Miracle-dz/UniFAR-FTUs huggingface dataset:
Multi-Label Tabular Synthetic Data Generation for Bundle Recommendation Problem

Recommender systems are a great academic and industrial success in e-commerce and other areas. However, access to real-world historical interaction data for evaluating the recommender systems remains a challenge due to privacy, cost, and multiple issues. The state of the art techniques for generating synthetic data that is closest in relation to real world data, needs atleast a sample of real world data, which is also a problem especially when it comes to bundled data. We propose a novel way of generating multi-label synthetic data specifically for Bundle Recommender system problems, that does not need a sample of real world data but still is closest in relation to real world data. The proposed approach is hybrid as it uses a combination of process based and data based synthetic data generation methods. We demonstrate the feasibility of this approach by generating bundled data for Cosmetics product purchases. Also, synthetic data generated by the state-of-the-art method and our hybrid approach were compared, and it was found that the two sets of data are comparable in terms of statistical characteristics. The generated synthetic data can be used for evaluating bundle recommender systems for the industry.
Exploring Quantum Key Distribution for Secure Communication: A Comprehensive Survey

Modern-day secure communication is made possible via cryptography. Modern cryptographic algorithms are based on the process of factoring large integers into their primes, as they are intractable. But the cryptography we use nowadays is vulnerable to technological advances in computing power like quantum computing and evolution in math to quickly reverse one-way functions like factorization of large integers. Incorporating quantum physics concepts into cryptography is the answer, which results in an assessment of quantum cryptography. A unique type of cryptography known as quantum cryptography makes advantage of quantum mechanics to provide complete protection against the transmitted message. Quantum Key Distribution (QKD), a random binary key distribution used in quantum cryptography, enables communication participants to recognize unauthorized listeners. Quantum Key Distribution (QKD) is likely the most advanced quantum technology currently accessible with full-stack systems already in use. This project’s goal is to develop secure and encrypted communication between the parties with the help of a web application using the BB84 protocol.
Spectral Data Migration of Seawater COD Using Generative Adversarial Networks to Address Differences in Spectral Features and Insufficient Samples Across Regions

Chemical oxygen demand (COD) is an indicator of organic matter content in water, and its sensitive spectral regions depend on the species and concentrations of organics present. In recent years, spectroscopic analysis methods using feature variable modeling have become important for seawater COD detection. However, due to differences in COD compositions across regions and insufficient single-region samples, issues like varying spectral features and inadequate samples make data processing with deep learning difficult. To address this, we propose a seawater COD spectral data migration method using generative adversarial networks, aiming to resolve problems caused by varying COD compositions and insufficient samples across regions. Our method employs WGAN-GP for network training to achieve spectral data migration between regions. The improved WGAN-GP generator produces simulated data highly similar to real data, thereby enabling COD spectral data migration. This method can effectively address issues of varying spectral features and inadequate samples across regions, improving the accuracy and reliability of seawater COD spectral data analysis.
A Chaos-Based Non-Linear Analysis Method for Detecting Human Attention Levels in EEG Signals

The paper presents chaos-theory-based human attention level detection from the electroencephalogram (EEG) signals. In the medical field, “human attention level” can be referred to as the “attentional state,”, which helps to understand an individual's attention capacity in various crucial moments. In this study, we have deployed secondary analysis on existing methods by implementing chaos theory on the PhysioNet dataset. We investigate different vital parameters and values to predict human attention level from the EEG dataset. We calculated time delay, embedding dimension, and correlation dimension from the participants' EEG data to determine the parameters' values: specifically, for detecting of human attention level from EEG signals. By calculating the 95% confidence interval (CI), the time delay has an average of 2.50 seconds, and the embedding dimension and correlation dimension have an average value of 4.41 and 2.23, respectively. We also observed a similar embedded signal in the reconstructed phase space (RPS) of participant's EEG signals. The statistical and chaos-based plot can potentially investigate human attention parameters and develop a robust EEG signal prediction system. Overall, this proposed framework serves as a resource on the latest nonlinearity detection techniques to detect human attention levels utilizing EEG signal analysis. Clinical Relevance - The effectiveness of the chaos-based non-linear analysis method for detecting human attention levels in EEG signals depends on its potential impact on diagnosis and treatment, integration into clinical practice, benefits, and risks.
TLBO Based Optimized Planning Framework of Distribution Network considering Electric Vehicle Charging Station and Solar Photovoltaic System

