This paper has been accepted by the "Unmanned Systems" journal. Paper link: https://doi.org/10.1142/S2301385025500839

In a world where traditional authentication systems are constrained, the introduction of voice-based authentication provides a viable option. This cutting-edge biometric security method uses unique vocal traits including pitch, tone, and speech patterns to create a unique voiceprint. Its sophisticated and dependable verification procedure, when combined with voice-activated services and secure access systems, not only solves au-thentication issues but also improves user experience overall. Our suggested method provides a thorough blueprint for developing a lightweight voice authentication system that is based on text. This system leverages an effective encoder model to convert voice spectrograms, making deployment easy even on edge devices with limited resources. Various dimensionality reduction techniques are explored to obtain optimal voice embeddings that capture speaker uniqueness while minimizing model complexity. A key novelty is the application of model compression techniques including lightweight architectures and Siamese Networks to obtain highly condensed voice embeddings for each user, reducing storage and infrastructure costs compared to traditional voice authentication methods. The proposed lightweight spectrogram embeddings leverage language-agnostic acoustic features, enabling language-independent speaker verification. Additionally, dimensionality reduction applied during voice registration allows the capturing of discriminative voice characteristics in a low-dimensional compact feature space. This significantly cuts down the storage requirements and infrastructure costs per user compared to standard voice biometric approaches, while retaining competitive verification performance. The architecture is optimized for responsiveness by leveraging lightweight frameworks. The proposed system delivers competitive voice authentication capabilities while minimizing memory, computational, and energy footprints. This makes the system useful for integration into smart devices and paves the way for ubiquitous voice biometrics.

This paper explores the problems caused by spam messages on the internet. It talks about different types of spam, like email spam and fake advertisements on social media. The paper also looks at how spam can make things difficult for people, like draining resources and tricking them into clicking on harmful links. The paper suggests fighting against spam by enabling filters in emails, using third-party spam filtering, avoiding clicking suspicious links, reporting, and blocking and avoiding using personal or professional emails. Lastly, the paper discusses what might happen with spam in the future and the challenges we may face. Overall, the paper aims to help and understand then find ways to deal with it for a safer and smoother experience.

Nearly ideal vertical AlxGa1-xN (\(0.7\ \le x<1.0\)) p-n diodes are fabricated on an AlN substrate. Distributed polarization doping (DPD) was employed for both p-type and n-type layers of the p-n junction, instead of conventional impurity doping, to overcome the major bottleneck of AlN-based material: the control of conductivity. Capacitance-voltage measurements revealed that the net charge concentration agreed well with the DPD charge concentration expected from the device layer structure. The fabricated devices exhibited a low turn-on voltage of 6.5 V, a low differential specific ON-resistance of 3 mΩ cm2, electroluminescence (maximum at 5.1 eV), and an ideality factor of 2 for a wide range of temperatures (room temperature-573 K). Moreover, the breakdown electric field was 7.3 MV cm-1, which was almost twice as high as the reported critical electric field of GaN at the same doping concentration. These results clearly demonstrate the usefulness of DPD in the fabrication of high-performance AlN-based power devices.

Emotions are considered to convey much meaning in communication. Hence, artificial methods for emotion categorization are being developed to meet the increasing demand to introduce intelligent systems, such as robots, into shared workspaces. Deep learning algorithms have demonstrated limited competency in categorizing images from posed datasets with the main features of the face being visible. However, the use of sunglasses and face masks are common in our daily lives, especially with the outbreak of communicable diseases such as the recent coronavirus. Anecdotally, partial coverings of the face reduce the effectiveness of human communication, so would this have hampering effects on computer vision, and if so, would different emotion categories be affected equally? Here, we analyze the performance of emotion classification systems when faces are partially covered with simulated sunglasses and face masks. Deep neural networks consider all pixels in an image as equally important unlike the neuroscientific findings on how humans recognize emotions. Hence, we propose a method that considers different constituent parts (e.g. mouth, eyes, and jaw) separately, giving more attention to relevant (uncovered) regions of the face. The method is compared with three standard, partial coverings-based and attention-based methods. We found that face coverings worsen emotion categorization by up to 74% for the state-of-the-art methods, whereby emotion categories are affected differently by different coverings, e.g. clear mouth coverings have little effect on categorizing happiness, but sadness is affected badly. The proposed method (on average 60.43%) has significantly improved the performance over the standard deep learning (< 46% on average), partial coverings-based (< 47% on average), as well as the attention-based (< 51% on average) methods for both the CK+, KDEF, and RAF-DB datasets when faces were partially covered with sunglasses or different face masks.

