Miracle-dz/UniFAR-FTUs huggingface dataset:
Unveiling the Potential of Natural Language Processing in Collaborative Robots (Cobots): A Comprehensive Survey

This paper delves into the integration of natural language processing (NLP) within the domain of cyber-physical systems, with a specific focus on collaborative robots (cobots), aiming to unravel the potential enhancements in communication, decision-making, and collaboration between humans and cobots. The primary objective is to conduct a comprehensive survey of NLP applications within the context of AI-assisted networking and systems, shedding light on how NLP can effectively elevate cobot capabilities, encompassing real-time decision-making, collaborative interactions, and training processes. The study meticulously categorizes the existing body of NLP research implemented on cobots, considering factors such as keywords, the number of published papers, and categorizations, allowing for a comprehensive assessment of their significance and impact.
Transmission Timing Scheduling Method for Station Groups with Different Packet Generation Periods in Smart Factory Environment

In smart IoT environments such as smart factory ones, periodically generated data packets as well as aperiodically generated ones with various sizes and generation time intervals are transferred, and they have various permissible transfer delay constraints. Reducing packet collision is the most essential effort for efficient data packet transfer meeting such constraints, because packet collision causes retransmissions and continuous collision events result in packet loss. In this paper, we propose a novel transmission timing scheduling method for wireless stations (STAs) that generate and transmit packets to their access point at various time periods, and verify its operation through experimental studies. In the proposed method, transmission durations during which each STA is permitted to transmit packets are assigned on the time axis so that the overlap of transmission durations from different STAs is minimized based on the periodicity of packet generation. This control is realized with a software buffer provided just before packets are passed from the upper IP layer to the MAC layer so that existing IEEE 802.11 devices are used without replacing them. We develop a prototype system and conduct several experiments to verify the operation of the proposed method. The results confirm the effective implementation of the proposed method, demonstrating its adherence to our planned schedule in accurately assigning transmission timings for each STA.
Shared DNN Model Ownership Verification in Cross-Silo Federated Learning: A GAN-Based Watermark Approach

Cross-silo federated learning, as a distributed learning paradigm, allows clients to collaboratively train an artificial intelligence (AI) model and jointly share the model ownership without local data transfer or exposure. However, the valuable AI models are facing fatal intellectual property (IP) infringement threats when offering AI services. Existing researches on IP protection mainly focus on the centralized models (i.e., single ownership), but leave federated models (i.e., shared ownership) unexplored. In this paper, we propose IPSF, a novel shared IP protection framework with all-round verification for multiple owners under cross-silo federated learning. Specifically, instead of embedding private watermarks individually, we adopt joint watermarks and soft labels as a conjoint fingerprint, and present a watermark generative adversarial network (WM-GAN) mechanism to fuse private watermarks and facilitate the integrated verification. We also design a diversity-and similarity-oriented assessment mechanism to support mutual evaluation between private and joint watermarks. Through the designed assessment mechanism, the correlation and variability between private and joint watermarks are dynamically maintained to ensure the stability of WM-GAN and the fairness among users in verification. Extensive experiments validates that our IPSF achieves desirable fidelity and high robustness under attacks.
Assessing the Impact of Air Pollution on Physiology: Implications and Prospects

Air pollution due to industrial activity, vehicles, and construction has shown immediate and long-term effects on human health. While respiratory condition manifests directly, long-term conditions like heart diseases, cancer, lung damage, and the impact on fetal health have been established. This study is aimed to explore the effects of air pollution on the physiological health of humans using a smart wearable watch. A total of 8 healthy security guards posted at the institute gate close to a very busy traffic junction for 12 hours a day were recruited. The physiological indicators such as heart rate (HR), body temperature (BT), and SPO2 levels were recorded by the wearable device for changes during the day/night for two seasons (summer and winter). Air pollution monitoring sensors were also deployed at the same site to record PM2.5, PM10, relative humidity (RH), and environmental temperature (ENT). A survey of 43 questions under three different sections: demographic, medical condition, and quality of life (QoL) were also filled by each participant. Descriptive statistics and inferential statistical analysis (Wilcoxon, Correlation) have been presented based on the collected data. The research shows an association between short-term fluctuations in physiological parameters due to air pollution. Still, the findings need to be weighted with the accuracy and consistency of the wearable devices.
Leveraging Neural Networks to Enhance Large-Scale Data Processing Performance

