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In recent years, advanced artificial intelligence technologies, such as ChatGPT, have significantly impacted various fields, including education and research. Developed by OpenAI, ChatGPT is a powerful language model that presents numerous opportunities for students and educators. It offers personalized feedback, enhances accessibility, enables interactive conversations, assists with lesson preparation and evaluation, and introduces new methods for teaching complex subjects. However, ChatGPT also poses challenges to traditional education and research systems. These challenges include the risk of cheating on online exams, the generation of human-like text that may compromise academic integrity, a potential decline in critical thinking skills, and difficulties in assessing the reliability of information generated by AI. This study examines both the opportunities and challenges ChatGPT brings to education from the perspectives of students and educators. Specifically, it explores the role of ChatGPT in helping students develop their subjective skills. To demonstrate its effectiveness, we conducted several subjective experiments using ChatGPT, such as generating solutions from subjective problem descriptions. Additionally, surveys were conducted with students and teachers to gather insights into how ChatGPT supports subjective learning and teaching. The results and analysis of these surveys are presented to highlight the impact of ChatGPT in this context.

Yoga has recently become an essential aspect of human existence for maintaining a healthy body and mind. People find it tough to devote time to the gym for workouts as their lives get more hectic and they work from home. This kind of human pose estimation is one of the notable problems as it has to deal with locating body key points or joints. Yoga-82, a benchmark dataset for large-scale yoga pose recognition with 82 classes, has challenging positions that could make precise annotations impossible. We have used VGG-16, ResNet-50, ResNet-101, and DenseNet-121 and finetuned them in different ways to get better results. We also used Neural Architecture Search to add more layers on top of this pre-trained architecture. The experimental result shows the best performance of DenseNet-121 having the top-1 accuracy of 85% and top-5 accuracy of 96% outperforming the current state-of-the-art result.

Bangla (or "Bengali") is a language spoken by approximately 240 million native speakers and around 300 million people worldwide. Despite being the 5th largest spoken language in the world, Bangla is still a "low-resource" language, and existing pretrained language models often struggle to perform well on Bangla Language Processing (BLP) tasks. This work addresses this gap by introducing BongLLaMA (i.e., Bangla-LLaMA), an open-source large language model fine-tuned exclusively on large Bangla corpora and instruction-tuning datasets. We present our methodology, data augmentation techniques, fine-tuning details, and comprehensive benchmarking results showcasing the utility of BongLLaMA on BLP tasks. We believe BongLLaMA will serve as the new standard baseline for Bangla Language Models and, thus, facilitate future benchmarking studies focused on this widely-spoken yet "low-resource" language. All BongLLaMA models are available for public use at https://huggingface.co/BanglaLLM., Comment: 19 pages

Objective: This research aims to develop a lifestyle intervention system, called MoveSense, that forecasts a patient's activity behavior to allow for early and personalized interventions in real-world clinical environments. Methods: We conducted two clinical studies involving 58 prediabetic veterans and 60 patients with obstructive sleep apnea to gather multimodal behavioral data using wearable devices. We develop multimodal long short-term memory (LSTM) network models, which are capable of forecasting the number of step counts of a patient up to 24 hours in advance by examining data from activity and engagement modalities. Furthermore, we design goal-based forecasting models to predict whether a person's next-day steps will be over a certain threshold. Results: Multimodal LSTM with early fusion achieves 33% and 37% lower mean absolute errors than linear regression and ARIMA respectively on the prediabetes dataset. LSTM also outperforms linear regression and ARIMA with a margin of 13% and 32% on the sleep dataset. Multimodal forecasting models also perform with 72% and 79% accuracy on the prediabetes dataset and sleep dataset respectively on goal-based forecasting. Conclusion: Our experiments conclude that multimodal LSTM models with early fusion are better than multimodal LSTM with late fusion and unimodal LSTM models and also than ARIMA and linear regression models. Significance: We address an important and challenging task of time-series forecasting in uncontrolled environments. Effective forecasting of a person's physical activity can aid in designing adaptive behavioral interventions to keep the user engaged and adherent to a prescribed routine., Comment: 9 pages, 5 figures

Early detection of intrapartum risk enables interventions to potentially prevent or mitigate adverse labor outcomes such as cerebral palsy. Currently, there is no accurate automated system to predict such events to assist with clinical decision-making. To fill this gap, we propose "Artificial Intelligence (AI) for Modeling and Explaining Neonatal Health" (AIMEN), a deep learning framework that not only predicts adverse labor outcomes from maternal, fetal, obstetrical, and intrapartum risk factors but also provides the model's reasoning behind the predictions made. The latter can provide insights into what modifications in the input variables of the model could have changed the predicted outcome. We address the challenges of imbalance and small datasets by synthesizing additional training data using Adaptive Synthetic Sampling (ADASYN) and Conditional Tabular Generative Adversarial Networks (CTGAN). AIMEN uses an ensemble of fully-connected neural networks as the backbone for its classification with the data augmentation supported by either ADASYN or CTGAN. AIMEN, supported by CTGAN, outperforms AIMEN supported by ADASYN in classification. AIMEN can predict a high risk for adverse labor outcomes with an average F1 score of 0.784. It also provides counterfactual explanations that can be achieved by changing 2 to 3 attributes on average. Resources available: https://github.com/ab9mamun/AIMEN., Comment: 17 pages, 8 figures

