Reviewerly/exHarmony huggingface dataset:
Comparisons on Scrum Team Strategies

Scrum is a type of agile process that incrementally, iteratively and continuously deliver software based on sprint time box. It is composed by User Stories, product backlog, sprint backlog, scrum team and sprints. Scrum team take user stories from product backlog into sprint backlog to start each sprint and deliver products at the end of each sprint. Sprint retrospective and review occurs at the end of each sprint to evaluate the delivered products and team performance. Based on the Scrum guide, scrum is easy to be understood but hard to be measured. Especially, it is depended largely on the performance of team dynamics referring to team compositions and task allocations, as its optimization make big impact on each sprint result. A new type of strategy called Intelligent pair strategies are tested in this paper to compare their performance under various task set and scrum team context.
The Impact of IR4 on Corporate Governance of Listed Companies

The digital revolution transforms business models and presents new privacy issues and ethical dilemmas. Research by MIT Sloan CISR reports that U.S. listed companies that have a digitally savvy board show substantially better financial performance. What is a digitally savvy board? What are the differences between the old and the new world? What are the new ethical dilemmas and how do you prevent making the same mistakes as big tech? Why does innovation fail so often within the existing structures of established companies? Why does the three lines of defense model for risk management have an inhibitory effect on innovation in practice? The author discusses these questions and provides suggestions for improvement of corporate governance of established companies. In the next chapter, the author provides rules of the road for how established companies can monetize their data including some pitfalls for established companies and discusses a number of ethical dilemmas that companies encounter in practice when implementing new digital technologies and services.
Towards a ubiquitous real-time COVID-19 detection system

Purpose In view of the intensive spread of Coronavirus disease 2019 (COVID-19) and in order to reduce the rate of spread of this disease; the objective of this article is to propose an approach to detect in real time suspect person of Coronavirus disease 2019 (COVID-19). Design/methodology/approach The ubiquitous computing offers a new opportunity to reshape the form of conventional solutions for personalized services according to the contextual situations of each environment. The health system is seen as a key part of ubiquitous computing, which means that health services are available anytime, anywhere to monitor patients based on their context. This paper aims to design and validate a contextual model for ubiquitous health systems designed to detect in real time suspect person of COVID-19, to reduce the propagation of this infectious disease and to take the necessary instructions. Findings This paper presents the performance results of the COVID-19 detection approach. Thus, the reduction of the COVID-19 propagation rate thanks to the real-time intervention of the system. Originality/value Following the COVID-19 pandemic spread, the authors tried to find a solution to detect the disease in real time. In this paper, a real-time COVID-19 detection system based on the ontological description supported by Semantic Web Rule Language (SWRL) rules was developed. The proposed ontology contains all relevant concepts related to COVID-19, including personal information, location, symptoms, risk factors, laboratory test results and treatment planning. The SWRL rules are constructed from medical recommendations.
Semi‐supervised joint learning for longitudinal clinical events classification using neural network models

The success of deep learning neural network models often relies on the accessibility of a large number of labelled training data. In many health care settings, however, only a small number of accurately labelled data are available while unlabelled data are abundant. Further, input variables such as clinical events in the medical settings are usually of longitudinal nature, which poses additional challenges. In this paper, we propose a semi‐supervised joint learning method for classifying longitudinal clinical events. Specifically, our model consists of a sequence generative model and a label prediction model, and the two parts are learned end to end using both labelled and unlabelled data in a joint manner to obtain better prediction performance. Using five mortality‐related classification tasks on the Medical Information Mart for Intensive Care (MIMIC) III database, we demonstrate that the proposed method outperforms the purely supervised method that uses labelled data only and existing two‐step semi‐supervised methods.
Privacy-preserving Data Aggregation for Big Data in Financial Institutions

Data aggregation plays an important role in the era of big data. Financial institutions aggregate user data to obtain the global information about their users. At the same time, they intend to hide the detailed data from third parties such as fusion center when participating in the aggregation due to concerns on data privacy. Based on the secure multi-party computation and pseudonym, in this paper, we propose a privacy preserving data aggregation mechanism. In this new mechanism, users are grouped into pairs anonymously, and their data is divided and mixed between pairwise users. Compared with the existing methods, this new scheme can achieve lower losses when link failure occurs, and resist the collusion attacks from users.
How Mobile Technologies Are Leading to Economic Development in Sub-Saharan Africa

