Open Issues Research Articles

Labels Need Prompts Too: Mask Matching for Natural Language Understanding Tasks

Textual label names (descriptions) are typically semantically rich in many natural language understanding (NLU) tasks. In this paper, we incorporate the prompting methodology, which is widely used to enrich model input, into the label side for the first time. Specifically, we propose a Mask Matching method, which equips an input with a prompt and its label with another, and then makes predictions by matching their mask representations. We evaluate our method extensively on 8 NLU tasks with 14 datasets. The experimental results show that Mask Matching significantly outperforms its counterparts of fine-tuning and conventional prompt-tuning, setting up state-of-the-art performances in several datasets. Mask Matching is particularly good at handling NLU tasks with large label counts and informative label names. As pioneering efforts that investigate the label-side prompt, we also discuss open issues for future study.

Machine Learning (In) Security: A Stream of Problems

Machine Learning (ML) has been widely applied to cybersecurity and is considered state-of-the-art for solving many of the open issues in that field. However, it is very difficult to evaluate how good the produced solutions are, since the challenges faced in security may not appear in other areas. One of these challenges is the concept drift, which increases the existing arms race between attackers and defenders: malicious actors can always create novel threats to overcome the defense solutions, which may not consider them in some approaches. Due to this, it is essential to know how to properly build and evaluate an ML-based security solution. In this article, we identify, detail, and discuss the main challenges in the correct application of ML techniques to cybersecurity data. We evaluate how concept drift, evolution, delayed labels, and adversarial ML impact the existing solutions. Moreover, we address how issues related to data collection affect the quality of the results presented in the security literature, showing that new strategies are needed to improve current solutions. Finally, we present how existing solutions may fail under certain circumstances and propose mitigations to them, presenting a novel checklist to help the development of future ML solutions for cybersecurity.

Urban Day-to-Day Travel and Its Development in an Information Environment: A Review

Urban day-to-day travel systems generally exist in various types of cities. Their modeling is difficult due to the uncertainty of individual travelers in micro travel decision-making. Moreover, with the advent of the information age, intelligent connected vehicles, smartphones, and other types of intelligent terminals have placed urban day-to-day travel systems in an information environment. In such an environment, the travel decision-making processes of travelers are significantly affected, making it even more difficult to give theoretical explanations for urban day-to-day travel systems. Considering that analyzing urban day-to-day travel patterns in an information environment is of great significance for governing the constantly developing and changing urban travel system and, thus, of great importance for the sustainable development of cities, this paper gives a systematic review of the theoretical research on urban day-to-day travel and its development in an information environment over the past few decades. More specifically, the basic explanation of an information environment for urban day-to-day travel is given first; subsequently, the theoretical development of micro decision-making related to individual day-to-day travelers in an information environment is discussed, and the theoretical development related to changes in urban macro traffic flow, which can be recognized as the aggregation effect formed by individual micro decision-making, is also discussed; in addition, the development of understanding different types of traffic information that travelers may obtain in an information environment is discussed; finally, some important open issues related to the deep impact of information environment on urban day-to-day travel systems that require further research are presented. These valuable research directions include using information methods to fit day-to-day travel patterns of cities and implementing macro and micro integrated modeling for urban day-to-day travel systems based on complex system dynamics and even quantum mechanics.

Detecting Wear and Tear in Pedestrian Crossings Using Computer Vision Techniques: Approaches, Challenges, and Opportunities

Pedestrian crossings are an essential part of the urban landscape, providing safe passage for pedestrians to cross busy streets. While some are regulated by timed signals and are marked with signs and lights, others are simply marked on the road and do not have additional infrastructure. Nevertheless, the markings undergo wear and tear due to traffic, weather, and road maintenance activities. If pedestrian crossing markings are excessively worn, drivers may not be able to see them, which creates road safety issues. This paper presents a study of computer vision techniques that can be used to identify and classify pedestrian crossings. It first introduces the related concepts. Then, it surveys related work and categorizes existing solutions, highlighting their key features, strengths, and limitations. The most promising techniques are identified and described: Convolutional Neural Networks, Histogram of Oriented Gradients, Maximally Stable Extremal Regions, Canny Edge, and thresholding methods. Their performance is evaluated and compared on a custom dataset developed for this work. Insights on open issues and research opportunities in the field are also provided. It is shown that managers responsible for road safety, in the context of a smart city, can benefit from computer vision approaches to automate the process of determining the wear and tear of pedestrian crossings.

