Miracle-dz/UniFAR-FTUs huggingface dataset:
Predicting Pedestrian Movement in Unsignalized Crossings: A Contextual Cue-Based Approach

To ensure safe and secure coexistence between pedestrians and autonomous vehicles (AVs), AVs must be able to anticipate pedestrian behavior and respond to it. This research gathers video data from real traffic scenes to predict pedestrian crossing intentions at unsignalized crossings. Computer vision techniques such as YOLOv4, Deep SORT, and perspective transformation are employed for road user detection, tracking, and mapping image coordinates to world coordinates to prepare trajectory datasets. Using trajectory data, several features influencing pedestrian intention like walking speed, location in the road environment, count and direction of approaching traffic, speed and type of closest approaching vehicle upstream, etc., are extracted. The dataset for this study was obtained by analyzing 1,411 pedestrians, resulting in 223,136 samples. To predict pedestrian crossing intentions, LSTM and Bi-LSTM with an attention mechanism model were built and trained to anticipate the pedestrian crossing intention at unsignalized crossing. The proposed model successfully combined the characteristics and surrounding dynamics of pedestrians to produce accurate predictions, Bi-LSTM with an attention mechanism outperformed LSTM, achieving an AUC of 95.3%, 91.1%, 89.2%, 87.5%, and 84% on the testing dataset at unsignalized crossing on the 0.6 sec, 1.2 s, 1.8 s, 2.4 s, and 3 s time horizons. These outcomes can be used to improve Connected and Autonomous Vehicle (CAV) technologies, infrastructure-to-vehicle (I2V) connectivity, and driver assistance systems to enhance pedestrian safety while navigating through pedestrian crosswalks.
Among adolescents, addiction susceptibility and sleep-related dysfunction have a common cognitive-emotional base and predict poor sleep quantity

ABSTRACT Background: Among adolescents, the self-pacing of sleep and experimentation with psychotropic substances increases. Poor sleep and susceptibility to addiction (SA) may therefore share common cognitive-emotional processes. The aim of the present study was to investigate the shared cognitive-emotional basis of SA and sleep-related dysfunction, and to explore their predictive value for sleep patterns. Methods: A total of 300 adolescents (mean age: 15.57 years; 50% females) took part in this questionnaire-based study. Participants completed a booklet covering sociodemographic, sleep-related (Sleep Hygiene Index: SHI; Pittsburgh Sleep Quality Index: PSQI) and SA-related variables. Results: Greater sleep-related dysfunction based on the SHI was strongly associated with higher SA scores. Parents’ educational level was negatively associated with SHI and SA scores. An exploratory factor analysis of SHI and SA items yielded three distinct factors: (i) risky behavior and irregular sleep-wake schedules, (ii) poor sleep environment and hygiene, and (iii) psychological arousal and distress. All three factors independently contributed to the prediction of poor sleep quality and quantity. Conclusions: Adolescents reporting dysfunctional cognitive-emotional processes are at increased risk of reporting poor sleep patterns. Additionally, poor sleep and SA were related to parents’ education, suggesting that both parents and their children might benefit from sleep hygiene counseling.
A Compact Slotted Double-Inverted Frustum-Shaped Dielectric Resonator Antenna for 5G Sub-6 GHz and 6G Bands Applications