Mobile technology today is increasingly being used to help improve underdeveloped and developing areas such as sub-Saharan Africa. With the statistics showing the number of adults in Africa owning mobile devices steadily increasing, mobile technology has been a popular area of interest to use to help improve areas such as healthcare and education throughout African cities and rural areas. Common that ways mobile technology is being used to help the residents of sub-Saharan Africa are in the sectors of health care and education. Mobile technologies being used in these areas whilst simple are incredibly effective and successful in helping to better the quality of health in education in sub-Saharan Africa. Many of the projects and systems developed using mobile technology focus mainly on urban areas. While reports state the huge increase in the number of those using mobile devices in Africa, along with the large estimated increase in the coming years, the difference between rural African countries and countries which contain urban cities is substantial.
Video project as a type of mass extracurricular activity of students to enhance a foreign language competence

The authors consider relevance of extracurricular activities with students of non-linguistic universities in order to form a foreign language competence, describe the forms and types of extracurricular activities. One of the successful types of extracurricular activities is an online video conference in a foreign language to stimulate students’ interest in learning English, as well as to develop speaking, listening, writing and reading skills. The use of innovative means of communication in extracurricular activities is based on the position of media pedagogy about the potential educational and educative resources of any mass communication medium. The online video conference held in Lobachevsky State University of Nizhny Novgorod arranges a competition of videos on a given topic, communication at the conference is carried out only in English.
Human Activity Recognition: Data Collection and Design Issues

Sensor-based Human Activity Recognition (HAR) has been explored by many research communities and industries for various applications. In the earlier chapters, we have presented methodologies to accomplish human activity recognition, pre-processing steps of raw data from sensors, segmentation of these data using various windowing approaches, feature extraction approaches, feature normalization schemes, sub-space methods for dimensional reduction and related issues. In this chapter, we present important challenges in activity recognition, data collection protocols, and design issues. This chapter also represents the basic requirement of training data, environmental set up for data collection, sensor requirement, sensor position, and energy consumption issues.
Multi-person Spatial Interaction in a Large Immersive Display Using Smartphones as Touchpads

In this paper, we present a multi-user interaction interface for a large immersive space that supports simultaneous screen interactions by combining (1) user input via personal smartphones and Bluetooth microphones, (2) spatial tracking via an overhead array of Kinect sensors, and (3) WebSocket interfaces to a webpage running on the large screen. Users are automatically, dynamically assigned personal and shared screen sub-spaces based on their tracked location with respect to the screen, and use a webpage on their personal smartphone for touchpad-type input. We report user experiments using our interaction framework that involve image selection and placement tasks, with the ultimate goal of realizing display-wall environments as viable, interactive workspaces with natural multimodal interfaces.
Research on the Trajectory Planning of Demolition Robot Attachment Changing

The process of changing the attachment of a demolition robot is a complex operation and requires a high docking accuracy, so it is hard for operators to control this process remotely through the camera's perspective. To solve this problem, this paper studies trajectory planning for changing a demolition robot attachment. This paper establishes a link parameter model of the demolition robot; the position and attitude of the attachment are obtained through a camera, the optimal docking point is calculated to minimize the distance error during angle alignment for attachment change, the inverse kinemics of the demolition robot are solved, the trajectory planning algorithm and visualization program are programmed, and then the trajectory planning for the demolition robot attachment changing method is proposed. The results of calculations and experiments show that the method in this paper can meet the accuracy, efficiency, and safety requirements of demolition robot attachment changing, and it has promising application prospects in the decommissioning and dismantling of nuclear facilities and other radioactive environments.
An Analysis of Lexical Chunks in Online Business Letters (OBL) and Business Letter Corpus (BLC): A Corpus-Based Study

This research investigated into the lexical density and frequencies of five types of lexical chunks located in 300 online business letters. Top 10 websites on business correspondence had been identified in terms of traffic visitors and bounce rate under one million web rankings worldwide. Criterion Sampling method was identified prior to extracting the sample letters from the websites. The data was then run with Antconc Concordance Program (ACP) for lexical density and frequency analysis. Top 15 lexical chunks in online business letters (OBL) were compared against those top 15 in Business Letter Corpus (BLC). Findings revealed that there was a total of 39 916-word tokens and 939 counts of lexical chunks found in this corpus. It was found that more lexical words do not imply more lexical chunks used in based on types of business letters. All 5 types of lexical chunks were identified and ranked in descending order; Sentence Builders (SB) as the most frequent type, followed by Collocations (COL), Deictic locutions (DLs), Polywords (POLs) and Institutionalized Expressions (IUs) as the least frequent type of lexical chunk. Sub-divisional analysis indicated that Grammatical Collocations (GCs) were more common than Lexical Collocations (LCs). Majority of lexical chunks were formed more at sentence level than phrasal level. Comparative analysis between top 15 lexical chunks in OBL and BLC discovered that most top lexical chunks in online business letters are representative of those corporate business letters in BLC. Pedagogical implications in terms of the reliability of online business letters for academic reference and future research considerations are also addressed.
Impact of Information Security Training on Recognition of Phishing Attacks: A Case Study of Vilnius Gediminas Technical University

