Computer Science Research Research Articles

Adoption of Blockchain Technology in Healthcare: Challenges, Solutions, and Comparisons

Blockchain technology was bestowed through bitcoin; research has continuously stretched out its applications in different sectors, proving blockchain as a versatile technology expanded in non-financial use cases. In the healthcare industry, blockchain is relied upon to have critical effects. Although exploration here is generally new yet developing quickly, along these lines, researchers in computer science, healthcare information technology, and professionals are continually geared to stay up with research progress. The study presents an exhaustive study on blockchain as a technology in depth from all possible perspectives and its adoption in the healthcare sector. A mapping study has been conducted to search different scientific databases to identify the existing challenges in healthcare management systems and to analyze the existing blockchain-based healthcare applications. Though blockchain has inherent highlights, such as distributed ledger, encryption, consensus, and immutability, blockchain adoption in healthcare has challenges. This paper also provides insights into the research challenges in blockchain and proposes solution taxonomy through comparative analysis.

Scaling Automated Programming Assessment Systems

The first automated assessment of student programs was reported more than 60 years ago, but this topic remains relevant and highly topical among computer science researchers and teachers. In the last decade, several factors have contributed to the popularity of this approach, such as the development of massive online courses, where large numbers of students can hardly be assessed manually, the COVID-19 pandemic with a strong online presence and physical relocation of students, and the ever-increasing shortage of personnel in the field CS. Modern Automated Programming Assessment Systems (APASs) are nowadays implemented as web applications. For such web applications, especially those that support immediate (on-demand) program assessments and feedback, it can be quite a challenge to implement the various system modules in a secure and scalable manner. Over the past six years, we have developed and actively deployed “Edgar”—a state-of-the-art APAS that enables immediate program evaluation and feedback in any programming language (SQL, C, Java, etc.). In this article, we look at the APAS web application architecture with a focus on scalability issues. We review fundamental features such as dynamic analysis and untrusted code execution, as well as more complex cases such as static analysis and plagiarism detection, and we summarize the lessons learned over the previous six years of research. We identify scalability challenges, show how they have been addressed in APAS Edgar, and then propose general architectural solutions, building blocks and patterns to address those challenges.

Exploring arXiv usage habits among Slovenian scientists

PurposeThis study investigates how important the preprint arXiv is for Slovenian scientists, whether there are differences between scientific disciplines and the reputation of arXiv among Slovenian scientists. We are also interested in what advantages and disadvantages scientists see in using arXiv.Design/methodology/approachA voluntary sample of active researchers from the scientific fields covered by arXiv was used. Data were collected over 21 days in September 2021 using a 40-question online survey. In addition to descriptive statistics, nonparametric statistical methods such as Pearson's chi-squared test for independence, Kruskal-Wallis' H-test and Mann-Whitney's U-test were applied to the collected data.FindingsAmong Slovenian scientists there is a wide range of different users of arXiv. The authors note differences among scientific disciplines. Physicists and astronomers are the most engaged, followed by mathematicians. Researchers in computer science, electrical engineering and systems science seem to have recognized the benefits of the archive, but are still hesitant to use it. Researchers from the other scientific fields participated in the survey to a lesser extent, suggesting that arXiv is less popular in these scientific fields. For Slovenian scientists, the main advantages of arXiv are faster access to knowledge, open access, greater impact of scientists' work and the fact that publishing in the archive is free of charge. A negative aspect of using the archive is the frustration caused by the difficulties in assessing the credibility of articles.Research limitations/implicationsA voluntary sample was used, which attracted a larger number of researchers but has a higher risk of sampling bias.Practical implicationsThe results are useful for international comparisons, but also provide bases and recommendations for institutional and national policies to evaluate researchers and their performance.Originality/valueThe results provide valuable insights into arXiv usage habits and the reasons for using or not using arXiv by Slovenian scientists. There is no comparable study conducted in Slovenia.

