Current Solutions Research Articles

Extensive Review of Security and Privacy Issues in Heterogeneous Networks

This paper studies advanced network security, privacy, and performance issues for Heterogeneous Networks (HetNets), which combine multiple types of cells to enhance wireless communication coverage and capacity. The coexistence of heterogeneous network elements inherently leads to considerable issues in terms of security, privacy, and performance. Previous research has primarily addressed security issues using common cryptographic techniques including data encryption with AES, secure key exchange with RSA, and handoff security with mutual authentication. Data anonymization methods and regulatory compliance have been used to address privacy concerns. Allocating resources and managing interference are some of the tactics that have been used in performance optimization. Although the current solutions offer a solid base, they frequently have issues with computational overhead, scalability, and continuous maintenance needs. Performance optimization solutions might not sufficiently handle dynamic network conditions, and privacy protections might not be sufficient to mitigate sophisticated data harvesting operations. This study evaluates existing HetNets solutions using a thorough evaluation and analytical technique. It assesses how well they handle issues with security, privacy, and performance, points out any shortcomings, and suggests areas for further investigation. Our analysis emphasizes the need for improved security mechanisms, including quantum-resistant cryptography, AI-driven threat detection, and technologies that improve privacy, such as differential privacy and homomorphic encryption. Innovative resource management and optimization strategies catered to HetNets' dynamic nature are needed to address performance issues. The necessity of developing new security, privacy, and performance solutions to guarantee the stability and dependability of HetNets is emphasized by this study. Stakeholders can promote the broad adoption and smooth integration of HetNet technology by tackling these obstacles. To sum up, this study offers a thorough analysis of the HetNets' architectural, security, privacy, and performance issues. It lists existing remedies, analyses their drawbacks, and suggests new lines of inquiry for future development to advance the area and improve HetNets' operational capabilities.

QoS-Aware Power-Optimized Path Selection for Data Center Networks (Q-PoPS)

Data centers consume significant amounts of energy, contributing indirectly to environmental pollution through greenhouse gas emissions during electricity generation. According to the Natural Resources Defense Council, information and communication technologies and networks account for roughly 10% of global energy consumption. Reducing power consumption in Data Center Networks (DCNs) is crucial, especially given that many data center components operate at full capacity even under low traffic conditions, resulting in high costs for both service providers and consumers. Current solutions often prioritize power optimization without considering Quality of Service (QoS). Services such as video streaming and Voice over IP (VoIP) are particularly sensitive to loss or delay and require QoS to be maintained below certain thresholds. This paper introduces a novel framework called QoS-Aware Power-Optimized Path Selection (Q-PoPS) for software-defined DCNs. The objective of Q-PoPS is to minimize DCN power consumption while ensuring that an acceptable QoS is provided, meeting the requirements of DCN services. This paper describes the implementation of a prototype for the Q-PoPS framework that leverages the POX Software-Defined Networking (SDN) controller. The performance of the prototype is evaluated using the Mininet emulator. Our findings demonstrate the performance of the proposed Q-PoPS algorithm in three scenarios. Best-case: Enhancing real-time traffic protocol quality without increasing power consumption. midrange-case: Replacing bottleneck links while preserving real-time traffic quality. Worst-case: Identifying new paths that may increase power consumption but maintain real-time traffic quality. This paper underscores the need for a holistic approach to DCN management, optimizing both power consumption and QoS for critical real-time applications. We present the Q-PoPS framework as evidence that such an approach is achievable.

Revolutionizing defect recognition in hard metal industry through AI explainability, human-in-the-loop approaches and cognitive mechanisms

Defect detection is one of the main areas that Industry 4.0 concepts like automation, IoT, digitization and AI aimed to provide solutions. In this work, a platform that extends the aforementioned concepts with ones coming from Industry 5.0 like reconciliation and collaboration between humans and machines is introduced. The proposed platform provides defect detection and localization services for hard metal industry by extending current AI solutions with exponentially growing technologies such as interpretable and explainable AI (XAI), human-in-the-loop (HITL) approaches and cognitive retraining mechanisms. In particular, the platform, that is built on a micro-service architecture to enable its software sustainability, is powered by machine and deep learning models for defect detection and localization. These models are able to recognize the defects, locate them with high precision and present them to end users so to maximize resiliency in quality inspection processes. Alongside the state-of-the-art AI algorithms that are utilized by the platform, the human in the loop mechanisms and interfaces that enable human experts to inject their knowledge for AI models training are considered as key innovative aspects of this work. To enhance further the human and AI collaboration, XAI mechanisms are included in the platform,to enable the experts to gain useful insights regarding the operation of the AI models in order to be able to provide further feedback that can trigger on-the-fly platform’s cognition mechanisms to improve models’ accuracy and performance.