Flying Ad-Hoc Networks (FANETs): A Review

INTRODUCTION: FANETs are a type of wireless communication network consisting of Unmanned Aerial Vehicles (UAVs) or drones that work collaboratively to process data and attain optimal results. These networks have achieved significant attention due to their potential applications in diverse engineering fields. The paper provides a comprehensive analysis of FANET, covering various aspects related to its classification, architecture, communication types, mobility models, challenges, characteristics, and design. It also discusses the importance of routing protocols and topology in FANETs. Furthermore, this paper identifies and presents open issues and challenges in the field of FANETs, urging researchers to focus on exploring and addressing these essential parameters and research areas. 
 OBJECTIVES: This paper will aims to promote further investigation and advancement in the field of FANETs and similar networks, enabling researchers to explore and overcome the challenges to unleash the full potential of these UAV-based ad-hoc networks shortly.
 METHODS: The data used in this paper was gathered from various research papers. A brief comparison among FANETs, MANETs, and VANETs has been shown and highlighted the main points. This paper also elaborates the general architecture, mobility models, routing, routing protocols in FANETs.
 RESULTS: It was discovered that the use of both deterministic and probabilistic techniques is suggested to enhance the performance and efficiency of FANETs. By combining these methods, the paper suggests that better results can be achieved in terms of network reliability, adaptability, and overall performance. 
 CONCLUSION: This paper discusses the importance of routing protocols and topology in FANETs. Furthermore, this paper identifies and presents open issues and challenges in the field of FANETs, urging researchers to focus on exploring and addressing these essential parameters and research areas.

Electrically Tunable Multiple-Effects Synergistic and Boosted Photoelectric Performance in Te/WSe2 Mixed-Dimensional Heterojunction Phototransistors.

Mix-dimensional heterojunctions (MDHJs) photodetectors (PDs) built from bulk and 2D materials are the research focus to develop hetero-integrated and multifunctional optoelectronic sensor systems. However, it is still an open issue for achieving multiple effects synergistic characteristics to boost sensitivity and enrich the prospect in artificial bionic systems. Herein, electrically tunable Te/WSe2 MDHJs phototransistors are constructed, and an ultralow dark current below 0.1pA and a large on/off rectification ratio of 106 is achieved. Photoconductive, photovoltaic, and photo-thermoelectric conversions are simultaneously demonstrated by tuning the gate and bias. By these synergistic effects, responsivity and detectivity respectively reach 13.9 AW-1 and 1.37×1012 Jones with 400 times increment. The Te/WSe2 MDHJs PDs can function as artificial bionic visual systems due to the comparable response time to those of the human visual system and the presence of transient positive and negative response signals. This work offers an available strategy for intelligent optoelectronic devices with hetero-integration and multifunctions.

Blockchain integration in big data: Review, vision, and opportunities

AbstractBig data has attracted extensive attention from industries and universities during the past few years. Big data is crucial in many fields, such as business analytics, healthcare, the Internet of Things (IoT), smart home, supply chain, transportation, and fraud detection. Nevertheless, some challenges must be addressed, such as decentralization, integration, data sharing, privacy, and security. On the other hand, blockchain has numerous potential for improving big data services and applications because of its decentralized, verifiable, and anti‐tamper features. We apply the Systematic Literature Review (SLR) approach in this study to investigate blockchain integrity in big data and understand the different topics and significant areas already presented. This paper aims to analyze studies on blockchain integration in big data published between 2017 and 2022. A technical taxonomy is presented for blockchain integration into big data, including data storage, security, and applications based on the field of articles selected by the SLR method. Finally, the achievements and shortcomings of each study are discussed, and future research challenges and open issues related to blockchain integration in big data are highlighted.