Synergies Between Operations Research and Quantum Information Science

This article highlights synergies between quantum information science (QIS) and operations research for QIS-curious operations researchers (and vice versa), and the author challenges the community to discover more. History: This “Challenge” paper was invited by the Editor in Chief and based on the topics raised by the author at his plenary address at the 2022 INFORMS Computing Society Conference in Tampa, Florida. Funding: This material is based on work supported by the U.S. Department of Energy, Office of Science, Office of Advanced Scientific Computing Research, Accelerated Research in Quantum Computing, Fundamental Algorithmic Research for Quantum Computing. This article has been authored by an employee of National Technology & Engineering Solutions of Sandia, LLC, under Contract DE-NA0003525 with the U.S. Department of Energy (DOE).

Why Are There So Few Women in Computer Systems Research?

Numerous explanations have been proposed for the underrepresentation of women in computer science research. Using an expansive dataset, we ask which of these explanations apply even more to researchers in computer systems.

РАЗРАБОТКА КОНЦЕПЦИИ ЦИФРОВОГО ДВОЙНИКА РАБОТНИКА РАДИАЦИОННО- И ЯДЕРНО-ОПАСНОГО ПРЕДПРИЯТИЯ И ПРОИЗВОДСТВА

Purpose: Analysis of the possibilities of a predictive model for assessing the risks of pathology of the cardiovascular, cerebrovascular systems and psychophysiological state in workers of radiation and nuclear hazardous enterprises and industries as part of the development of the concept of a digital twin. Material and methods: The study involved employees of one of the leading radiation and nuclear hazardous enterprises, who underwent periodic medical examinations at comprehensive medical health center of the A.I. Burnazyan Federal Medical and Biological Center in September–October 2022. In total 154 patients (115 men and 39 women) were questioned, examined, and analyzed medical records. Diagnostic criteria for risk factors and other pathological conditions and diseases that increase the likelihood of developing chronic non-communicable diseases were evaluated both in accordance with the orders of the Ministry of Health and using the AI-HIPPOCRAT digital prognostic model developed by the Computer Science and Management Federal Research Center. Results: In accordance with the criteria specified in the orders of the Ministry of Health, it was revealed that high or very high risks of developing diseases such as heart attack, stroke, hypertension and depression are observed in 120 people (77.9 %), and according to the analysis in the AI-HIPPOCRAT system, these risks occur in 131 people (85.1 %). Conclusion: As a result of the study, the capabilities of the AI-HIPPOCRATE system were analyzed and the following recommendations were given for its use by workers of radiation and nuclear hazardous enterprises and industries: to integrate the AI-HIPPOCRAT system into the medical information system used at the enterprise; to divide the presented risk factors into three groups: manageable (on which the doctor or patient can influence), conditionally manageable (which can be influenced by the use of drugs) and uncontrollable, which the doctor and the patient cannot manage (for example, gender, age, etc.); to raise the threshold for setting the risk of a particular disease to “high and above”.

KC Two-Way Clustering Algorithms For Multi-Child Semantic Maps In Image Mining

Image mining is now a thriving and expanding field of computer science research. Image mining is linked to the advancement of data mining in image preparation. Image mining is used to extract hidden information and in other situations where the photos do not clearly describe the situation. Image mining combines machine learning, data handling, application autonomy, and image preparation concepts. Semantic maps are used to visualize image data stored in image databases. We recommend using Multi-Child Semantic Maps to build semantic maps which fully display the image. In this study, we propose two path clustering on Multi-Child Semantic Maps (MCSM) using the K-C Means Clustering Algorithm, also known as the MCSMK-C algorithm. This algorithm causes image clustering and instructs the mining system to look at the image's top area. When mining, the MCSMK-C algorithm considers the X and Y coordinates. The system looks for groups by examining each object's territory in the database, and it saves a region if it contains more objects than the required number.