Rationale: Whether working together to move a table or supporting a child learning to ride a bike, physically connected individuals use the exchange of haptic information to improve motor performance. However, this improvement occurs at the cost of additional effort for the more skilled partner. Objective: Here, we aim to assess whether an asym-metric connection, consisting of a stiffer link to the less skilled partner, could increase performance without additional effort in collaborative tasks. Methods: Through computational modelling, we first evaluated such a hypothesis on simulated human dyads tracking a common target. The approach was then experimentally validated on a three degree-of-freedom tracking task using two commercial robots as individual interfaces. Results: The simulation and experimental results confirm that using an asymmetric connection stiffness can improve joint performance without requiring additional effort from either partner compared to solo performance. Conclusion: This suggests that the training of motor skills with a proficient partner-like a physical therapist assisting a patient or a violin teacher demonstrating bowing techniques-may be enhanced through the use of robot-mediated asymmetric haptic communication. Index Terms-Human-robot interaction, joint action, human motor control Impact Statement-Using an asymmetric connection in a dyad enables the partners to improve their common performance without additional effort.

We compute the weight distribution of R(4, 9) by combining the approach described in D. V. Sarwate's Ph.D. thesis from 1973 with knowledge on the affine equivalence classification of Boolean functions. To solve this problem posed, e.g., in the MacWilliams and Sloane book [12, p. 447], we apply a refined approach based on the classification of Boolean quartic forms in eight variables due to Ph. Langevin and G. Leander, and recent results on the classification of the quotient space R(4, 7)/R(2, 7) due to V. Gillot and Ph. Langevin.

In developing economies, logistics inefficiencies impede supply chain operations, leading to increased costs and hampering economic growth. This article investigates the pivotal role of mobile technology in optimizing logistics and enhancing Supply Chain Management (SCM), with a specific focus on Uganda. It delves into the landscape of mobile technology utilization, addressing the benefits and associated risks within logistics operations, encompassing storage, transportation, and management. The evolving concept of SCM is discussed, emphasizing its strategic significance in improving efficiency and customer satisfaction. The article systematically examines the advantages of integrating mobile technology into logistics operations, emphasizing improved collaboration, enhanced supply chain performance, resilience to disruptions, and advancements in service quality and global competitiveness. Practical insights from Uganda illustrate the application of mobile technology in the local context. However, integration introduces risks, including cybersecurity vulnerabilities, technological dependencies, and integration challenges. The paper recommends proactive measures, such as robust security protocols and contingency plans, to mitigate risks and ensure effective integration. This article targets policymakers, industry professionals, and researchers. It provides valuable insights into strategically employing mobile technology for enhanced logistics operations and SCM in developing economies, emphasizing its transformative potential in fostering efficient, resilient, and competitive supply chain ecosystems.

Molecular communication is a communication paradigm inspired by biological systems, where chemical signals are used to encode and transmit information. MoSK (Molecule Shift Keying) is proposed as a modulation technique that utilizes different types of signaling molecules to encode digital information. MoSK mimics natural molecular communication by leveraging the diversity of signaling molecules and their specific roles. Experimental testbeds have been developed using mi-crofluidic systems, droplet-based platforms, and biological cells to validate MoSK. A prototype platform for MoSK implementation is presented, including a transmitter with infusion and selection valves, and a fluorescence-based receiver. The receiver detects and decodes fluorescence signals emitted by Graphene Quantum Dots (GQDs), which are water-soluble and fluorescent molecular messengers. The fluorescence signals of blue-GQDs and cyan-GQDs are acquired by the receiver, and the performance of the system is evaluated in terms of synchronization, detection threshold, and symbol recognition using Principal Component Analysis (PCA). The results demonstrate the successful detection and recognition of different symbols, even at lower concentrations. PCA proves to be an efficient method for qualitative recognition of molecular messengers in MoSK-based molecular communication systems.

