Miracle-dz/UniFAR-FTUs huggingface dataset:
Machine Learning Based Approaches for Predicting Stock Closing Price

Due to random, uncertain and dynamic nature of stock market, predicting the price is quite a tough and challenging task. Dominance of machine learning in all sectors of life is noticeable. Hence, researchers have paid attention on predicting stock price through different machine learning architectures. This study has focused on observing performance of six machine learning techniques and has proposed three hybrid machine learning approaches which consists of Light Gradient Boosting Machine for feature selection and Recurrent Recurrent Neural Network, Long Short Term Memory and Gated Recurrent Network for prediction. The hybrid approaches showed better results. But among all of them, hybrid model of Light Gradient Boosting Machine and Gated Recurrent Network performed best. Data of seven years is collected from two technology groups and is pre-processed. Eight technical indicators are utilized as inputs which are obtained from existing values of dataset. Performance comparison is observed through different evaluation metrics: MAE, MSE, RMSE and RI. 2 . The investigational output proves the dominance of the presented approach over the other implemented models.
QEdu: A Quantum-Safe Blockchain Framework to Secure and Verify Educational Credentials

Distributed Ledger Technologies (DLT), notably blockchain, have advanced significantly, finding applications in various domains, including education, for enhanced transparency and accountability. The utilization of hash functions and public-key cryptography (e.g., ECDSA and RSA) in blockchain ensures these attributes. However, the rapid progress in quantum computing has unveiled vulnerabilities in classical public-key cryptography, necessitating a reassessment of blockchain architecture for quantum resistance. This article explores diverse blockchain architectures in education, evaluating their susceptibility to quantum attacks. It introduces QEdu, a consortium blockchain framework securing educational credentials using quantum-safe Elgamal-Like public-key cryptography based on matrices over grouping. Comparative analysis with existing works underscores the efficacy of the proposed framework.
Prediction of Diabetes Using Machine Learning Classifiers

Diabetes is the most prevalent chronic disease and a number of people are suffering from it all over the world. Diabetes could be due to hereditary reasons, obesity, etc. Diabetes can cause other complications like heart disease, so prediction and prevention of diabetes is of utmost importance. In this paper, five machine learning algorithms that are Logistic Regression, Decision Tree, Random Forest, Support Vector Machine and K-Nearest Neighbor have been used for the prediction of diabetes. It was found that Logistic Regression performed the best amongst the five classifiers and obtained highest accuracy of 0.7359, precision of 0.7312, recall of 0.7359 and f1-score of 0.7329.
Autism Detection Based on MRI Images Using Deep Learning

Autism spectrum disorder (ASD) is a neurodysfunction or neurodevelopmental disorder. This causes a patient to have trouble with social interaction which causes social instability. It also causes speech problems or difficulty with any sort of verbal communication as well as nonverbal communication. The primary challenge associated with autism lies in its intricacy of early diagnosis. The difficulty in diagnosing is due to the lack of a proper medical test for it. Researchers have yet to discover a biomarker or specific gene that can detect autism. Doctors still use outdated methods to identify autism nowadays. In order to tackle this issue and facilitate the diagnosis of autism, we employed deep learning techniques to create a method for ASD diagnosis. This study presents a deep learning-based approach using Functional MRI and Structural MRI images. We used the ABIDE dataset for this research. After examining the MRI pictures, a method was developed to pick out particular layers from the MRI images. Our dataset was then constructed using images from ABIDE for our models to train and test without performing any pre-processing. Various cutting-edge deep learning architectures were chosen to train using our created dataset. Novel architectures were used to attain an accuracy of 80% to practically 84%. A custom block was used later in the research to expand the dataset and achieve more accuracy. Based on our findings, it is evident that the deep learning model with our modified block demonstrates superior performance when compared to alternative techniques. Consequently, it offers a feasible solution for the diagnosis of ASD using MRI images.
Real-Time Point Cloud Action Recognition System with Automated Point Cloud Preprocessing

Point cloud action recognition has the advantage of being less affected by changes in lighting and viewing angle, as it focuses on the three-dimensional position of an object rather than pixel values. This enables robust recognition performance even in complex and dark environments. Additionally, point cloud action recognition finds widespread applications in fields such as robotics, virtual reality, autonomous driving, human-computer interaction, and game development. For instance, understanding human actions is crucial for better interaction and collaboration in robotics, while in virtual reality, it can capture and reproduce user movements to enhance realism and interactivity. To build a smoothly operating point cloud action recognition system, it is often necessary to filter out background and irrelevant points, resulting in clean and aligned data. In previous methods, point cloud filtering and action recognition were usually performed separately, with fewer systems operating together or action recognition without background filtering. In this paper, we propose a pipeline that enables users to directly acquire point cloud data from the Azure Kinect DK and perform comprehensive automated preprocessing. This generates cleaner point cloud data without background points, suitable for action recognition. Our approach utilizes PSTNet for point cloud action recognition and trains the model on the dataset obtained through automated preprocessing, which includes 12 action classes. Finally, we have developed a real-time point cloud action recognition system that combines automated point cloud preprocessing.
Hybrid Beamforming Design with Overlapped Subarrays for Massive MIMO-ISAC Systems