An Analysis of Black Faculty in CS Research Departments

Exploring Black faculty at computer science research departments where Ph.D. programs exist.

Reporting non-consensual pornography: clarity, efficiency and distress

According to recent legislative initiatives, non-consensual pornography is a crime in several countries and social media providers have a duty to provide their users easy to use mechanisms to report abuses. In this paper, we analyse the state of the art of the interfaces for reporting non-consensual pornography from the victim’s perspective. Firstly, we analysed 45 content sharing platforms where aggressors might post non-consensual pornography. The analysis identified three distinct interaction styles for reporting the crime: Scriptum (a text-field where the user verbally describes the abuse), Bonam (a multilayered menu that includes a correct option), and Malam (a multilayered menu that does not include a correct option). Secondly, we conducted a within-subject study to evaluate the experience elicited by these interaction styles. Participants (N = 39) were given a scenario and asked to report six blurred images as non-consensual pornography using a medium-fidelity prototype. The results exposed complex trade-offs between clarity, efficiency, and distress among the different interaction styles. These trade-offs open foundational research directions transcending boundaries between human-computer interaction and multimedia studies and interfacing computer science research with the law.

Forging Emotions: a deep learning experiment on emotions and art

Affective computing is an interdisciplinary field that studies computational methods that relate to or influence emotion. These methods have been applied to interactive media artworks, but they have focused on affect detection rather than affect generation. For affect generation, computationally creative methods need to be explored that have recently been driven by the use of Generative Adversarial Networks (GANs), a deep learning method. The experiment presented in this paper, Forging Emotions, explores the use of visual emotion datasets and the working processes of GANs for visual affect generation, that is, for generating images that can convey or trigger specified emotions. This experiment concludes that the methodology used so far by computer science researchers to build image datasets for describing high-level concepts such as emotions is insufficient and proposes utilizing emotional networks of associations according to psychology research. Forging Emotions also concludes that to generate affect visually, merely corresponding to basic psychology findings, such as bright or dark colours, does not seem adequate. Therefore, research efforts should aim to understand the structure of trained GANs and compositional GANs in order to produce genuinely novel compositions that can convey or trigger emotions through the subject matter of generated images.

Enterprise Platform and Integration Concepts Research at HPI

The Hasso Plattner Institute (HPI), academically structured as the independent Faculty of Digital Engineering at the University of Potsdam, unites computer science research and teaching with the advantages of a privately financed institute and a tuition-free study program. Founder and namesake of the institute is the SAP co-founder Hasso Plattner, who also heads the Enterprise Platform and Integration Concepts (EPIC) research center which focuses on the technical aspects of business software with a vision to provide the fastest way to get insights out of enterprise data. Founded in 2006, the EPIC combines three research groups comprising autonomous data management, enterprise software engineering, and data-driven decision support.

Toward the Development of a Computer-Assisted, Real-Time Assessment of Ideational Dynamics in Collaborative Creative Groups

ABSTRACT We present the results of an ongoing collaboration between computer science and psychology researchers that employs Natural Language Processing (NLP) methods to examine the trajectory of semantic space used during group idea generation sessions. Specifically, we track and estimate the region of semantic space being used and the degree to which new ideas expand that space. We present a visualization of this space mapping endeavor and compare human ratings of creativity dimensions (i.e., novelty, task-relevance, and elaboration) to algorithm-based estimations of those same dimensions. The semantic space mapping and algorithm development projects can be used to deliver real-time feedback to human creative groups in order to optimize the collaborative creativity process. The overall goal of this research is to increase the “survival” of novel ideas and their elaboration in the collaborative ideation and subsequent decision processes.

