Miracle-dz/UniFAR-FTUs huggingface dataset:
Review of Stress Detection Methods Using Wearable Sensors

Stress is a significant factor that affects well-being and health. Factors that trigger stress include work, social interactions, and economic and environmental factors. Stress may cause lower labor productivity, physical and mental health problems, and malfunctions in all social aspects of life. Psychosomatic health can be improved if proper stress detection mechanisms are present in daily life and stress reduction methods can occur. Wearable sensors are currently used in many commercial and scientific applications in a non-invasive or unobtrusive manner. These devices are used in daily routines. In this paper, a comprehensive review of the latest literature and developments in stress detection methods is presented through extensive and holistic research on stress response, both at the level of the autonomic nervous system (ANS) and hypothalamic-pituitary-adrenal axis (HPA). This review focuses on the exploitation of various methods, technologies, and data analysis systems to understand stress in a multifaceted and comprehensive manner. Various stress-related factors are presented along with biological signal measurements, and physical secretions or biomarkers are primarily used for stress detection. Furthermore, the manner in which body movement and posture measurements may be related to stress was investigated, together with speech and hand tremors. Various stress-detection technologies have been analyzed, and existing data analysis methods that can be applied to stress-detection systems have been highlighted. This review serves as a reference and guideline for exploring this area of interest, identifying research opportunities, and offering ideas, options, and suggestions for optimized solutions regarding future applications and research.
Signal Processing Techniques from Accelerometers on Railway Transport Based on the Wavelet Transform

Railway transport safety is directly connected with the condition of railway tracks and also the condition of railway wheels’ surface. One of the main reasons for abnormal situations on railways are various defects such as irregularities of a railway track. Thus, we need to estimate the length of short and impulse irregularities. It is also important to carry out joint analysis of vibration acceleration signals recorded by accelerometers in order to study types and sizes of railway track irregularities.
Federated Learning for Network Anomaly Detection in a Distributed Industrial Environment

Industrial control systems have been targeted by numerous cyber attacks over the past few decades which causes different problems related to data privacy, financial losses and operational failures. One potential approach to detect these attacks is by analyzing network data using machine learning and employing network anomaly detection techniques. However, the nature of these systems often involves their geographical dispersion across multiple zones, which poses a challenge in applying local machine learning methods for detecting anomalies. Additionally, there are instances where sharing complete operational data between different zones is restricted due to security concerns. As a result, a promising solution emerges by implementing a federated model for anomaly detection in these systems. In this study, we investigate the application of machine learning techniques for anomaly detection in network data, considering centralized, local, and federated approaches. We implemented the local and centralized methods using several simple machine-learning techniques and observed that Random Forest and Artificial Neural Networks exhibited superior performance compared to other methods. As a result, we extended our analysis to develop a federated version of Random Forest and Artificial Neural Network. Our findings reveal that the federated model surpasses the performance of the local models, and achieves comparable or even superior results compared to the centralized model, while it ensures data privacy and maintains the confidentiality of sensitive information.
Emotion-Aware Fake News Detection on Social Media with BERT Embeddings

Fake news dissemination on social media poses a significant threat to the integrity of information and public discourse. This research proposes an emotion-aware fake news detection model using BERT embeddings. Leveraging the power of BERT, our model captures contextual relations in text, enabling accurate classification of fake news. Through experimentation with different BERT models, “bert-large-cased” emerges as the top-performing variant, achieving a remarkable training accuracy of 98% and an F1 score of 0.77. Integrating emotion-aware features enhances the model's efficacy in identifying fake news while minimizing false positives and negatives. Our study contributes to the field of fake news detection, offering a potent tool for safeguarding social media from disinformation.
A dual-band notched UWB antenna with a slot and a parasitic resonator

An ultra-wideband monopole antenna with dual-band notches is presented in this paper. A square slot is introduced on the upper edge of ground plane to improve the impedance bandwidth performance. The -10 dB impedance bandwidth is realized for UWB antenna ranges from 2.2 GHz to 15 GHz. The dual-band notches are realized with a U-shaped slot etched in monopole antenna for Wi-MAX band and a U-shaped parasitic resonator integrated on back side of the substrate for WLAN band. The notch bands achieved in two frequency bands from 3.1 GHz to 3.9 GHz and 4.6 GHz to 6.2 GHz. The proposed antenna is successfully simulated and provides good impedance bandwidth and stable gain over the UWB band.
Classification of Short-Interfering RNA Through Transformer Encoder Model

