Open Issues Research Articles

Fast audio signal denoising with low-rank approximation using windowed singular value decomposition of frequency division

The method of constructing Hankel matrices and using low-rank approximation has been demonstrated to be an effective approach to audio denoising. However, the significant computational complexity and the trade-off between the denoising quality and the loss of effective signal remain open issues. This paper proposes a denoising method based on frequency-divided Windowed Singular Value Decomposition (WSVD) and exploits the low-rank characteristics and frequency features commonly found in audio signals including speech and music recordings. The method incorporates an improved Lanczos Bidiagonalization algorithm to accelerate the singular value decomposition with low error and high tolerance. Furthermore, techniques are added at the window junctions to maintain the continuity and smoothness of the final audio, thus achieving denoising efficiently and effectively. This paper also assesses the influence of window segmentation length, main frequency domain characteristics, rank selection of Hankel matrix and characteristics of different noises on the final denoising effect. Finally, the denoising algorithm's robustness and effectiveness are validated through simulations and experiments.

Risk prediction based on oversampling technology and ensemble model optimized by tree-structured parzed estimator

High accuracy prediction of urban flood risk is conducive to avoid potential losses, however, it's negatively affected by unbalanced data. Furthermore, ensemble model has been demonstrated to have the ability to improve to prediction accuracy. Nevertheless, the performance of ensemble model is influenced by basic model and ensemble rules, and determining the best ensemble model remains an open issue. To improve the accuracy of flood risk prediction, an approach covering data optimization and ensemble modeling was presented to optimize unbalanced flood data and the selection of various ensemble models based on efficiency and performance. A practical application in Zhengzhou City shows that Borderline-SMOTE2 is the most applicable for optimizing the flood risk data among the state-of-the-art oversampling algorithm utilized, because of the excellent entropy value. The effect of unbalanced data on the performance of the basic models was pervasive according to changes of the common indicators. The optimal ensemble model for flood risk prediction is composed of K-Nearest Neighbor, Decision Tree, Gaussian Naive Bayes and Extreme Gradient Boosting under Stacking rule in the current study. The results of this study supply the valuable reference for the flood prediction and mitigation.

Roadmap of Adversarial Machine Learning in Internet of Things-Enabled Security Systems.

Machine learning (ML) represents one of the main pillars of the current digital era, specifically in modern real-world applications. The Internet of Things (IoT) technology is foundational in developing advanced intelligent systems. The convergence of ML and IoT drives significant advancements across various domains, such as making IoT-based security systems smarter and more efficient. However, ML-based IoT systems are vulnerable to lurking attacks during the training and testing phases. An adversarial attack aims to corrupt the ML model's functionality by introducing perturbed inputs. Consequently, it can pose significant risks leading to devices' malfunction, services' interruption, and personal data misuse. This article examines the severity of adversarial attacks and accentuates the importance of designing secure and robust ML models in the IoT context. A comprehensive classification of adversarial machine learning (AML) is provided. Moreover, a systematic literature review of the latest research trends (from 2020 to 2024) of the intersection of AML and IoT-based security systems is presented. The results revealed the availability of various AML attack techniques, where the Fast Gradient Signed Method (FGSM) is the most employed. Several studies recommend the adversarial training technique to defend against such attacks. Finally, potential open issues and main research directions are highlighted for future consideration and enhancement.

A Comprehensive Survey of Aerial Mesh Networks (AMN): Characteristics, Application, Open Issues, Challenges, and Research Directions

A Comprehensive Survey of Aerial Mesh Networks (AMN): Characteristics, Application, Open Issues, Challenges, and Research Directions

Retraction Note: A comprehensive survey 5G wireless communication systems: open issues, research challenges, channel estimation, multi carrier modulation and 5G applications

Retraction Note: A comprehensive survey 5G wireless communication systems: open issues, research challenges, channel estimation, multi carrier modulation and 5G applications

Open Issues in the Choice and Management of Direct Oral Anticoagulants in Patients with Cancer-Related Venous Thromboembolism.

Open Issues in the Choice and Management of Direct Oral Anticoagulants in Patients with Cancer-Related Venous Thromboembolism.

Muscle Synergy Analysis as a Tool for Assessing the Effectiveness of Gait Rehabilitation Therapies: A Methodological Review and Perspective.

