This paper focuses on multiple-access protocol design in a wireless network assisted by multiple reconfigurable intelligent surfaces (RISs). By extending the existing approaches in single-user or single-RIS cases, we present two benchmark schemes for this multi-user multi-RIS scenario. Inspecting their shortcomings, a simple but efficient method coined opportunistic multi-user reflection (OMUR) is proposed.

The full realization of smart city technology is dependent on the secure and honest collaboration between IoT applications and edge-computing. In particular, resource constrained IoT devices may rely on fog-computing to alleviate the computing load of IoT tasks. Mutual authentication is needed between IoT and fog to preserve IoT data security, and monetization of fog services is needed to promote the fog service ecosystem. However, there is no guarantee that fog nodes will always respond to IoT requests correctly, either intentionally or accidentally. In the public decentralized IoT-fog environment, it is crucial to enforce integrity among fog nodes. In this paper, we propose a blockchain-based system that 1) streamlines the mutual authentication service monetization between IoT and fog, 2) verifies the integrity of fog nodes via service audits, and 3) discourages malicious activity and promotes honesty among fog nodes through incentives and penalties.

In the contemporary realm of cybersecurity, the dynamic and rapidly evolving nature of cyber threats necessitates advanced protective measures. Large Language Models (LLMs), with their sophisticated capabilities in natural language processing, have emerged as a cutting-edge tool in bolstering cybersecurity defenses. This paper investigates the application of LLMs in two pivotal cybersecurity domains: penetration testing and threat detection. It underscores the innovations introduced by tools such as Open Interpreter, which enable LLMs to interact with system terminals for executing code and conducting comprehensive security protocols. Furthermore, the paper critically examines the integration of LLMs in creating intelligent firewalls and smart defense systems, harnessing databases like MITRE's ATT&CK and NIST frameworks [a][b] for proactive threat management. However, the powerful functionalities of LLMs also present potential risks and ethical dilemmas, particularly concerning their dual-use nature and potential for misuse in accelerating hacking techniques or automating malware creation. The paper delves into the ethical landscape, emphasizing the importance of security by design, ethical guidelines, and regulatory frameworks to ensure responsible AI development and application in cybersecurity. Moreover, privacy concerns are addressed, highlighting the necessity for stringent data protection measures in LLM deployment. In conclusion, the Experimental Procedure section of this paper demonstrates the effective application of Open Interpreter in system vulnerability assessments, highlighting the critical role of AI-driven security measures in modern cybersecurity. The findings emphasize the necessity for continuous investment in these technologies and collaboration among various stakeholders to create a cybersecurity environment that is both resilient and adaptable to emerging threats.

This a topical review on the applications of silicene and the Xenes, the elemental 2D materials family beyond graphene. Two paradigmatic applications of silicene, i.e. field-effect transistors and flexible membranes, are here reviewed and an overview on the applications involving Xenes out of silicene is discussed. Finally, an outlook on the lab-to-fab transition for silicene and the Xenes is provided.

Functional near-infrared spectroscopy (fNIRS) is a valuable non-invasive tool for monitoring brain activity. The classification of fNIRS data in relation to conscious activity holds significance for advancing our understanding of the brain and facilitating the development of brain-computer interfaces (BCI). Many researchers have turned to deep learning to tackle the classification challenges inherent in fNIRS data due to its strong generalization and robustness. In the application of fNIRS, reliability is really important, and one mathematical formulation of the reliability of confidence is calibration. However, many researchers overlook the important issue of calibration. To address this gap, we propose integrating calibration into fNIRS field and assess the reliability of existing models. Surprisingly, our results indicate poor calibration performance in many proposed models. To advance calibration development in the fNIRS field, we summarize three practical tips. Through this letter, we hope to emphasize the critical role of calibration in fNIRS research and argue for enhancing the reliability of deep learning-based predictions in fNIRS classification tasks. All data from our experimental process are openly available on GitHub.