Phishing attack is a type of social engineering attack and often used as the initial stage of a larger campaign. It is dangerous as users might inadvertently reveal to the attackers personal data or sensitive corporate information. Therefore, inability to recognize and properly react to phishing attacks must be treated as one of the main security risks in the enterprise. In this paper, we present a methodology for evaluating employees' resistance to phishing attacks. We also analyze the changes to the situation after the employees participated in information security training. Experiments with employees of Vilnius Gediminas Technical University were carried out within a period of one year to gather information on how credulous they are to phishing attacks before and after security training. Results of the experiment reveal the benefit of security training, however there is still room for improvement and need to pay attention in the future.
The failed promise of <scp>IeCT</scp> for development: Aunt Ofelia replies to her letter 20 years later

Abstract This paper reflects on the nature of social changes brought about by information and communication technologies for development (ICTD or ICT4D) over the past two decades. We respond to the 2002 Letter to Aunt Ofelia, a publication that resulted from collective workshop of ICTD practitioners in Latin America. The Letter to Aunt Ofelia, written in the form of a letter to an imaginary aunt, presented seven proposals for equitable development using ICT. In this note, we use the epistolary format again as a response from the imaginary aunt, to present 10 challenges that represent failures and setbacks of ICT working against rather than for human development. These challenges pertain to commerce, services, media, surveillance, algorithmic control, addiction, misinformation, acceleration, loneliness, and environmental degradation. These 10 issues deserve our critical attention and creative energies if we are to truly harness the power of ICT for human development with equity, prosperity, and social justice.
Gap in the Wall

The Writing Center is the newest, innovative service, established as a project-based initiative within the organization of the Library of Corvinus University Budapest. The present and future goals of the Writing Center require a wide spectrum of services if wanting to cater for the needs of doctoral students and faculty members. This includes traditional and novel tasks, such as fostering publication activities, combating information overload, being familiar with abstract writing, and Open Access offered to experienced and to early career researchers. The goal in this chapter is to demonstrate how the learning and research support activities of a library, comprising curricular and extra-curricular courses, trainings, and consultations can be integrated into the knowledge structures of the university as a whole. The authors place special emphasis on the role of group-based and individual mentoring throughout a university career, spanning from student to researcher, and on the development of transversal skills through the training programs of the Writing Center.
Secure Anti-piracy System

This paper proposes a secure movie distribution and playing system which can avoid piracy in theaters. In the proposed system, an IR LED based anti-piracy screen is set up for discouraging illegal recording of movies. The optimization of power is achieved through progressive activation and deactivation of IR LEDs. Copyright protection of movie file is provided using invisible watermarking, and confidentiality and security are provided by encrypting the movie file as well as by enabling the decryption based on the authorized location verification of theaters. The system is also designed to allow only the authorized personnel to operate the system upon receiving the one-time password (OTP) from the owner. The experimental results proved that the developed system is secure and discourages the illegal video recording by creating artificial degradation in the recoded video. The system also helps to resolve rightful ownership via invisible watermarking.
Enhancement of Synthetic-Aperture Radar (SAR) Images Based on Dynamic Unsharp Masking

Synthetic-aperture radar (SAR) images are speckle corrupted at the initial stage of their acquisition. For this reason, despeckling of the images is required to preserve the finer details without losing them. But, quite often it was seen that even after despeckling, there persists residual noise at higher noise levels and obscured or weak edges. Therefore, to overcome these problems, in this paper, a method of enhancement using dynamic unsharp masking has been proposed for various noise levels. It uses the concept of variable weight factor which is calculated for a single pixel of the image. Dynamic unsharp masking controls the enhancement of the images adaptively such that there is no overshooting in images. The sharpening competency of the proposed method is then scrutinized and compared with conventional unsharp masking using an evaluation parameter called measure of enhancement (EME).
Towards a Better Security in Public Cloud Computing

