Miracle-dz/UniFAR-FTUs huggingface dataset:
Bike-sharing Demand Prediction based on Artificial Intelligence Algorithm Using Weather Data

To alleviate traffic congestion and environmental pollution in heavily populated Seoul, South Korea, the city has actively introduced a bike-sharing service. As the demand for this service increases, efficient supply and management of shared bikes are becoming critically important. Because bike-sharing demand correlates with the weather, we performed a prediction for bike demand based on artificial intelligence algorithms utilizing weather data. The Multiple Linear Regression, Random Forest Regression, and Multi-Layer Perceptron artificial intelligence algorithms were considered and applied to processed weather data collected in Seoul using various methods. The results revealed that the best performance in predicting bike-sharing demand was achieved by a model trained by the Random Forest Regression algorithm, which considers historical weather and demand data and separates itself into two sub-datasets by weekdays and weekends, but without distinguishing itself with the information of the four seasons.
WebGuardML: Safeguarding Users with Malicious URL Detection Using Machine Learning

The prevalence of cyber security threats stemming from rogue URLs is widespread. A malicious URL contains phishing and spam to initiate assaults. Such websites attract innocent people who fall for frauds like identity theft and financial loss. Such threats must be identified and addressed promptly. The main goal of this research is to create a machine-learning-based malicious URL detection system. The method uses Extreme Gradient Boosting, Categorical Boosting, Decision Trees, Random Forest, Extra Trees, Gradient Boosting and others to detect hazardous URLs. The process begins with dataset collection, extracts domain attributes, path components, length features and addresses class imbalance. The best model is chosen after lengthy training and testing, considering accuracy, precision, recall and F1-score. Hyperparameter tuning and ensemble techniques are optional optimization procedures. The process of testing serves to assess the performance of a system in real-world scenarios. After deployment, the system is monitored and maintained to keep up with emerging dangerous URL trends. Overall, this research improves cyber security by automating URL protection.
A Comparative Analysis of Deep Learning Models Towards Precise Recognition of Intracranial Hemorrhage Utilizing CT Images

Intracranial hemorrhage (ICH) is a critical medical condition that requires swift and accurate diagnosis to ensure timely intervention and improved patient outcomes. Computed Tomography (CT) imaging is widely utilized for the diagnosis of ICH due to its speed and accuracy. With the advent of deep learning techniques, there is a growing interest in leveraging artificial intelligence to aid radiologists in the rapid and precise detection of ICH. This study presents a comprehensive comparative analysis of various deep learning models employed for the recognition of ICH in CT images, with a focus on achieving enhanced accuracy and efficiency. Our research assesses and compares the performance of multiple deep learning architectures, including VGG-16, GoogleNet, ResNet-50, Inception-v3, Inception-v4, ResNet-101, and DenseNet-169. We employ the Radiological Society of North America (RSNA) stage-2 dataset, which includes 752,803 training images and 121,232 test images to evaluate the models. We explore image type conversion, resizing, and normalization as data preprocessing techniques and investigate the impact of hyperparameter tuning to optimize model performance. Our findings reveal the strengths and weaknesses of each deep learning model in terms of accuracy, precision, recall, f1-score, and ROC-AUC value. Our study makes recommendations for the best model to use depending on individual clinical needs and computing resources.
Exploring self-supervised learning in Multiview captcha recognition

Text-based CAPTCHA (Completely Automated Public Turing test to tell Computers and Humans Apart) systems play a crucial role in safeguarding online platforms against automated bot attacks. However, with the increasing complexity of text-CAPTCHA designs, traditional supervised learning approaches face challenges in accurately recognizing the underlying text in text-CAPTCHA images. One of the main challenges is the limited availability of labeled data, as manually annotating large amounts of text-CAPTCHAs is a time-consuming and expensive process. To address these challenges, we propose a novel Self-Supervised Multiview Captcha Recognition Model (SS-MCR) that leverages self-supervised learning with contrastive loss to learn representations from limited labeled data. Our model redefines the text-CAPTCHA solution as a multiview problem, using image manipulations to create a pseudo-view of the original single-view image. The SS-MCR employs a siamese architecture with 2D convolutional neural networks and recurrent networks to learn robust features from both the pseudo-view and the original view. The contrastive loss encourages the model to distinguish between corresponding pairs and non-corresponding pairs, facilitating the extraction of informative and discriminative features. Through extensive experiments on synthesized datasets and publicly available datasets, we demonstrate that the SS-MCR outperforms traditional supervised learning models and achieves state-of-the-art performance in text-CAPTCHA recognition. Our proposed approach offers a promising direction for improving text-CAPTCHA security while reducing the dependence on large labeled datasets, making it a valuable contribution to the field of computer vision and captcha recognition.
The production of space and construction of frontier: Contesting a Cambodian resource landscape

i Acknowledgements . iii List of figures: . x List of tables: . xiii Acronyms . xiv Chapter 1: Introduction . 1 1.1. Resource landscape, frontier and space . 1 1.2. Research problems . 3 1.3. Rationale and research questions . 7 1.4. Key arguments . 9 1.5. Thesis structure . 11 Chapter 2: Production of Space and Construction of Frontier . 14 2.
A DRL Empowered Multipath Cooperative Routing for Ultra-Dense LEO Satellite Networks

