in terms of their expressive power. This classification enables us to investigate reducibility relations between collective deontic admissibility, collective agency, and collective obligations, on the one hand, and individual deontic admissibility, individual agency, and individual obligations, on the other.
Multi-Objective and Model-Predictive Tree Search for Spatiotemporal Informative Planning

Adaptive sampling and planning in robotic environmental monitoring are challenging when the target environmental process varies over space and time. The underlying environmental dynamics require the planning module to integrate future environmental changes so that action decisions made earlier do not quickly become outdated. We propose a Monte Carlo tree search method which not only well balances the environment exploration and exploitation in space, but also catches up to the environmental dynamics that are related to time. This is achieved by incorporating multi-objective optimization and a look-ahead model-predictive rewarding mechanism. We show that by allowing the robot to leverage the simulated and predicted spatiotemporal environmental process, the proposed informative planning approach achieves a superior performance after comparing with other baseline methods in terms of the root mean square error of the environment model and the distance to the ground truth.
Redirected Walking in VR

Virtual Reality (VR) content creators usually design large virtual environments. However, the available physical spaces are typically limited and contain several interior obstacles such as furniture, cables or computers. Thus, users may experience discontinuity or even dangers while naturally walking in VR. Redirected walking (RDW) approaches consist of a series of VR-based locomotion techniques, which introduce perceptually unnoticeable virtual camera motion offset from a user's movements. In this chapter we summarize the state-of-art RDW techniques on how to more significantly redirect users towards infinite walking while avoiding both static and dynamic dangers.
Automated Resume Evaluation System using NLP

Recruiting candidates to fit a particular job profile is a task crucial to most of the companies. Due to increasing growth in online recruitment, traditional hiring methods are becoming inefficient. The conventional techniques usually include a labor-intensive process of manually searching through the applied candidates, reviewing their resumes, and then producing a shortlist of suitable candidates to be interviewed. In this era of technology, job searching has become smarter and more accessible at the same time. The companies receive enormous numbers of resumes/CVs, which are not always structured. There have been lots of work done for the job searching process. Whereas, the process of selecting a candidate based on their resume has not been entirely automated. This research proposes a model of extracting valuable information from the resume and ranking it according to the preference and requirement of the company. To achieve the desired goal, the entire process has been divided into three segments. The first segment consists of converting the unstructured resumes in structured data using NLP, and the second segment consists of the extraction phase, where the relevant information is extracted from the resume and giving them an identifier value. Finally, based on the values assigned, the resumes are ranked accordingly in the final segment.
Recent Progress of Named Entity Recognition over the Most Popular Datasets

Named entity recognition (NER) has been considered as an initial step for many applications and tasks such as information retrieval and extraction, question answering, topic modelling, open information extraction, knowledge graph construction, and so forth. Therefore, NER has been receiving increasing attention in the research community. Despite the abundant availability of previous studies on NER, few of them have been applied for more than one dataset. Hence, one system might outperform other systems in one dataset and fail to do in another one. The previous NER surveys have mostly focused on reporting the NER systems without providing a clear comparison for all systems proposed for each dataset. In this paper, we will track the NER performance progress for the most commonly used datasets in NER and report the most recent best systems that have been proposed for each dataset during the last few years.
Adaptation of Professional Engineering Training to the Challenges of Modern Digital Production

The work is concerned with the adaptation of professional engineering training to the requirements of modern digital production. The analysis of the typical infrastructure of engineering education, ensuring the implementation of project-based methods of teaching in the conditions of smart education, is carried out. The structural-functional model for adaptation of engineering training to the requirements of production digitalization trends is considered in detail. The proposed approach is based on the implementation of the concept of an educational and industrial smart cluster. The conditions for broad professional training and mobility of the specialists are created through the “digital production – university” smart infrastructure. The result of the social responsibility of such cooperation of educational institutions and enterprises is the creation of a new type of infrastructure – an educational and industrial smart cluster.
Privacy-Aware Location Sharing with Deep Reinforcement Learning

Location-based services (LBSs) have become widely popular. Despite their utility, these services raise concerns for privacy since they require sharing location information with untrusted third parties. In this work, we study privacy-utility trade-off in location sharing mechanisms. Existing approaches are mainly focused on privacy of sharing a single location or myopic location trace privacy; neither of them taking into account the temporal correlations between the past and current locations. Although these methods preserve the privacy for the current time, they may leak significant amount of information at the trace level as the adversary can exploit temporal correlations in a trace. We propose an information theoretically optimal privacy preserving location release mechanism that takes temporal correlations into account. We measure the privacy leakage by the mutual information between the user's true and released location traces. To tackle the history-dependent mutual information minimization, we reformulate the problem as a Markov decision process (MDP), and solve it using asynchronous actor-critic deep reinforcement learning (RL).
A Precise Breast Cancer Detection Approach Using Ensemble of Random Forest with AdaBoost