Particle-producing targets in high-energy research facilities are often made from refractory metals, and they typically require dedicated cooling systems due to the challenging thermomechanical conditions they experience. However, direct contact of water with target blocks can induce erosion, corrosion, and embrittlement, especially of tungsten (W). One approach to overcoming this problem is cladding the blocks with tantalum (Ta). Unfortunately, Ta generates high decay heat when irradiated, raising safety concerns in the event of a loss-of-cooling accident. This study explored the capacity of niobium (Nb) and its alloys to form diffusion bonds with W and TZM (a molybdenum alloy with titanium and zirconium). This is because the Beam Dump Facility (BDF), a planned new fixed-target installation in CERN's North Area, uses these target materials. The bonding quality of pure Nb, Nb1Zr, and C103 (a Nb alloy with 10% hafnium and 1% titanium) with TZM and W obtained using hot isostatic pressing (HIP) was evaluated. The effects of different HIP temperatures and the introduction of a Ta interlayer were examined. Optical microscopy indicated promising bonding interfaces, which were further characterized using tensile tests and thermal-diffusivity measurements. Their performance under high-energy beam impact was validated using thermomechanical simulations. C103 exhibited higher interface strengths and safety factors than Ta2.5W, positioning it as a potential alternative cladding material for the BDF production target. The findings highlight the viability of Nb-based materials, particularly C103, for improving operational safety and efficiency in fixed-target physics experiments; however, considerations regarding the long half-life of 94Nb require further attention.

Context: The advent of Large Language Model-driven tools like ChatGPT offers software engineers an interactive alternative to community question-answering (CQA) platforms like Stack Overflow. While Stack Overflow provides benefits from the accumulated crowd-sourced knowledge, it often suffers from unpleasant comments, reactions, and long waiting times. Objective: In this study, we assess the efficacy of ChatGPT in providing solutions to software engineering questions by analyzing its performance specifically against human solutions. Method: We empirically analyze 2564 Python and JavaScript questions from StackOverflow that were asked between January 2022 and December 2022. We parse the questions and answers from Stack Overflow, then collect the answers to the same questions from ChatGPT through API, and employ four textual and four cognitive metrics to compare the answers generated by ChatGPT with the accepted answers provided by human subject matter experts to find out the potential reasons for which future knowledge seekers may prefer ChatGPT over CQA platforms. We also measure the accuracy of the answers provided by ChatGPT. We also measure user interaction on StackOverflow over the past two years using three metrics to determine how ChatGPT affects it. Results: Our analysis indicates that ChatGPT's responses are 66% shorter and share 35% more words with the questions, showing a 25% increase in positive sentiment compared to human responses. ChatGPT's answers' accuracy rate is between 71 to 75%, with a variation in response characteristics between JavaScript and Python. Additionally, our findings suggest a recent 38% decrease in comment interactions on Stack Overflow, indicating a shift in community engagement patterns. A supplementary survey with 14 Python and JavaScript professionals validated these findings.

Objective: Variation of forearm orientation is one of the crucial factors that drastically degrades the forearm orientation invariant hand gesture recognition performance or the degree of freedom and limits the successful commercialization of myoelectric prosthetic hand or electromyogram (EMG) signal-based human-computer interfacing devices. This study investigates the impact of surface EMG electrode positions (elbow and forearm) on forearm orientation invariant hand gesture recognition. Methods: The study has been performed over 19 intact limbed subjects, considering 12 daily living hand gestures. The quality of the EMG signal is confirmed in terms of three indices. Then, the recognition performance is evaluated and validated by considering three training strategies, six feature extraction methods, and three classifiers. Results: The forearm electrode position provides comparable to or better EMG signal quality considering three indices. In this research, the forearm electrode position achieves up to 5.35% improved forearm orientation invariant hand gesture recognition performance compared to the elbow electrode position. The obtained performance is validated by considering six feature extraction methods, three classifiers, and real-time experiments. In addition, the forearm electrode position shows its robustness with the existence of recent works, considering recognition performance, investigated gestures, the number of channels, the dimensionality of feature space, and the number of subjects. Conclusion: The forearm electrode position can be the best choice for getting improved forearm orientation invariant hand gesture recognition performance. Significance: The performance of myoelectric prosthesis and human-computer interfacing devices can be improved with this optimized electrode position., Comment: 10 pages, 4 figures, 5 tables