This manuscript introduces the design and development of a slotted double-inverted frustum-shaped (SDIFS) dielectric resonator antenna (DRA) for 5G sub-6 GHz and 6G band applications. The designed antenna employs a coplanar waveguide-fed SDIFS dielectric resonator (DR) configuration. The proposed SDIFS-DR is built by stacking two inverted frustum-shaped conical DRAs, aligning their base and apex. The recommended SDIFS-DRA shows dual hybrid (HEM) modes. The SDIFS-DRA achieves a -10 dB measured impedance bandwidth of 60.81% within the operating frequency band (OFB) of 5.15 GHz to 9.65 GHz, with a measured maximum peak gain of 6.52 dBi and an average peak gain of 4.55 dBi. This proposed antenna maintains an average simulated radiation efficiency of 86.61% in the entire OFB with omnidirectional-like radiation patterns. The dimensions of the SDIFS-DRA are given by $\mathbf{1}. \mathbf{6}\lambda_{\mathbf{g}}\times \mathbf{1}. \mathbf{5}\lambda_{\mathbf{g}}\times \mathbf{0}. \mathbf{54}\lambda_{\mathbf{g}}$, where $\lambda_{\mathbf{g}}$ represents the guided wavelength at a lower cut-off frequency of 5.15 GHz with effective permittivity of 7.1. A prototype of the proposed SDIFS-DRA is developed to authenticate the precision of the designed antenna simulation results with the measured results.
Design of a High Gain Planar Cross Dipole Antenna for WLAN Range Extension in IoT Paradigms

A fractal-based planar crossed dipole circularly polarized (CP) antenna is designed in this paper which provides an axial ratio bandwidth of 49% (obtained from full-wave simulation). The proposed antenna is fed with the help of a coaxial probe and it has a square cavity reflector for gain enhancement. The proposed antenna obtains impedance bandwidth covering from 1.55 to 3.01 GHz and axial ratio bandwidth ranging from 1.71 to 2.82 GHz. The dimensions of the proposed antenna are $1.12\lambda_{0} \times 1.12\lambda_{0}\times 0.27\lambda_{0}$. The simulated gain varies from 8.14 to 10.70 dBic throughout the operating frequency band. The proposed antenna uses a 5G sub-6 GHz frequency band and it is suitable for Wireless Local Area Network (WLAN) communication with extended range in the Internet of Things (IoT).
A Comprehensive Review on Energy Efficient Internet of Things based Wireless Sensor Network

The Internet of Things (loT) has become an important aspect in various domains such as smart cities, healthcare and industrial revolution. There are various concerns of energy consumption ever since the widespread of IOT due to their imitation on the use of resources when it comes to lOT based Wireless sensor Networks, energy efficiency is essential to ensure optimal and cost-effective deployment. This study is a review of energy-efficient lOT based Wireless sensor Networks (WSN), mail focusing on the methods and techniques that can be employed to optimize energy consumption of lOT devices. This study covers various aspects of energy-optimal lOT based WSN including machine leaning approaches and energy efficient communication protocols.
Improving Cybersecurity Named Entity Recognition with Large Language Models

A lot of attention has been paid to cybersecurity threat intelligence analysis based on security knowledge graphs because they can evaluate multi-source threat intelligence data at a fine-grained level. The primary effort in building knowledge graphs is named entity recognition (NER). However, the retrieved features from classic NER algorithms are insufficient to identify novel security entities in the cybersecurity field. Currently, the natural language processing(NLP) field has made unprecedented progress, driven by large language models(LLMs). In this paper, we present a BERT- BiLSTM-CRF cybersecurity NER method based on BiLSTM-CRF that combines the pre-trained large language model BERT. The preprocessed annotated corpus is fed into the Bi-LSTM and CRF models, which employ the word vectors to extract contextual features and annotate 9 different categories of named entities. The proposed cyberecurity NER method is effective in identifying cybersecurity entities on large-scale cybersecurity datasets, and the relevant evaluation indexes are superior to other algorithms, according to comparative experiments carried out within the cyberspace security corpus.
WaveRFN-Nest: A Multi-Scale Feature Fusion Method Based on Wavelet Transform and Deep Learning

Infrared and visible images fusion is a technique that combines two types of images and has broad application prospects. With the development of deep learning, various high-performance fusion models have emerged in this field, including STDFusionNet, DRF, RFN-Nest, and others. However, most of these models focus on optimizing the model architecture and fusion performance, without considering the key features that can support subsequent tasks such as classification and object detection in the images. To address these issues, this paper proposes a multi-scale feature fusion algorithm called WaveRFN-Nest. Firstly, the spatial attention module called WaveAtten is introduced to extract important detail information from visible light images. Then, the WaveAtten module is embedded into the RFN-Nest model using a multi-scale feature fusion module, allowing the model to focus more on crucial features. The Experiments conducted on multiple datasets show that the composite model proposed in this paper ensures the quality of fused images with small parameter increments, improves the proportion of important detailed features in the images, and facilitates subsequent tasks.
Machine Learning and Deep Learning Models for Early Detection of Heart Disease