Electric vehicles (EVs) are significantly increasing the burden on the electricity sector, resulting in power loss, a poor voltage profile, and voltage stability difficulties. To address this, a solid planning framework for efficient deployment of EV charging stations and solar energy resources in the distribution network is being established in the present work. A practical probabilistic model of EV is developed using Probability Distribution Function (PDF) considering uncertainty parameters i.e.trip distance, trip end time, types of battery and battery capacity extracted from real National Household Travel Survey (NHTS) datasheet. The Teaching Learning Based Optimization (TLBO) algorithm is used to solve the problem of optimum joint deployment of EV charging station and solar energy, minimizing power loss reduction index (PLRI) and voltage deviation index(VDI) while considering system constraints. Further the voltage stability of the system is also analyzed with the incorporation of devices in the network. In this paper, the optimization problem is solved using MATLAB (version R2018a) software, and the IEEE 69 bus system is employed as a test system. The simulation outcomes show that optimal deployment of EV charging stations with photovoltaic (PV) units improves voltage profile, the penetration level of EV charging station and also voltage stability index (VSI) of the system, but reduces total real loss. The technique is compared with three different algorithms, including Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Differential Evolution (DE), in terms of convergence characteristics.
Exploring ChatGPT for Email Content Compression and Summarization

In our daily lives, email communication is essential, yet monitoring the voluminous amount of emails can be time-consuming. The goal of email summarizing techniques is to quickly and accurately summarize email content in order to increase productivity and efficiency. In this study, we investigate the use of ChatGPT, a cutting-edge language model, for email synthesis. We offer a fresh strategy to take advantage of ChatGPT's conversational features for summarization while optimizing it on a big dataset of email discussions. Our tests show that ChatGPT-based email summarization outperforms other approaches in terms of performance. The generated summaries provide a clear and succinct summary of the email content that successfully captures the important details. To assess the value and user satisfaction of the summaries produced by ChatGPT, we also perform user studies. According to the findings, ChatGPT-based email summarizing has potential as an efficient and convenient method for controlling email overload. This work demonstrates the potential of conversational language models in tackling the difficulties of email management and advances email summarizing approaches.
Software Framework for Physical Layer of Home-Plug GreenPhy

Of the several powerline communication (PLC) specifications available, Home Plug Green PHY (HPGP) specification is aimed at the smart grid, Electric Vehicle (EV) and other automation networks within domestic premises. This work showcases a software implementation of HPGP physical layer specification on MATLAB Simulink. Sub-modules are implemented or developed in Simulink based on the requirements extracted from the specification. Integrating the individual blocks forms the complete Phy layer after ensuring conformance to specifications and the sub-modules’ functionalities. Implementation of electric vehicle-supply equipment and EV communication and a custom home automation system over this model demonstrates the message exchange capability for real-world applications. Future extensions of this work can include channel modeling with analog front end and Medium Access Control (MAC) layer implementation.
GraphEEG: Uniting CNN and GCN for Superior Personal Identification via EEG Analysis

Electroencephalography (EEG), are increasingly utilized for personal identification. This paper introduce a novel solution that fuse convolutional neural networks and Graph Convolutional Network, dubbed GraphEEG. GraphEEG utilize the advantage of CNN in automatic feature extraction and GCN for exploring the potential connectivity among EEG electrodes, achieves excellent performance in personal identification tasks. We assess the performance of the proposed GraphEEG on real-world datasets, including brain-computer interface tasks and compare it to other models. Extensive experiments on real-world datasets demonstrate that the proposed model outperform all baselines, based on popular evaluation metrics, the identification rates is up to ~99% via utilizing intra-session data in the event related potential task.
A Specialized and Fortified Model for Vehicle Brand Detection Based on YOLOv8

Recognizing the brand and model of vehicles is of paramount importance in areas ranging from security to market analysis, but it is also a complex challenge due to the range of visual attributes, scales, and orientations vehicles present. Despite its importance, there’s a noticeable gap in research dedicated to vehicle brand detection. Addressing this, this paper introduce a refined You Only Look Once Version 8 (YOLOv8) frame- work, termed YOLOv8-A-DH. This enhancement integrates an attention module, concentrating on distinctive vehicle features, which aids in sharper brand distinction. Furthermore, this study incorporated a dynamic head to the model, specifically devised to navigate challenges related to differing scales, spatial complexities, and varied tasks. Evaluations using a customized vehicle dataset show the robustness of YOLOv8-A-DH, with a mAP50-95 of 0.995 and a precision rate of 0.967, marking an enhancement over the conventional YOLOv8 model. This underscores the value of attention module and dynamic head in optimizing vehicle brand detection accuracy.
Analyzing Data Access Characteristics of Deep Learning Workloads and Implications