Utilization of synthetic system intelligence as a new industrial asset

System knowledge and reasoning mechanisms are essential means for intellectualization of cyber-physical systems (CPSs). As enablers of system intelligence, they make such systems able to solve application problems and to maintain their efficient operation. Normally, system intelligence has a human-created initial part and a system-produced (extending) part, called synthetic system intelligence (SSI). This position paper claims that SSI can be converted to a new industrial asset and utilized as such. Unfortunately, no overall theory of SSI exists and its conceptual framework, management strategy, and computational methodologies are still in a premature stage. This is the main reason why no significant progress has been achieved in this field, contrary to the latent potentials. This paper intends to contribute to: (i) understanding the nature and fundamentals of SSI, (ii) systematizing the elicitation and transfer of SSI, (iii) exploration of analogical approaches to utilization of SSI, and (iv) road-mapping and scenario development for the exploitation of SSI as an industrial asset. First, the state of the art is surveyed and the major findings are presented. Then, four families of analogical approaches to SSI transfer are analyzed. These are: (i) knowledge transfer based on repositories, (ii) transfer among agents, (iii) transfer of learning resources, and (iv) transfer by emerging approaches. A procedural framework is proposed that identifies the generic functionalities needed for a quasi-autonomous handling of SSI as an industrial asset. The last section casts light on some important open issues and necessary follow-up research and development activities.

Frequency Design of a Vertical Cantilever Beam Carrying Tip Mass

Previous research has revealed that vertical cantilever beam structures, after considering the effects of gravity, can exhibit a 1:3 internal resonance between two transverse modes. However, whether a similar phenomenon would still occur in the system when a proof body is attached to the free end of cantilever beams, and how to perform parameter design to deliberately induce internal resonance are still open issues. The present study conducts research on the natural frequencies of a vertical cantilever beam with a tip mass. Within the framework of the Euler–Bernoulli beam theory, Hamilton’s principle is utilized to derive the integral-partial differential equation of free vibration of the system. The corresponding frequency equation is then solved using the Chebyshev pseudospectral method, with an algebraic equation of the natural frequency obtained. Moreover, an explicit analytical expression for the fundamental frequency is obtained using parameter fitting, which is then used to reveal the relationship among parameters under the critical state of buckling and evaluate the extent of gravity’s impact on the fundamental frequency. The study finally investigates the ratio of the first two natural frequencies. A parameter design method is proposed, which derives a parametric curve in terms of the fundamental frequency, ensuring a 1:3 ratio between the first two natural frequencies. The free vibration time history at the midpoint of the beam is acquired by directly solving the system’s partial differential equation through the finite element method. Fourier transformation of this time history verifies that the fundamental frequency aligns with a predetermined value, and the ratio between the first two natural frequencies is 1:3, thus confirming the efficacy of the proposed method. It is found that the addition of a tip mass provides more flexibility in adjusting parameters to achieve internal resonance, which facilitates the miniaturization of actual structures.

IoT based Health Monitoring System

The Internet of Things (IoT) is essential in innovative applications such as smart cities, smart homes, education, healthcare, transportation, and defense operations. IoT applications are particularly beneficial for providing healthcare because they enable secure and real- time remote patient monitoring to improve the quality of people’s lives. This review paper explores the latest trends in healthcare-monitoring systems by implementing the role of the IoT. The work discusses the benefits of IoT-based healthcare systems with regard to their significance, and the benefits of IoT healthcare. We provide a systematic review on recent studies of IoT- based healthcare-monitoring systems through literature review. The literature review compares various systems’ effectiveness, efficiency, data protection, privacy, security, and monitoring. The paper also explores wireless- and wearable-sensor-based IoT monitoring systems and provides a classification of healthcare- monitoring sensors. We also elaborate, in detail, on the challenges and open issues regarding healthcare security and privacy, and QoS. Finally, suggestions and recommendations for IoT healthcare applications are laid down at the end of the study along with future directions related to various recent technology trends. Keywords: IoT, IoWT, healthcare, monitoring, remote

Remote mobile health monitoring frameworks and mobile applications: Taxonomy, open challenges, motivation, and recommendations

