Open Challenges Research Articles

A multidisciplinary perspective on the present and future of particle imaging

The imaging of particulate media – encompassing both the imaging of the particles themselves, as well as the study of their dynamics and bulk properties and behaviours – is crucial to improving our understanding of a diverse range of phenomena and processes spanning numerous scientific disciplines and industrial sectors. Despite interdisciplinary interest in the field and the availability, and continuous development, of a wide range of different imaging techniques, there exist nonetheless a number of limitations of these techniques, and open challenges – both technical and non-technical – facing the field as a whole. In this perspective, we discuss in detail five such challenges, identified by a team of interdisciplinary experts spanning both academia and industry: how can we work toward the imaging of systems which are more ‘real-world-relevant’, both in terms of composition and scale? How can we extract detailed, quantitative information regarding stresses from such systems? How can we image processes which are both rapid and transient, when most current technologies can manage (at best) only one of these states? How can we ensure closer and more fruitful collaboration between the academics developing particle imaging technologies and the potential industrial end-users who stand to benefit from them? How can we improve the visibility of the field and the educational opportunities available to the potential next generation of particle technologists? As one may expect for such a broad range of questions, the answers to the above are diverse and numerous. However, there are certain key themes running through them. Above all, our work highlights a need for improved collaboration, be that in terms of experts in multiple different imaging technologies working together to perform multi-modal studies so as to address the technical limitations highlighted above, researchers and industry professionals finding new ways to engage, or academics co-creating open-source educational tools to support the next generation of particle imaging experts.

UAV-Assisted IoT Applications, Cybersecurity Threats, AI-Enabled Solutions, Open Challenges With Future Research Directions

UAV-Assisted IoT Applications, Cybersecurity Threats, AI-Enabled Solutions, Open Challenges With Future Research Directions

Federated Learning for Digital Twin-Based Vehicular Networks: Architecture and Challenges

A digital twin uses a virtual model of the physical system to fulfill the diverse requirements (e.g., latency, reliability, quality of physical experience) for emerging vehicular network applications. Although a twin-based implementation of vehicular networks can offer performance optimization, modeling a digital twin is a significantly challenging task. Federated learning (FL) is a better privacy-preserving, distributed learning scheme that can be used to model twin models. Although FL can offer performance enhancement, it requires careful design. Therefore, in this article, we present an overview of FL for a twinbased vehicular network. A general architecture for FL-enabled digital twins for a vehicular network is presented. Our proposed architecture consists of two spaces, such as twin space and the physical space. A physical space consists of all the physical entities (e.g., cars and edge servers) required for vehicular networks, whereas, the twin space refers to the logical space that is used for the deployment of twins. A twin space can be implemented either using edge servers and cloud servers. We also outline a few use cases of FL for the twin-based vehicular network. Finally, the paper is concluded and an outlook on open challenges is presented.

An Approach for Engineering Peptides for Competitive Inhibition of the SARS-COV-2 Spike Protein.

SARS-CoV-2 is the virus responsible for a respiratory disease called COVID-19 that devastated global public health. Since 2020, there has been an intense effort by the scientific community to develop safe and effective prophylactic and therapeutic agents against this disease. In this context, peptides have emerged as an alternative for inhibiting the causative agent. However, designing peptides that bind efficiently is still an open challenge. Here, we show an algorithm for peptide engineering. Our strategy consists of starting with a peptide whose structure is similar to the interaction region of the human ACE2 protein with the SPIKE protein, which is important for SARS-COV-2 infection. Our methodology is based on a genetic algorithm performing systematic steps of random mutation, protein-peptide docking (using the PyRosetta library) and selecting the best-optimized peptides based on the contacts made at the peptide-protein interface. We performed three case studies to evaluate the tool parameters and compared our results with proposals presented in the literature. Additionally, we performed molecular dynamics (MD) simulations (three systems, 200 ns each) to probe whether our suggested peptides could interact with the spike protein. Our results suggest that our methodology could be a good strategy for designing peptides.

