Miracle-dz/UniFAR-FTUs huggingface dataset:
Convolutional Feature based Vision Transformer Model for Speech Command Recognition

Speech command recognition (SCR) is the process of recognising the short speech commands uttered by speakers using statistical and deep learning (DL) based models. With emerging developments in the field of DL research, deep convolutional neural network (DCNN) models have appeared as a better alternative over the conventional statistical models. However, in recent years vision transformer models (ViTs) have shown superior performance over DCNN for image classification tasks. Moreover, in ViTs the number of trainable parameters are comparatively less than DCNN models. Therefore, in this work we have proposed an improved ViT model incorporated with convolutional features for the speech command recognition task. The motivation behind the incorporation of ViT model is that it extracts the information of formant distribution from small patches of the input mel-spectrogram. Whereas, the convolutional blocks incorporated to the ViT model captures the prominent local features more effectively. The proposed convolutional feature based improved ViT model is trained and tested with the google speech commands v2 (SCV2) dataset. From the experimental findings it is observed that the proposed improved ViT model performed better than the existing ViT model in terms of accuracy for the SCR task.
A PSO and Random Forest-based Hybrid Model for Effective Intrusion Detection

Recent technological advances have led to the need to store information electronically, which is why information security has become an increasingly important issue in recent years. Traditional intrusion detection takes care of unusual activity. It is crucial to improve the true positive while reducing false positives to having a trustworthy IDS in a network. We present a methodology intrusion detection system (IDS) that relies on machine learning classifiers and multi-objective PSO in our proposed model. The KDDCUP99 dataset is picked up as an input for evaluating the outcome of the proposed model. Based on the performance analysis and the comparative study, it can be concluded that the model is valid. PSO-RF proposed model gives better classification accuracy compared with DT, KNN, SVM, and Naïve Bayes and the highest Gbest value with maximum TPR and minimum FPR.
A Simple Proportional Resonant Current Controller with Floor Function Modulation Strategy For a Three-Phase, Three-Level Reduced Component Count Based Active Front-End Converter

In literature, most of the authors have discussed the concept of a 3-phase two-level active front-end (AFE) converter with different control techniques. Generally, concerning circuit configuration, the 2L-converter is very simple but has many disadvantages when considering high power with medium voltage applications. To alleviate the drawbacks of 2L-converter, there are different types of multilevel topologies have been reported and these have a greater operating voltage in conjunction with low voltage blocking semiconductor devices. In this paper, by considering the recently developed reduced component count (RCC) based on a three-level topology, the AFE operation has been explained. The AFE operation includes the objectives of regulation of DC bus voltage with low ripple, sinusoidal input source current with low harmonic distortion, unity power factor (UPF) operation at input source terminals, and regenerative operation. To achieve all these objectives, a simple proportional resonant (PR) current controller with floor function (F2) modulation strategy has been proposed and of course, it is well-suitable to different types of AFE-based multilevel topologies. Both transient state and steady-state analysis results of the RCC-based 3P3L-AFE converter have been elucidated vividly with the utilization of the PLECS platform.
A Deep Dive into Accurate Garbage Classification Using Transfer Learning Techniques for Environmental Sustainability

Accurate garbage classification is a pillar in modern waste management systems, carrying profound implications for sustainability and environmental impact reduction. This study represents a significant leap forward in the domain of garbage classification, driven by the application of state-of-the-art deep learning algorithms enhanced by transfer learning methodologies. We employ a comprehensive garbage classification dataset, encompassing a diverse array of waste categories including plastics, paper, glass, and organic materials, to explore the potential of selecting pre-trained deep learning architectures. Models such as ResNet-50, DenseNet-161, InceptionV3, and EfficientNet-B7 are employed and subjected to thorough evaluation for their classification capabilities. Leveraging transfer learning to enhance their performance, we incorporate logistic regression as a multi-class classifier. Our findings reveal remarkable test accuracies for these models. ResNet-50 shines with a notable accuracy of 94.70%, closely followed by DenseNet-161 at 93.25%. InceptionV3 maintains competitive performance with an accuracy of 92.54%, while EfficientNet-B7 impresses with a significant test accuracy of 93.75%. These results collectively underscore the exceptional performance of these models in automating waste classification tasks, showing the effectiveness of deep learning coupled with transfer learning and logistic regression methodologies. Such advancements hold the potential to revolutionize waste management practices, fostering sustainability and responsible waste handling.
Intelligent Search Engine Technology for Power Dispatching Cloud Platform