Cashflow-driven investment beyond expectations

ABSTRACT This paper presents a computationally tractable optimization model for cashflow-driven investment where the aim is to find asset portfolios whose future payouts cover given liability payments as well as possible. While current industry solutions are largely based on expected future cash flows, we use a stochastic optimization model that seeks portfolios that give the best possible match across time as well as scenarios. Cashflow matching across scenarios is controlled by risk aversion while the timing is driven by illiquidity of money markets. When illiquidity increases, the hedging strategy quickly shifts towards portfolios suggested by deterministic models, but significant uncertainty remains. The model can incorporate hundreds of quoted instruments in the construction of optimal hedging strategies. The hedging strategies are able to employ any statistical connections between the liabilities and publicly quoted assets. The model is solved with simple Monte Carlo approximations and off-the-shelf convex optimization software. Besides optimal hedging strategies, we find the least cost of hedging which provides a market-consistent valuation based on the current quotes and the liquidity factors as well as the views and risk preferences of the investor/regulator. The approach is illustrated by pricing and hedging defined benefit pension liabilities which depend on uncertain longevity developments and the consumer price index. The hedging strategies are constructed from 128 publicly quoted instruments including index-linked bonds and equities. We find that the optimized hedging strategies achieve lower risk at a lower cost than the strategies obtained by matching expected cashflows. The hedging-based liability valuations are robust with respect to model parameters and the additional risk reduction achieved by optimization does not add much to the overall hedging cost.

Detecting airport luggage dimensions through low-cost depth sensors

A factor that impacts airlines' resources is the verification of luggage’s dimensions during the boarding process. Companies often rely on a human operator to perform this check using a manual template, which can cause delays. As an alternative, companies are investing in self bag drop systems. This process introduces new technological challenges since, in this scenario, checking the conformity of luggage dimensions is delegated to the passenger, which can lead to errors. In addition, current solutions use specific computational devices, such as laser scanners, that are expressive in size and cost, which may require interventions in the airport infrastructure. To overcome this, isolated initiatives are observed with alternative technologies, such as low-cost depth sensors, but they usually come without a scientific investigation. In this sense, this work investigates the technical viability of using such low-cost devices to obtain the dimensions of airport baggage. To do so, we developed a model that obtains a 3D point cloud of the luggage surface through a Microsoft Kinect V2 sensor. This cloud is treated and processed to extract the dimensions of the luggage. In order to validate this approach, a full-scale physical prototype was built and tested. The results indicate that the mean absolute error of the obtained dimension by the proposed model is 2.86 cm. Such data suggest that this technology has the potential to become an alternative to detect the dimensions of airport luggage.

A Mathematical Model to Enhance Creativity in Generative AI Systems

People often think it will be hard for artificial intelligence (AI) systems to copy creativity because it is a complicated and important part of human intelligence. Generative AI, which tries to make new and useful things, has shown promise in being able to copy creative processes. However, generative AI systems that are already in use often have trouble making results that are truly original. This is mostly because there isn't a clear scientific framework to help people be creative. We present a new mathematical model to improve creativity in creative AI systems in this work. Our model is based on the ideas of lateral and convergent thinking, which are important parts of how people think creatively. Divergent thinking means coming up with many ideas or solutions, while convergent thinking means picking the best ones and making them even better. To put our model into action, we come up with the idea of a "creativity score," which measures how new and useful the results are. The creativity score is determined by looking at a number of things, such as the variety of outputs, how different they are from current solutions, and how useful they are for fixing the problem at hand. Finding the right balance between divergent and convergent thinking is one of the hardest parts of making generative AI systems more creative. Divergent thinking is important for coming up with many ideas, but convergent thinking is needed to pick out the best ones and make them even better. This problem is solved by our model, which uses both divergent and convergent thought. To make sure our model works, we test it on different creative AI tasks, such as making images and writing text. Our results show that our model can greatly improve the creativity of generative AI systems, resulting in more varied, unique, and useful outputs.