Permutation entropy analysis of EEG signals for distinguishing eyes-open and eyes-closed brain states: Comparison of different approaches.

Developing reliable methodologies to decode brain state information from electroencephalogram (EEG) signals is an open challenge, crucial to implementing EEG-based brain-computer interfaces (BCIs). For example, signal processing methods that identify brain states could allow motor-impaired patients to communicate via non-invasive, EEG-based BCIs. In this work, we focus on the problem of distinguishing between the states of eyes closed (EC) and eyes open (EO), employing quantities based on permutation entropy (PE). An advantage of PE analysis is that it uses symbols (ordinal patterns) defined by the ordering of the data points (disregarding the actual values), hence providing robustness to noise and outliers due to motion artifacts. However, we show that for the analysis of multichannel EEG recordings, the performance of PE in discriminating the EO and EC states depends on the symbols' definition and how their probabilities are estimated. Here, we study the performance of PE-based features for EC/EO state classification in a dataset of N=107 subjects with one-minute 64-channel EEG recordings in each state. We analyze features obtained from patterns encoding temporal or spatial information, and we compare different approaches to estimate their probabilities (by averaging over time, over channels, or by "pooling"). We find that some PE-based features provide about 75% classification accuracy, comparable to the performance of features extracted with other statistical analysis techniques. Our work highlights the limitations of PE methods in distinguishing the eyes' state, but, at the same time, it points to the possibility that subject-specific training could overcome these limitations.

Multimodal and Multiuser Semantic Communications for Channel-Level Information Fusion

Recently, semantic communication emerged as one of the enabling technologies for intelligent communications and multimodal information fusion plays an important role therein, where various sensors collect environment information to generate complementary multimodal data. In this paper, we propose a new multimodal information fusion scheme for multi-user semantic communications, namely channel-level information fusion, taking into account the nature of signal transmission in wireless channels. In the proposed scheme, wireless channel acts as a medium to fuse multimodal data and the received signal is viewed as the fused information, such that a receiver can retrieve semantic information without a need to perform multiuser signal detection. In addition, semantic precoding is used to mitigate the randomness effect of wireless channels in fusion. A case study is conducted to validate the effectiveness of the proposed fusion scheme, followed by a discussion on the open issues and challenges to be tackled.

Memristive tonotopic mapping with volatile resistive switching memory devices

To reach the energy efficiency and the computing capability of biological neural networks, novel hardware systems and paradigms are required where the information needs to be processed in both spatial and temporal domains. Resistive switching memory (RRAM) devices appear as key enablers for the implementation of large-scale neuromorphic computing systems with high energy efficiency and extended scalability. Demonstrating a full set of spatiotemporal primitives with RRAM-based circuits remains an open challenge. By taking inspiration from the neurobiological processes in the human auditory systems, we develop neuromorphic circuits for memristive tonotopic mapping via volatile RRAM devices. Based on a generalized stochastic device-level approach, we demonstrate the main features of signal processing of cochlea, namely logarithmic integration and tonotopic mapping of signals. We also show that our tonotopic classification is suitable for speech recognition. These results support memristive devices for physical processing of temporal signals, thus paving the way for energy efficient, high density neuromorphic systems.