Over the years in object detection several efficient Convolutional Neural Networks (CNN) networks, such as DenseNet201, InceptionV3, ResNet152v2, SEresNet152, VGG19, Xception gained significant attention due to their performance. Moreover, CNN paradigms have expanded to transfer learning and ensemble models from original CNN architectures. Research studies suggest that transfer learning and ensemble models are capable of increasing the accuracy of deep learning (DL) models. However, very few studies have conducted comprehensive experiments utilizing these techniques in detecting and localizing blood malignancies. Realizing the gap, this study conducted three experiments; in the first experiment -- six original CNNs were used, in the second experiment -- transfer learning and, in the third experiment a novel ensemble model DIX (DenseNet201, InceptionV3, and Xception) was developed to detect and classify blood cancer. The statistical result suggests that DIX outperformed the original and transfer learning performance, providing an accuracy of 99.12%. However, this study also provides a negative result in the case of transfer learning, as the transfer learning did not increase the accuracy of the original CNNs. Like many other cancers, blood cancer diseases require timely identification for effective treatment plans and increased survival possibilities. The high accuracy in detecting and categorization blood cancer detection using CNN suggests that the CNN model is promising in blood cancer disease detection. This research is significant in the fields of biomedical engineering, computer-aided disease diagnosis, and ML-based disease detection.

Surface electromyography (sEMG) signal holds great potential in the research fields of gesture recognition and the development of robust prosthetic hands. However, the sEMG signal is compromised with physiological or dynamic factors such as forearm orientations, electrode displacement, limb position, etc. The existing dataset of sEMG is limited as they often ignore these dynamic factors during recording. In this paper, we have proposed a dataset of multichannel sEMG signals to evaluate common daily living hand gestures performed with three forearm orientations. The dataset is collected from nineteen intact-limed subjects, performing twelve hand gestures with three forearm orientations: supination, rest, and pronation.Additionally, two electrode placement positions (elbow and forearm) are considered while recording the sEMG signal. The dataset is open for public access in MATLAB file format. The key purpose of the dataset is to offer an extensive resource for developing a robust machine learning classification algorithm and hand gesture recognition applications. We validated the high quality of the dataset by assessing the signal quality matrices and classification performance, utilizing popular machine learning algorithms, various feature extraction methods, and variable window size. The obtained result highlighted the significant potential of this novel sEMG dataset that can be used as a benchmark for developing hand gesture recognition systems, conducting clinical research on sEMG, and developing human-computer interaction applications. Dataset:https://www.kaggle.com/datasets/ummerummanchaity/fors-emg-a-novel-semg-dataset/data, Comment: 11 pages, 9 figures

High voltage (~2 kV) AlGaN-channel HEMTs were fabricated with 64% Aluminum composition in the channel. The average on-resistance was ~75 ohm. mm (~21 miliohm. cm^2) for LGD = 20 microns. Breakdown voltage reached >3 kV (tool limit) before passivation however it reduced to ~2 kV after SiN surface passivation and field plates. The apparent high breakdown voltage prior to passivation can possibly be attributed to the field plate effect of the charged trap states of the surface. The breakdown voltage and RON demonstrated a strong linear correlation in a scattered plot with ~50 measured transistors. In pulsed IV measurements with 100 microsecond pulse width and 40 V of off-state bias (tool limit), the dynamic RON increased by ~5% compared to DC RON and current collapse was <10%., Comment: 4 pages, 6 figures

This study investigates thermal transport across nanocrystalline diamond/AlGaN interfaces, crucial for enhancing thermal management in AlGaN/AlGaN-based devices. Chemical vapor deposition growth of diamond directly on AlGaN resulted in a disordered interface with a high thermal boundary resistance (TBR) of 20.6 m^2-K/GW. We employed sputtered carbide interlayers (e.g., $B_4C$, $SiC$, $B_4C/SiC$) to reduce thermal boundary resistance in diamond/AlGaN interfaces. The carbide interlayers resulted in record-low thermal boundary resistance values of 3.4 and 3.7 m^2-K/GW for Al$_{0.65}$Ga$_{0.35}$N samples with $B_4C$ and $SiC$ interlayers, respectively. STEM imaging of the interface reveals interlayer thicknesses between 1.7-2.5 nm, with an amorphous structure. Additionally, Fast-Fourier Transform (FFT) characterization of sections of the STEM images displayed sharp crystalline fringes in the AlGaN layer, confirming it was properly protected from damage from hydrogen plasma during the diamond growth. In order to accurately measure the thermal boundary resistance we develop a hybrid technique, combining time-domain thermoreflectance and steady-state thermoreflectance fitting, offering superior sensitivity to buried thermal resistances. Our findings underscore the efficacy of interlayer engineering in enhancing thermal transport and demonstrate the importance of innovative measurement techniques in accurately characterizing complex thermal interfaces. This study provides a foundation for future research in improving thermal properties of semiconductor devices through interface engineering and advanced measurement methodologies.