Features of the structure and properties of stone mastic asphalt

AbstractThe purpose of this study is to investigate the features and properties of stone mastic asphalt in the context of its potential for practical application in road repair works, aiming to create a high‐quality road surface and replace outdated structural materials with newer alternatives. The leading approach combines a systematic analysis of the issues submitted for consideration and an experimental study of the properties of stone mastic asphalt, which are important in the context of the practical use of this building material to ensure high quality parameters of the roadway and the timeliness of its repair, if necessary. Within the framework of this study, results were obtained that indicate a significant variety of approaches to determining the characteristics and properties of the material under consideration, clearly demonstrating current solutions to the problem of improving the performance of stone mastic asphalt by introducing special admixtures into its composition. The findings and conclusions of this research are valuable for road service employees and developers of modern construction materials, aiding in high‐quality road surface repair and creating durable asphalt for safe road use in the future.

REACT: Autonomous intrusion response system for intelligent vehicles

Autonomous and connected vehicles are rapidly evolving, integrating numerous technologies and software. This progress, however, has made them appealing targets for cybersecurity attacks. As the risk of cyber threats escalates with this advancement, the focus is shifting from solely preventing these attacks to also mitigating their impact. Current solutions rely on vehicle security operation centers, where attack information is analyzed before deciding on a response strategy. However, this process can be time-consuming and faces scalability challenges, along with other issues stemming from vehicle connectivity. This paper proposes a dynamic intrusion response system integrated within the vehicle. This system enables the vehicle to respond to a variety of incidents almost instantly, thereby reducing the need for interaction with the vehicle security operation center. The system offers a comprehensive list of potential responses, a methodology for response evaluation, and various response selection methods. The proposed solution was implemented on an embedded platform. Two distinct cyberattack use cases served as the basis for evaluating the system. The evaluation highlights the system’s adaptability, its ability to respond swiftly, its minimal memory footprint, and its capacity for dynamic system parameter adjustments. The proposed solution underscores the necessity and feasibility of incorporating dynamic response mechanisms in smart vehicles. This is a crucial factor in ensuring the safety and resilience of future smart mobility.

Considering radial basis function neural network for effective solution generation in metaheuristic algorithms

In many engineering optimization problems, the number of function evaluations is severely limited by the time or cost constraints. These limitations present a significant challenge in the field of global optimization, because existing metaheuristic methods typically require a substantial number of function evaluations to find optimal solutions. This paper presents a new metaheuristic optimization algorithm that considers the information obtained by a radial basis function neural network (RBFNN) in terms of the objective function for guiding the search process. Initially, the algorithm uses the maximum design approach to strategically distribute a set of solutions across the entire search space. It then enters a cycle in which the RBFNN models the objective function values from the current solutions. The algorithm identifies and uses key neurons in the hidden layer that correspond to the highest objective function values to generate new solutions. The centroids and standard deviations of these neurons guide the sampling process, which continues until the desired number of solutions is reached. By focusing on the areas of the search space that yield high objective function values, the algorithm avoids exhaustive solution evaluations and significantly reduces the number of function evaluations. The effectiveness of the method is demonstrated through a comparison with popular metaheuristic algorithms across several test functions, where it consistently outperforms existing techniques, delivers higher-quality solutions, and improves convergence rates.