Recent advances in neural networks have enabled them to become powerful tools in data processing for tasks such as pattern recognition and classification. This has enabled neural networks to be applied to large-scale data processing problems, where they have been shown to perform very well. However, due to the large amount of data involved in such tasks, it is essential that the processing performance is also improved. This paper presents several methods to leverage available resources such as distributed computing and improved algorithmic techniques to improve the performance of large-scale data processing tasks using neural networks. Examples of such techniques are given for both supervised and unsupervised learning tasks, which can significantly enhance the performance of large-scale data processing applications. The results of several experiments are presented, demonstrating the effectiveness of the proposed techniques. The authors also provide insights for future work that could further enhance the capabilities of neural networks when applied to large-scale data processing applications.
Deep Learning Based Text Extraction System from Handwritten Bengali Word

Extracting text from handwritten Bengali images poses unique challenges for Optical Character Recognition (OCR) systems due to varied handwriting styles, diverse character representations, alignment issues, character similarities, and distinguishing marks. By overcoming these challenges, our research contributes to advancing OCR technology and facilitates improved text extraction from handwritten Bengali documents. We developed an intelligent system that uses Deep Learning to localize and classify Bengali characters. Post-processing techniques are then applied to convert these characters into readable text. Our system encompasses 82 classes, including vowels, consonants, diacritics, numbers, and compound characters. Evaluation shows promising results, with an average accuracy of 91% for character localization and classification, and an overall system accuracy of 86.59%.
A Low-Power Bandgap Voltage Reference Circuit With Ultra-Low Temperature Coefficient

A highly accurate bandgap reference with ultralow sensitivity to temperature variations has been proposed. In the presented work, new techniques and circuits have been proposed to provide voltages with negative and positive temperature coefficients. Moreover, a novel fully MOS CTAT voltage generator without using BJT transistors has been proposed. This voltage and PTAT voltages with different sensitivities to temperature are finally summed up leading to the generation of a reference with a very low temperature dependency. The proposed bandgap reference has been simulated in a 0.18$\mu$m CMOS process using ADS. The temperature coefficient is almost 5 ppm$/^{\circ}\mathrm{C}$, over $-40^{\circ}\mathrm{C}$ to $100^{\circ}\mathrm{C}$ temperature variation, consuming almost 2$\mu$A quiescent current.
Improving Diabetes Management with Support Vector Machine based Fundus Image Analysis

Diabetes poses a widespread health concern and, when left untreated, can lead to costly complications. Fundus imaging serves as a crucial tool in diabetes monitoring by identifying retinal abnormalities that could have severe consequences. However, the challenge arises from the absence of standardized databases and evaluation protocols among various research groups that have proposed fundus image analysis algorithms. This lack of standardization makes it challenging to assess the accuracy and reliability of these algorithms. In medical diagnosis, the evaluation of disease classification accuracy commonly relies on sensitivity and specificity measures. In the context of diabetic retinopathy, sensitivity and specificity are evaluated on a preimage basis for fundus images. Support-vector machine (SVM) classifiers have proven effective in detecting abnormalities in fundus images. This paper introduces an innovative approach that combines descriptive features of flow areas to enhance the robustness of results in the presence of phase noise and multiple scattering. The proposed approach, utilizing an SVM classifier, achieved higher levels of sensitivity and specificity compared to existing methods. This tool holds significant potential for aiding in the early detection and treatment of diabetic retinopathy and could be seamlessly integrated into non-invasive screening tools. To further enhance the accuracy and reliability of fundus image analysis algorithms, the adoption of standardized evaluation protocols and representative fundus image databases is imperative.
Comparative Analysis of PM and Non-PM Design Options for Small-Scale Wind Generators

For small-scale wind generators to operate efficiently in remote and residential areas, careful consideration must be given to the selection of the generator topology. The purpose of this study is to conduct an in-depth comparison of various design options available for wind generators. These designs include a permanent magnet vernier generator (PMVG), a permanent magnet synchronous generator (PMSG), and a wound-rotor synchronous generator (WRSG). The study thoroughly evaluates various performance aspects, such as cost, efficiency, torque density, power factor, and ripple torque, to help make an informed decision when choosing the most appropriate generator structure for small-scale wind power applications. Evaluation of the designs is validated by comparing two-dimensional finite element results. The findings of this study provide valuable guidance in choosing a generator that is both high-performing and cost-effective for small-scale wind power generation.
A Channel Robust RF Fingerprint Identification Scheme for LTE Devices Based on DMRS Signals