The onset of Industry 4.0 is rapidly transforming the manufacturing world through the integration of cloud computing, machine learning (ML), artificial intelligence (AI), and universal network connectivity, resulting in performance optimization and increase productivity. Digital Twins (DT) are one such transformational technology that leverages software systems to replicate physical process behavior, representing the physical process in a digital environment. This paper aims to explore the use of photogrammetry (which is the process of reconstructing physical objects into virtual 3D models using photographs) and 3D Scanning techniques to create accurate visual representation of the 'Physical Process', to interact with the ML/AI based behavior models. To achieve this, we have used a readily available consumer device, the iPhone 15 Pro, which features stereo vision capabilities, to capture the depth of an Industry 4.0 system. By processing these images using 3D scanning tools, we created a raw 3D model for 3D modeling and rendering software for the creation of a DT model. The paper highlights the reliability of this method by measuring the error rate in between the ground truth (measurements done manually using a tape measure) and the final 3D model created using this method. The overall mean error is 4.97\% and the overall standard deviation error is 5.54\% between the ground truth measurements and their photogrammetry counterparts. The results from this work indicate that photogrammetry using consumer-grade devices can be an efficient and cost-efficient approach to creating DTs for smart manufacturing, while the approaches flexibility allows for iterative improvements of the models over time.

This study investigates the critical role of buffer layers in enhancing the efficiency of thin-film CdTe solar cells. We explore non-toxic buffer materials, specifically ZnSe, ZnMgO, 3C-SiC, and WS2, as potential replacements for the traditional Cd-based CdS buffer layer. Our analysis includes a thorough evaluation of the electrical and optical performance of these buffer materials in conjunction with CdTe absorbers. We optimize the thickness of both the buffer and absorber layers to achieve the best performance. Additionally, we examine the impact of defect density variations in the buffer materials and their corresponding temperature effects. Among the tested materials, ZnSe and ZnMgO demonstrate the highest potential, achieving power conversion efficiencies of 20.74% and 20.73%, respectively., Comment: 19 pages, 7 Figures, 3 Tables

Graphene nanoribbons (GNRs) are unique quasi-one-dimensional (1D) materials that have garnered a lot of research interest in the field of topological insulators. While the topological phases exhibited by GNRs are primarily governed by their chemical structures, the ability to externally control these phases is crucial for their potential utilization in quantum electronics and spintronics. Here we propose a class of GNRs featured by mirror symmetry and four zigzag segments in a unit cell that has unique topological properties induced and controlled by an externally applied electric field. Their band structures manifest two finite gaps which support topological solitons, as described by an effective square-root model. To demonstrate the experimental feasibility, we design and synthesize a representative partially zigzag chevron-type GNR (pzc-GNR) with the desired zigzag segments using a bottom-up approach. First-principles calculations on pzc-GNR reveal band inversions at the two finite gaps by switching the direction of the electric field, which is in accordance with predictions from the square-root Hamiltonian. We show different topological phases can be achieved by controlling the direction of the field and the chemical potential of the system in square-root GNRs. Consequently, upon adding a step-function electric field, solitons states can be generated at the domain wall. We discuss the properties of two types of soliton states, depending on whether the terminating commensurate unit cell is mirror symmetric.

The use of convolutional neural networks (CNNs) has accelerated the progress of handwritten character classification/recognition. Handwritten character recognition (HCR) has found applications in various domains, such as traffic signal detection, language translation, and document information extraction. However, the widespread use of existing HCR technology is yet to be seen as it does not provide reliable character recognition with outstanding accuracy. One of the reasons for unreliable HCR is that existing HCR methods do not take the handwriting styles of non-native writers into account. Hence, further improvement is needed to ensure the reliability and extensive deployment of character recognition technologies for critical tasks. In this work, the classification of English characters written by non-native users is performed by proposing a custom-tailored CNN model. We train this CNN with a new dataset called the handwritten isolated English character (HIEC) dataset. This dataset consists of 16,496 images collected from 260 persons. This paper also includes an ablation study of our CNN by adjusting hyperparameters to identify the best model for the HIEC dataset. The proposed model with five convolutional layers and one hidden layer outperforms state-of-the-art models in terms of character recognition accuracy and achieves an accuracy of $\mathbf{97.04}$%. Compared with the second-best model, the relative improvement of our model in terms of classification accuracy is $\mathbf{4.38}$%.

The success of autonomous navigation relies on robust and precise vehicle recognition, hindered by the scarcity of region-specific vehicle detection datasets, impeding the development of context-aware systems. To advance terrestrial object detection research, this paper proposes a native vehicle detection dataset for the most commonly appeared vehicle classes in Bangladesh. 17 distinct vehicle classes have been taken into account, with fully annotated 81542 instances of 17326 images. Each image width is set to at least 1280px. The dataset's average vehicle bounding box-to-image ratio is 4.7036. This Bangladesh Native Vehicle Dataset (BNVD) has accounted for several geographical, illumination, variety of vehicle sizes, and orientations to be more robust on surprised scenarios. In the context of examining the BNVD dataset, this work provides a thorough assessment with four successive You Only Look Once (YOLO) models, namely YOLO v5, v6, v7, and v8. These dataset's effectiveness is methodically evaluated and contrasted with other vehicle datasets already in use. The BNVD dataset exhibits mean average precision(mAP) at 50% intersection over union (IoU) is 0.848 corresponding precision and recall values of 0.841 and 0.774. The research findings indicate a mAP of 0.643 at an IoU range of 0.5 to 0.95. The experiments show that the BNVD dataset serves as a reliable representation of vehicle distribution and presents considerable complexities., Comment: 13 pages, 8 figures

