Current Open Research Research Articles

Maximal extractable value: Current understanding, categorization, and open research questions

In traditional financial markets, front-running is a well-structured phenomenon. It represents a form of privileged actors utilizing knowledge or power advantages to extract undue profit at the cost of other stakeholders. Various mitigation strategies have emerged, ranging from market design to regulatory measures. More recently, a similar and substantially richer variety of means to gain unethical profit from power asymmetries has appeared in the context of blockchain-based decentralized applications. This phenomenon is called “maximal extractable value” (MEV). Despite the decentralized nature and inherent transparency of blockchain ledgers, MEV is particularly prevalent and challenging to mitigate. While related work in computer science and algorithmic game theory has already identified several different ways in which MEV manifests in decentralized finance (DeFi) and outlined partial solution approaches, a discussion of its impacts in the information systems (IS) domain is still absent. A holistic definition of MEV and how it can be exploited is necessary for the discussion of its potential implications for blockchain-based IS for businesses and public institutions. This paper conducts a systematic literature review to close this gap. It consolidates the diverging definitions of MEV and provides a categorization of the different ways in which it can manifest. As such, we synthesize and review the existing state of knowledge on MEV and point to undiscovered areas relevant to decentralized electronic markets in the form of a research agenda.

Interdisciplinary system lifecycle management – a systematic literature review

AbstractThe increasing proportion of software in technical products means that both the products and the associated development processes are becoming more complex. An integration of the existing lifecycle considerations Application Lifecycle Management and Product Lifecycle Management into an interdisciplinary System Lifecycle Management promises to make the complexity manageable. To obtain an overview of the current benefits, challenges, requirements, approaches and open research gaps in the context of an ALM-PLM integration, this contribution presents the results of a Systematic Literature Review.

A Comprehensive Survey on the Data-Driven Approaches used for Tackling the COVID-19 Pandemic

The current evolution of Artificial Intelligence (AI) is fueled by the massive data sources generated by the Internet of Things (IoT), social media, and a diverse range of mobile and web applications. Machine learning (ML) and deep learning become the key to analyzing these data intelligently and developing complementary intelligent data-driven services in the healthcare sector. The world witnessed many AI-enabled tools that contributed to fighting against the COVID-19 pandemic and accelerated with unprecedented accuracy the development and the deployment of many countermeasures. The main objective of this study is to provide a comprehensive survey on the role of AI and ML methods in the healthcare sector. The study offers cases on how AI/ML can arm the world against future pandemics. Specifically, the study presents all available datasets, the main research problems related to COVID-19, and the solutions that AI and ML technologies offer. Finally, based on the analysis of the current literature, the limitations and open research challenges are highlighted. Our findings show that AI and ML technologies can play an essential role in COVID-19 forecasting, prediction, diagnosis, and analysis. In comparison, most of the previous works did not deploy a comprehensive framework that integrates the ML and DL with network security. This work emphasizes the mandate of including network security in all COVID-19 applications and providing complete and secure healthcare services.

Formal Methods and Validation Techniques for Ensuring Automotive Systems Security

The increasing complexity and connectivity of automotive systems have raised concerns about their vulnerability to security breaches. As a result, the integration of formal methods and validation techniques has become crucial in ensuring the security of automotive systems. This survey research paper aims to provide a comprehensive overview of the current state-of-the-art formal methods and validation techniques employed in the automotive industry for system security. The paper begins by discussing the challenges associated with automotive system security and the potential consequences of security breaches. Then, it explores various formal methods, such as model checking, theorem proving, and abstract interpretation, which have been widely used to analyze and verify the security properties of automotive systems. Additionally, the survey highlights the validation techniques employed to ensure the effectiveness of security measures, including penetration testing, fault injection, and fuzz testing. Furthermore, the paper examines the integration of formal methods and validation techniques within the automotive development lifecycle, including requirements engineering, design, implementation, and testing phases. It discusses the benefits and limitations of these approaches, considering factors such as scalability, efficiency, and applicability to real-world automotive systems. Through an extensive review of relevant literature and case studies, this survey provides insights into the current research trends, challenges, and open research questions in the field of formal methods and validation techniques for automotive system security. The findings of this survey can serve as a valuable resource for researchers, practitioners, and policymakers involved in the design, development, and evaluation of secure automotive systems.