Nowadays, the ultra-dense low earth orbit (LEO) satellite network has become an attractive solution for providing global Internet coverage and services. With the ever-increasing demand for higher transmission performance, multipath also attracts much attention due to its great potential. In this paper, we consider the multipath cooperative routing in the ultra-dense LEO satellite network. To cope with the high dynamics of the network environment, a deep reinforcement learning (DRL) empowered intelligent routing algorithm is proposed, where each satellite only observes the local network state and independently makes the next-hop forwarding decision. Meanwhile, the perceived conditions of each path are recorded hop by hop in a format-specific packet. In this way, multiple available paths can be found for cooperative transmission. To balance multipath load, an adaptive traffic scheduling scheme is further developed on the sender to adjust traffic distribution according to the varying path conditions, so that multiple sub-flows can be efficiently maintained. Simulation results show the superiority and the effectiveness of the proposed multipath cooperative routing scheme compared with other baseline schemes.
Association of CYP1A1*2A polymorphism and Breast Cancer risk: a meta analysis

Cytochrome P450 1A1 (CYP1A1) is an enzyme well recognized for its role in the metabolism of various substances, particularly in the detoxification of xenobiotics (foreign compounds) and the biochemical conversion of endogenic compounds. A person’s vulnerability to certain diseases, including breast cancer, may be impacted by genetic changes in the CYP1A1 gene, such as the CYP1A1 m1 polymorphism (rs4646903). Our article conducts an extensive statistical analysis, integrating a significant number of recent studies, and delivers updated insights derived from case-control research. 37 Studies (13,160 cases and 16,155 controls) were eligible from PRISMA steps, and 28 studies (10,994 cases and 14,516 controls) from these were used to get the overall pooled OR and other statistical values as they followed the Hardy-Weinberg equilibrium (HWE). Other subgroup analyses were performed on these, and breaking down the data based on ethnicity, premenopausal and postmenopausal conditions, and smoking habits provided valuable perceptions. Combined odds ratios (OR) and their respective 95% confidence intervals (CI) were calculated using an appropriate model (fixed-effect or random-effect). We found that the population in India had a higher risk of breast cancer: OR (2.05, 95% CI: 1.04-4.05) for the homozygous versus wild type and OR (1.88, 95% CI: 1.05-3.36) for the recessive model. For overall association, the homozygous carrier’s OR is notable: (1.34, 95% CI: 1.05-1.71). No significant change was seen after adding the studies deviating from HWE to the analysis.
Variational Autoencoder-Based Parameter Estimation in Beam-Space OFDM Integrated Sensing and Communication

In this work, we propose a framework based on Deep Neural Networks (DNNs) for radar parameter estimation in an Integrated Sensing and Communication (ISAC) system em-ploying a realistic and hardware-efficient Hybrid Digital-Analog (HDA) architecture that uses Orthogonal Frequency Division Multiplexing (OFDM) digital modulation. This framework takes raw signals as input and utilizes a Variational Autoencoder (VAE) followed by a regression network to output the spatial extent and location of extended targets. Owing to the HDA setup, the co-located radar receiver uses multi-block measurements to perform parameter estimation. The proposed solution is motivated as a remedy for the increasing computational complexity associated with high-resolution extended target estimation in multi-carrier digital modulations such as OFDM. In addition, it is well known that off-grid delay-Doppler shifts which are present in the doubly-dispersive channels in the high mobility scenarios expected in ISAC applications, exhibit leakage effects that adversely affect the parameter estimation performance. Due to the data-centric nature of the proposed method, these effects can be learned by the network. We provide numerical results to showcase the effectiveness of the proposed framework for parameter estimation. 1
Utilizing Autoencoder to Improve the Robustness of Intrusion Detection Systems Against Adversarial Attacks