According to the modular hypothesis for the control of movement, muscles are recruited in synergies, which capture muscle coordination in space, time, or both. In the last two decades, muscle synergy analysis has become a well-established framework in the motor control field and for the characterization of motor impairments in neurological patients. Altered modular control during a locomotion task has been often proposed as a potential quantitative metric for characterizing pathological conditions. Therefore, the purpose of this systematic review is to analyze the recent literature that used a muscle synergy analysis of neurological patients' locomotion as an indicator of motor rehabilitation therapy effectiveness, encompassing the key methodological elements to date. Searches for the relevant literature were made in Web of Science, PubMed, and Scopus. Most of the 15 full-text articles which were retrieved and included in this review identified an effect of the rehabilitation intervention on muscle synergies. However, the used experimental and methodological approaches varied across studies. Despite the scarcity of studies that investigated the effect of rehabilitation on muscle synergies, this review supports the utility of muscle synergies as a marker of the effectiveness of rehabilitative therapy and highlights the challenges and open issues that future works need to address to introduce the muscle synergies in the clinical practice and decisional process.

Anomaly and intrusion detection using deep learning for software-defined networks: A survey

Software-Defined Networks (SDN) represent an adaptable paradigm for dealing with network users’ dynamic demands. Confidentiality, integrity, and availability are fundamental pillars for the security of the networks, which are often targeted by cyberattacks. The scientific community has been recently exploring deep learning to implement Network Intrusion Detection Systems (NIDS) against network attacks. In this survey, we aim to present an empirical literature review on state-of-the-art NIDS based on deep learning for defending SDNs. The essential steps to develop such systems are carefully examined: benchmark datasets, data preprocessing, deep learning modeling, hyperparameter tuning, and performance evaluation. There has been a growing trend in published works since 2021, underpinning the importance of the research field, which is still active and under investigation. We support the development of the area by discussing the identified open issues and future research directions.

A generalized weighted evidence fusion algorithm based on quantum modeling

In the field of evidence theory, the Dempster-Shafer framework is widely used for combining evidence. However, it often produces counterintuitive results when dealing with highly conflicting evidence. To mitigate this issue, the weighted evidence fusion method has gained popularity. However, with the generalization of the mass function from the perspective of quantum theory, the weighting of conflicting evidence under quantum effects remains an open issue. To address this issue, a generalized weighted evidence fusion algorithm based on quantum modeling is proposed in this study. First, when quantum interference effects are considered, Dempster's rule is generalized into a generalized evidence combination rule based on quantum modeling. Second, a generalized quantum evidence distance is proposed to measure the similarity of quantum evidence in the presence of phase-term interference. Third, a global and nonlinear quantum evidence weighting operator is designed to reduce the impact of conflicting quantum evidence. Then, based on this operator and the similarities between pieces of quantum evidence, the weighted quantum evidence is fused to yield the final conclusion using the proposed generalized combination rule. Finally, the rationality, reliability, and accuracy of the proposed method are effectively proven through experiments using a few numerical examples and applications.

Организация и обеспечение безопасности судебной деятельности на территории Донбасса, Запорожья и Херсона: вопросы теории и практики

Aleksandr V. Orlov, Presiding Judge of the Commercial Court of the Central District, PhD (Law) The article is devoted to the study of certain theoretical and practical issues of the organization and security of judicial activity in the territory of the DPR, LPR, Zaporizhia and Kherson regions. The article discusses the issues of financing courts, public procurement to meet the needs of judicial authorities, and some issues of openness of judicial activity.

Why consider quantum instead classical pattern recognition techniques?

This article delves into the evolving landscape of pattern recognition, transitioning from classical methodologies to quantum-based techniques. It underscores how quantum algorithms offer a new paradigm with the potential to overcome the limitations of classical techniques. Unlike conventional methods, which, while effective, often struggle with complex and high-dimensional datasets, quantum algorithms are poised to surpass these limitations. This study explores the applications, benefits, drawbacks, and open issues surrounding quantum pattern recognition methods and provides a comprehensive overview of the current state of quantum technology and outlines potential future directions, highlighting the intersection of quantum computing and pattern recognition for breakthroughs.

Investigating the impact of supply voltage fluctuations on phase noise in quartz crystal oscillators.