Congenital anomalies arising as a result of a defect in the structure of the heart and great vessels are known as congenital heart diseases or CHDs. A PCG can provide essential details about the mechanical conduction system of the heart and point out specific patterns linked to different kinds of CHD. This study aims to investigate the minimum signal duration required for the automatic classification of heart sounds. This study also investigated the optimum signal quality assessment indicator (Root Mean Square of Successive Differences) RMSSD and (Zero Crossings Rate) ZCR value. Mel-frequency cepstral coefficients (MFCCs) based feature is used as an input to build a Transformer-Based residual one-dimensional convolutional neural network, which is then used for classifying the heart sound. The study showed that 0.4 is the ideal threshold for getting suitable signals for the RMSSD and ZCR indicators. Moreover, a minimum signal length of 5s is required for effective heart sound classification. It also shows that a shorter signal (3 s heart sound) does not have enough information to categorize heart sounds accurately, and the longer signal (15 s heart sound) may contain more noise. The best accuracy, 93.69%, is obtained for the 5s signal to distinguish the heart sound., Comment: 16 pages,11 Figures

A recent research trend involves treating database index structures as Machine Learning (ML) models. In this domain, single or multiple ML models are trained to learn the mapping from keys to positions inside a data set. This class of indexes is known as "Learned Indexes." Learned indexes have demonstrated improved search performance and reduced space requirements for one-dimensional data. The concept of one-dimensional learned indexes has naturally been extended to multi-dimensional (e.g., spatial) data, leading to the development of "Learned Multi-dimensional Indexes". This survey focuses on learned multi-dimensional index structures. Specifically, it reviews the current state of this research area, explains the core concepts behind each proposed method, and classifies these methods based on several well-defined criteria. We present a taxonomy that classifies and categorizes each learned multi-dimensional index, and survey the existing literature on learned multi-dimensional indexes according to this taxonomy. Additionally, we present a timeline to illustrate the evolution of research on learned indexes. Finally, we highlight several open challenges and future research directions in this emerging and highly active field.

The Center for Disease Control estimates that over 37 million US adults suffer from chronic kidney disease (CKD), yet 9 out of 10 of these individuals are unaware of their condition due to the absence of symptoms in the early stages. It has a significant impact on patients' quality of life, particularly when it progresses to the need for dialysis. Early prediction of dialysis is crucial as it can significantly improve patient outcomes and assist healthcare providers in making timely and informed decisions. However, developing an effective machine learning (ML)-based Clinical Decision Support System (CDSS) for early dialysis prediction poses a key challenge due to the imbalanced nature of data. To address this challenge, this study evaluates various data augmentation techniques to understand their effectiveness on real-world datasets. We propose a new approach named Binary Gaussian Copula Synthesis (BGCS). BGCS is tailored for binary medical datasets and excels in generating synthetic minority data that mirrors the distribution of the original data. BGCS enhances early dialysis prediction by outperforming traditional methods in detecting dialysis patients. For the best ML model, Random Forest, BCGS achieved a 72% improvement, surpassing the state-of-the-art augmentation approaches. Also, we present a ML-based CDSS, designed to aid clinicians in making informed decisions. CDSS, which utilizes decision tree models, is developed to improve patient outcomes, identify critical variables, and thereby enable clinicians to make proactive decisions, and strategize treatment plans effectively for CKD patients who are more likely to require dialysis in the near future. Through comprehensive feature analysis and meticulous data preparation, we ensure that the CDSS's dialysis predictions are not only accurate but also actionable, providing a valuable tool in the management and treatment of CKD.

Bayesian optimization (BO) protocol based on Active Learning (AL) principles has garnered significant attention due to its ability to optimize black-box objective functions efficiently. This capability is a prerequisite for guiding autonomous and high-throughput materials design and discovery processes. However, its application in materials science, particularly for novel alloy designs with multiple targeted properties, remains limited. This limitation is due to the computational complexity and the lack of reliable and robust acquisition functions for multiobjective optimization. In recent years, expected hypervolume-based geometrical acquisition functions have demonstrated superior performance and speed compared to other multiobjective optimization algorithms, such as Thompson Sampling Efficient Multiobjective Optimization (TSEMO), Pareto Efficient Global Optimization (parEGO), etc. This work compares several state-of-the-art multiobjective BO acquisition functions, i.e., parallel expected hypervolume improvement (qEHVI), noisy parallel expected hypervolume improvement (qNEHVI), parallel Pareto efficient global optimization (parEGO), and parallel noisy Pareto efficient global optimization (qNparEGO) for the multiobjective optimization of physical properties in multi-component alloys. We demonstrate the impressive performance of the qEHVI acquisition function in finding the optimum Pareto front in 1-, 2-, and 3-objective Aluminium alloy optimization problems within a limited evaluation budget and reasonable computational cost. In addition, we discuss the role of different surrogate model optimization methods from a computational cost and efficiency perspective.