In silico exploration of 4(α-l-rhamnosyloxy)-benzyl isothiocyanate: A promising phytochemical-based drug discovery approach for combating multi-drug resistant Staphylococcus aureus

Multidrug-resistant (MDR) Staphylococcus aureus infections significantly threaten global health. With rising resistance to current antibiotics and limited solutions, the urgent discovery of new, effective, and affordable antibacterials with low toxicity is imperative to combat diverse MDR S. aureus strains. Hence, in this study, we introduce an in silico phytochemical-based approach for discovering novel antibacterial agents, underscoring the potential of computational approaches in therapeutic discovery. Glucomoringin Isothiocyanate (GMG-ITC) from Moringa oleifera Lam. is one of the phytochemical compounds with several biological activities, including antimicrobial, anti-inflammatory, and antioxidant activities, and is also effective against S. aureus. This study focuses on screening GMG-ITC as a potential drug candidate to combat MDR S. aureus infections through a molecular docking approach. Moreover, interaction amino acid analysis, in silico pharmacokinetics, compound target prediction, pathway enrichment analysis and molecular dynamics (MD) simulations were conducted for further investigation. Molecular docking and interaction analysis showed strong binding affinity towards S. aureus lipase, dihydrofolate reductase, and other MDR S. aureus proteins, including penicillin-binding protein 2a, MepR, D-Ala:D-Ala ligase, and RPP TetM, through hydrophilic and hydrophobic interactions. GMG-ITC also showed a strong binding affinity to cyclooxygenase-2 and FAD-dependent NAD(P)H oxidase, suggesting that it is a potential anti-inflammatory and antioxidant candidate that may eliminate inflammation and oxidative stress associated with S. aureus infections. MD simulations validated the stability of the GMG-ITC molecular interactions determined by molecular docking. In silico pharmacokinetic analysis highlights its potency as a drug candidate, showing strong absorption, distribution, and excretion properties in combination with low toxicity. It acts as an active protease and enzyme inhibitor with moderate activity against GPCR ligands, ion channels, nuclear receptor ligands, and kinases. Enrichment analysis further elucidated its involvement in important biological, molecular, and cellular functions with potential therapeutic applications in diseases like cancer, hepatitis B, and influenza. Results suggest that GMG-ITC is an effective antibacterial agent that could treat MDR S. aureus-associated infections.

High precision control of industrial robot based on flexible joint response model

For industrial robots, their repetition accuracy is high, but their absolute accuracy and trajectory accuracy are low, which limits the application of industrial robots in more scenarios. Current solutions mainly address precision issues caused by static errors through adjustments in kinematic parameters. Approaches such as dynamic feedforward control, torque computation methods, intelligent control, dual encoder control, visual servoing, etc., are employed to enhance dynamic tracking accuracy, yet they commonly suffer from insufficient precision, poor robustness, high costs, and usability challenges. In response to the difficulty in reducing dynamic errors, this paper proposes a high-precision control technique based on compensating for flexible joint responses. By compensating for deviations in flexible joint responses, the trajectory precision at the end-effector of industrial robots is improved.

A Novel Shape Memory Alloy Modular Robot with Spatially Stable Structure

Soft robots exhibit significant flexibility but normally lack stability owing to their inherent low stiffness. Current solutions for achieving variable stiffness or implementing lock mechanisms tend to involve complex structures. Additionally, passive solutions like bistable and multistate mechanisms lack spatial stable characteristics. This study presents a novel shape memory alloy (SMA) modular robot with spatially stable structure, by utilizing gooseneck as the backbone. This is the first time that a concept of spatially stable structure is proposed. When the power is off, the robot can still maintain its current posture in three‐dimensional space and resist external disturbance. The SMA spring and gooseneck are characterized, elucidating the mechanism behind achieving spatial stability. Then, a controller based on the inverse kinematics is designed, and validated by experiments. The results demonstrate the structural stability of the robot. Specifically, it can withstand a maximum external force of 2.5 N (0.0875 Nm) when bent at an angle of 20° without consuming energy. Moreover, with the assistance of the SMA spring, this resistance capacity surpasses 5 N (0.175 Nm).

Developing Human Resources in the Era of Digital Transformation in Vietnam: Current Status and Solutions

Digital transformation is becoming an inevitable and powerful trend across all sectors of socio-economic life, contributing to the enhancement of business efficiency, government management, and the quality of life for the public. High-quality human resources with digital skills and adaptability to new technologies are crucial to the success of digital transformation. However, Vietnam is currently facing numerous challenges in developing IT human resources. This paper examines the current state of human resources in Vietnam’s digital transformation, discusses the solutions that have been and are being implemented, and proposes specific steps to develop high-quality human resources. The policies and training programs from the government, enterprises, and educational institutions are analyzed and evaluated. Finally, the paper provides recommendations on policies and long-term strategies to improve the quality and quantity of IT human resources, ensuring that Vietnam can seize opportunities from the Fourth Industrial Revolution.