Data protection is considered to be a main facet in order to have a favorable development of the infrastructure of public cloud. This is due to the fact that it mainly indicates the well working of infrastructure especially for the cloud users and also for the cloud providers. Unfortunately, data protection has not yet been provided with the required attention. Nowadays, in order to achieve data protection, cryptographic approaches are recommended as solutions because they can grant some degree of control on data users. Nevertheless, they can reduce the efficiency and also set up an important overhead of any cloud system. In this work, we studied the storage and retrieval in the cloud, which are protection methods. Then, we evaluated their strengths and also their limitations in order to propose a new approach.
The Impact of Rule Evaluation Metrics as a Conflict Resolution Strategy

It is crucial for each rule induced via machine learning algorithm is to be associated with a numerical value(s) which can reflect its properties like accuracy, coverage, likelihood. The accumulation of these properties is the so-called evaluation metrics. These metrics are important for both rule induction systems (for stopping rules generation) and rule classification systems (for solving rules conflict). This paper describes the most important of both statistical and empirical rule evaluation metrics. Thereafter, the paper presents an approach that utilizes and shows the impact of these metrics as a rule conflict resolution strategy during classification tasks when combining two heterogeneous classifiers (Naïve Bayes (henceforth, NB) and decision tree J48). To accomplish this goal, authors have extracted rule-set from J48 and presented a new method for rule construction from NB. Experiments have been conducted on (WBC, Vote, and Diabetes) datasets. The experimental results show that different evaluation metrics have a different impact on the classification accuracy when used as a conflict resolution strategy.
Gemini-Chain: A Regulatable Digital Currency Model based on Blockchain

Blockchain technology is the fundamental technology for digital currency such as Bitcoin. Because blockchain can establish credit between unfamiliar nodes in distributed systems, there are no more centralized credit entities in emerging digital currency systems. It's challenging for financial regulators and state agencies to supervise and manage the participants in the digital money system and the transactions between them. In this paper, a double-chain based regulatable model is proposed to solve this problem. In the aspect of the consensus mechanism, we introduce an agreement protocol based on the Credit Practical Byzantine Fault Tolerance to improve the system efficiency. The experimental results show that the proposed double-chain model works effectively, and the consensus mechanism and overall performance meet the design requirements.
Image Generation from Layout via Pair-Wise RaGAN

Despite recent remarkable progress in image generation from layout, synthesizing vivid images with recognizable objects remains a challenging problem, object distortion and color imbalance occasionally happened in the generated images. To overcome these limitations, we propose a novel approach called Pair-wise Relativistic average Generative Adversarial Network (P-RaGAN) which includes a pair-wise relativistic average discriminator for enhancing the generative ability of network. We also introduce a consistency loss into our model to keep the consistency of original latent code and reconstructed or generated latent code for reducing the scope of solution space. A series of ablation experiments demonstrate the capability of our model in the task from layout to image on the complicated COCO-stuff and Visual Genome datasets. Extensive experimental results show that our model outperforms the state-of-the-art methods.
An Approach for Collaborative Data Publishing Using Self-adaptive Genetic Grey Wolf Optimizer

This paper introduces an algorithm, termed self-adaptive genetic grey wolf optimizer (self-adaptive genetic GWO), for privacy preservation using a C-mixture factor. The C-mixture factor improves the privacy of data, in which the data does not satisfy the privacy constraints, such as l-diversity, m-privacy, and k-anonymity. Experimentation is carried out using the adult dataset, and the effectiveness of the proposed self-adaptive genetic GWO is checked depending on the information loss and the average equivalence class metric values and is evaluated to be the best when compared to other existing techniques with low information loss value as 0.1724 and average equivalence class value as 0.71, respectively.
Coal Power Technology Exhibition and Intelligent Power Generation

This paper introduces the development trend of coal industry up to 2019. Compares with the global coal consumption volume and policy requirements, it is concluded that in the future, the amount of coal power generation will still occupy the main position of gross generation. With the requirements of environmental protection and clean energy, coal industry will gradually upgrade into coal gasification processing cluster. After gasification, the fuel can be used in the combustion of gas turbine to generate power, so as to achieve the advantages of higher efficiency and lower pollution. At the same time, the combined fuel cell technology can directly convert chemical energy into electrical energy, which will become the ultimate goal of the transformation and upgrading of the coal power generation industry in the future. At present, the coal power generation group is actively exploring the production application and management decision-making of intelligent power plants. The CFD software can be fully used to simulate and calculate the malfunction of main equipment in the power plant, and help technicians to determine the transformation scheme and fault causes. Artificial intelligence analysis method can analyze the weight proportion of a large number of production data stored in the SIS system of the power plant according to target requirements, quickly get the cause of main equipment failure, and predict the operation data for 3 to 5 min. The upgraded DCS management system can become intelligent and centralized big data analysis system.