A Multiagent Cooperative Learning System With Evolution of Social Roles

Recent developments in reinforcement learning have been able to derive optimal policies for sophisticated and capable agents, and shown to achieve human-level performance on a number of challenging tasks. Unfortunately, when it comes to multi-agent systems (MASs), complexities such as non-stationarity and partial observability bring new challenges to the field. Building a flexible and efficient multi-agent reinforcement learning algorithm capable of handling complex tasks has to date remained an open challenge. This paper presents an Multi-Agent learning system with the evolution of Social Roles (eSRMA). The main interest is placed on solving the key issues in the definition and evolution of suitable roles, and optimizing the policies accompanied by social roles in MAS efficiently. Specifically, eSRMA incorporates and cultivates role division awareness of agents to improve the ability to deal with complex cooperative tasks. Each agent is assigned a role module, which can dynamically generate roles based on the individuals’ local observations. A novel multi-agent reinforcement learning algorithm (MARL) is designed as the principal driving force that governs the role-policy learning process by a role-attention credit assignment mechanism. Moreover, a role evolution process is developed to help agents dynamically choose appropriate roles in decision-making. Comprehensive experiments on the StarCraft II micromanagement benchmark have demonstrated that eSRMA exhibits superiority in achieving higher learning capability and efficiency for multiple agents compared to the state-of-the-art MARL methods.

A Survey on Unbalanced Classification: How Can Evolutionary Computation Help?

Unbalanced classification is an essential machine learning task, which has attracted widespread attention from both the academic and industrial communities due mainly to its broad applications. Evolutionary computation (EC) has contributed greatly to unbalanced classification. However, to the best of our knowledge, there have not been any comprehensive investigations on the strengths and weaknesses of alternative EC methods in addressing various challenging problems in unbalanced classification. This paper reviews the literature which utilize EC techniques for unbalanced classification, with the aim of revealing the contributions of EC to unbalanced classification, providing an overview of recent advances, and identifying limitations of existing works. In addition, we present a series of real-world applications, and identify open challenges as well as possible research directions for the future.

SoK: Secure Human-centered Wireless Sensing

Human-centered wireless sensing (HCWS) aims to understand the fine-grained environment and activities of a human using the diverse wireless signals around him/her. While the sensed information about a human can be used for many good purposes such as enhancing life quality, an adversary can also abuse it to steal private information about the human (e.g., location and person's identity). However, the literature lacks a systematic understanding of the privacy vulnerabilities of wireless sensing and the defenses against them, resulting in the privacy-compromising HCWS design. In this work, we aim to bridge this gap to achieve the vision of secure human-centered wireless sensing. First, we propose a signal processing pipeline to identify private information leakage and further understand the benefits and tradeoffs of wireless sensing-based inference attacks and defenses. Based on this framework, we present the taxonomy of existing inference attacks and defenses. As a result, we can identify the open challenges and gaps in achieving privacy-preserving human-centered wireless sensing in the era of machine learning and further propose directions for future research in this field.

Recognition human walking and running actions using temporal foot-lift features

The recognition of human walking and running actions becomes essential part of many different practical applications such as smart video-surveillance, patient and elderly people monitoring, health care as well as human-robot interaction. However, the requirements of a large spatial information and a large number of frames for each recognition phase are still open challenges. Aiming at reducing the number frames and joint information required, temporal foot-lift features were introduced in this study. The temporal foot-lift features and weighted KNN classifier were used to recognize “Walkin and“Running”actions from four different human action datasets. Half of the datasets were trained and the other half of datasets were experimentally tested for performance evaluation. The experimental results were presented and explained with justifications. An overall recognition accuracy of 88.6% was achieved using 5 frames and it was 90.7% when using 7 frames. The performance of proposed method was compared with the performances of existing methods. Skeleton joint information and temporal foot-lift features are promising features for real-time human moving action recognition.

Exploring a Spectrum of Deep Learning Models for Automated Image Captioning: A Comprehensive Survey