The Internet of Things (IoT) is pivotal in transforming the way we live and interact with our surroundings. To cope with the advancement in technologies, it is vital to acquire accuracy with the speed. A phase frequency detector (PFD) is a critical device to regulate and provide accurate frequency in IoT devices. Designing a PFD poses challenges in achieving precise phase detection, minimising dead zones, optimising power consumption, and ensuring robust performance across various operational frequencies, necessitating complex engineering and innovative solutions. This study delves into optimising a PFD circuit, designed using 90 nm standard CMOS technology, aiming to achieve superior operational frequencies. An efficient and high-frequency PFD design is crafted and analysed using cadence virtuoso. The study focused on investigating the impact of optimising PFD design. With the optimised PFD, an operational frequency of 5 GHz has been achieved, along with a power consumption of only 29 {\mu}W. The dead zone of the PFD was only 25 ps.

One of the most critical applications undertaken by coalitions of Unmanned Aerial Vehicles (UAVs) and Unmanned Ground Vehicles (UGVs) is reaching predefined targets by following the most time-efficient routes while avoiding collisions. Unfortunately, UAVs are hampered by limited battery life, and UGVs face challenges in reachability due to obstacles and elevation variations. Existing literature primarily focuses on one-to-one coalitions, which constrains the efficiency of reaching targets. In this work, we introduce a novel approach for a UAV-UGV coalition with a variable number of vehicles, employing a modified mean-shift clustering algorithm to segment targets into multiple zones. Each vehicle utilizes Multi-agent Deep Deterministic Policy Gradient (MADDPG) and Multi-agent Proximal Policy Optimization (MAPPO), two advanced reinforcement learning algorithms, to form an effective coalition for navigating obstructed environments without collisions. This approach of assigning targets to various circular zones, based on density and range, significantly reduces the time required to reach these targets. Moreover, introducing variability in the number of UAVs and UGVs in a coalition enhances task efficiency by enabling simultaneous multi-target engagement. The results of our experimental evaluation demonstrate that our proposed method substantially surpasses current state-of-the-art techniques, nearly doubling efficiency in terms of target navigation time and task completion rate.

This study developed a generative adversarial network (GAN)-based defense method for traffic sign classification in an autonomous vehicle (AV), referred to as the attack-resilient GAN (AR-GAN). The novelty of the AR-GAN lies in (i) assuming zero knowledge of adversarial attack models and samples and (ii) providing consistently high traffic sign classification performance under various adversarial attack types. The AR-GAN classification system consists of a generator that denoises an image by reconstruction, and a classifier that classifies the reconstructed image. The authors have tested the AR-GAN under no-attack and under various adversarial attacks, such as Fast Gradient Sign Method (FGSM), DeepFool, Carlini and Wagner (C&W), and Projected Gradient Descent (PGD). The authors considered two forms of these attacks, i.e., (i) black-box attacks (assuming the attackers possess no prior knowledge of the classifier), and (ii) white-box attacks (assuming the attackers possess full knowledge of the classifier). The classification performance of the AR-GAN was compared with several benchmark adversarial defense methods. The results showed that both the AR-GAN and the benchmark defense methods are resilient against black-box attacks and could achieve similar classification performance to that of the unperturbed images. However, for all the white-box attacks considered in this study, the AR-GAN method outperformed the benchmark defense methods. In addition, the AR-GAN was able to maintain its high classification performance under varied white-box adversarial perturbation magnitudes, whereas the performance of the other defense methods dropped abruptly at increased perturbation magnitudes.

The escalating complexity of modern computing frameworks has resulted in a surge in the cybersecurity vulnerabilities reported to the National Vulnerability Database (NVD) by practitioners. Despite the fact that the stature of NVD is one of the most significant databases for the latest insights into vulnerabilities, extracting meaningful trends from such a large amount of unstructured data is still challenging without the application of suitable technological methodologies. Previous efforts have mostly concentrated on software vulnerabilities; however, a holistic strategy incorporates approaches for mitigating vulnerabilities, score prediction, and a knowledge-generating system that may extract relevant insights from the Common Weakness Enumeration (CWE) and Common Vulnerability Exchange (CVE) databases is notably absent. As the number of hardware attacks on Internet of Things (IoT) devices continues to rapidly increase, we present the Hardware Vulnerability to Weakness Mapping (HW-V2W-Map) Framework, which is a Machine Learning (ML) framework focusing on hardware vulnerabilities and IoT security. The architecture that we have proposed incorporates an Ontology-driven Storytelling framework, which automates the process of updating the ontology in order to recognize patterns and evolution of vulnerabilities over time and provides approaches for mitigating the vulnerabilities. The repercussions of vulnerabilities can be mitigated as a result of this, and conversely, future exposures can be predicted and prevented. Furthermore, our proposed framework utilized Generative Pre-trained Transformer (GPT) Large Language Models (LLMs) to provide mitigation suggestions., Comment: 22 pages, 10 pages appendix, 10 figures, Submitted to ACM TODAES