The impact of power supply voltage fluctuations on the phase noise of quartz crystal oscillators (XOs) remains an open issue. Currently, there is a lack of comprehensive analysis on this matter. This work presents a novel phase-noise drive sensitivity (PNDS) model for the XO to reveal its mechanism. This model based on five noise modulation processes demonstrates the distribution of the PNDS in the frequency domain clearly, and there exists a PNDS bandwidth fS that limits the supply voltage fluctuation, which is similar to the effect of the noise bandwidth of the classical Leeson model. Using the PNDS, we successfully predict the phase noise of a 10MHz oscillator under different supply voltage noises. In addition, experimental results show that the PNDS floor is determined by the phase modulation of the sustaining amplifier, while the amplitude-frequency effect of the resonator and the tuning of the diode often play crucial roles in the near-carrier PNDS.

Workload Placement on Heterogeneous CPU-GPU Systems

The popularity of heterogeneous CPU-GPU processing has increased considerably in recent years. To efficiently utilize heterogeneous resources, data processing systems depend on an appropriate workload placement strategy to assign the right amount of compute to the right processor. However, finding an optimal placement strategy is not trivial due to various complex and conflicting tradeoffs related to the characteristics of processors, the nature of the workload, and data locality. In addition, placement decisions impact workload runtime and performance cost, and also depend on the availability of potentially different implementations for CPUs and GPUs, which adds extra complexity in such heterogeneous environments. In this tutorial, we review and compare state-of-the-art strategies for workload placement on heterogeneous CPU-GPU architectures, along with runtime prediction techniques and methods to support multi-device code. We also discuss open issues and identify potentially promising future research directions.

Multiscale insights on compression cast sea sand and seawater concrete

Reinforced concrete (RC) structures in coastal and marine regions face two primary challenges, the shortage of raw materials and severe service environments. Concrete made from sea sand and seawater (SS-SW) can address the shortage of natural sand by utilizing locally available resources. Meanwhile, the newly developed compression cast technology (CCT) significantly improves the micro and macro properties of concrete, enhancing its durability in harsh environments. Therefore, the combination of SS-SW concrete and CCT to form a new compression-cast SS-SW concrete can simultaneously solve the problems of materials shortage and durability issues for coastal RC structures. However, till now, research on this type of concrete is still an open issue. This paper aims to carry out multilevel investigations on the macro and micro performance of compressed SS-SW concrete. First, 69 concrete columns were tested to understand the stress-strain behavior and associated mechanical indices. The compressive strength of compressed concrete was found to be up to 1.86 times greater than that of uncompressed concrete. Meanwhile, the porosities in both mortar and the interfacial transition zone (ITZ) of concrete under a casting pressure of 15 MPa were approximately 50 % of those in uncompressed counterpart. Existing models are inadequate for predicting the behavior of compressed SS-SW concrete, leading to the development of new and accurate models to predict its compressive strength, elastic modulus, peak strain and stress-strain curve. These findings pave the way for the design and application of this kind of concrete.

Digital Sentinels and Antagonists: The Dual Nature of Chatbots in Cybersecurity

Advancements in artificial intelligence, machine learning, and natural language processing have culminated in sophisticated technologies such as transformer models, generative AI models, and chatbots. Chatbots are sophisticated software applications created to simulate conversation with human users. Chatbots have surged in popularity owing to their versatility and user-friendly nature, which have made them indispensable across a wide range of tasks. This article explores the dual nature of chatbots in the realm of cybersecurity and highlights their roles as both defensive tools and offensive tools. On the one hand, chatbots enhance organizational cyber defenses by providing real-time threat responses and fortifying existing security measures. On the other hand, adversaries exploit chatbots to perform advanced cyberattacks, since chatbots have lowered the technical barrier to generate phishing, malware, and other cyberthreats. Despite the implementation of censorship systems, malicious actors find ways to bypass these safeguards. Thus, this paper first provides an overview of the historical development of chatbots and large language models (LLMs), including their functionality, applications, and societal effects. Next, we explore the dualistic applications of chatbots in cybersecurity by surveying the most representative works on both attacks involving chatbots and chatbots’ defensive uses. We also present experimental analyses to illustrate and evaluate different offensive applications of chatbots. Finally, open issues and challenges regarding the duality of chatbots are highlighted and potential future research directions are discussed to promote responsible usage and enhance both offensive and defensive cybersecurity strategies.