Automatic caption generation from images has emerged as a fundamental and challenging problem at the intersection of computer vision and natural language processing. This paper presents a comprehensive survey of the techniques, methodologies, and advancements in the field of automatic caption generation from images. The primary objective is to provide an extensive review of the state-of-the-art models, evaluation metrics, datasets, and applications associated with this domain. The survey begins by elucidating the underlying principles of image feature extraction and caption generation. Various neural network architectures, including Convolutional Neural Networks (CNNs) and recurrent models such as Long Short-Term Memory (LSTM) networks, are discussed in detail. Additionally, the paper explores the integration of attention mechanisms and reinforcement learning strategies to enhance the quality and relevance of generated captions. A thorough examination of evaluation metrics, encompassing both automated and human-centric approaches, is presented to evaluate the generated captions quantitatively and qualitatively. The survey also highlights prominent datasets that have significantly contributed to the advancement of research in this field, facilitating a deeper understanding of challenges and trends. Furthermore, the paper discusses practical applications and real-world use cases where automatic caption generation plays a pivotal role, including accessibility, multimedia indexing, and assistive technologies. The discussion concludes by outlining open challenges and future directions, aiming to inspire further research and innovation in automatic caption generation from images. The aim of this paper is to examine and contrast diverse end-to-end learning frameworks for image captioning, employing established evaluation metrics to comprehend their applicability across different research domains. In addition to the comparative analysis, the paper addresses future challenges in this domain.

Amphibious Robots Locomotion Strategies in Unstructured Complex Environments: A Review

In the previous literature, amphibious robots focused mainly on locomotion in underwater and flat land surface manoeuvring. Few amphibious robots focused on unstructured land environments. The amphibious robot designs were more emphasised in academics, leading to more work done in building amphibious robots that mimic biological amphibians, imitating the geometry and overall functionality of the amphibious robots. Developing amphibious robots with propulsive mechanisms for manoeuvring in a water environment received more attention than other functionalities like adaptability on rough natural terrain and obstacle repositioning capability. However, practical applications like reconnaissance and surveying posed challenges in the ground environment, which had unstructured and complex terrain profiles, especially in the transition area. Therefore, reviewing the amphibious robots focused on manoeuvring complex uneven surfaces was essential. The literature had comprehensive review papers on navigation strategies encompassing manoeuvring on flat ground surfaces and underwater locomotion. There was a need for a focused study that highlighted the amphibious robot that manoeuvred in an unstructured land environment. The open challenges and recent solutions by designing new mechanisms and deployment issues were highlighted and reviewed. Hence, the paper addressed a more specific review of amphibious robot locomotion in an unstructured environment. The paper also discussed a case study of an amphibious robot capable of locomotion in unstructured environments. It was envisaged that the review would provide directions and insights to researchers and robotic system designers on developing robust propulsive mechanisms for amphibious robots capable of locomotion in unstructured environments.Keywords: manoeuvrability, mobility, terrain, unified locomotion, mechanism, propulsion

Synthesis, solubility and thermodynamic properties of N-A-S-H gels with various target Si/Al ratios

The synthesis of N-A-S-H gel with high Si/Al ratios (>2) has been rarely reported in the literature, leaving the establishment of a reliable synthesis route as an open challenge. This paper aims to synthesize N-A-S-H gels with Si/Al ratios ranging from 1 to 3 and establish their thermodynamic database. The effects of reaction temperature, reaction time, initial Si/Al, concentration of reactants and pH of the matrix on the Si/Al ratios of the synthesized N-A-S-H gel were investigated. Results showed that N-A-S-H gels with target Si/Al ratios can be synthesized by controlling the concentration of reactants, pH and initial Si/Al ratios. The solubility products of the obtained N-A-S-H gels were determined via dissolution tests at different temperatures, to determine thermodynamic data. The development of this experimentally derived thermodynamic database of N-A-S-H gels constitutes a crucial step in the advancement of thermodynamic modeling of geopolymer, providing valuable insight into geopolymer reactions and phase assemblages.

Biofilm contamination in confined space stations: reduction, coexistence or an opportunity?