With the continuous deepening of energy transformation and the continuous promotion of electricity marketization reform, the structural form, system characteristics, and operational organization of the power system have undergone significant changes, and the power system has entered a new era. The design goal of intelligent search services is to provide a simple, efficient, reliable, and flexible search engine framework, providing comprehensive support for improving the search efficiency of users and applications. Through intelligent search services, three major indicators such as accuracy, coverage, and search speed can be effectively improved for users and applications. Intelligent search services provide search functions for structured and unstructured data, supporting visual display and intelligent sorting of search results.
Discovering Complex Correlations Among Multiple IoT Devices in Smart Environments

The ubiquity of the Internet of Things (IoT) in a vast range of consumer applications is unparalleled. Unfortunately, despite the benefits of IoT, its widespread integration comes with significant security challenges. Considering IoT devices' capability to interact with the physical environment, there is an urgent need for effective anomaly detection. The state-of-the-art anomaly detection method, HAWatcher, models the normal behaviors of smart homes with inter-device correlations and demonstrates great results. Nonetheless, it is limited to capturing only simple one-to-one correlations between two events or states, which undermines its capability to detect anomalies in more complicated environments. To address this issue, we present a novel correlation discovering method to mine complex two-to-one correlations in such complicated IoT-enabled environments. We conduct experiments over two weeks on four smart home testbeds and obtain 70 two-to-one correlations. The correlations are applied to 9 anomaly scenarios, which show significant improvements in detecting anomalies over one-to-one correlations.
A Study on Music Genre Classification And Lyrics Generation of Bangla Songs Dataset

This study is divided into two crucial sections. We first gathered Bangla song names and lyrics using web crawling and a dataset that was only slightly annotated. A comprehensive dataset was then thoroughly annotated across five different genres. Utilizing transformer models to classify the genre of Bangla songs was our main goal. Noteworthy accomplishments are the 94% accuracy and F1-score of csebuetnlp/banglabert. Additionally, we created a text generation system using ritog/bangla-gpt2 and flax-community/gpt2-bengali. This method allows for the development of scenario-based music material, expanding the creative possibilities for the entertainment business, by choosing the genre with the highest F1 score from the classification findings.
Theoretical Analysis of Serial/Parallel Variations of Hash-Mining for Smaller Variance of Confirmation Time

In Bitcoin, the computational problem that miners try to solve is an equation described in terms of a hash function. The hash-mining time consumed for solving the problem is known to follow the exponential distribution. In this paper, we study two variations of computational problems that are constructed from the computational problem of Bitcoin. The first variation is a serial composition and the second variation is a parallel composition. We deduce the formulas of the variances of the hash-mining times of the variations in relation to the variance of the original Bitcoin. Also, we see the needed nonce data to be kept in the corresponding blockchains. We conclude that there is a trade-off between the decrease of variance and the data size of the nonce to be kept in the blockchain.
An Intelligent Pothole Management System Using Multi-Tasking CNN on Android

Potholes on the road can pose significant risks to drivers and pedestrians. They are a common cause of accidents and vehicle damage. Hence, their timely repair is critical for road safety. However, pothole repairs are frequently delayed due to lack of information about their location and risk level. To address this issue, we propose an Intelligent Pothole Management System (IPMS) that promotes using citizen-sourced information through an android application in a citizen-government collaborative environment. The android application employs a novel lightweight multitasking CNN to detect and classify potholes based on their risk level instead of traditional computer vision methods that rely on calculation of geometric dimensions. The proposed model runs on the smartphone, compared to a server environment, as it relies on perspective vision instead of exact calculations and is trained using a carefully curated dataset of about 30,000 images. Our system, similar to Google Maps’ traffic view, shows citizens the pothole density as well as pothole severity on each road to avoid potential accidents. Citizens report potholes through the application which captures geo-location and extracts pothole information (risk level). Similarly, public works department as access to pothole relevant data in a dashboard, which they can use to prioritize repairs based on the risk levels of potholes and their location. By allowing citizens to report potholes directly, we hope to foster a sense of community ownership and responsibility for road safety. Our proposed system can also assist public works department in making more informed repair decisions, resulting in more efficient repairs and safer roads. We believe that our Intelligent Pothole Management System (IPMS) has the potential to improve road safety and is a valuable contribution to the development of intelligent transportation systems.
Energy Harvesting in NOMA based UAV-Acoustic system

This paper presents the analysis of energy harvested cooperative unmanned arial vehicle (UAV)-underwater acoustic communication (UWAC) system with non orthogonal multiple access (NOMA) based underwater sensor controllers. An energy constrained decode and forward (DF) relay has been considered at the buoy. The DF relay at the buoy harvests energy from the received signal using power splitting (PS) scheme and transmits the decoded signal to the two underwater sensor controllers. Shadowed κ-µ, being an accurate channel model for UWAC, has been used to characterise link between buoy and underwater sensor controllers and also the link between base station and the buoy. We have analysed closed form expressions for bit error rate (BER) and sum rate for the considered system at both underwater nodes. The results have been verified by monte carlo simulations.
PSNR Based Comparative Analysis for Visible and LSB Watermarking to Ascertain Authenticity