Due to breast cancer, a number of women die every year. With an early diagnosis, breast cancer can be cured. Prognosis and early detection of cancer types have become a necessity in cancer research. Thus, a reliable and accurate system is required for the classification of benign and malignant tumor types of breast cancer. This paper explores a supervised machine learning model for classification of malignant and benign tumor types from Wisconsin Breast Cancer dataset retrieved from UCI machine learning repository. The dataset has 458 (65.50%) of benign data and 241 (34.50%) of malignant data, the total of 699 instances, 11 features and 10 attributes. Random Forest (RF) ensemble learning method is implemented with AdaBoost algorithm manifest improved metrics of performance in binary classification between tumor classes. For more accurate estimation of model prediction performance, 10-fold cross-validation is applied. The structure provided accuracy of 98.5714% along with sensitivity and specificity of 100% and 96.296% respectively in the testing phase. Matthews Correlation Coefficient is calculated 0.97 which validates of the structure being a pure binary classifier for this work. The proposed structure outperformed conventional RF classifier for classifying tumor types. Additionally, this model enhances the performance of conventional classifiers.
Smart System of a Real-Time Pedestrian Detection for Smart City

This paper proposes the possibility of recognizing pedestrians in real time on the end devices using a microcomputer to automate the process of signaling traffic lights and improve system mobility. The paper is devoted to a brief analysis of existing systems for the detection of pedestrians, with the subsequent development of its own system based on those studied, and its further implementation on the Raspberry Pi microcomputer. A natural experiment was conducted to detect pedestrians in an image with the subsequent determination of their location at a pedestrian crossing. A number of popular pedestrian detection systems were analyzed in terms of speed, accuracy of determination, and the possibility of implementation on microcomputers. Recommendations on the choice of the system, conditions of use and improvement of characteristics. The developed system automates the work of the traffic light through the introduction of additional functions (tracking a pedestrian's posture, moving pedestrians along the roadway), ensuring the safety of road users, which allows to improve the level of the urban environment as a whole.
Understanding SPARQL Endpoints through Targeted Exploration and Visualization

The Resource Description Framework (RDF) has provided a unified way for everyone to publish their data. The SPARQL query language has been developed to facilitate the exploration of this information. The RDF format, however, is addressed mainly to machines and it is not easily comprehended by humans. Due to the value of the information available through the SPARQL endpoints many efforts have been dedicated to facilitate the access and exploration of this information from users with limited knowledge of the Semantic Web. The main challenge of such approaches is the diversity of the information contained the endpoints, which renders holistic or schema specific solutions obsolete. We present here an integrated platform that supports the users to the querying, exploration and visualization of information contained in SPARQL endpoints. The platform handles each query result independently based only on its characteristics, offering an endpoint and data schema agnostic solution. This is achieved through a Decision Support System, developed based on knowledge base containing raw data from many endpoints, that allows us to provide case-specific visualization strategies for SPARQL query results based exclusively on features extracted from the result.
Efficient Spoofing Attack Detection against Unknown Sample using End-to-End Anomaly Detection

With the evolution of a high precision sensor, printing machine, and manufacturing machine, spoofing attacks become a significant threat to the biometric systems. In order to mitigate the threats of diverse and unexpected attacks, conventional spoofing attack detection methods which aim to detect a specific attack are not sufficient. In this study, we propose a end-to-end machine learning technique which can model biometric information with the complicated structure of high dimension by a probability distribution. The proposed system can recognize whether inputted sample is spoofing one or not even if it is an unknown attack.
Data Mining in Healthcare and Predicting Obesity

Humanity has been in the pursuit of lengthening its lifespan for centuries. Healthcare provides many solutions to illnesses, injuries, and data mining, which provides useful patterns in data, is being actively used in healthcare sectors for the betterment of services provided by healthcare institutions. Obesity is a major issue in the current world and is a known cause of a multitude of other health problems. Data mining is a rapidly growing field with many applications in the field of healthcare. One of its many uses is the prediction of diseases. In this paper, we use classification, a data mining technique, to predict obesity in patients. We compare several classification techniques, such as random forest generation, decision trees, and neural net, to find the most accurate technique.
Credit Card Fraud Detection using Deep Learning

Changing technology leads to various advancements. Here we talk about online transactions that is by the means of credit cards, which leads to credit card frauds, and this paper is about making a credit card fraud detection system using Deep Learning Neural Networks. Even if the Neural Network is trained over a large number of iterations, it is not accurate enough to classify the data as fraudulent or non-fraudulent because of the skewness in the dataset. We use two sampling mechanisms: Under-Sampling, by reducing number of non-fraudulent observations and Over-Sampling, where the fraudulent class observations are duplicated. The results obtained show that these methods increase the accuracy of the prediction process.
Learned Patch-Based Regularization for Inverse Problems in Imaging

Many modern approaches to image reconstruction are based on learning a regularizer that implicitly encodes a prior over the space of images. For large-scale images common in imaging domains like remote sensing, medical imaging, astronomy, and others, learning the entire image prior requires an often-impractical amount of training data. This work describes a deep image patch-based regularization approach that can be incorporated into a variety of modern algorithms. Learning a regularizer amounts to learning the a prior for image patches, greatly reducing the dimension of the space to be learned and hence the sample complexity. Demonstrations in a remote sensing application illustrates that learning patch-based regularizers produces high-quality reconstructions and even permits learning from a single ground-truth image.