Development and Evaluation of a Reusable, Force Measuring Tool for the Robot-Assisted Insertion of Cochlear Implant Electrode Arrays.

Limitations in human kinematics during cochlear implantation induce pressure transients and increased forces on intracochlear structures. Herein, we present a novel head-mounted surgical tool designed for the motorized insertion of cochlear implant electrode arrays. The tool integrates a force measurement feature to overcome the lack of haptic feedback in current robotic solutions. Utilizing a prototype device, we compare force measurements with those exerted on intracochlear structures in a realistic temporal bone model. Furthermore, we test the tool on six temporal bone specimens in a surgical setting to assess its performance in various anatomies. Force measurements exhibit good agreement with intracochlear forces, offering significantly improved resolution over manual, tactile sensing. Successful electrode array insertions in six cadaver specimens affirmed the feasibility of tool setup, motorized insertion and tool removal in different anatomies. The tool allows the robot-assisted insertion of cochlear implant electrode arrays and offers valuable insights during the surgical procedure, demonstrating promise for application in clinical contexts. This instrument has the potential to aid surgeons in achieving atraumatic placement of electrodes, consequently contributing to the improvement of hearing outcomes in cochlear implantation.

An overview of implementing security and privacy in federated learning

Federated learning has received a great deal of research attention recently,with privacy protection becoming a key factor in the development of artificial intelligence. Federated learning is a special kind of distributed learning framework, which allows multiple users to participate in model training while ensuring that their privacy is not compromised; however, this paradigm is still vulnerable to security and privacy threats from various attackers. This paper focuses on the security and privacy threats related to federated learning. First, we analyse the current research and development status of federated learning through use of the CiteSpace literature search tool. Next, we describe the basic concepts and threat models, and then analyse the security and privacy vulnerabilities within current federated learning architectures. Finally, the directions of development in this area are further discussed in the context of current advanced defence solutions, for which we provide a summary and comparison.

Thermal analysis on Ferro Casson nanofluid flow over a Riga plate with thermal radiation and non-uniform heat source/sink

Ferro hybrid nanofluids can be used in electronics and microelectronics cooling applications to reduce heat accumulation and efficiently remove surplus heat. These nanofluids aid to maintain optimum operating temperatures and reduce device overheating by enhancing the heat transfer rate. With this motivation, the aim of the present numerical analysis is to study the three-dimensional incompressible hybrid nanofluid flow over a slippery Riga surface by combining the Casson fluid model. Mathematical modeling is constructed with nanoparticles as Fe3O4 and CoFe2O4 with base fluid as water. The non-uniform heat source/sink and thermal linear radiation effects are taken into account with the Hamilton–Crosser thermal conductivity model. A system of nonlinear PDEs is produced by the proposed problem and then the relevant similarity variables are implemented to transform the set of partial differential equations and their accompanying boundary conditions into the coupled nonlinear differential equations with one independent variable. These modified ordinary differential equations (ODEs) were then successfully solved with the Runge–Kutta fourth-order method by combining via the shooting technique. With the aid of graphical representations, the effects of various influencing parameters were presented and analyzed comprehensively. Furthermore, the impacts of relevant parameters on heat transfer rates and shear stress are concisely discussed and illustrated in tabular forms. The significant findings include the enhancement of the radiation parameter increases the thermal boundary layer thickness and the thickness increases whenever surface experiences slippery conditions. The axial and transverse momentum of the fluid are controlled with the Casson parameter. An effective connection is noticed once the current numerical solutions are verified under particular conditions that were previously described.

A systematic overview of single-cell transcriptomics databases, their use cases, and limitations.