Integrated sensing and communications (ISAC), supported by massive multiple-input multiple-output (MIMO), can provide simultaneously improvement of sensing capability and communication capacity. However, employing the conventional fully digital beamforming architecture with a large-scale antenna array will incur the prohibitively high hardware cost and power consumption. In this paper, we propose a hybrid beamforming design with the overlapped subarrays (OSA)-based hybrid architecture for massive MIMO-ISAC systems. We design the analog and digital beamformers by jointly optimizing the spectral efficiency of communication and beampattern mean squared error of sensing under the specific constraints of OSA structures, power budget, and constant modulus. To tackle the resulting non-convex problem, we relax it as a weighted summation minimization problem, where the Euclidean distance between the designed hybrid beamformers and the optimal communication/desired sensing beamformers is minimized. We further decompose the formulated problem into three subproblems and develop an effective alternating minimization algorithm. Numerical simulations demonstrate the effectiveness and flexibility of the proposed OSA-based hybrid beamforming design in terms of spectral efficiency and sensing beampattern performance.
Exploration of Transformer Ensemble and Auto Regressive Approaches to Enhance Performance of Clickbait Title Detection

"ClickBait" is a term which refers to online content titles designed to attract the viewers to click, often with the intention of misleading or exaggerating the actual content. Clickbait can be used for various malicious purposes such as social engineering, phishing, misinformation spreading, click fraud etc. Therefore, it is important to design a mechanism for detecting clickbait titles. For this purpose, we have utilised an auto regressive model called XLNet-base-case and 3 different types of transformer embedding models such as BERT-base-en-case, ROBERTa-base-en-case and XLM-ROBERTa-multi-case. We individually trained these models using a large diversified dataset and also performed ensemble by majority voting of BERT-base-en-case, ROBERTa-base-en-case and XLM-ROBERTa-multi-case eventually achieving test accuracy of 90% on imbalanced class dataset and 89% on balanced class dataset where the auto regressive approach gave validation accuracy of about 90.7% and 88.19% respectively on imbalanced class and balanced class datasets.
RBINet: A Convolutional Neural Network for Automatic Building Identification

Convolutional neural networks (CNNs) have been used to analyze visual data in several applications related to computer vision, including image classification and segmentation. The aim of this paper is to use a trained CNN model to accurately recognize various building from images. However, the exploration of this task has been hampered by the lack of well-organized, sizable architectural picture datasets with a particular focus on building identification. To address this, we created a dataset of building pictures with related labels to train and test our CNN model for building identification. The name of the dataset is the RUET-building image (RBI) dataset as the photographed buildings are located at the RUET (a prestigious engineering University) campus. There are 7075 photos in this collection, which were gathered from 15 distinct buildings. In order to find the ideal model for the RBI dataset, this work also contains an ablation analysis of our proposed CNN named RBINet (RUET-building identification network). In terms of building recognition accuracy, RBINet (with four convolutional layers and three hidden layers) outperforms leading models and achieves 98.88% accuracy. The authors also report that the EfficientNetb0 and DenseNet121 models give the second (98.44%) and third (98.22%) highest classification accuracy, respectively, for the RBI dataset.
ANTI: An Adaptive Network Traffic Indexing Algorithm for High-Speed Networks

Network packets record communication behaviors and details, which is important for security audits, attack detection, and forensic analysis. For the effectiveness and timeliness of security analysis, it is necessary to fully store network packets and build an efficient packet index. However, the existing packet indexing algorithms based on the radix tree ignore the distribution characteristics of network traffic and use internal nodes with the same capacity for index construction, resulting in wasted disk space and poor retrieval performance. As a solution, $w$e propose ANTI, an adaptive network traffic indexing algorithm similar to Adaptive Radix Tree, which can adaptively switch internal nodes with different capacity according to the density of network traffic and compress the common prefix and distinct suffix of traffic attributes to balance the index construction performance and space utilization. We also implement a packet-aware network traffic archiving and indexing system to achieve full packet archival, efficient indexing, and fast retrieval. Finally, we empirically evaluate ANTI in IPv4 (IPv6) traffic scenarios, and the results confirm the effectiveness of ANTI as well as the benefit of adopting ANTI for enhancing indexing and retrieval performance compared with other state-of-art algorithms.
Towards Stable Federated Fog Formation Using Federated Learning and Evolutionary Game Theory