Challenges and prospects in big data analytics: a comprehensive review of developments, hurdles, and future research directions

Big data and business analytics are trends that are positively affecting the business world. This comprehensive review article explores the shifting paradigms and dynamic trends within Big Data Technology (BDT), predominantly for last 5 years, based on an extensive literature review and comparative analysis methodology. It elucidates the transformative influence of big data analytics (BDA) in various sectors, emphasizing the rapid ascendance of cloud computing, Artificial Intelligence (AI) integration, and development of sophisticated analytics tools. The review leverages a wealth of academic literature and market research to underscore the predicted expansion of the big data market. This projected growth indicates the widespread adoption of BDT across industries, with healthcare becoming a significant consumer, motivated by the demand for personalized medicine and improved patient care. The review then navigates emerging trends such as open data usage and ethical concerns surrounding big data, indicating the increasing necessity for stringent guidelines for data use and robust individual data control mechanisms. This is derived from a methodical analysis of recent scholarly articles and industry reports. The article also scrutinizes the evolving definition of "big data" through comparative study of the 3V model and the expanded 7V model in various literature sources, reflecting the evolving nature of data and the unique challenges introduced by modern big data analytics. The review also outlines the challenges for successful implementation of big data projects and highlights the current open research directions of big data analytics. The reviewed areas of big data suggest that good management and manipulation of the large data sets using the techniques and tools of big data can deliver actionable insights that create business values.

Ten Topics to Get Started in Medical Informatics Research.

The vast and heterogeneous data being constantly generated in clinics can provide great wealth for patients and research alike. The quickly evolving field of medical informatics research has contributed numerous concepts, algorithms, and standards to facilitate this development. However, these difficult relationships, complex terminologies, and multiple implementations can present obstacles for people who want to get active in the field. With a particular focus on medical informatics research conducted in Germany, we present in our Viewpoint a set of 10 important topics to improve the overall interdisciplinary communication between different stakeholders (eg, physicians, computational experts, experimentalists, students, patient representatives). This may lower the barriers to entry and offer a starting point for collaborations at different levels. The suggested topics are briefly introduced, then general best practice guidance is given, and further resources for in-depth reading or hands-on tutorials are recommended. In addition, the topics are set to cover current aspects and open research gaps of the medical informatics domain, including data regulations and concepts; data harmonization and processing; and data evaluation, visualization, and dissemination. In addition, we give an example on how these topics can be integrated in a medical informatics curriculum for higher education. By recognizing these topics, readers will be able to (1) set clinical and research data into the context of medical informatics, understanding what is possible to achieve with data or how data should be handled in terms of data privacy and storage; (2) distinguish current interoperability standards and obtain first insights into the processes leading to effective data transfer and analysis; and (3) value the use of newly developed technical approaches to utilize the full potential of clinical data.

Wild Patterns Reloaded: A Survey of Machine Learning Security against Training Data Poisoning

The success of machine learning is fueled by the increasing availability of computing power and large training datasets. The training data is used to learn new models or update existing ones, assuming that it is sufficiently representative of the data that will be encountered at test time. This assumption is challenged by the threat of poisoning, an attack that manipulates the training data to compromise the model’s performance at test time. Although poisoning has been acknowledged as a relevant threat in industry applications, and a variety of different attacks and defenses have been proposed so far, a complete systematization and critical review of the field is still missing. In this survey, we provide a comprehensive systematization of poisoning attacks and defenses in machine learning, reviewing more than 100 papers published in the field in the past 15 years. We start by categorizing the current threat models and attacks and then organize existing defenses accordingly. While we focus mostly on computer-vision applications, we argue that our systematization also encompasses state-of-the-art attacks and defenses for other data modalities. Finally, we discuss existing resources for research in poisoning and shed light on the current limitations and open research questions in this research field.