This paper presents the use of natural language processing (NLP) to classify short-interfering RNA (siRNA) sequences based on their knockdown efficiency. The proposed method utilizes a transformer encoder model. The siRNA sequences are treated as text input, tokenized, and embedded through positional encoding to allow the transformer encoder model to extract significant features of the siRNA nucleotides and classify them based on their silencing efficiency. The transformer encoder model achieved a high level of accuracy and performance on the test sequences with an overall accuracy of 98%. Despite the imbalanced number of samples of each class, the proposed method for classifying the knockdown efficiency is proven effective. This claim is supported by examining the results of precision, recall, and f1-score of each class with class 1 (knockdown efficiency from 50-100% having scores of precision equals 0.98, recall is 0.99, and f1-score of 0.99. Class 0 with precision, recall, and f1-score of 0.90, 0.76, and 0.82. The ability of the model to process large datasets and capture dependencies, as well as parallel data processing and positional embedding contributed to its high accuracy in predicting complex siRNA sequences.
Vector Databases and Vector Embeddings-Review

This research paper aims to present a comprehensive survey of vector databases and vector embedding techniques. A concise overview of the evolution, architecture, advantages and challenges of vector databases are presented in this paper. To convert unstructured data into vectors, various embedding techniques with their in-depth technical description are also surveyed in this research paper. The existing vector databases’ characteristics and features are described to select an appropriate vector database. The embedding and indexing techniques are thoroughly discussed to help the research community experiment with combining them to develop their application. The challenges of vector databases, like selecting distance metrics, dimensionality, data integrity, cost, etc. are presented. This information on vector database systems will help researchers to exhibit further advancements in their data science applications.
Mining for geographically disperse communities in social networks by leveraging distance modularity

Social networks where the actors occupy geospatial locations are prevalent in military, intelligence, and policing operations such as counter-terrorism, counter-insurgency, and combating organized crime. These networks are often derived from a variety of intelligence sources. The discovery of communities that are geographically disperse stems from the requirement to identify higher-level organizational structures, such as a logistics group that provides support to various geographically disperse terrorist cells. We apply a variant of Newman-Girvan modularity to this problem known as distance modularity. To address the problem of finding geographically disperse communities, we modify the well-known Louvain algorithm to find partitions of networks that provide near-optimal solutions to this quantity. We apply this algorithm to numerous samples from two real-world social networks and a terrorism network data set whose nodes have associated geospatial locations. Our experiments show this to be an effective approach and highlight various practical considerations when applying the algorithm to distance modularity maximization. Several military, intelligence, and law-enforcement organizations are working with us to further test and field software for this emerging application.
Design of Non-Quadrature Branch Line Coupler with Unequal Power Division using Asymmetric T-Structure

In this work, it is shown that non-quadrature branch-line couplers can be designed using asymmetric two-port networks of T-Structure instead of transmission lines as in the conventional branch-line coupler. The design equations of the proposed coupler have been obtained using the Y-matrix method. The primary advantage is that it provides design flexibility for equal and unequal power division with quadrature and non-quadrature phase differences across ports. A practical design of the proposed coupler has been implemented on FR4 Substrate.
Detailed study of electrical interference coupling between AC OHTL and buried metallic pipelines

Electric power lines that share the same track as metallic pipelines can induce electromagnetic field interference and induced voltage. These voltages may affect the metallic pipeline itself, cathodic protection, operational staff, and related equipment. In a system of parallel buried metallic pipelines and transmission lines using a horizontal transmission configuration, problems of interference and stray currents may cause serious damage to neighboring buried metallic pipelines. A method for calculating the longitudinal electric field (LEF), profile of induced voltages and stray currents is presented and verified using the MATLAB software m-file. In addition, LEF curves for the vertical alignment of the transmission line conductors were established. The induced voltages are influenced by a number of variables, including the geometry of the structures, type and arrangement of conductors, amplitude of the current, type of pipeline coating, and electrical resistivity of the soil. The voltage profile in the case of vertical alignment with a metallic pipeline of length L was obtained in this study.
g-Planner: Real-time Motion Planning and Global Navigation using GPUs

We present novel randomized algorithms for solving global motion planning problems that exploit the computational capabilities of many-core GPUs. Our approach uses thread and data parallelism to achieve high performance for all components of sample-based algorithms, including random sampling, nearest neighbor computation, local planning, collision queries and graph search. The approach can efficiently solve both the multi-query and single-query versions of the problem and obtain considerable speedups over prior CPU-based algorithms. We demonstrate the efficiency of our algorithms by applying them to a number of 6DOF planning benchmarks in 3D environments. Overall, this is the first algorithm that can perform real-time motion planning and global navigation using commodity hardware.
Factors Influencing the Adoption and Usage of E-Wallets