One important approach to improve the performance of the convolutional accelerator is to reduce its memory access latency. When the DDR memory connected to the accelerator is fixed, the memory access latency of the convolutional accelerator can be decreased by reducing the number of memory accesses or memory row conflicts, and using DMA to optimize memory access time intervals and so on. The convolutional process involves accessing a large amount of data with different sizes and types, which are usually stored in different memory rows. Accessing such data always causes a significant number of memory row conflicts, resulting in high memory access latency of the convolutional accelerator. This paper firstly analyzes the composition of DDR access latency for the convolutional accelerator and finds that the number of memory row conflicts is mainly decided by the number of loop tiling columns and padding size. Then, an OptTc method is proposed to calculate the optimal number of loop tiling columns. FPGA experiments of proposed convolution accelerator show that using the optimal number of loop tiling columns can reduce the memory access latency by 24% with negligible increase in FPGA resources in the case of VGG-16. The proposed OptTc method can effectively assist in the design of convolutional accelerators.

Unmanned Aerial Vehicle (UAV) detection in real-time is an emerging field of study that focuses on computer vision and deep learning algorithms. However, the increasing use of UAVs in numerous applications has generated worries about possible risks and misuse. The purpose of this research is to detect UAVs, under adverse weather conditions (such as rain) and image distortions (such as motion blur and noise). The goal is to examine how these adverse conditions affect UAV detection performance and to provide techniques to increase model robustness. To achieve this, a custom training dataset was constructed by combining multiple existing datasets, supplementing them with complex backgrounds. In addition, a custom testing dataset was generated containing UAV images affected by adverse conditions.  On the proposed dataset, the performance of well-known object detection algorithms including YOLOv5, YOLOv8, Faster-RCNN, RetinaNet, and YOLO-NAS was investigated. In comparison to clean images, the results demonstrated a considerable performance decrease under adverse conditions. However, training the models on the augmented dataset containing samples of distorted and weather-affected images significantly enhanced the models' performance under challenging settings. These findings highlight the importance of taking adverse weather conditions into account during model training and underscore the significance of data enrichment for improving model generalization. The work also accentuates the need for further research into advanced techniques and architectures to ensure reliable UAV detection under extreme weather conditions and image distortions. (Note: This is a pre-print of a paper submitted to IEEE for potential journal publication and final version may vary upon acceptance and publication)

A document by Stan Zurek. Click on the document to view its contents.

The performance of machine learning models is closely linked to the quality of training data, underpinning the 'garbage in, garbage out' principle. Label noise in datasets is a key challenge in training and evaluation. This study introduces two innovative ChatGPT-based methods, ChatGPT-Predict and ChatGPT-Detect, for effective noise detection in labeled datasets. We assess the efficacy of these methods against conventional vote-based techniques, focusing on factors like noise characteristics , dataset complexity, and the impact of prompt-engineering. Comprehensive evaluations using both artificial and real-world datasets demonstrate the adaptability of our methods to different noise types and levels. Key findings emphasize the critical role of prompt design in language model performance and the distinct contrasts in handling artificial versus real-world noise. The research acknowledges potential limitations due to prompt design variability and suggests possible enhancements with more advanced models like GPT-4. Future research avenues include applying these methods with GPT-4, exploring diverse prompt templates, and extending the methodology to real-world datasets with high noise levels. This study contributes to the field by refining noise detection methodologies, thereby enhancing the robustness and reliability of machine learning models.