Advancements in mobile technology have propelled the rapid progress of remote health monitoring, particularly in the domain of mobile health (mHealth). This progress is underscored by sophisticated smart health monitoring applications designed for mobile devices. mHealth monitoring, a standout area within remote health monitoring, excels in providing continuous health monitoring for patients and enabling remote observation by physicians. These applications cater to ambulatory patients with chronic diseases, offering essential features such as Telemedicine services, doctor availabilitys, and emergency ambulance services. Despite the development of various frameworks for mHealth monitoring, existing review papers often fall short in addressing critical parameters and issues related to Telemedicine. Many reviews provide a broad overview but lack the depth needed to dissect intricate aspects like implementation challenges in Telemedicine, nuances in decision-making applications, and the dynamics of mobility for both patients and healthcare providers. Patient rescue protocols, especially in diverse locations, are also underexplored. To fill this gap, our research delves into these dimensions, providing a nuanced understanding of challenges and opportunities in mobile health monitoring, particularly within the Telemedicine context. Leveraging a comprehensive literature review, we meticulously selected over 100 research articles from reputable databases like ScienceDirect, IEEE Xplore, and Springer. We have assessed and ranked the significance of each article. The study aims to address open issues in telemedicine, contributing valuable insights to enhance remote health monitoring.

Inflammatory Bowel Diseases and Non-Alcoholic Fatty Liver Disease: Piecing a Complex Puzzle Together.

Inflammatory bowel diseases (IBD), comprising Crohn's disease and ulcerative colitis, are systemic and multifaceted disorders which affect other organs in addition to the gastrointestinal tract in up to 50% of cases. Extraintestinal manifestations may present before or after IBD diagnosis and negatively impact the intestinal disease course and patients' quality of life, often requiring additional diagnostic evaluations or specific treatments. Non-alcoholic fatty liver disease (NAFLD) is the most common liver disease worldwide. Current evidence shows an increased prevalence of NAFLD (and its more advanced stages, such as liver fibrosis and steatohepatitis) in IBD patients compared to the general population. Many different IBD-specific etiopathogenetic mechanisms have been hypothesized, including chronic inflammation, malabsorption, previous surgical interventions, changes in fecal microbiota, and drugs. However, the pathophysiological link between these two diseases is still poorly understood. In this review, we aim to provide a comprehensive overview of the potential mechanisms which have been investigated so far and highlight open issues still to be addressed for future studies.

Face Mask Recognition based on YOLOV3

Wearing masks is considered one of the effective ways to prevent epidemics and protect physical health. In densely populated public places, detecting whether the mask is correctly worn has important application value, which can help managers reduce the potential risk of disease transmission. Previous works mainly rely on manual features, which are limited to fully expressing complex scene information. Thanks to the rapid development of artificial intelligence technology, automatic mask detection methods based on deep learning have achieved breakthroughs in speed and accuracy, while mask detection in complex scenes is still an open issue. To alleviate this issue, this paper proposes a mask detection method based on YOLOV3. Specifically, this study introduces regularization methods such as Drop Block and Label Smoothing, as well as data augmentation strategies such as Gridmask and Mosaic. This study further explores the recognition performance for refined scenarios including no mask, standard mask-wearing and incorrect mask-wearing. Extensive experimental results show the effectiveness of our method. By introducing the improved YOLOV5 algorithm to realize the mask detection function, the data structure is optimized through pruning, the data volume is reduced, the calculation speed is accelerated, and the mask detection is well realized.

Trajectory-Aware Offloading Decision in UAV-Aided Edge Computing: A Comprehensive Survey.

Recently, the integration of unmanned aerial vehicles (UAVs) with edge computing has emerged as a promising paradigm for providing computational support for Internet of Things (IoT) applications in remote, disaster-stricken, and maritime areas. In UAV-aided edge computing, the offloading decision plays a central role in optimizing the overall system performance. However, the trajectory directly affects the offloading decision. In general, IoT devices use ground offload computation-intensive tasks on UAV-aided edge servers. The UAVs plan their trajectories based on the task generation rate. Therefore, researchers are attempting to optimize the offloading decision along with the trajectory, and numerous studies are ongoing to determine the impact of the trajectory on offloading decisions. In this survey, we review existing trajectory-aware offloading decision techniques by focusing on design concepts, operational features, and outstanding characteristics. Moreover, they are compared in terms of design principles and operational characteristics. Open issues and research challenges are discussed, along with future directions.