In today's digital era, financial transactions have witnessed a significant shift with the advent of digital payment methods. Among these, E-wallets have emerged as a popular choice, offering users a convenient and secure means of managing their finances. As E-wallet adoption continues to rise, it is crucial to explore the factors that drive users to incorporate E-wallets into their daily routines. The key factors identified in this paper can serve as an opportunity for financial service providers to enhance their services, thereby persuading individuals to adopt digital payments in the future. This research uses a qualitative method with a systematic literature review approach, we identified 30 factors from a pool of 52 selected papers. Among these factors, 13 fall under the personal category, while 6 are categorized as technological. Additionally, it's noteworthy that the Technology Acceptance Model (TAM) emerged as the most frequently utilized research model in these 52 papers, featuring in 18 of them. In conclusion, perceived security, take the most influential factor in adopting E-Wallet.
On the Fly Proxy Generation for Home Devices Interoperability

Different plug and play protocols coexist in home networks and each protocol defines its own profiles, data semantics and networking layers, thus interoperability between different devices cannot be applied. We take advantage of the device description annunciation to automatically generate ontologies representing the device, then we apply semi automatic alignment heuristics to find correspondences between equivalent entities. Once the alignment is validated and updated by a human, it is used with predefined code templates to automatically generate software modules "proxies" to hide the device heterogeneity. We validated our approach on a real DPWS printer
Deeper Investigation on Extractive-Abstractive Summarization for Indonesian Text

The demand for text summarization is rising due to the increasing amount of online content, particularly in the creation of brief news from news documents. However, text summarization in Indonesian is still not widely used. Indonesian language processing itself poses several difficulties, particularly when it comes to text summarization. This study uses the pre-trained model to combine extractive and abstractive methodolo-gies to provide a hybrid text summarizing method designed for the Indonesian language. The IndoSUM and Liputan6 Indonesian language corpus datasets are used to train the proposed model. The ROUGE score evaluation metric and human evaluation are used to assess the summary findings from the suggested model. It is evident from the evaluation results that the suggested model is comparable to earlier studies. For further study, a deeper investigation was conducted regarding the impact of extractive summarization on the outcomes of abstractive summarization. The experimental results showed that the length of the selected extractive summaries has a slight effect on the outcomes of abstractive summaries. The greater the percentage of sentences' length selected in each dataset, the higher the ROUGE score.
Matrix Zoom: A Visual Interface to Semi-External Graphs

In Web data, telecommunications traffic and in epidemiological studies, dense subgraphs correspond to subsets of subjects (i.e. users, patients) that share a collection of attributes values (i.e. accessed Web pages, email-calling patterns or disease diagnostic profiles). Visual and computational identification of these "clusters" becomes useful when domain experts desire to determine those factors of major influence in the formation of access and communication clusters or in the detection and contention of disease spread. With the current increases in graphic hardware capabilities and RAM sizes, it is more useful to relate graph sizes to the available screen real estate S and the amount of available RAM M, instead of the number of edges or nodes in the graph. We offer a visual interface that is parameterized by M and S and is particularly suited for navigation tasks that require the identification of subgraphs whose edge density is above certain threshold. This is achieved by providing a zoomable matrix view of the underlying data. This view is strongly coupled to a hierarchical view of the essential information elements present in the data domain. We illustrate the applicability of this work to the visual navigation of cancer incidence data and to an aggregated sample of phone call traffic
Study of 6-phase Pole Change Induction Motor with Double Inverter Drive

Induction motors are attracting renewed attention as motors that do not use permanent magnets. Among them, 6-phase pole change induction motor (hereafter, 6-phase PCIM) can switch the number of poles from 8 to 4 by changing the rotating magnetic field. By switching the number of poles, the constant power range can be expanded in the same volume, and the induction motor can be made smaller than the rated 4-pole induction motor. However, there is a problem that torque shock occurs when switching the number of poles. This torque shock is caused by the first-order delay in the rise of the secondary magnetic flux. If both poles are driven simultaneously to suppress the torque shock, a voltage rise may occur, and the inverter output is limited. In this paper, a prototype 6-phase PCIM was created using the JMAG/(FEA: Finite Element Analysis) model and the control system using MATLAB/Simulink. Direct coupled analysis was performed using these two to generate command values that suppress torque shock and voltage rise. Using these command values, the effect of torque shock suppression during pole number switching was verified.
A Machine Learning Model for Sentiment Classification of Mobile Products Data