Multi-level Federated Learning for Industry 4.0 - A Crowdsourcing Approach

Federated learning is one of the emerging areas of research in computer science. It has shown great potential in some application areas and we are witnessing evidence of new approaches where millions or even billions of IoT devices can contribute collectively to achieve a common goal of machine learning through federation. However, existing approaches are primarily suitable for single-task learning with a single objective in a single task owner where it is assumed that the majority of devices contributing to federated learning have a similar design or device type and restrictions. We argue that the true potential of federated learning can only be realised if we have a dynamic and open ecosystem where devices, industrial units, machine manufacturers, non-governmental agencies, and governmental entities can contribute toward learning for multiple tasks and objectives in a crowdsourced manner. In this article, we propose a multi-level framework that shows how federated learning, IoT, and crowdsourcing can come hand-in-hand with each other to make a robust ecosystem of multi-level federated learning for Industry 4.0. This helps build future intelligent applications for Industry 4.0 such as predictive maintenance and fault detection for systems in smart manufacturing units. In addition, we also highlight several use-cases of multi-level federated learning where this approach can be implemented in Industry 4.0. Moreover, if the approach is implemented successfully, besides enhancement in performance it will also help towards a greater common goal e.g. UN Sustainable Goal No 13 i.e. reduction in carbon footprint.

Application of Machine Learning in Digital Human Resource Management

In today's high-speed information age, many problems of the traditional human resource management (HRM) system make it impossible to effectively analyze massive data in today's enterprise competition and development. Machine learning, as a novel field of research in computer science in recent decades, provides a potential solution to the current problems faced by HRM systems. This study focuses on strategic HRM, an essential means of shaping employee behavior, and a vital information transmission channel in the CEO's transformational leadership behavior. By reviewing past literature on machine learning and exploring the principles of the practical application of machine learning in HRM, this paper also focuses on the intermediary mechanism between enterprise performance and the latest progress in machine learning and HRM. Further, the study tries to summarize and refine the opportunities and challenges that HRM faces in developing HRM and possible future research directions and hotspots.

Insect Classification Framework based on a Novel Fusion of High-level and Shallow Features

The largest and most diversified group of organisms are insects. Since insects play a crucial role in many ecosystems, it must be precisely identified for efficient management. However, it is difficult and labor-intensive to identify insect species. Due to advancements in deep learning, computer vision, and sensor technologies, there is rising interest in image-based systems for quick, accurate identification. This study investigates a novel hybrid feature set of shallow features from the tiger beetle dataset, such as texture and wavelet features and high-level features from SqueezeNet. The tiger beetle insect is classified into the cicindelini and collyridini classes with a random forest classifier with 97.65 percent accuracy using this hybrid feature set, which considers the texture and structural characteristics of the insect. Thus the technique provides insight into various features and indicates promising future directions for image-based insect identification and species classification relevant to Computer Science, Agriculture, and Ecology research.

Annalen der Physik – Wiley's Physics Forum and Center of Excellence

<i>Annalen der Physik</i> – Wiley's Physics Forum and Center of Excellence

Visual User-Generated Content Verification in Journalism: An Overview

Over the past few years, social media has become an indispensable part of the news generation and dissemination cycle on the global stage. These digital channels along with the easy-to-use editing tools have unfortunately created a medium for spreading mis-/disinformation containing visual content. Media practitioners and fact-checkers continue to struggle with scrutinising and debunking visual user-generated content (UGC) quickly and thoroughly as verification of visual content requires a high level of expertise and could be exceedingly complex amid the existing computational tools employed in newsrooms. The aim of this study is to present a forward-looking perspective on how visual UGC verification in journalism can be transformed by multimedia forensics research. We elaborate on a comprehensive overview of the five elements of the UGC verification and propose multimedia forensics as the sixth element. In addition, different types of visual content forgeries and detection approaches proposed by the computer science research community are explained. Finally, a mapping of the available verification tools media practitioners rely on is created along with their limitations and future research directions to gain the confidence of media professionals in using multimedia forensics tools in their day-to-day routine.