In this paper, we analyze the data access characteristics of deep learning workloads and observe that they differ significantly from traditional workloads. In memory access, access types (instruction vs. data), operations (read vs. write), access bias, and access distributions show unique patterns. In file access, a large volume of data are randomly accessed and their access bias is very weak. For this reason, it is difficult to efficiently manage memory and file data in deep learning workloads. Also, a large volume of data accessed during the training phase of deep learning can lead to severe memory thrashing. To cope with this situation, we present a new memory architecture that makes use of persistent memory to accelerate deep learning memory systems. By considering the access characteristics of deep learning, our experiments show that the proposed architecture with our preliminary management policy improves memory performance by 80% compared to the second-chance algorithm under the same persistent memory based architecture and 98% compared to an original memory architecture without persistent memory.
Walking to Authenticate: Identifying Robust Behavioral Biometrics from Step Count Data

Step count data, readily available from smartphones and wearable devices, offers valuable insights into a user’s physical activities and lifestyle patterns, positioning it as a viable contender for integration into biometric authentication systems. In this study, we introduce Step Count Print (SCP), a novel behavioral biometric derived from coarse-grained minute-level step count data, featuring daily step count distribution to capture an individual’s unique physical activity pattern. We conduct an ablation study with data from 100 users collected over a five-year period, utilizing mainstream machine learning algorithms. Our experimental results demonstrate SCP’s non-redundancy in user authentication scenarios, achieving an impressive average accuracy of $92.3 \%$. We showcase the universality and effectiveness of SCP across various classification algorithms through user accuracy histograms. Our proposed strategy holds promise for user-friendly and reliable biometric authentication, leveraging step count data to enhance security and usability in diverse applications.
A Fast Point-Line Visual-Inertial Odometry with Structural Regularity

In this paper, a fast point-line visual-inertial odometry (VIO) with structural regularity is presented. First, the processing of point and line features is improved. After point feature detection and tracking, we further filter out unstable points to make sure they are more reliable. As for lines, a modified detection and matching method is developed to guarantee the proposed VIO system with good real-time performance. Second, the detected line features are used to calculate three orthogonal vanishing points. With the help of vanishing points, some line features can be recognized as structural lines. Further, a structural constraint is created using vanishing points and structural lines, which is added to the sliding window optimization framework to estimate camera poses and landmark positions. Finally, experiments conducted on the public dataset indicate that our system has superior accuracy, efficiency, and mapping quality.
A Methodological Approach of LCL filter design for Industrial Application using MATLAB script

Three-phase power factor correction (PFC) converters are the core power conversion stage for grid-tied inverter systems to meet international standards for power factor and THD. A high order LCL filter is a widely used solution to accomplish this task. This paper presents a simple yet an effective way to design an LCL filter. The proposed method provides a step-by-step systematic approach to design the LCL filter. The methodological design procedure can aid and streamline the efforts of researchers and design engineers. Additionally, the proposed method is simpler then its analogous procedures. The design is validated using MATLAB for a two-level and three-level grid connected inverter.
Improving Recognition for Disordered Speech in Indonesian Language: a Data Augmentation approach

For individuals with speech disorders, speech recognition serves as a crucial communication tool. But most speech recognition systems are trained on normal speech data, worsen by limited disordered speech data which mostly available in English language. Data augmentation is one possible approach, where we can apply signal processing such as speed perturbation to transform normal speech into disordered speech. In this work, we studied how augmented data composition in a dataset affects the system performance on recognizing disordered speech. Using QuartzNet CNN as the acoustic model, we evaluated the speech recognition system on an augmented dataset built based on an Indonesian language speech database from Mozilla Corpus. Using speed perturbation to generate disordered speech, the initial results show that augmenting normal speech dataset with 25-50% more disordered speech data could help improve the Word Error Rate (WER) of the model in recognizing disordered speech. This result, however, is limited as we use the same speed perturbation method for training and testing datasets.
Implementation of smart sensors with Swarm Intelligence for Disease Diagnosis in Smart Environments

Disease diagnosis in smart settings has been transformed by the fast development of technology, especially the Internet of Things (IoT) and Swarm Intelligence. Various health-related data may be continually monitored and collected in smart environments due to the abundance of sensors. However, this data's vast quantity and complexity complicates accurate illness diagnosis and early management. Inspired by the coordinated actions of flocks of birds and swarms of insects, Swarm Intelligence provides a novel paradigm for analyzing and understanding this mountain of data. Hence, this paper proposes a Swarm Intelligence-enabled Disease Diagnosis (SI-DD) in smart environments using IoT-based sensors. Smart systems use algorithms motivated by swarm behaviour to assess data from several sensors in concert, allowing for faster, more accurate illness diagnosis. As a shape of collective swarm intelligence, Ant Colony Optimization (ACO) enables make healthcare treatments more effective. As a result, sufferers get care tailor-made to their unique desires, thinking about their scientific history and the kingdom of the healthcare machine. This article examines the most distinguished makes use of the Internet of Things and Swarm Intelligence in healthcare. It highlights how those techniques have already shown their promise for higher contamination detection. With this in thoughts, it's clear that the usage of clever sensors prepared with Swarm Intelligence for infection detection in clever settings is a viable path toward enhancing healthcare results and the first-rate of existence for sufferers. This novel approach has the capability to revolutionize contamination surveillance, prognosis, and control, leading to greater cost-effective scientific services and higher results for sufferers.
Comparative Study of Collaborative Filtering Models - Matrix Factorization and Factorization Machine