A novel Dual-Magnetic circuit actuated Fast-Switching valve with Multi-Stage excitation control algorithm

Solenoid type fast-switching valve is the commonly used control element in digital hydraulics, and its magnetic circuit is completely composed of soft magnetic materials. The current research primarily focuses on reducing the response time of the valve, but it neglects the influence of the relationship between the opening and closing response times on the control accuracy. However, it faces the issue of opening and closing processes asymmetry aggravated by coil severe heating and various operating pressures, which would lead to flow errors and restrict control accuracy. Hence, this paper proposed a novel dual-magnetic circuit actuated fast-switching valve (DAFV) with a multi-stage excitation control algorithm (MSEC). The DAFV utilizes a permanent magnet (PM) to maintain the open state without electrical, avoiding large temperature rises. The variable excitation control of MSEC is achieved by precisely adjusting the duty cycle of both the delay and movement stages, ensuring stability and symmetry in response time. Among in, the duty cycles are set online for each stage to adapt to the operating pressure and target response time. The experiment shows that the response times of opening and closing almost remain the same at approximately 1.16 ms under different operating pressures. The relative flow error is reduced to within 4.1 %, while the maximum temperature rise is 15 ℃.

Thermofluid-dynamic and thermal–structural assessment of the EU-DEMO WCLL “double bundle” Breeding Blanket concept left outboard segment

As part of the activities performed by the DEMO Central Team (DCT) to study alternative configurations of the Water-Cooled Lead Lithium (WCLL) Breeding Blanket (BB), to overcome the open issues that emerged at the end of the Pre-Conceptual Design phase, a new concept, the WCLL BB “double bundle” (db), was developed. This concept adopts an array of db tubes poloidally distributed inside the breeding zone, mimicking the arrangement inside heat exchangers. These are coaxial pipes the gap between which is filled with PbBi (or alternatively with gas and fins), which avoids direct contact between PbLi and water in case of in-box LOCA events. Similarly, the First Wall channel cooling water is separated from the PbLi. The use of PbBi as an inert fluid makes the Segment Box (SB) sizing less stringent: in the case of an in-box LOCA due to a break of the water cooling circuit inside the BB SB, the pressurized volume is only the PbBi one, instead of the whole SB volume as in the reference WCLL design configuration. Consequently, the total amount of steel inside the BB can be reduced, with benefits in terms of increased tritium breeding ratio. Within this framework, University of Palermo, in support of the DCT and under the EUROfusion action, preliminarily analysed the steady-state thermal, thermo-hydraulic and normal and off-normal thermo-mechanical performances of this concept, following a theoretical-numerical approach based on finite volume and finite element methods, and adopting the ANSYS CFX and ANSYS Mechanical codes. The analyses carried out allowed a preliminary optimization of the design, improving the thermal and thermo-mechanical performances of the WCLL-db BB, in compliance with the design requirements, while reducing the total steel amount. Models, assumptions and main results are herewith reported and critically discussed.

A review of the current state-of-the-art on in situ monitoring in electron beam powder bed fusion

The industrial development of electron beam powder bed fusion (PBF-EB) is relatively younger and much more limited in terms of global widespread and revenues compared to laser powder bed fusion (PBF-L). Nevertheless, PBF-EB has been adopted in some of the most successful industrial case studies of metal AM, as it provides specific benefits and capabilities that make it a key enabling technology in a variety of industrial applications. Moreover, the recent years have seen a rapid evolution with new actors and new systems entering the market, together with a considerable increase of research and innovation programs. A field of major interest is the development and continuous improvement of in situ sensing and monitoring methods to anticipate the detection of defects, to predict the final quality of the part, and to rethink product qualification procedures. The technological features of the PBF-EB process have motivated the development of solutions that differ from the ones in PBF-L. Some of them have reached a good maturity level, being recently integrated into industrial machines, while others still deserve further research. This study explores the current state-of-the-art on in situ and in-line monitoring of the PBF-EB process, aiming to provide an up-to-date overview of the major differences with respect to PBF-L, currently available methods and their performances, as well as open issues, challenges to be tackled, and perspective for future research and industrial developments.