The prolonged human permanence in confined environments in space, such as in the case of the International Space Station, has inadvertently fostered conditions leading to uncontrolled microbial proliferation on surfaces, known as biofilm. Biofilm presence represents a challenge in critical spacecraft systems, that can lead to contamination issues and systems loss of function due to biofouling phenomena. This scenario is further complicated by microgravity that has a controversial role on biofilm growth and formation. Biocontamination can be a limiting factor in human long-term mission in outer Earth orbit and an economic and health issue on ISS. This study addresses the pressing need for effective antimicrobial strategies against such resilient biofilms in confined environments where the usage of biocidal chemical compounds is strictly controlled due to toxicity dangers. Traditional methods can be complemented by advanced antimicrobial coatings techniques. A promising approach is based on the oxygen plasma as coating platform. The technology can be potentially extended to a wide range of antibiofilm agents (e.g., peptides, bacteriophages, nanoparticles, quorum sensing disrupting agents, etc.) and substrates (e.g., metal, plastic, ceramic) showing an exceptional flexibility. An alternative vision of the biofilm challenge can be inspired by the dual nature of biofilms, addressed as “good” or “bad” depending on the specific application. Indeed, biofilm have a great potential in closed systems as small space habitat (e.g., ISS) that can be inspired by their role in “large space habitat” as planet Earth itself. The replication of such a complex biological equilibrium is an open challenge.

Impact of power consumption in containerized clouds: A comprehensive analysis of open-source power measurement tools

Recently, container-based solutions have become de facto compute units of modern cloud-native applications. However, the exponential growth in data traffic and the power consumption of these technologies to handle high data traffic alarm the strong need for energy evaluation approaches in containerized clouds. Furthermore, the proliferation of highly distributed edge clouds raises additional concerns regarding the power consumption of future cloud architectures. This article presents a detailed overview of methods and techniques for monitoring power consumption within popular cloud platforms. The study offers an in-depth evaluation of these approaches, demonstrating variations in measured power consumption based on the chosen technique. A well-known container orchestration platform named Kubernetes (K8s) has been applied in our extensive measurements. This work argues that energy-efficient container clouds will play a vital role in building a more sustainable and eco-friendly digital infrastructure by optimizing power consumption and reducing carbon footprint, paving the way for a greener future. The paper also discusses open challenges and future research directions on energy sustainability, leading to the conclusion, offering lessons learned and prospects on potential solutions to foster sustainable practices within the container ecosystem.

A comprehensive survey of digital twins: Applications, technologies and security challenges

Alongside advancements in Artificial Intelligence (AI), significant progress has been made in big data processing, edge/cloud computing, and ubiquitous computing in the past two decades. These advancements catalyzed the development and adoption of Digital Twins (DT) across various domains, serving as virtual replicas of Physical Objects (POs). DTs provide advanced visualization and simulation capabilities, enabling effective estimation, optimization, and forecasting of PO's behaviors. However, the widespread adoption of DTs has introduced various security threats, vulnerabilities, and attacks. Despite ongoing research in DT applications and security, there is a lack of systematic review of the DT security literature across domains and architectural layers. This study fills this gap by systematically reviewing DT research, focusing on three interrelated aspects: DT applications, architectural layers, and security. We explore DT's architectural layers, functional requirements, application, and creation software to identify potential threats, attacks, and vulnerabilities specific to DT layers and application domains. We then systematize our findings under a unified security framework and pinpoint countermeasures against identified security challenges. Furthermore, our study explores DT's role in mitigating existing cyber threats, and we conclude our work by identifying open challenges and potential research directions.

Noncentrosymmetric Triangular Magnet CaMnTeO6: Strong Quantum Fluctuations and Role of s0 versus s2 Electronic States in Competing Exchange Interactions.