Multi-Antenna Array-Based Massive MIMO for B5G/6G: State of the Art, Challenges, and Future Research Directions

This comprehensive article explores the massive MIMO (M-MIMO) design and its associated concepts, focusing on the seamless integration requirements for Beyond 5G (B5G) and 6G networks. Addressing critical aspects such as RF chain reduction, pilot contamination, cell-free MIMO, and security considerations, this article delves into the intricacies of M-MIMO in the evolving landscape of B5G. Moreover, the emerging MIMO concepts in this article include AI-enabled M-MIMO three-dimensional beamforming, reconfigurable intelligent surfaces, visible light communication, and THz spectrum utilization. This review highlights the challenges and open research issues, including Narrow Aperture Antenna Nodes, Plasmonic Antenna Arrays, Integrated Sensing with M-MIMO, and the application of federated learning in M-MIMO systems. By examining these cutting-edge developments, this article aims to advance knowledge in the field and inspire future research directions in the exciting realm of B5G and 6G networks.

Theories of Democratic Backsliding

We review recent contributions to the modeling of democratic backsliding. We organize these theories according to (a) the source of constraints on the executive (vertical or horizontal restrainers) and (b) the target of backsliding (electoral manipulation or executive aggrandizement), and then use these concepts to build a scaffold for a meta-model of democratic backsliding. This meta-model allows us to describe and compare the premises and insights of this scholarship. We further apply our two-dimensional classification to more than 30 empirical papers and show how these theories can guide research design. We conclude by highlighting open issues for future research.

An Analysis on Intelligent Systems for Remote Sensing Satellite Image Processing and Classification

The integration of intelligent systems to the remote sensing satellite image processing and classification has greatly changed. This paper offers a synthesis of the subject, with respect to intelligent systems’ contribution to the improvement of these processes’ accuracy and speed. Accuracy of different methods such as machine learning algorithms, artificial neural networks, and deep learning techniques in the extraction of information from satellite image comprehension is considered a research interest. The presented problems and open issues are data complexity, feature extraction, and classification accuracy over the data, along with new methods in enhancing the intelligent systems to minimize those problems. It expands knowledge of intelligent systems’ contribution to remote sensing applications by outlining how these advancements have influenced the progression of image analysis for the given research goals. This research work gives the summary of our research by outlining the techniques used in the study, the problems solved, and the general outcomes of incorporating intelligent systems in the area of remote sensing and satellite image analysis. Accuracy analysis results for the SVM based methodology with spatial-spectral features include 90% of accuracy, 88% of the precision, and 90% of the F1-score, which in turn makes it easy to make sound decisions when using satellite imagery in different fields like agriculture, urban development, and environment.

Is There a Functional Role for the Knotted Topology in Protein UCH-L1?

Knotted proteins are present in nature, but there is still an open issue regarding the existence of a universal role for these remarkable structures. To address this question, we used classical molecular dynamics (MD) simulations combined with in vitro experiments to investigate the role of the Gordian knot in the catalytic activity of UCH-L1. To create an unknotted form of UCH-L1, we modified its amino acid sequence by truncating several residues from its N-terminus. Remarkably, we find that deleting the first two N-terminal residues leads to a partial loss of enzyme activity with conservation of secondary structural content and knotted topological state. This happens because the integrity of the N-terminus is critical to ensure the correct alignment of the catalytic triad. However, the removal of five residues from the N-terminus, which significantly disrupts the native structure and the topological state, leads to a complete loss of enzymatic activity. Overall, our findings indicate that UCH-L1's catalytic activity depends critically on the integrity of the N-terminus and the secondary structure content, with the latter being strongly coupled with the knotted topological state.

Semantic mapping techniques for indoor mobile robots: Review and prospect

With the continuous development of robotics and computer vision technology, mobile robots have been widely applied in various fields. In this process, semantic maps for robots have attracted considerable attention because they provide a comprehensive and anthropomorphic representation of the environment. On the one hand, semantic maps are a tool for robots to depict the environment, which can enhance the robot’s cognitive expression of space and build the communication bond between robots and humans. On the other hand, semantic maps contain spatial location and semantic properties of entities, which helps robots realize intelligent decision-making in human-centered indoor environments. In this paper, we review the primary approaches of semantic mapping proposed over the last few decades, and group them according to the type of information used to extract semantics. First, we give a formal definition of semantic map and describe the techniques of semantic extraction. Then, the characteristics of different solutions are comprehensively analyzed from different perspectives. Finally, the open issues and future trends regarding semantic maps are discussed in detail. We wish this review provides a comprehensive reference for researchers to drive future research in related field.