The use of multi-band matching for rectifiers leads to design complexity. Instead, recent advancements suggested self-matched branches combined in parallel to enable multiband operation. However, this method controls only the imaginary part. In this letter, we propose an efficient dual-band rectifier with compact realization. The rectifier consists of two self-matched parallel branches. Each branch provides comprehensive impedance control over real and imaginary parts in the corresponding band independent of the design frequency. The branch impedance matching is analyzed theoretically, and design equations are presented. To verify the proposed theory, a compact dual-band rectifier was fabricated with a compact area of only 0.42 cm2 after excluding the area required for the RF connector. The measured RF-DC power conversion efficiency (PCE) was > 50% for input power (𝑷𝒊𝒏) ranging from -5.5 dBm to 11 dBm at 390 MHz with a peak of 69%. Also, the PCE was > 50% for 𝑷𝒊𝒏 ranging from -4 dBm to 12 dBm at 690 MHz with a peak of 68%. The fabricated rectifier operates with a wide load range from 0.5 KΩ to 3 KΩ with PCE > 50% at both bands when 𝑷𝒊𝒏 = 𝟓 dBm.

M-estmators including the Welsch and Cauchy have been widely adopted for robustness against outliers, but they also down-weigh the uncontaminated data. To address this issue, we devise a framework to generate a class of nonconvex functions which only down-weigh outlier-corrupted observations. Our framework is then applied to the Welsch, Cauchy and lp-norm functions to produce the corresponding robust loss functions. Targeting on the application of robust matrix completion, efficient algorithms based on these functions are developed and their convergence is analyzed. Finally, extensive numerical results demonstrate that the proposed methods are superior to the competitors in terms of recovery accuracy and runtime.

Meta-learning, or "learning to learn", enables machines to acquire general priors with minimal supervision and rapidly adapt to new tasks. Unlike traditional AI methods that approach each task from scratch using a fixed learning algorithm, meta-learning refines the learning algorithm itself through experience across various tasks, enhancing transferability and generalization. This is especially valuable when data collection is difficult or costly, allowing for effective learning from task sequences while reducing the dependency on extensive target domain data. Consequently, meta-learning has emerged as a promising field in machine learning. Although existing surveys provide valuable insights into meta-learning, they often present methods and applications in isolation and lack coverage of the latest advancements. Given the rapid growth of the field, a comprehensive survey is both necessary and challenging. Moreover, meta-learning algorithms often remain disconnected, with no unified framework to explain how they facilitate "learning to learn". This survey seeks to bridge that gap by systematizing meta-learning research, offering a thorough overview of strategies to enhance understanding. Additionally, the paper reviews over thirty representative meta-learning methods across models, tasks, and applications, analyzing their characteristics and challenges. To illustrate method performance, we evaluate more than fifteen models on six problems spanning thirteen scenarios, emphasizing the importance of selecting appropriate meta-learning approaches for practical applications.

Ultra-low magnetic field sensing is rapidly emerging as a technology in various applications, providing a non-invasive and instantaneous method of data acquisition. The increasing availability of improved or innovative types of magnetometers promotes the need for a benchmark metric. This contribution focuses on the evaluation of single magnetometer systems that detect magnetic fields in the femtotesla range and are usually used in a magnetically shielded environment. For assessment, a Device Alignment Actuator system is presented that enables an accurate 3D alignment of the Device Under Test around a single point within a known, homogeneous magnetic test field inside the highly magnetically shielded room, called BMSR-2.1 at the Physikalisch-Technische Bundesanstalt Berlin, Germany. The defined benchmark metric includes several key parameters, such as frequency response, amplitude stability, frequency stability, and directivity, to name just a few. These parameters are demonstrated using several commercial state-of-the-art optically pumped magnetometers (SERF-OPMs) and compared to the reference system based on superconducting quantum interference devices, which remain the gold standard in magnetometry. This approach ensures consistent evaluation across different magnetometer types, provided they operate at room temperature and fit within the evaluation platform. The platform can also be adapted for multi-channel magnetometer systems.