Rapid advancements in high-throughput single-cell RNA-seq (scRNA-seq) technologies and experimental protocols have led to the generation of vast amounts of transcriptomic data that populates several online databases and repositories. Here, we systematically examined large-scale scRNA-seq databases, categorizing them based on their scope and purpose such as general, tissue-specific databases, disease-specific databases, cancer-focused databases, and cell type-focused databases. Next, we discuss the technical and methodological challenges associated with curating large-scale scRNA-seq databases, along with current computational solutions. We argue that understanding scRNA-seq databases, including their limitations and assumptions, is crucial for effectively utilizing this data to make robust discoveries and identify novel biological insights. Such platforms can help bridge the gap between computational and wet lab scientists through user-friendly web-based interfaces needed for democratizing access to single-cell data. These platforms would facilitate interdisciplinary research, enabling researchers from various disciplines to collaborate effectively. This review underscores the importance of leveraging computational approaches to unravel the complexities of single-cell data and offers a promising direction for future research in the field.

On Representation Learning-based Methods for Effective, Efficient, and Scalable Code Retrieval

Code retrieval consists of finding relevant code snippets regarding a programmer’s query — an increasingly important task due to software ubiquity. Although significant progress has been made, there is still a need for refinement and improvement in current code retrieval solutions. Besides effective, we argue that solutions for the code retrieval task should be efficient and scalable to ever-increasing large code repositories. This paper introduces xCoFormer, the first representation learning-based model for the code retrieval task in the literature, which meets all these requirements. Our contributions include: (i) an interactive tag-training method that efficiently places a query closer to its relevant codes in the embedding space and (ii) use of a specialized loss function (N-pair) better suited to the code retrieval task. To evaluate our proposal, we conducted experiments with different versions of xCoFormer against several state-of-the-art deep learning models in datasets with different properties. In our experiments, xCoFormer produced the best cost-effectiveness tradeoff — it is as effective as our closest competitor while being thousands of times faster at search time. xCoFormer’s attention mechanism also (partially) meets the desirable requirement of explainability by exposing the “reasoning” of the model’s predictions. Finally, when used as a “proxy” method, fine-tuned with domain-specific code language models, such as Unixcoder and CodeBERT, our proposal achieves gains of more than 33% in effectiveness when compared to the use of general purpose language models such as BERT and RoBERTa.

A Secure Data Aggregation Algorithm Based on a Trust Mechanism.

Due to the uniqueness of the underwater environment, traditional data aggregation schemes face many challenges. Most existing data aggregation solutions do not fully consider node trustworthiness, which may result in the inclusion of falsified data sent by malicious nodes during the aggregation process, thereby affecting the accuracy of the aggregated results. Additionally, because of the dynamically changing nature of the underwater environment, current solutions often lack sufficient flexibility to handle situations such as node movement and network topology changes, significantly impacting the stability and reliability of data transmission. To address the aforementioned issues, this paper proposes a secure data aggregation algorithm based on a trust mechanism. By dynamically adjusting the number and size of node slices based on node trust values and transmission distances, the proposed algorithm effectively reduces network communication overhead and improves the accuracy of data aggregation. Due to the variability in the number of node slices, even if attackers intercept some slices, it is difficult for them to reconstruct the complete data, thereby ensuring data security.

The freedom to run: developing an autonomous robot matching the needs of visually impaired citizens to technology opportunities

Purpose: Visual impairment poses significant challenges in daily life, especially when navigating unfamiliar environments, resulting in inequalities and reduced quality of life. This study aimed to gain an in-depth understanding of the needs and perspectives of visually impaired people in sports-related contexts through surveys and focus groups, and to understand whether their needs are being met by current technological solutions. Materials and methods: To accomplish this, opinions gathered from focus groups and interviews were compared to the technological solutions found in the literature. Since many unmet needs were identified, participants from associations and organizations were asked to identify key characteristics for the development of a robot guide. The results underscored the paramount importance of an easy-to-use guide that offers accurate and personalized assistance. Participants expressed a strong desire for advanced features such as object recognition and navigation in complex environments, as well as adaptability to the user’s speed while providing the necessary safety features to ensure a high level of autonomy. Results: This research serves as a bridge between technological advances and the needs of the visually impaired, contributing to a more accessible and inclusive society. By addressing the unique challenges faced by the visually impaired individuals and tailoring technology to meet their needs, this study takes a significant step toward reducing disparities and improving the independence and quality of life for this community. Conclusions: As technology continues to advance, it has the potential to be a powerful tool in breaking down barriers and fostering a world where everyone, regardless of their visual ability, can navigate the world with confidence and ease.