Social search: Retrieving information in Online Social platforms – A survey

Social Search research studies methodologies exploiting social information to better satisfy user information needs in Online Social Media while simplifying the search effort and consequently reducing the time spent and the computational resources utilized. Starting from previous studies, in this work, we analyze the current state of the art of the Social Search area, proposing a new taxonomy and highlighting current limitations and open research directions. We divide the Social Search area into three subcategories, where the social aspect plays a pivotal role: Social Question&Answering, Social Content Search, and Social Collaborative Search. For each subcategory, we present the key concepts and selected representative approaches in the literature in greater detail. We found that, up to now, a large body of studies model users’ preferences and their relations by simply combining social features made available by social platforms. It paves the way for significant research to exploit more structured information about users’ social profiles and behaviours (as they can be inferred from data available on social platforms) to optimize their information needs further.

Injecting cognitive intelligence into beyond-5G networks: A MAC layer perspective

The rapid rise of heterogeneous data traffic exposes the shortcomings of fifth-generation (5G) technology, which was initially designed to form self-organizing and self-sustaining networks to facilitate the adoption of the Internet-of-Everything (IoE). This study presents the applications and service requirements of future communication networks. This study details flexible design agreements of the Medium Access Control (MAC) layer of Beyond-5G (B5G) from the current 3rd Generation Partnership (3GPP) study and highlights the current open research issues and challenges which are yet to be optimized. To ensure that the network is self-sustaining and self-organized for B5G paradigm, an intelligent network design is required. Artificial Intelligence (AI) is revolutionizing every aspect of life, therefore, this article provides an overview of how AI plays an important role in improving future-generation communication by solving MAC-related issues.

Comparison of open education research between China and other countries from knowledge graph perspective

Based on the Chinese Social Science Citation Index (CSSCI) and Social Science Citation Index (SSCI), this paper uses visualization software CiteSpace to draw the knowledge graph, and makes a bibliometric analysis of open education research between China and other countries from 1998 year to 2020 year. Keywords co-occurrence, word frequency distribution and keyword clustering are used to analyze hot topics and research trends. The research finds that research hotspots mainly focus on the current situation, practical achievements, sustainable development of open education resources, the design of open curriculum and analysis of learner characteristics. The paper points out that the current open education research lacks of in-depth research on resource evaluation, and it should be strengthened to sustainable development of open education resources in the future.

Spatio-Temporal Forecasting: A Survey of Data-Driven Models Using Exogenous Data

Forecasting Spatio-Temporal processes has been attracting a great deal of interest within the research community. In this context, there is an increasing trend of proposing and improving methodologies to gather and use vast amounts of Spatio-Temporal data. Spatio Temporal Forecasting (STF) problems present complex interactions and non-linearities as the temporal dimension and the spatial one are usually entangled. To address these problems, statistical, Machine Learning based, and Deep Learning based models are introduced and developed. The use of exogenous data has proven to be beneficial in many STF models. Various techniques of incorporating exogenous data in STF problems have been proposed in the literature. This survey aims at providing a systematic review of the data-driven STF models, with a focus on those that incorporate exogenous data. We first investigate the data properties, including their dynamics, types and representations. Next, we propose a new taxonomy of the reviewed models and inspect the different complexities of STF problems. Exogenous data incorporation techniques are then presented and analyzed. We conclude our paper by highlighting the current open challenges and future research directions.