Cloud media video encoding: review and challenges

AbstractIn recent years, Internet traffic patterns have been changing. Most of the traffic demand by end users is multimedia, in particular, video streaming accounts for over 53%. This demand has led to improved network infrastructures and computing architectures to meet the challenges of delivering these multimedia services while maintaining an adequate quality of experience. Focusing on the preparation and adequacy of multimedia content for broadcasting, Cloud and Edge Computing infrastructures have been and will be crucial to offer high and ultra-high definition multimedia content in live, real-time, or video-on-demand scenarios. For these reasons, this review paper presents a detailed study of research papers related to encoding and transcoding techniques in cloud computing environments. It begins by discussing the evolution of streaming and the importance of the encoding process, with a focus on the latest streaming methods and codecs. Then, it examines the role of cloud systems in multimedia environments and provides details on the cloud infrastructure for media scenarios. After doing a systematic literature review, we have been able to find 49 valid papers that meet the requirements specified in the research questions. Each paper has been analyzed and classified according to several criteria, besides to inspect their relevance. To conclude this review, we have identified and elaborated on several challenges and open research issues associated with the development of video codecs optimized for diverse factors within both cloud and edge architectures. Additionally, we have discussed emerging challenges in designing new cloud/edge architectures aimed at more efficient delivery of media traffic. This involves investigating ways to improve the overall performance, reliability, and resource utilization of architectures that support the transmission of multimedia content over both cloud and edge computing environments ensuring a good quality of experience for the final user.

Opportunities and Challenges in AI Based Modern Power System

Nowadays, the power grid has transformed into a dynamic and extensive resource generation and management system. This transformation is primarily driven by the widespread adoption of renewable energy sources and the utilization of intelligent information and communication technologies to handle dynamic workloads. The smart grid encompasses various innovative operations, including power electrification, intelligent information integration at the physical layer, and intricate interconnections. These operations leverage data-driven deep learning, big data, and machine learning paradigms to effectively analyze and control transient issues within the electric power system. Artificial intelligence (AI) has emerged as a crucial tool in addressing and resolving challenges associated with transient stability assessment (TSA) and power generation control. In this research paper, we present a comprehensive review that explores the role of AI and its sub-procedures in tackling TSA problems. The article outlines an AI-based intelligent power system structure, along with the rationality of applying AI to transient situations in power system TSA. Distinguishing itself from other reviews, this paper delves into the AI-based TSA framework and design process, highlighting intelligent applications and their analytical capabilities in addressing power system transient problems. Furthermore, our analysis extends beyond AI alone, as we also explore the integration of big data, which aligns seamlessly with AI. The paper discusses future trends, opportunities, challenges, and open issues pertaining to AI-Big data based transient stability assessment in the smart power grid.

Stress Evaluation Through the Layers of a Fibre-Metal Hybrid Composite by IHD: An Experimental Study

BackgroundIncremental hole-drilling (IHD) has shown its importance in the measurement of the residual stress distribution within the layers of composite laminates. However, validation of these results is still an open issue, especially near the interfaces between plies. ObjectivesIn this context, this study is focused on experimentally verifying its applicability to fibre metal laminates.MethodsTensile loads are applied to cross-ply GFRP-steel [0/90/steel]s samples. Due to the difference in the mechanical properties of each ply, Classical Lamination Theory (CLT) predicts a distribution of the uniform stress within each layer, with pulse gradients between them. The interfaces act as discontinuous regions between the plies. The experimental determination of such stress variation is challenging and is the focus of this research. A horizontal tensile test device was designed and built for this purpose. A differential method is used to eliminate the effect of the existing residual stresses in the samples, providing a procedure to evaluate the ability of the IHD technique to determine the distribution of stress due to the applied tensile loads only. The experimentally measured strain-depth relaxation curves are compared with those determined numerically using the finite element method (FEM) to simulate the hole-drilling. Both are used as input for the IHD stress calculation method (unit pulse integral method). The distribution of stress through the composite laminate, determined by classical lamination theory (CLT), is used as a reference.ResultsUnit pulse integral method results, using the experimental and numerical strain-depth relaxation curves, compare reasonably well with those predicted by CLT, provided that there is no material damage due to high applied loads.ConclusionsIHD seems to be an important measurement technique to determine the distribution of residual stresses in fibre metal laminates and should be further developed for a better assessment of the residual stresses at the interfaces between plies.