In telemedicine, it is very important to transmit medical data securely over the internet. To ensure that data transferred over the channel is not tampered and data belongs to the legitimate patient. To ensure this authentication and ownership of medical data becomes very important. This article is focused on why authentication and ownership are important in telemedicine. For carrying out this survey, results are calculated based on different techniques used for watermarking, MATLAB and OpenCV of Python are used. The images used are X-Rays, CT- Scans which are taken from DICOM. This survey has been done by choosing articles from IEEE, Elsevier, A CM, Google Scholar, etc. This article is focused on ensuring the authenticity of medical data during transfer over the network. This article compares the different techniques used for watermarking for ensuring authenticity, robustness against attacks, and imperceptibility. It also compares all these using PSNR values which plays a vital role in defining the performance of a particular technique. This article further helps in deciding a novel approach to ascertain the authenticity and ownership of medical data, transferred through the internet.
Asynchronous Federated Learning via Over-the-Air Computation

The emerging field of federated learning (FL) provides great potential for edge intelligence while protecting data privacy. However, as the system grows in scale or becomes more heterogeneous, new challenges, such as the spectrum shortage and stragglers issues, arise. These issues can potentially be addressed by over-the-air computation (AirComp) and asynchronous FL, respectively, however, their combination is difficult due to their conflicting requirements. In this paper, we propose a novel asynchronous FL with AirComp in a time-triggered manner (async-AirFed). The conventional async aggregation requests the historical data to be used for model updates, which can cause the accumulation of channel noise and interference when AirComp is applied. To address this issue, we propose a simple but effective truncation method which retains a limited length of historical data. Convergence analysis presents that our proposed async-AirFed converges on non-convex optimality function with sub-linear rate. Simulation results show that our proposed scheme achieves more than 34% faster convergence than the benchmarks, by achieving an accuracy of 85%, which also improves the time utilization efficiency and reduces the impact of staleness and the channel.
Spiking Neural Network-Based Digit Pattern Recognition in FPGA

The scientific community has major challenges in understanding how the brain works and recreating it in various forms, whether biological or computer. There is strong evidence that such a feat could be accomplished, given that we ourselves are an example of an intelligent system, even though time travel and faster-than-light travel remain unattainable goals. It may be possible to develop a system capable of awareness and responsive interaction with the outside world by mimicking brain function. The fundamental goal of this work is to build a digital system that can mimic the behavior of spiking neural networks on a single chip. It attempts to carry out mental processes including reasoning, speech recognition, movement, and visual processing. The system is described using VHDL, a high-level hardware description language, to create a compromise between simplicity, efficiency, and performance without sacrificing the precision necessary for simulating neural networks.
A Novel Digital Signature Scheme Based on Linear Congruence

A digital signature serves as a mechanism providing authentication, data integrity, and non-repudiation services. The digital signature of a message relies on a secret component that is known only to the signer and must be verifiable. In this study, we designed a digital signature scheme based on linear congruence, referred to as the Wasser scheme. The Wasser scheme is a follow-up study to the linear congruence-based public key encryption scheme by Dwi Karyadi in 2019. Wasser's scheme demonstrates that the linear congruence basis may be used to implement digital signature schemes. The scheme consists of three general processes of a digital signature, namely key generation, signing, and verification. In principle, the Wasser scheme satisfies the requirements for a digital signature scheme. The signing and verification processes in the Wasser scheme utilize a hash function. The hash function used in the scheme is the Modification Detection Codes (MDCs), which is one of the Collision Resistant Hash Functions (CRHFs). Respectively, the computing process in the signing and verification processes has a time complexity of ((log n)3) and O(log n), which suggests that the processes take a relatively short time.
Rethinking the Game of Adversarial Robustness and Generalization with Diverse Strategies

Deep neural networks are suffering potential and critical hidden danger when facing adversarial examples, which poses great threat on artificial intelligence applications. Generally speaking, a generalizable model has more accuracy on clean data. But when considering adversarial examples, which add intended and imperceptible perturbations on clean data, a model should be more robust on adversarial attacks to have more accuracy on adversarial examples. In this paper, we propose an input diversity method to balance the adversarial robustness and network generalization. It leverages the baseline methods with probability strategy to control the white-box rate and black-box rate. Experiments verify that the proposed method maintains relatively high white-box rate while boosting considerable black-box rate.
UAV based Human Detection for Search and Rescue Operations in Flood