Recent Advances in Baggage Threat Detection: A Comprehensive and Systematic Survey

X-ray imagery systems have enabled security personnel to identify potential threats contained within the baggage and cargo since the early 1970s. However, the manual process of screening the threatening items is time-consuming and vulnerable to human error. Hence, researchers have utilized recent advancements in computer vision techniques, revolutionized by machine learning models, to aid in baggage security threat identification via 2D X-ray and 3D CT imagery. However, the performance of these approaches is severely affected by heavy occlusion, class imbalance, and limited labeled data, further complicated by ingeniously concealed emerging threats. Hence, the research community must devise suitable approaches by leveraging the findings from existing literature to move in new directions. Towards that goal, we present a structured survey providing systematic insight into state-of-the-art advances in baggage threat detection. Furthermore, we also present a comprehensible understanding of X-ray-based imaging systems and the challenges faced within the threat identification domain. We include a taxonomy to classify the approaches proposed within the context of 2D and 3D CT X-ray-based baggage security threat screening and provide a comparative analysis of the performance of the methods evaluated on four benchmarks. Besides, we also discuss current open challenges and potential future research avenues.

A Roadmap of Next-Generation Wireless Technology for 6G-Enabled Vehicular Networks

Ultra-reliable connectivity and delay-sensitive data transmission is the ever increasing demand for the next generation transportation system. However, substantial network congestion and long-distance data travel always create significant delays even with conventional 4G or 5G cellular technology. Situations become critical even in autonomous transportation systems in various domain-specific applications, where smart vehicles continuously generate an enormous amount of data to make optimal decisions, for example, self-driving cars, autonomous robots, and industrial transportation. Thus, the importance of beyond 5G or 6G technology in the transportation system is prominent and requires a different level of investigation. With this motivation, in this article, we briefly analyze next-generation 6G-supported technologies and their advantages on adopting 6G into the Internet of Vehicles (IoV). We also shed light on several 6G-based frameworks in IoV networks. Finally, we conclude our discussion by addressing current open research challenges and future directions of solving those challenges with 6G technology.

Erzählen als multimodale Aktivität – zur Einführung in das Themenheft

This special issue of Linguistik online is dedicated to the study of storytelling in face-to-face interaction. More specifically, the individual contributions to this special issue approach conversational storytelling from a multimodal perspective and provide analyses on the fine-grained coordination of verbal, para-verbal, and nonverbal action in face-to-face storytelling. This introduction first frames the individual contributions by providing an overview of the current topics and open research questions in the developing field of multimodal storytelling research. All individual papers are then briefly summarized and discussed in terms of their commonalities and their contribution to the developing field of multimodally-oriented research on conversational storytelling.

An Overview of Reinforcement Learning Methods for Variable Speed Limit Control

Variable Speed Limit (VSL) control systems are widely studied as solutions for improving safety and throughput on urban motorways. Machine learning techniques, specifically Reinforcement Learning (RL) methods, are a promising alternative for setting up VSL since they can learn and react to different traffic situations without knowing the explicit model of the motorway dynamics. However, the efficiency of combined RL-VSL is highly related to the class of the used RL algorithm, and description of the managed motorway section in which the RL-VSL agent sets the appropriate speed limits. Currently, there is no existing overview of RL algorithm applications in the domain of VSL. Therefore, a comprehensive survey on the state of the art of RL-VSL is presented. Best practices are summarized, and new viewpoints and future research directions, including an overview of current open research questions are presented.

Stability and control of power systems with high penetrations of inverter-based resources: An accessible review of current knowledge and open questions

As power system renewable energy penetrations increase, the ways in which key renewable technologies such as wind and solar photovoltaics (PV) differ from thermal generators become more apparent. Many studies have examined the variability and uncertainty of such generators and described how generation and load can be balanced for a wide variety of annual energy penetrations, at timescales from seconds to years. Another important characteristic of these resources is asynchronicity, the result of using inverters to interface the prime energy source with the power system as opposed to synchronous generators. Unlike synchronous generators, whose frequency of alternating current (AC) injection is physically coupled to the rotation of the machine itself, inverter based asynchronous generators do not share the same physical coupling with the generated frequency. These subtle differences impact the operations of power systems developed around the characteristics of synchronous generators. In this paper we review current knowledge and open research questions concerning the interplay between asynchronous inverter-based resources (IBRs) and cycle- to second-scale power system dynamics, with a focus on how stability and control may be impacted or need to be achieved differently when there are high instantaneous penetrations of IBRs across an interconnection. This work does not seek to provide a comprehensive review of the latest developments, but is instead intended to be accessible to any reader with an engineering background and an interest in power systems and renewable energy. As such, the paper includes basic material on power electronics, control schemes for IBRs, and power system stability; and uses this background material to describe potential impacts of IBRs on power system stability, operational challenges associated with large amounts of distributed IBR generation, and modern power system simulation trends driven by IBR characteristics.