In physical-layer security schemes, radio frequency fingerprint (RFF) identification is vulnerable to the channel variations, and the identification performance of mobile devices using long term evolution (LTE) signals remains to be validated. In this paper, we propose an RFF extraction method based on LTE demodulation reference signal (DMRS) signal processing for LTE mobile devices. First, we analyze the impacts of the RFF and channel fading on DMRS in the LTE uplink channel. Then, we propose an RFF extraction method based on the wavelet decomposition and reconstruction of DMRS. By removing the low-frequency components of DMRS, which are mainly affected by the channel effects, our proposed method is robust to the channel impairments. Finally, our simulation and experimental results show that our method can effectively reduce the channel impacts and retain the RFF of devices. The effectiveness of this method is verified via different classification tasks. The classification accuracy can reach 98.5% and 93.9% in the stationary and mobile scenarios, respectively.
Hardware Design on Face Recognition System by Deep Neural Network for Access Control System

Based on the advance in GPU and big data, deep learning has brought progress, especially in computer vision. But the acceleration by GPU is not cost-effective on the dedicated design. This paper implements a face recognition system with the deep learning technique as an access control system. We accelerate the algorithm and design it with the combination of FPGA and ARM to realize an access control system. The recognition system can only be executed when there is an object near the camera, and only compute the detected face.
A Novel Heuristic Technique for Task Scheduling in Public Clouds

Cloud computing has increased in usage greatly over the last decade. As an increasing number of businesses migrate computation tasks to public clouds, the problem of managing costs of Cloud usage has come to the center of attention in Cloud computing research. Many heuristic techniques have been proposed to allocate tasks to Public Clouds so as to reduce the costs incurred. Among these, Shortest Job First (SJF) and Longest Job First (LJF) are two prominent techniques. In this work, the authors propose a novel heuristic technique that aims to combine SJF and LJF heuristic techniques and improve cost compared to both the techniques. Using Cloudsim, the authors also show the optimum combinations of Amazon Elastic Compute Cloud(EC2) instances where it is most cost effective to schedule tasks.
Revolutionizing Digital Banking: Harnessing Blockchain Smart Contracts for Enhanced Security and Efficiency

Blockchain technology stands out for its cryptographic connections between chronologically ordered records, forming the core of integrity within the expansive blockchain ecosystem. Leading the charge in this blockchain revolution are digital currencies, reshaping the financial landscape with seamless peer-to-peer transactions. As trailblazers in the realm of blockchain, digital currencies have catalyzed a shift towards decentralized, trust-based financial systems, fundamentally reimagining the global financial stage. This research embarks on an exploration at the intersection of Google Trends analytics and blockchain-based smart contracts. Its objective is to transform and fortify financial transactions within the digital banking sector. Moreover, the study offers a third-person perspective, unveiling valuable insights and recommendations for key stakeholders, such as financial institutions, regulators, and policymakers. These policy directives aim to promote greater financial inclusion, bolster security measures, and cultivate trust within the digital banking realm.
Analyzing The Effect of Edge Computing on Real-Time Data Processing and Latency Reduction

The concept of edge computing is a fresh approach that has garnered notable attention in recent times, primarily driven by the use of the Internet of Thingsand the requirement for instantaneous data processing and examination. Edge computing technology offers various benefits, including diminished latency, enhanced efficiency, and heightened dependability. Nonetheless, it also introduces a range of obstacles, such as the safeguarding of security and the management of dispersed computational assets. Edge computing minimizes latency and cuts down on bandwidth expenditures in the realm of Health Information Technology by relocating a portion of the data processing and analytical tasks from the central server or cloud to the periphery of the network, precisely where data originates or is utilized. This approach ensures that only indispensable or pertinent data is dispatched to the cloud or server, while the remaining data is either managed locally or disregarded. As a result, the volume of data requiring transmission is reduced, leading to bandwidth preservation and heightened swiftness. Furthermore, edge computing facilitates real-time or nearly real-time data processing and analysis, contributing to latency reduction and overall improved responsiveness. Edge gateways function as intermediaries connecting edge devices and cloud servers, aiding in data routing and supplying supplementary processing capabilities whenever required. Cloud servers furnish extra computational potency and storage aptitude, affording enterprises the ability to handle and scrutinize substantial data quantities instantaneously. Edge computing architecture latency reduction accuracy. The main contribution of the study is to analyze the effects of edge computing on latency reduction and real-time data processing.
Hardware Implementation of Low-latency Image Denoising with Wavelet Shrinkage and Noise2Noise-based Learning for Ultra-high Definition Cameras