Due to the escalating utilization of communication networks and the prevailing occurrence of cyber attacks, intrusion detection systems (IDSs) have emerged as imperative components in network security. Machine learning (ML) and deep learning (DL) based IDSs have gained popularity due to their detection capability and adaptability. However, this type of schemes are susceptible to adversarial attacks, which involve minor perturbations to attack features causing misclassification. Autoencoders (AEs) have proven effective in mitigating adversarial attacks in computer vision, but their capacity for enhancing IDSs remains relatively unexplored. In this paper, we focus on the use of AEs to detect adversarial network flows. Specifically, we propose an AE-enhanced IDS (AE-IDS) that leverages the power of AEs to improve the robustness of IDSs against adversarial attacks. Our experimental results indicate that AE-IDS outperforms the baseline schemes under investigation in terms of accuracy and detection rate. We believe that AE-IDS showcases the potential of using AEs to enhance the robustness of IDSs, providing improved security against sophisticated and evolving cyber threats.
Federated Optimal Framework with Low-bitwidth Quantization for Distribution System

Federated learning is an attractive solution for efficient data processing and optimization in distributed networks. However, communication bottlenecks often result in sluggish optimization and increased resource consumption, leading to reduced system efficiency. To address these challenges, this paper proposes a novel approach based on Federated Low-bitwidth Quantization (FLQ) framework, which optimizes resource utilization in distributed communications. FLQ quantizes network parameters and gradients, significantly reducing computational and communication costs related to parameter broadcasting and gradient uploading. By utilizing an 8-bit low-bitwidth training and binary vector compression technique, our algorithm greatly enhances network convergence and is well-suited to the energy consumption characteristics of servers and end devices. Moreover, to enhance overall device coordination, we develop a dynamic resource allocation scheme that adapts to changing requirements of individual nodes. Results from extensive experiments demonstrate that our approach leads to faster convergence, lower computational and communication costs and optimized joint network deployment in Internet of Things scenarios.
Joint Partner Pairing and Resource Scheduling for D2D-U-Based Decentralized Mutual Learning

In this paper, we design a novel system named as decentralized mutual learning over unlicensed spectrum (DML-U), where edge devices are allowed to learn from each other via bidirectional device-to-device communications over unlicensed spectrum. We further formulate a non-convex optimization problem to minimize energy consumption and accelerate knowledge sharing with constrained power, bandwidth and transmission latency. Under this context, we propose a two-layer iterative algorithm, which contains an enumeration-based outer layer for the pairing scheme and a sum-of-ratios-based inner layer for obtaining a globally optimal allocation of communication resources. Simulation results verify that our obtained algorithm converges fast and finds efficiently the balance between knowledge sharing and communication energy consumption.
Development of Parameter Bit Operation Framework for Deep Neural Networks

Recently, research on lightweighting of Deep Neural Network(DNN) models suitable for embedded IoT devices and edge computing is rapidly increasing. One of the important points of lightweighting DNN is model parameter optimization. Parameter precision increases model accuracy, but also increases memory and processing overhead. In this paper, we developed a parameter bitwise adjustment framework of DNN models to analyze the correlation between model’s parameter precision and performance. The framework can set each bit of parameter value to 0, 1, or random in a specific range for DNN models. With the framework we measured the accuracy of the model while modifying the parameter values to 0 by step by step from LSB, for three representative CNN models. As a result, it was identified that the accuracy robustness for the range of valid parameters is different each other depending on the model.
FeatureMix: A General Adversarial Defense Method for Pretrained Language Models

Pretrained language models (PLMs) that are trained over large-scale data and then finetuned on downstream tasks have achieved great success. However, they are vulnerable to adversarial attacks. Adversarial training with both clean and adversarial data is a widely-used technique to improve model robustness. In this paper, we propose FeatureMix, a straightforward yet effective adversarial defense strategy for PLMs by finetuning on both discrete adversarial examples and online virtual examples. During finetuning, we augment clean data with discrete attacks first and generate virtual examples in each finetuning epoch by randomly mixing local latent features in the hidden layers of augmented data pairs. The virtual examples serve as additional training signals, regularizing the PLMs to favor mixing of latent features between discrete augmented examples and thus enhance adversarial robustness. The experimental evaluation results show that FeatureMix outperforms prevailing baseline methods in terms of robustness against adversarial attacks, without significantly reducing generalization performance.
Martian Terrain Classification through Federated Learning: A Decentralized Approach for Understanding the Mars