Noncentrosymmetric triangular magnets offer a unique platform for realizing strong quantum fluctuations. However, designing these quantum materials remains an open challenge attributable to a knowledge gap in the tunability of competing exchange interactions at the atomic level. Here, a new noncentrosymmetric triangular S=3/2 magnet CaMnTeO6 is created based on careful chemical and physical considerations. The model material displays competing magnetic interactions and features nonlinear optical responses with the capability of generating coherent photons. The incommensurate magnetic ground state of CaMnTeO6 with an unusually large spin rotation angle of 127°(1) indicates that the anisotropic interlayer exchange is strong and competing with the isotropic interlayer Heisenberg interaction. The moment of 1.39(1)µB, extracted from low-temperature heat capacity and neutron diffraction measurements, is only 46% of the expected value of the static moment 3µB. This reduction indicates the presence of strong quantum fluctuations in the half-integer spin S=3/2 CaMnTeO6 magnet, which is rare. By comparing the spin-polarized band structure, chemical bonding, and physical properties of AMnTeO6 (A=Ca, Sr, Pb), how quantum-chemical interpretation can illuminate insights into the fundamentals of magnetic exchange interactions, providing a powerful tool for modulating spin dynamics with atomically precise control is demonstrated.

On the role of audio frontends in bird species recognition

Automatic acoustic monitoring of bird populations and their diversity is in demand for conservation planning. This requirement and recent advances in deep learning have inspired sophisticated species recognizers. However, there are still open challenges in creating reliable monitoring systems of natural habitats. One of many open questions is whether predominantly used audio features like mel-filterbanks are appropriate for such analysis since their design follows human's perception of the sound, making them susceptible to discarding fine details from other animals' vocalization. Although research shows that different audio features work better for particular tasks and datasets, it is hard to attribute all advantages to input features since the experimental setups vary. A general solution is to design a learnable audio frontend to extract task-relevant features from raw waveform since it contains all the information in other audio features. The current paper thoroughly analyzes the role of such frontends in bird species recognition, which helped to evaluate the adequacy of traditional time-frequency representations (static frontends) in capturing the relevant information from bird vocalization. In particular, this work shows that the main performance gain in learnable audio frontends comes from the normalization and compression operations rather than the data-driven frequency selectivity and functional form of filters. We observed no significant discrepancy between the frequency bands of the learned and static frontends for bird vocalization. Although the performance of learnable frontends was much higher, we will show that adequate normalization and compression enhance the accuracy of traditional frontends by more than 16% to achieve comparable results for bird species recognition. Ablation studies of the frontends under different configurations and detailed analysis of noise robustness provide evidence for the conclusions, validate the use of mel-filterbanks and similar features in prior works, and provide guidelines for designing future species recognizers. The code is available at https://github.com/houtan-ghaffari/bird-frontends.

Exploring the use of 3D radar measurements in predicting the evolution of single-core convective cells

Object-based radar rainfall nowcasting is a widely used technique for convective storm prediction. Currently, most existing object-based nowcasting methods primarily focus on predicting cell movements, neglecting the temporal evolution of cell properties such as size, shape, and intensity. Incorporating this evolution is critical for improving predictability in convective storms. While previous studies have used three-dimensional (3D) radar observations to capture vertical changes during convective cell formation, these efforts often analyse or reconstruct specific convective events. Integrating 3D radar information into operational object-based radar rainfall nowcasting remains an open challenge. This research addresses this challenge using deep learning (DL) techniques. More specifically, a DL-based prediction model is developed, which uses 2D and 3D cells' properties retrieved from 3D radar reflectivity data at the current time and across the past 15 min to predict the evolution of these properties over the next 15 min. This model could eventually be integrated into existing object-based nowcasting models. A total of 4708 cell lifecycles, extracted from high-resolution (5-min, 1-km, 24 levels at 0.5 km intervals) 3D radar data across the UK, are used to train the model, and a total of 1177 lifecycles are used for testing. The proposed model is shown to predict the evolution of single-core convective cells effectively, including changes in 2D projected geometry and mean 2D and 3D reflectivity. In particular, by incorporating information on the vertical evolution of convective cores, the prediction errors of mean reflectivity (in both 2D and 3D) can be reduced by approximately 50% at 15-min forecast lead time, as compared to a persistence forecast. Keywords: radar, tracking, convective cell, nowcasting, 3D, deep learning, lstm.