Recommendation systems help business organizations in suggesting to their customers the most relevant and preferred items to purchase. For designing these recommendation systems industry analysts have to choose among various Collaborative Filtering (CF) models. Choosing the right CF model for a specific recommender system design based on historical data remains a challenge. In this study among the different models available, we have compared Matrix Factorization (MF) and Factorization Machine (FM) models of CF using two real-world datasets and experimentally evaluated based on optimized model parameters with various metrics. When using MF we also studied the effect of mean, item bias, and user bias terms. The quality of recommendation is measured using RMSE, Precision, recall, and F1 score as evaluation metrics. The results are plotted graphically providing insight into the quality of the two models. The comparative study will offer researchers and industry analysts an in-depth understanding of the two models, allowing them to make an informed decision for their specific application of their recommender system.
Drone-Based Corrosion Detection on High-Voltage Transmission Towers Using Hyperspectral Imaging

High-voltage transmission towers require regular inspections to identify corrosion. Traditionally, these inspections are performed through climbing, involving skilled technicians. This method is both tedious and hazardous, often necessitating the shut-down of sections of the high-voltage grid. In this paper we propose a workflow that relies on drone-based hyperspectral imaging, which enables remote assessment without endangering the technician. Currently, drones are equipped with conventional RGB cameras. However, these cameras have limited spectral resolution and range, which compromises their ability to reliably detect corrosion and often leads to false alarms. Moreover, conventional RGB cameras are unsuitable for accurately assessing the severity of corroded areas. To address these challenges, this study proposes a solution that leverages hyperspectral imaging and a dedicated processing pipeline to robustly detect corrosion and classify it based on severity level. Experiments using drones equipped with imec’s VIS-NIR hyperspectral payload demonstrated the effectiveness of our developed solution.
Blockchain technology in the Greek Supply Chain sector: An empirical examination of adoption patterns

This paper explores the implementation of blockchain technology within the Greek supply chain sector, delineating both its potential advantages and accompanying challenges. A quantitative survey was executed, collecting data from participants engaged in the Greek supply chain sector via structured questionnaires. Through non-parametric tests, it was revealed that certain "independent" variables exert a significant influence on the perceived benefits and challenges associated with the adoption of blockchain technology in the Greek logistics sector. For example, long-standing companies in the sector experience technological, organizational, and financial challenges, often stemming from adapting to new technologies or deep-rooted processes. However, these businesses, especially those larger in scale or advanced in blockchain integration, also see significant benefits from the technology. The findings lay the groundwork for future research, which may include comparative studies examining the impact of blockchain technology across distinct sectors such as supply chain and retail.
Optimization of Bioanalytical Liquid Chromatography - Tandem Mass Spectrometric Method for Quantification of Tazemetostat: An Epithelioid Sarcoma Treatment Drug in Human Plasma

The pharmaceutical drug tazemetostat prevent spreading of cancer cells in adults and was available in the form of tablets in market. The literature reveals that, no analytical method reported for evaluation of tazemetostat in biological samples and hence this study planned to propose a simple and sensitive LCMS/MS method for quantification of tazemetostat in spiked plasma using vorinostat as internal standard. The extraction of tazemetostat along with vorinostat was performed using a simple protein precipitation followed by liquid-liquid extraction with dichloromethane. The Agilent eclipse plus C18 (100x4.6 mm, 3.5 μm) column with 0.1 % formic acid in water, 0.1 % formic acid in acetonitrile at 65:35 (v/v) pH 3.9 at 0.3 mL/min isocratic flow was optimized and finalized for resolution of tazemetostat and internal standard. The resolved analytes were monitored with mass analyser operated in multiple reaction monitoring positive ion mode. The characteristic mass transition at m/z 571→136 and m/z 265→94 was noticed for tazemetostat and vorinostat respectively. The method produces linear calibration curve in the concentration level of 0.1 - 400 ng/mL that suggest method sensitivity. The stability during handling, extraction and well as analysing the tazemetostat solution was evaluated by performing various stability studies. The analytes were confirmed to be acceptable in every studied stability studies. The validation study produces acceptable results in all parameters and can successfully utilized for evaluation of tazemetostat in biological samples.