The exploration of the Martian landmass holds paramount importance in advancing spatial research as it offers valuable insights into planetary evolution and the potential for habitability beyond Earth. In this paper, a novel approach is presented to perform multi-class classification of Martian terrain into seven distinct classes, namely crater, dark dune, slope streak, bright dune, impact ejecta, swiss cheese and spider. Accurate terrain classification provides crucial insights into Martian ge-ological processes and enables informed decisions for selecting optimal landing sites for future missions. Additionally, by facili-tating the planning of safe and efficient traverses during explo-ration missions, precise terrain classification enhances the overall success and safety of these ambitious endeavors, ultimately advancing our knowledge of Mars and supporting humanity's quest for space exploration. This research harnesses the power of federated learning, an emerging decentralized machine learning paradigm, in conjunction with the DenseNet-121 architecture. This collaborative learning technique enables the model training across distributed data sources while preserving data privacy and security. Through extensive experimentation using the HiRISE dataset, obtained from the Mars Reconnaissance Orbiter, this research demonstrates the efficacy of the presented approach in achieving better performance and robustness. The federated DenseNet-121 model showcases promising results, laying the foundation for efficient multi-class Martian terrain classification in future spatial exploration missions and contributing to a deeper understanding of the Martian landscape.
Task Provisioning in Unreliable Edge Networks: Inferring Utility

Edge computing can satisfy the requirements of latency-critical and data-intensive applications by exploiting com-putational resources of end devices. However, such devices inherently suffer from dynamic user behavior, cyclic task-switching, varying link qualities which often impact their reliability. In addition, in incentivized systems, they may often over-estimate their advertised capabilities and consequently fail on delivering. In this paper, we propose the Reputation-based Task Assignment and Replication (RTAR) scheme. RTAR is the first scheme that uses a black box approach to perform cost-efficient task replication that accounts for workers' reliability and preserves workers' privacy by not requiring or soliciting any information about their devices. RTAR incorporates a reputation model using beta distribution to estimate the worker's reputation based on past performance. We formulate the problem as an Integer Linear Program (ILP) that strives to maximize the overall reputation of recruited workers, while abiding by a certain budget limit for each task. We also propose the RTAR-Heuristic (RTAR-H) scheme. RTAR-H uses matching theory to solve the optimization problem in a time-efficient manner. Extensive evaluations show that RTAR yields 63% and 68% reduction in recruitment cost and number of replicas, respectively, compared to a baseline scheme that blindly maximizes the number of replicas. Moreover, RTAR-H closely approaches the optimal solution, rendering a small gap of up to 1% and 1.2% in terms of task drop rate and recruitment cost, respectively.
The Analysis of the Expected Change in the Classification Probability of the Predicted Label

We present a formalism for estimating the expected change in the probability distribution of the predicted label of an object, with respect to all small perturbations to the object. We first derive analytically an estimate of the expected probability change as a function of the input noise. We then conduct three empirical studies: in the first study, experimental results on image classification show that the proposed measure can be used to distinguish the not-robust label predictions from those that are robust, even when they are all predicted with high confidence. The second study shows that the proposed robustness measure is almost always higher for the predictions on the corrupted images, compared to the predictions on the original versions of them. The final study shows that the proposed measure is lower for models when they are trained using adversarial training approaches.
Review of Dual-Edge Triggered Low-Power D Flip-Flops

The most recent dual-edge triggered flip-flops forlow-power applications are reviewed in this paper. The Dual Edge triggered flip-flops(DETFFs) can sample the data at twice the rate compared to Single-Edge triggered flip-flops, which can sample at both positive and negative edges of the clock. The parameters such as power, power delay product, Clock to Q delay, and minimum voltage of operation are calculated in this work for various DETFFs at standard conditions. This will help the reader to understand different types of DETFFs and what to use depending upon the application of interest. This work also focuses on calculating power by adding an extra test driver circuit to the main design for accurate values.
Navigating Bengali Linguistics: Insights from Machine and Deep Learning Perspectives for Categorization of Sentences

The paper presents an innovative exploration into the classification of Bengali sentences, an essential aspect of advancing natural language processing (NLP) for languages characterized by rich linguistic diversity. In this comprehensive study, various machine learning and deep learning methodologies were meticulously applied to navigate the intricate linguistic landscape of the Bengali language. The research draws upon and synthesizes insights from previous studies that have focused on aspects such as sentence attributes, simplification algorithms, context-free grammar applications, and grammar-rule based transformations. The core of this study is anchored in the deployment of three distinct vectorization approaches - BERT-based, Glove-based, and TF-IDF-based - for the nuanced classification of Bengali sentences into simple, complex, and compound categories. A series of classifiers were applied, with the BERT-based vectorization demonstrating exemplary proficiency, registering an impressive 98.07% overall test accuracy. This finding underscores the model’s robustness and versatility, illuminating its potential applicability beyond the designated dataset to encompass bespoke, real-life datasets. The research is set against the backdrop of a recently published large-scale Bengali sentence dataset titled Bangla Transformation of Sentence Dataset (BTSD) consisting of 3793 samples, which has filled a significant void in the existing literature, enabling a more intricate and detailed analysis. The findings of this study not only contribute to the enriched understanding of Bengali sentence structures but also hold implications for the broader field of computational linguistics and NLP.