Audit logs containing system level events are frequently used for behavior modeling as they can provide detailed insight into cyber-threat occurrences. However, mapping low-level system events in audit logs to highlevel behaviors has been a major challenge in identifying host contextual behavior for the purpose of detecting potential cyber threats. Relying on domain expert knowledge may limit its practical implementation. This paper presents TapTree, an automated process-tree based technique to extract host behavior by compiling system events' semantic information. After extracting behaviors as system generated process trees, TapTree integrates event semantics as a representation of behaviors. To further reduce pattern matching workloads for the analyst, TapTree aggregates semantically equivalent patterns and optimizes representative behaviors. In our evaluation against a recent benchmark audit log dataset (DARPA OpTC), TapTree employs tree pattern queries and sequential pattern mining techniques to deduce the semantics of connected system events, achieving high accuracy for behavior abstraction and then Advanced Persistent Threat (APT) attack detection. Moreover, we illustrate how to update the baseline model gradually online, allowing it to adapt to new log patterns over time., Comment: 20pages

Social Media is a repository of digital literature including user-generated content. The users of social media are expressing their opinion with diverse mediums such as text, emojis, memes, and also through other visual and textual mediums. A major portion of these media elements could be treated as harmful to others and they are known by many words including Cyberbullying and Toxic Language . The goal of this research paper is to analyze a curated and value-added dataset of toxic language titled ToxLex_bn . It is an exhaustive wordlist that can be used as classifier material to detect toxicity in social media. The toxic language/script used by the Bengali community as cyberbullying, hate speech and moral policing became major trends in social media culture in Bangladesh and West Bengal. The toxicity became so high that the victims has to post as a counter or release explanation video for the haters. Most cases are pointed to women celebrity and their relation, dress, lifestyle are became trolled and toxicity flooded in comments boxes. Not only celebrity bashing but also hates occurred between Hindu Muslims, India-Bangladesh, Two opponents of 1971 and these are very common for virtual conflict in the comment thread. Even many times facebook comment causes sue and legal matters in Bangladesh and thus it requires more study. In this study, a Bangla toxic language dataset has been analyzed which was inputted by the user in Bengali script & language. For this, about 1968 unique bigrams or phrases as wordlists have been analyzed which are derived from 2207590 comments. It is assumed that this analysis will reinforce the detection of Bangla's toxic language used in social media and thus cure this virtual disease.

Atrial fibrillation (AF) increases the risk of stroke. Electrocardiogram (ECG) is used for AF detection, while photoplethysmography (PPG) is simple to use and appropriate for long-term monitoring. We have developed a novel approach to detect AF from smartwatch-based wrist PPG signals. To the best of our knowledge, this is the first study to employ 1D CycleGAN for the reconstruction of 1D wrist PPG signals, since the quality of wrist PPG signals is very poor due to motion artifacts and acquisition site limitations. Our proposed approach is validated on a dataset of 21,278 10s long wrist PPG segments. The dataset was divided into two halves and two experiments were conducted. One half was used for training and the other half for testing and vice versa. Our classification model (Self-AFNet) with 1D-CycleGAN for restoration achieves an accuracy of 96.41% and 97.09% for both splits, respectively. There is a noteworthy improvement in accuracy (2.94% and 5.08% for test splits, respectively) for restored signals as compared to unrestored PPG. AF detection task was also performed using ECG signals with the matched PPG signals to assess the validity of employing reconstructed PPG-based AF classification. Self-AFNet managed to achieve an accuracy of 98.07% and 98.97%, respectively using two ECG splits which is comparable to the performance of AF detection utilizing reconstructed PPG segments. This study reflects that reconstructed wrist PPG signals acquired from wearable devices can be used for reliable detection of atrial fibrillation, which can significantly help in the reduction of the risk of stroke.

Chest X-rays are widely used to diagnose thoracic diseases, but the lack of detailed information about these abnormalities makes it challenging to develop accurate automated diagnosis systems, which is crucial for early detection and effective treatment. To address this challenge, we employed deep learning techniques to identify patterns in chest X-rays that correspond to different diseases. We conducted experiments on the "ChestX-ray14" dataset using various pre-trained CNNs, transformers, hybrid(CNN+Transformer) models and classical models. The best individual model was the CoAtNet, which achieved an area under the receiver operating characteristic curve (AUROC) of 84.2%. By combining the predictions of all trained models using a weighted average ensemble where the weight of each model was determined using differential evolution, we further improved the AUROC to 85.4%, outperforming other state-of-the-art methods in this field. Our findings demonstrate the potential of deep learning techniques, particularly ensemble deep learning, for improving the accuracy of automatic diagnosis of thoracic diseases from chest X-rays. Code available at:https://github.com/syednabilashraf/SynthEnsemble, Comment: Published in International Conference on Computer and Information Technology (ICCIT) 2023

Lemmatization holds significance in both natural language processing (NLP) and linguistics, as it effectively decreases data density and aids in comprehending contextual meaning. However, due to the highly inflected nature and morphological richness, lemmatization in Bangla text poses a complex challenge. In this study, we propose linguistic rules for lemmatization and utilize a dictionary along with the rules to design a lemmatizer specifically for Bangla. Our system aims to lemmatize words based on their parts of speech class within a given sentence. Unlike previous rule-based approaches, we analyzed the suffix marker occurrence according to the morpho-syntactic values and then utilized sequences of suffix markers instead of entire suffixes. To develop our rules, we analyze a large corpus of Bangla text from various domains, sources, and time periods to observe the word formation of inflected words. The lemmatizer achieves an accuracy of 96.36% when tested against a manually annotated test dataset by trained linguists and demonstrates competitive performance on three previously published Bangla lemmatization datasets. We are making the code and datasets publicly available at https://github.com/eblict-gigatech/BanLemma in order to contribute to the further advancement of Bangla NLP.