Radio Frequency Interference (RFI) in Synthetic Aperture Radar (SAR) is a daunting challenge that affects the SAR sensing reliability and its image quality. To ensure that SAR remains a powerful tool for Earth observation, this paper presents an effective two-dimensional tuneable attenuation space-frequency (azimuth-range) filtration. This framework is based on key components including time-frequency features of level-0 SAR data, radar antenna pattern, and the estimated parameters of the interference signal. Additionally, a signal power localization method is applied to estimate the relative position of the interference source with 95% accuracy to facilitate applying the tuneable filter. Simulated results are obtained using a public open-source spaceborne SAR emulator, SEMUS, for generating emulated clean and contaminated SAR raw data. Furthermore, the filtration framework is successfully tested on real-life interference events on the TerraSAR-X satellite raw data.

Data cooperatives with fiduciary obligations to members provide a useful source of truthful information regarding a given member whose personal data is managed by the cooperative. Since one of the main propositions the cooperative model is to protect the data privacy of members, we explore the notion of blinded attestations in which the identity of the subject is removed from the attestations issued by the cooperative regarding one of its members. This is performed at the request of the individual member. We propose the use of a legal entity to countersign the blinded attestation, one that has an attorney-client relationship with the cooperative, and which can henceforth become the legal point of contact for inquiries regarding the individual related to the attribute being attested. There are several use-cases for this feature, including the Funds Travel Rule in transactions in digital assets, and the protection of privacy in decentralized social networks.

This study focuses on iris segmentation, a crucial aspect of iris recognition systems with implications in security and authentication. Leveraging Convolutional Neural Networks (CNNs), the study integrates preprocessing,

This review paper explores advanced methods to prompt Large Language Models (LLMs) into generating objectionable or unintended behaviors through adversarial prompt injection attacks. We examine a series of novel projects like HOUYI, Robustly Aligned LLM (RA-LLM), StruQ, and Virtual Prompt Injection, that compel LLMs to produce affirmative responses to harmful queries. Additionally, the paper investigates the robustness of these attacks across different models and prompts. Several new benchmarks, such as PromptBench, AdvBench, AttackEval, INJECAGENT, and RobustnessSuite, have been created to evaluate the overall performance and resilience of LLMs against adversarial prompt injection attacks. Results show significant success rates in misleading models like Vicuna-7B, Llama-2-7B-Chat, GPT-3.5, and GPT-4. The review highlights limitations in existing defense mechanisms and proposes future directions for enhancing LLM alignment and safety mechanisms such as LLM SELF DEFENSE, Unlike previous studies, this paper provides a more comprehensive evaluation by integrating a broader range of attack methods and target models. Our study shows that there is a need for improved robustness in LLMs, which will potentially shape the future of AI-driven applications and security protocols.

Talking head generation based on neural radiance fields (NeRF) has gained prominence, primarily owing to its implicit 3D representation capabilities within neural networks. However, most NeRF-based methods often intertwine audio-tovideo conversion in a joint training process, resulting in challenges such as inadequate lip synchronization, limited learning efficiency, large memory requirement and lack of editability. In response to these issues, this paper introduces a fully decoupled NeRF-based method for generating talking head. This method separates the audio-to-video conversion into two stages through the use of facial landmarks. Notably, the Transformer network is used to establish the cross-modal connection between audio and landmarks effectively and generate landmarks conforming to the distribution of training data. Then, these landmarks are combined with Gaussian relative position coding to refine the sampling points on the rays, thereby constructing a dynamic neural radiation field conditioned on these landmarks for rendering the generated head. This decoupled setup enhances both the fidelity and flexibility of mapping audio to video with two independent small-scale networks. Additionally, it supports the generation of the torso part from the head-only image with deformable convolution, further enhancing the realism of the generated talking head. The experimental results demonstrate that our method excels in producing lifelike talking head, and the lightweight neural network models also exhibit superior speed and learning efficiency with less memory requirement.