In our study, we explored the genetic intricacies of thyroid cancer, a complex endocrine malignancy, with a primary focus on identifying specific single nucleotide polymorphisms (SNPs) associated with the disease. The multifaceted nature of thyroid cancer, marked by a diversity of genetic variations, underscores the importance of research aimed at decoding its genetic architecture. Our investigation began with a focused examination of the NRAS gene region, noted for its documented significance in thyroid cancer pathogenesis. However, recognizing the potential of a broader genetic landscape, our study expanded to encompass the entirety of chromosome 1. We used Genome-Wide Sequencing data from the Sequence Read Archive and constructed robust pipelines to align sequences against chromosome 1 using the Bowtie 2 tool. The subsequent process involved indexing and variant calling. Our stringent protocols resulted in the identification of over 700 statistically significant SNPs within the thyroid cancer cohort. Interestingly, these SNPs were notably absent in the non-cancer cohort. This revelation accentuates possible genetic foundations of thyroid cancer and emphasizes the genetic disparity between cancer-affected and unaffected cohorts. Our findings suggest that the identified SNPs could be useful in developing innovative strategies for early thyroid cancer diagnosis and prevention.

This hypothesis explores the possibility of a function that describes how the effective mass of a particle changes over time during a quantum process. We introduce a Temporal Mass Function, \(m(t)=m_0e^{-λt}\) , to reflect changes in the energy and properties of the particle, particularly in the context of deferred annihilation. Substituting this function into Einstein's equation, \(E=mc^2\) , we obtain a new energy-time equation that shows how the energy of the particle changes due to the variation of its mass. The temporal derivative of this equation reveals that the rate of change of the particle's energy decreases over time, suggesting a novel quantum interpretation of annihilation. Furthermore, we propose replacing the constant speed of light, , with a constant representing the Universe's expansion speed, , leading to a reformulation of the mass-energy equation as . This approach suggests that a particle's energy could be linked to the expansion of space itself, raising significant questions about the relationship between mass, energy, and the expansion of the Universe in the quantum context.  

This work presents the complete design process of a 3D fully-metallic reflectarray. The structure is composed of two unit cells with resonators in z direction that are phase-shifted 180 • and distributed in the x-y direction. Here will be shown how those unit cells have been designed and how we determine their distribution. Furthermore, a tolerance analysis is carried out with the aim of obtaining a prototype that is robust to manufacturing imperfections. Lastly, that protoype is measured to verify the results.

Masking is one of the most well-established methods to thwart side-channel attacks. Many masking schemes have been proposed in the literature, and code-based masking emerges and unifies several masking schemes in a coding-theoretic framework. In this work, we investigate the side-channel resistance of code-based masking from a non-profiling perspective by utilizing correlation-based side-channel attacks. We present a systematic evaluation of correlation attacks with various higher-order (centered) moments and then present the form of optimal correlation attacks. Interestingly, the Pearson correlation coefficient between the hypothetical leakage and the measured traces is connected to the signal-to-noise ratio in higher-order moments, and it turns out to be easy to evaluate rather than launch repeated attacks. We also identify some ineffective higher-order correlation attacks at certain orders when the device leaks under the Hamming weight leakage model. Our theoretical findings are verified through both simulated and real-world measurements.

This paper discusses a new modeling framework for switched circuits. The core idea is to avoid the distinction between the topologies defined by the short-and open-circuits corresponding to the on-and off-states of each ideal switch. We treat both cases in a uniform manner by describing Ohm's law in terms of a single variable u, via the relations i = pu, v = qu. The parameters p and q describe the state of the switch, either ideal or nonideal. An ideal switch is described by the values p = pon = 1, q = qon = 0 in the on-state and p = poff = 0, q = qoff = 1 in the off-state, whereas nonideal switches are described by nonzero values of qon and/or p off. Regardless of the number of switches, all working states of a given circuit can be described by means of a single model which, in essence, may be set up by conventional methods. This avoids the need to draw different schematics and write down a variety of models depending on the state of each switch, as it is almost invariably done when modeling switched circuits. We use this framework to address the state-space formulation problem for switched circuits. The results are then applied to the analysis of several DC-DC power converters and a multilevel inverter.