Network delays cause a reduction in the Quality-of-Service (QoS) for Internet of Things (IoT) applications, and even render time-critical applications inoperative. The paper tackles the problem of forming fog federations that aim to improve the QoS. However, instabilities within fog federations might cause some providers to withdraw from the federation, and thus decrease the profit of the federations and the expected QoS. Moreover, federation formation techniques could potentially create privacy risks for end-users whose data is utilized in the process. This paper introduces a decentralized evolutionary game theoretic algorithm that tackles the problem of fog federation formation, as well as, providing a decentralized privacy-aware federated learning algorithm that predicts the QoS between fog servers for optimizing the formation procedure. The devised method provides better stability and increased QoS when compared to other benchmarks.
Data Transfer for Balancing Model Convergence and Training Time in Federated Learning

Federated learning is a distributed machine learning technique that addresses the challenges of traditional centralized machine learning, such as high computational expenses and network congestion. In federated learning, a model is distributed to each server from a central server, and then each server trains the model using its own data. After training, the models are integrated at the central server, and the integrated model is re-distributed to the servers for further training. Federated learning can train a model based on all the data without collecting the data in one place, reducing the burden on the network and addressing the problem of training costs for extensive amounts of data. However, federated learning has two unique challenges: prolonged training time depending on the amount of data in each server, the processing capacity of each server, and the bandwidth of links in a network, and the accuracy of the trained model, which is influenced by the heterogeneity of the datasets in each server. We propose an optimization problem for the training time and the convergence behavior in federated learning. Specifically, we present a nonlinear programming problem to minimize the total training time while optimizing the data transfer route and destination of each router, subject to constraints on network congestion avoidance and convergence of the trained model. We evaluate the proposed method in an experiment using a virtual GPU cluster and show that the proposed method improves both the accuracy of the trained models and the training time compared to a prior method.
High-level synthesis-based approach for CNN acceleration on FPGA

This paper presents a comprehensive approach to implementing Convolutional Neural Networks (CNNs) on Field-Programmable Gate Arrays (FPGAs). CNNs have become a cornerstone in numerous fields, enabling breakthroughs in areas such as computer vision, natural language processing, and speech recognition. CNNs comprise multiple layers designed to perform various computations. In this research, we propose a general methodology using High-level synthesis(HLS) tools for implementing CNNs on FPGAs and provide several use cases demonstrating competitive FPGA resource utilization in comparison to state-of-the-art works. Our experimental results demonstrate a significant reduction in resource utilization for DPS units, amounting to approximately 80% when compared to other neural network accelerators deployed on FPGAs. Furthermore, we have accomplished a noteworthy 50% reduction in Look-Up Table (LUT) usage compared to alternative accelerators, alongside an overall superior performance in comparison to CPU or GPU implementations.
Demonstration of a PECVD SiOx-Based RRAM Dendritic Device

Synaptic plasticity has been traditionally credited for learning in the brain. The prevalent view on learning through synapses forms the backbone behind all the significant developments in the area of artificial neural networks (ANN). However, more recent studies in Neuroscience reveal that dendritic junctions play a crucial role in the dynamics of learning, leading to increased efficiency and faster learning. Consequently, there is a need to implement dendritic computation/learning at a hardware level in ANNs. Resistive Random Access Memory (RRAM) devices have been frequently used as non-volatile synaptic elements for in-memory-computing (IMC) or neuromorphic computing applications. However, their usage in implementing dendritic dynamics has been rarely investigated. This work reports a SiOx-based RRAM device with gradual and volatile resistance switching behavior. We demonstrate that the switching dynamics of this device can be used to emulate the behavior of a dendritic junction. We also report robust endurance of this device up to 1 million cycles and investigate the transport mechanism responsible for the switching dynamics. The demonstrated dendritic devices makes room for the monolithic integration of all-SiOx neural networks in the future.
Compile-Time Analysis of Compiler Frameworks for Query Compilation

Low compilation times are highly important in contexts of Just-in-time compilation. This not only applies to language runtimes for Java, WebAssembly, or JavaScript, but is also crucial for database systems that employ query compilation as the primary measure for achieving high throughput in combination with low query execution time. We present a performance comparison and detailed analysis of the compile times of the JIT compilation back-ends provided by GCC, LLVM, Cranelift, and a single-pass compiler in the context of database queries. Our results show that LLVM achieves the highest execution performance, but can compile substantially faster when tuning for low compilation time. Cranelift achieves a similar run-time performance to unoptimized LLVM, but compiles just 20–35% faster and is outperformed by the single-pass compiler, which compiles code 16x faster than Cranelift at similar execution performance.
Roosting patterns of the Edible-Nest Swiftlet (Aerodramus fuciphagus) of the Andaman Islands: effects of lunar phase and breeding chronology