Negative or positive opinions regarding a product affect the purchasing decisions of potential customers and hence the producing company. Based on these opinions, there is a need to determine the features affecting the product and its producer. In this paper, we present a machine learning model we have built for sentiment analysis of mobile product sentiment data coming from Twitter. It aims to categorize sentiments for a particular product of a particular mobile phone company. The model applies three classification algorithms; Naive Bayes (NB), Logistic Regression (LR), and Support Vector Machine (SVM) using two types of features, namely discrete features and continues features. We have compared their results using two types of classification models, supervised and semi-supervised with several test cases. With Fl-score of 80.26% for supervised and 87.64% for semi-supervised, the semi-supervised model outperforms the supervised model. Further research on continuous features is needed in the future as it does not require the use of a feature engineering process. This includes the use of word embeddings which perform well in related tasks.
Deep Learning-Based Object Detection System for Crowd Monitoring in High-Security Areas

In order to maintain crowd control and assure safety, contemporary security systems require effective monitoring tools for sensitive and high-traffic locations. In particular, headcount estimate in sensitive areas like high-security buildings, crowded areas is the subject of this research’s novel using deep learning algorithms for real-time object identification in surveillance settings. The main goal is to create a reliable system that can count the number of people in a certain area, recognize their presence, and sound an alarm when the number of people in the crowd rises over pre-established levels. YOLO (You Only Look Once), RetinaNet, SSD (Single Shot MultiBox Detector), and RCNN (Region-based Convolutional Neural Networks) are four of the cutting-edge deep learning models that are applied in this study of object recognition. In order to identify the best method for precise and effective headcount estimation, the study comprises a comparison review of various models. And of all the models examined, YOLO consistently showed the greatest results for precision, recall, and average precision, making it the most efficient model for this specific project.
Stiction-free poly-SiGe resonators for monolithic integration of biosensors with CMOS

This work presents the first fabricated and characterized MEMS in polycrystalline silicon-germanium (poly-SiGe) for biosensing applications. The devices, clamped-clamped microresonators, combine the possibility of above-IC MEMS-CMOS integration with a stiction-free design that enables biomolecule mass sensing in air. By perforating the resonant beam with square or hexagonal holes, a higher surface-to-volume ratio is achieved leading to an increased relative frequency shift for a certain analyte surface density. A minimum detectable mass of ∼2pg in air was calculated. A mass sensing test was performed on these perforated devices by immobilization of biotin and the extraction of the resulting resonance frequency shift.
A Fuzzy Logic-Based Opinion Update Model for Online Social Networks with Influential Social Bots

Social media is a platform where people express their opinions through user-generated text. Investigating opinion changes of people based on the influence of legitimate users and bots over time is crucial. The influence of bots on opinion evolution can be different from the influence of individuals in a network since the opinion of bots does not evolve over time, irrespective of the opinion of the rest of the network. This work aims at investigating the influence of social bots on users' opinions by considering the user's bias and interactions with other users and bots over time. A fuzzy logic-based approach is designed for opinion updates, allowing for uncertainty in the opinion. By considering the tweet's sentiment, we develop a neighbor-biased-fuzzy opinion influence model and analyze the effect of bots in social media. Experiments are conducted on the Social-Honeypot Twitter dataset to evaluate the performance of the proposed approach. The change in user opinion in the presence of different bots is analyzed. Further, the shift in public opinion over time due to the influence of bots is predicted.
Design and Simulation of PMUT for Fingerprint Sensing Applicatons

In this paper, an Aluminium Nitride (AIN)-based circular Piezoelectric Micromachined Ultrasonic Transducer (PMUT) ideal for fingerprint sensing applications is designed and simulated using COMSOL™. The resonant frequencies of the PMUT, along with voltage induced due to pressure and the displacement of the membrane, are studied. Further, studies are performed to determine the relation between the thickness and radius of the piezoelectric layer (AIN) with eigenfrequency. Moreover, resonant frequencies of hexagonal diaphragm PMUTs with similar dimensions were studied. The PMUT is also simulated with Lead Zirconate Titanate (PZT) as the piezoelectric layer to compare the device’s performance. The simulations show a resonant frequency of 5.58 MHz, a $0.52 \mu \mathrm{m}$ displacement, and a pressure-induced voltage of 0.14 V. The resonant frequency increases with an increase in the thickness of the piezoelectric layer and decreases with an increase in the radius of the piezoelectric layer. The device is also modeled analytically using MATLAB™.