The domain of safe multi-agent reinforcement learning (MARL), despite its potential applications in areas ranging from drone delivery and vehicle automation to the development of zero-energy communities, remains relatively unexplored. The primary challenge involves training agents to learn optimal policies that maximize rewards while adhering to stringent safety constraints, all without the oversight of a central controller. These constraints are critical in a wide array of applications. Moreover, ensuring the privacy of sensitive information in decentralized settings introduces an additional layer of complexity, necessitating innovative solutions that uphold privacy while achieving the system's safety and efficiency goals. In this paper, we address the problem of multi-agent policy optimization in a decentralized setting, where agents communicate with their neighbors to maximize the sum of their cumulative rewards while also satisfying each agent's safety constraints. We consider both peak and average constraints. In this scenario, there is no central controller coordinating the agents and both the rewards and constraints are only known to each agent locally/privately. We formulate the problem as a decentralized constrained multi-agent Markov Decision Problem and propose a momentum-based decentralized policy gradient method, DePAint, to solve it. To the best of our knowledge, this is the first privacy-preserving fully decentralized multi-agent reinforcement learning algorithm that considers both peak and average constraints. We then provide theoretical analysis and empirical evaluation of our algorithm in a number of scenarios and compare its performance to centralized algorithms that consider similar constraints., Comment: accepted for publication in Springer Applied Intelligence Journal

Large Language Models (LLMs) are swiftly advancing in architecture and capability, and as they integrate more deeply into complex systems, the urgency to scrutinize their security properties grows. This paper surveys research in the emerging interdisciplinary field of adversarial attacks on LLMs, a subfield of trustworthy ML, combining the perspectives of Natural Language Processing and Security. Prior work has shown that even safety-aligned LLMs (via instruction tuning and reinforcement learning through human feedback) can be susceptible to adversarial attacks, which exploit weaknesses and mislead AI systems, as evidenced by the prevalence of `jailbreak' attacks on models like ChatGPT and Bard. In this survey, we first provide an overview of large language models, describe their safety alignment, and categorize existing research based on various learning structures: textual-only attacks, multi-modal attacks, and additional attack methods specifically targeting complex systems, such as federated learning or multi-agent systems. We also offer comprehensive remarks on works that focus on the fundamental sources of vulnerabilities and potential defenses. To make this field more accessible to newcomers, we present a systematic review of existing works, a structured typology of adversarial attack concepts, and additional resources, including slides for presentations on related topics at the 62nd Annual Meeting of the Association for Computational Linguistics (ACL'24).

Recently, machine learning of the branch and bound algorithm has shown promise in approximating competent solutions to NP-hard problems. In this paper, we utilize and comprehensively compare the outcomes of three neural networks--graph convolutional neural network (GCNN), GraphSAGE, and graph attention network (GAT)--to solve the capacitated vehicle routing problem. We train these neural networks to emulate the decision-making process of the computationally expensive Strong Branching strategy. The neural networks are trained on six instances with distinct topologies from the CVRPLIB and evaluated on eight additional instances. Moreover, we reduced the minimum number of vehicles required to solve a CVRP instance to a bin-packing problem, which was addressed in a similar manner. Through rigorous experimentation, we found that this approach can match or improve upon the performance of the branch and bound algorithm with the Strong Branching strategy while requiring significantly less computational time. The source code that corresponds to our research findings and methodology is readily accessible and available for reference at the following web address: https://isotlaboratory.github.io/ml4vrp

Integer factorization is a significant problem, with implications for the security of widely-used cryptographic schemes. No efficient classical algorithm for polynomial-time integer factorization has been found despite extensive research. Although Peter Shor's breakthrough quantum algorithm offers a viable solution, current limitations of noisy intermediate-scale quantum (NISQ) computers hinder its practical implementation. To address this, researchers have explored alternative methods for factorization suitable for NISQ devices. One such method is the Quantum Approximate Optimization Algorithm, which treats factoring as an optimization problem defined over binary bits, resulting in various problematic aspects. In this paper, we explore the Func-QAOA approach for factorization, which premises overcoming some of the limitations of previous approaches and allows the incorporation of more advanced factorization techniques. After reviewing the most promising quantum implementations for integer arithmetics, we present a few illustrative examples to demonstrate the efficacy of the Func-QAOA approach and discuss methods to reduce the search space to speed up the optimization process.