SYNTHESIS OF THE UTILITY FUNCTION OF THE IT COMPANY'S SOFTWARE DEVELOPMENT PROCESS BASED ON THE SPICE REFERENCE MODEL

The work distinguishes two types of complex systems: "real systems" and "virtual systems". The first ones are the object of computer science research, and the second ones are the object of software engineering. The latter include software systems (SS) and the software development process (SDP), which is the object of scientific work research. SDP refers to goal-oriented systems. In such systems, on the basis of the set goals, the task of synthesizing their structure (individual elements and the relationship between them) arises, which is aimed at achieving the set goals related to the quality of SDP. It is believed that the success of SS development directly depends on the quality of SDP. The work considers the problem of assessment and improvement of SDP on the basis of two maturity models: SMMI, SPICE. Both models use a point scale of the first type, when the assessment is made according to objective criteria, so that individual assessments are some fluctuations of the real values. At the same time, there are generally accepted standards that correspond to the gradations of the point scale. The main component of the SMMI model is the focal area, which is characterized by the "level of opportunity" on a point scale from 0 to 3. The main component of the SPICE model is a process characterized by a "level of possibility" on a point scale from 1 to 5. For the discrete version of the SMMI model, the concept of "maturity level" is used, thanks to which the quality of the SDP of the entire IT organization is assessed. The formalization of the model was carried out and static and dynamic planning models of the SDP of the IT organization have been implemented, which are integrated into a single technology of "sliding" planning. One of the main disadvantages of the discrete version of the SMMI model is that it does not take into account the specifics of the IT organization activity. On the other hand, the SPICE model does not allow for the assessment of the entire SDP of the IT organization. Therefore, a synthesis of a discrete maturity model based on the SPICE reference model was proposed. Its main goal is to minimize the main drawbacks of the SMMI model. For this purpose, the use of utility theory was proposed. A hierarchical structure of criteria is formed, on the basis of which the utility function is synthesized. Previously, at the level of a separate process, the SPICE model was formalized and the structure of the utility function was determined on this basis. Taking into account the fundamental properties of the systems, the trend of its change based on the used resources is presented. Next, the experts compare the characteristics of the processes of the SPICE model and the IT company from the point of view of the importance of the processes for increasing the level of maturity of the SDP. This information from the experts is provided to the analysts, who place the processes in a queue depending on the importance from the point of view of the utility function.

Software: Protection, Licensing and Rewarding Researchers in Computer Science – An Overview of Challenges in the European Innovation Ecosystem

With the transition of innovation to the digital sphere, the software has become an essential part of contemporary inventions and creations. It is also an essential part of intellectual property. The state of software protection in the European innovation ecosystem can still not be considered fully regulated. However, researchers in computer science also face several challenges when exploiting intellectual property rights in software. The field, therefore, offers opportunities for researching, which should steer research to (1) present the critical points and (2) update some of the legal frameworks to address more clearly the field that deals with the issue of remuneration for researchers in computer science. The overview paper discusses software, focusing mainly on the challenges that researchers in computer science face in protecting and licensing software in the European innovation ecosystem. The following paper presents the state of the researcher’s remuneration under protection and licensing.

Twitter spam and false accounts prevalence, detection, and characterization: A survey

The issue of quantifying and characterizing various forms of social media manipulation and abuse has been at the forefront of the computational social science research community for over a decade. In this paper, I provide a (non-comprehensive) survey of research efforts aimed at estimating the prevalence of spam and false accounts on Twitter, as well as characterizing their use, activity, and behavior. I propose a taxonomy of spam and false accounts, enumerating known techniques used to create and detect them. Then, I summarize studies estimating the prevalence of spam and false accounts on Twitter. Finally, I report on research that illustrates how spam and false accounts are used for scams and frauds, stock market manipulation, political disinformation and deception, conspiracy amplification, coordinated influence, public health misinformation campaigns, radical propaganda and recruitment, and more. I will conclude with a set of recommendations aimed at charting the path forward to combat these problems.