ABSTRACT Environmental conditions and biological rhythms can affect the behavioural routines of animals. However, the effect of lunar phase on individual roosting behaviour remains uninvestigated in most species. Here, we monitored the effect of lunar phase, sunset time, temperature, humidity and the breeding chronology on the roosting patterns of the Edible-Nest Swiftlet (Aerodramus fuciphagus) of the Andaman Islands, across breeding stages. Counts revealed that more than 98% of the population returned to the roosting caves during peak roosting hours, i.e. 1700–2000h. The proportion of birds roosting in caves was highest during the ‘new moon’ phase and when birds were at the nest-building and fledging stage of their breeding cycle. We confirmed that the variation in the roosting behaviour of the Edible-Nest Swiftlet is linked both to the stages in the breeding cycle and to the lunar phase. We hypothesise that the cause for the lunarphilic roosting pattern is an anti-predator strategy. Feeding habits and food requirements during different breeding stages are presumed to influence the roosting pattern of the species. An improved understanding of the association of the behaviour, physiology and the environmental conditions which influence these traits can only help us improve conservation outcomes for this economically important species.
Using the YOLO Network's Games Object to Classify Sports

One of the most favored types of entertainment today is sports. This study identifies different sports from real-world photographs, which can be a foundation for action recognition. Although they only occupy a small portion of the pictures, minor objects like athletes or sporting goods can be challenging to identify because they are essential for understanding the situations. Athletes and sporting items frequently have various distances from the camera and adopt different postures than what object detectors for everyday activities are used to learn. Convolutional Neural Network (CNN) is the most frequently used sports detection technique. Due to the varying angles of photos, such as when an image is taken against a different background, at a different angle, or with a variable amount of light, detecting images in CNN becomes extremely difficult. Detecting sports photos is a difficult task due to the poor image accuracy. To help with better decisions, new varieties of seeing instruments are critically needed. We therefore suggest a model called Reconstruction of Sports Images with the Aid of Darknet-53. To see sports Utilizing YOLO and evaluating it with data from real-world sports and a portion of data from virtual world sports games. We trained a computer to recognize eight different kinds of sports classes using a dataset of 2400 pictures. The model achieved a mAP@0.5 of 97.25% on train data and a mAP@0.5 of 95.74% on test data. This study could be used to identify and categorize different sports and the analytical data associated with them.
Enhancing Person Re-identification Using Polynomial Expansion of Cross Entropy Loss

In the advance of machine learning, person re-identification (re-ID) algorithms have gained a dramatic improvement to identify a person without a clear face or frontal image in the real world. Since recent studies have found that the polynomial expansion of cross entropy loss function can learn more effectively than the original version on training neural networks for object detection tasks, we are motivated to utilize this finding to make an improvement on the deep metric learning for person re-ID. In this work, we utilize a linear combination of a polynomial cross entropy and a triplet loss function to train the well-known AGW baseline. Experimental results have shown that the proposed method outperforms the original AGW, reaching rank-1 accuracy of 96.4% (with mAP: 94.6) and rank-1 accuracy of 93.6% (with mAP: 91.4) on Market1501 and DukeMTMC datasets, respectively.
Beam Tracking: A Channel Charting and Neighborhood Search Based Method

Beam tracking is an essential procedure in millimeter Wave (mmWave) communication systems for providing reliable and robust service. However, beam tracking is a challenging task due to dynamically unpredictable channels. In this paper, we propose a Channel Charting (CC) based beam tracking algorithm to provide robust communication service with low beam search complexity. By projecting the beam direction information to the beam feature domains, the beam tracking problem is transformed into the search of the beam cluster in the beam feature domains. Since the channel chart enables clustering of high-dimension Angles of Arrival (AoA) and Angles of Departure (AoD) to low-dimension beam domains regardless of geographic location, the search complexity is significantly reduced. Furthermore, the neighborhood search algorithm can be applied to obtain the optimal features. The proposed method reduces the search times about 50% in simulation work, with the average tracking accuracy of 98.27%. We also perform the field tests, the results show a high similarity to the simulation. The proposed method exhibits low search complexity with high tracking accuracy in the real scenario.
Beam Depth Analysis for Large Rectangular Arrays

Most wireless communication systems operate in the far-field region of antennas and antenna arrays, where waves are planar and beams have infinite depth. When antenna arrays become electrically large, it is possible that the receiver is in the radiative near-field of the transmitter, and vice versa. Recent works have shown that near-field beamforming exhibits a finite depth, which enables a new depth-based spatial multiplexing paradigm. In this paper, we explore how the shape and size of a rectangular array determine the near-field beam behaviors. In particular, we investigate the 3 dB beam depth (BD), defined as the range of distances where the gain is greater than half of the peak gain. We derive analytical gain and BD expressions and prove how they depend on the aperture area and length. The largest BD is obtained for a square array while the smallest BD is obtained by a uniform linear array.