During disastrous events like floods, the urgency to rescue people facing immediate danger poses a significant challenge. Traditional search operations led by humans often struggle due to dangerous conditions and limited resources. As a response, a new application has emerged: employing Unmanned Aerial Vehicle (UAV) for rescue missions, utilizing image processing to locate individuals in distress. However, existing application relies on deep learning based approach for image analysis suffer from slow human detection, a crucial drawback given the time sensitive nature of such scenarios. To tackle this issue, we have proposed a novel approach using You Look Only Once version 4 (YOLOv4) for rapid human detection. YOLOv4 is renowned for its speed, surpassing traditional methods by swiftly identifying humans within flooded environments. To train and validate our approach, we have used Common Objects in Context (COCO) dataset (pretrained or labeled). The COCO dataset's large size and wide variety (real-world scenarios, including images from natural scenes, indoor environments, and crowded public spaces) of images make it a valuable asset for training and testing human detection models. Through exhaustive experimentation, our method showcases an impressive average accuracy of 79.46% in accurately identifying humans amidst flood-ravaged surroundings. By harnessing the efficiency of YOLOv4 and the diversity of the COCO dataset, our approach holds the potential to significantly enhance the efficiency of UAV-assisted search and rescue operations during critical moments, ultimately resulting in the preservation of more lives in the face of disasters. As well as the human detection information collected from the model facilitates the rescue team, assisting in tasks such as distributing food, providing first aid, and formulating rescue strategies.
Towards Efficient and Privacy-Preserving Federated Learning for HMM Training

The hidden Markov model (HMM) has played a pivotal role in various IoT applications due to its ability to model time-varying sequences. Since the datasets usually live in isolated islands and their privacy naturally demands to be seriously considered, the HMM should be trained in a privacy-preserving manner. A typical HMM training framework is federated learning, in which a federated server and many data owners collaboratively train an HMM without revealing data owners' data to the federated server and the trained model to data owners. Since existing HMM training schemes are computationally intensive, we propose an efficient and privacy-preserving federated learning scheme for HMM training to address the efficiency issue in this paper. First, we transform all HMM training computations into matrices- and vectors-based computations over real domains. Then, we introduce our federated HMM training scheme by applying matrix encryption to protect the HMM training privacy. After that, we show that our scheme is privacy-preserving through a rigorous analysis on the security of our scheme. We illustrate that our scheme is efficient through extensive experimental evaluation on the performance of our scheme.
Predicting Graduation and Dropout Rates : A Machine Learning Approach

Student dropout is a complicated and detrimental issue in the educational process, with costs to both students and institutions on a social and financial level. The tool presented in the paper uses machine learning techniques to forecast first-year undergraduate student dropouts. In order to calculate the risk of dropping an academic course, the tool takes into account personal information, secondary school academic records, and first-year course credits. In this paper, a broad study on the applications of the algorithms of machine learning is presented for predicting educational enrollment status. The research models in this situation. The used dataset includes a wide range of financial, academic, and demographic variables, offering a rich source of data for modeling. Researchers and practitioners looking to use similar predictive models in educational contexts can benefit greatly from the methodology and insights presented here.Focuses on comparing the efficacy of Support Vector Machine (SVM) and Gaussian Naive Bayes (GNB). The results of this study show that the SVM Model demonstrates higher accuracy than the GNB Model. Accuracy of these models then performs an increase when techniques like SMOTE is applied. This study lays the groundwork for future developments in the field of predictive modeling for student enrollment and adds to the body of knowledge in that area.
Deep Learning Based Joint CSI Compression and Prediction for Beyond-5G Systems

We consider a deep learning (DL) based approach to optimize channel state information (CSI) reporting in massive multiple-input and multiple-output (MIMO) systems. For CSI compression, existing methods use frequency-domain (FD) and spatial-domain (SD) correlation, whereas correlation also exists in the time-domain (TD) under fading conditions. Hence, we propose a DL-based three-dimensional compression (DL-3DC) approach to improve CSI reporting accuracy by using correlation in FD, SD and TD. In addition, to reduce the feedback overhead, we propose two DL-based CSI prediction methods: eigenvector based and MIMO channel based. We then integrate CSI prediction with DL-3DC at the UE and propose a joint CSI compression and prediction (JCCP) scheme to improve the CSI accuracy and reporting overhead trade-off. Although, UE-side JCCP is efficient when the future CSI application time instance is known in advance. To support prediction when this is unknown, we propose BS-side JCCP to predict the CSI at the BS after it is received. Through simulations, we show that DL-3DC improves the CSI reporting accuracy by up to 13.8% compared to the latest CSI reporting mechanism in New Radio (NR). Further, we show that JCCP significantly reduces the reporting overhead by up to 87.5% compared to NR CSI reporting.