In recent years, there has been a growing interest in using image super-resolution (SR) techniques in remote sensing. These techniques aim to reconstruct high-resolution (HR) imagery from low-resolution (LR) sources. Despite the development of sophisticated SR methodologies, determining what constitutes 'good' SR is still a matter of debate. Present-day literature often presents SR models through a strong computer vision perspective, heavily relying on synthetic datasets. Moreover, commonly used metrics often prioritize attributes that do not necessarily correspond to improvements in spatial resolution. To address this challenge, we present OpenSR-test, a comprehensive benchmark designed exclusively for evaluating SR of remote sensing images. Our framework incorporates specific quality metrics and curated cross-sensor datasets, each spanning various scale factors with consistent metadata. Utilizing OpenSR-test, we evaluate state-ofthe-art SR algorithms from a remote sensing perspective. The OpenSR-test framework and datasets are publicly available at https://esaopensr.github.io/opensr-test/.

Time measurements are challenging in electronics 1 given their various applications. The main focus lies not in achieving greater precision, as conventional architectures have already reached picosecond levels. Instead, the challenge stems from the use of low resources and the substantial expansion in the number of channels. This study presents a novel architecture for the implementation of TDCs in applications where resources are constrained. The introduced FPGA-based TDC offers a resolution of 415.84 ps, a single-shot precision of 0.45 LSB (186 ps r.m.s), while maintaining a minimal resource occupancy. Built upon a multi-shift phase counter, the TDC is extended with a tap delay using the input delay available in the FPGA hardware input, doubling the resolution of the TDC. The resource utilization is minimized when compared to low-resources state-of-the-art TDCs. The number of LUTs has been reduced up to 102, and the number of registers to 213. Furthermore, the presented TDC exhibits favorable DNL (0.2 LSB) and INL 17 (0.15 LSB). The TDC has been successfully implemented on an Artix7-2 FPGA from Xilinx. This design provides a resource-effective solution for applications requiring high precision and  low resource consumption.

This article targets first-year engineering studentsâ\euro™ self-efficacy beliefs and their relationship with performance attainments in the first half of the semester and the final grade of the programming course. Self-efficacy is the belief in oneâ\euro™s capabilities to reach a desired goal or outcome by setting and implementing the required courses of action. In education, self-efficacy is crucial to academic growth, for it helps students take charge of their own learning, develop their skills, set goals, and regulate their motivation in order to accomplish these goals. Considering the difficulty of the programming course, self-efficacy plays a vital role in the challenges the students face in the programming course, their magnitude, and the skills the students use to overcome them. Self-efficacy beliefs were measured through a survey administered twice to 118 engineering students in the first half of the semester and correlated with the first quiz of the semester and the midterm grade, then with the final grade. The analyses targeted all engineering students, then each engineering major separately: Computer, Electrical, Mechatronics, Mechanical, and Industrial engineering. The results showed that there is in fact a relationship between studentsâ\euro™ self-efficacy beliefs and performance in the programming course. However, the relationship was shown to be inverse in the first half of the semester but positive with the final grade. These findings highlight the importance of student efficacy beliefs especially in the first half of the semester while also presenting new empirical findings regarding each engineering major targeted.

This work investigates a Holographic Multiple-Input Multiple-Output (HMIMO) communication system consisting of a source and a receiver being two surfaces in the LoS configuration. The closed-form expressions of the electric field and the channel coupling coefficients are obtained leveraging an asymptotic expansion of the Green's function. The derived approximations show satisfactory accuracy in both near-and farfield conditions, across low and high frequencies. These are then employed to obtain insights regarding (i) the spatial distribution of the beams, and (ii) the mapping relation between source and receiver modes while ensuring sufficient orthogonality under prescribed conditions.