Privacy and data protection in mobile cloud computing: A systematic mapping study.

As a result of a shift in the world of technology, the combination of ubiquitous mobile networks and cloud computing produced the mobile cloud computing (MCC) domain. As a consequence of a major concern of cloud users, privacy and data protection are getting substantial attention in the field. Currently, a considerable number of papers have been published on MCC with a growing interest in privacy and data protection. Along with this advance in MCC, however, no specific investigation highlights the results of the existing studies in privacy and data protection. In addition, there are no particular exploration highlights trends and open issues in the domain. Accordingly, the objective of this paper is to highlight the results of existing primary studies published in privacy and data protection in MCC to identify current trends and open issues. In this investigation, a systematic mapping study was conducted with a set of six research questions. A total of 1711 studies published from 2009 to 2019 were obtained. Following a filtering process, a collection of 74 primary studies were selected. As a result, the present data privacy threats, attacks, and solutions were identified. Also, the ongoing trends of data privacy exercise were observed. Moreover, the most utilized measures, research type, and contribution type facets were emphasized. Additionally, the current open research issues in privacy and data protection in MCC were highlighted. Furthermore, the results demonstrate the current state-of-the-art of privacy and data protection in MCC, and the conclusion will help to identify research trends and open issues in MCC for researchers and offer useful information in MCC for practitioners.

Emotion Recognition Using Eye-Tracking: Taxonomy, Review and Current Challenges.

The ability to detect users’ emotions for the purpose of emotion engineering is currently one of the main endeavors of machine learning in affective computing. Among the more common approaches to emotion detection are methods that rely on electroencephalography (EEG), facial image processing and speech inflections. Although eye-tracking is fast in becoming one of the most commonly used sensor modalities in affective computing, it is still a relatively new approach for emotion detection, especially when it is used exclusively. In this survey paper, we present a review on emotion recognition using eye-tracking technology, including a brief introductory background on emotion modeling, eye-tracking devices and approaches, emotion stimulation methods, the emotional-relevant features extractable from eye-tracking data, and most importantly, a categorical summary and taxonomy of the current literature which relates to emotion recognition using eye-tracking. This review concludes with a discussion on the current open research problems and prospective future research directions that will be beneficial for expanding the body of knowledge in emotion detection using eye-tracking as the primary sensor modality.

A review of prognostics and health management of machine tools

This paper presents a survey of the applications of prognostics and health management maintenance strategy to machine tools. A complete perspective on this Industry 4.0 cutting-edge maintenance policy, through the analysis of all its preliminary phases, is given as an introduction. Then, attention is given to prognostics, whose different approaches are briefly classified and explained, pointing out their advantages and shortcomings. After that, all the works on prognostics of machine tools and their main subsystem are reviewed, highlighting current open research areas for improvement.

Lab-on-chip: principle, design, technology and diagnostic targets

Lab-on-Chip (LoC) integrates various analyses such as biochemical operations, chemical synthesis, DNA sequencing onto a single chip which otherwise would have been performed in laboratory taking sufficient amount of time. Due to the miniaturization of these biochemical operations, better diagnostic speed, cost efficiency, ergonomy, sensitivity and so on can be achieved. This paper gives the detailed description of Lab-on Chip technology includingits system components. Ongoing worldwide research projects based on LoC technology have been investigated andvarious constraints that need to be fulfilled for designing a LoC system are presented. The biomedical applicationsof LoC in different fields like in diagnostics, cellomics, in environmental studies to control the effect of pathogens,to check the food quality have also been discussed. The current open research issues of this technology along withthe possible future research scope in the biomedical area have been presented.