There is a growing demand for real-time image denoising in low-light shooting with ultra-high definition cameras. This paper presents a denoising method that incorporates Haar-wavelet shrinkage denoising and a minimum mean square error estimation based on an extension of the Noise2Noise learning technique, which can perform low-latency processing and can be adapted to non-linear processed signals without prior knowledge of noise and signal distributions. By introducing a two-dimensional kernel to reduce memory usage in the coring function estimator, we demonstrate real-time hardware denoising for luminance signals of 4K 60 fps video with a delay of less than one millisecond.
Federated learning optimisation algorithm based on Non-IID data

With the rapid development of the Internet of Things (IoT), more and more devices are being connected to the Internet. These devices are not only capable of collecting large amounts of data, but also of making intelligent analyses and decisions through artificial intelligence technology. However, due to data security and privacy issues, these devices are often unable to upload data to the cloud for processing, leading to the data silo problem. To solve this problem, researchers have proposed the concept of federated learning. Federated Learning (FL) is a distributed machine learning approach that allows multiple devices to collaboratively train a shared model while keeping the data localised. This approach not only protects user privacy, but also makes full use of the data on each device. However, in practice, federated learning faces an important challenge: the problem of Non- Independent and Identically Distributed (Non-IID) data. Non-IID data means that the data on each device is not independently and identically distributed. This leads to a decrease in the model training effect and affects the performance of the final model. Therefore, it is important to study the optimisation algorithm in federated learning based on Non-IID data. The traditional federated learning algorithms converge slowly during the training process under Non-IID data, and the model performance degrades seriously. In this paper, two algorithms with optimisation directions are proposed based on the client selection step and model aggregation step in the federated learning process. In this paper, we first propose a variance-based hierarchical client selection algorithm FedDR-H-CS (Federated Dispersion-Reduced Hierarchical Client Selection), which is able to stratify the clients into data distributions that are approximately independently and identically distributed, and then perform the optimal selection within the layers to achieve the effect of eliminating the Non-IID problem. Another algorithm proposed in this paper is, FedFLA (Federated Adaptive Local Aggregation), a flexible model aggregation algorithm based on local goals, which allows fine-grained aggregation of global and local models to adapt to local goals on each client for personalised local model initialisation. Local gradient updates are also tracked and recorded for unbiased gradient global parameter aggregation on the server side.
Aspect Based Sentiment Detection from Natural Bangla Text

Aspect-based sentiment detection is one of the text analysis methods that extracts and separates each aspect phrase as well as identifies the polarity of the sentiment related to each aspect term. In the Bangla language, studying sentiment analysis is an important topic. Aspect-based sentiment analysis (ABSA) of the Bangla language is thought to be a difficult problem because there aren’t enough resources. In this paper, three high-quality, manually annotated datasets in Bengali are presented that are annotated with aspects and their corresponding sentiments in order to carry out the ABSA task in Bangla. The datasets are comprised of user reviews of restaurants, movies and cricket. To analyze the datasets, a baseline evaluation was performed with an emphasis on machine learning models. Three supervised machine learning models NB, LR and SVM were trained using the three different datasets. SVM achieved the highest f1-score for aspect-opinion term extraction, aspect category and sentiment polarity classification on the three different datasets. For aspect-opinion term extraction, they were 95.67%, 95.67% and 94.67% respectively. For aspect category classification, the values were 97.00%, 95.60% and 95.88% respectively. For sentiment polarity classification, the scores were 95.67%, 83.67% and 91.33% respectively.
Performance Enhancement using Data Augmentation of Depth Estimation for Autonomous Driving

For autonomous driving, various sensors such as cameras, LiDAR, and radar are required to accurately perceive the surrounding environment. These sensors provide information for tasks like object recognition, lane detection, path planning, and distance estimation. However, processing information from these multiple sensors for perception tasks demands significant costs, computational resources, and latency. These challenges hinder the practical implementation of real-time edge computing in autonomous driving systems. Consequently, research is actively exploring methods to perform perception using only cameras, particularly to alleviate the computational burden and cost associated with 3D point cloud data from LiDAR or radar sensors. In this study, we investigate techniques to optimize the performance of Monocular Depth Estimation (MDE) methods, which utilize a single camera to extract 3D information about the surrounding environment. We focus on enhancing accuracy through classical data augmentation techniques and synthetic data generation methods. Additionally, we explore the selection of an optimal loss function. Experimental results demonstrate that employing our proposed data augmentation approach reduces REL by approximately 3.9%, showcasing the potential of this method.