The prevalence of cardiovascular disease is a significant worldwide health concern. health concern and is responsible for an increased fatality rate. Effective treatment and prevention of heart disease depend on early detection and precise prediction. This work proposes various machine learning (ML) and deep learning (DL) models for the prediction of heart disease based on a large dataset of patient traits and medical indicators defining the presence of heart disease. This work uses various classifiers namely Naive Bayes, Multilayer Perceptron (MLP), decision tree, and Logistic Regression for developing an effective heart disease prediction model. These models have been fed with a dataset containing 1025 tuples and 11 attributes such as sex, age, thalach, restecg etc. The proposed model has been developed with a train and test ratio of 80:20, The aforementioned phenomenon has led to the emergence of certain measures, including precision, recall, accuracy, and Fl-measure, as indicators of the achieved results. The results have depicted that out of all the ML and DL models, the Decision Tree exhibited the highest accuracy with a rate of 98.04%. However, MLP has been also identified as the second outperforming model by showing 95.51 % for binary heart disease classification. Furthermore, the performance of Naive Bayes and logistic regression have shown an accuracy of 83.12%, and 84.48%, respectively.
Prediction of Whiteleg Shrimp Feed Requirement Using k-Nearest Neighbours Regression

Some Whiteleg shrimp farms had minimum facilities for collecting data, so they had a limited amount of data variance. This research aimed to predict Whiteleg shrimp feed requirements with limited variations and the amount of available data to get a minimum error rate. Because the available data is only 69 daily feed consumption data, the knearest Neighbours (kNN) Regression method is suitable to predict the following feed requirements target. The raw data was divided into 50 training data and 19 test data. The raw data will be converted into 2-dimensional data with columns indicating the number of features. Several experiments were carried out by changing the number of features and value of knearest neighbors. Overall, the MAPE range obtained was in a good or highly accurate category, which is 6.11% - 14.52%. The best MAPE (6.11%) was achieved when the number of features and the k value were two.
Modeling Research on Tropospheric Delay Mapping Function

Due to the increasing demands for atmospheric refraction in radio systems, the investigation of atmospheric propagation errors has emerged as a primary approach to enhancing system accuracy. The mapping function plays a crucial role in accurately converting zenith direction delay to the signal transmission path direction, thus serving as a vital parameter for analyzing tropospheric delays. To enhance the accuracy of slant path delay in radio systems, this study utilizes meteorological sounding data from the Qingdao station in 2022, including pressure, temperature, humidity, and other variables. By employing the parameter fitting method, a rational mapping function model is established. The results demonstrate that the new mapping function model outperforms traditional models in calculating slant path delays, particularly in reducing error in the wet term delay. This improvement carries significant implications for enhancing the accuracy of radio systems.
Innovative Flexible Body Sensor For In Vitro Vital Signs Monitoring

Vitals are a reliable, accurate and simple means of understanding the generic situation of the body of an individual. Clinicians can assess the state of the body based on the nature of temperature and pulse of the patient. There is a need for a non-invasive technique to read these parameters in real time, to use when there is a shortage in staff or some emergency. This paper proposes an in vitro temperature and pulse monitoring system that records the patient's health in real time, for specific applications post-surgery. The system comprises an innovative flexible body sensor which can be placed on the surface of the body non-invasively and record the temperature of the individual simultaneously with pulse readings. The same can be seen on a Liquid Crystal Display, as well as a Bluetooth mobile application. Multi-walled carbon nanotubes (MWCNT) and a polydimethylsiloxane (PDMS) matrix were chosen to make the flexible body sensor, to perform non-invasive and remote temperature monitoring. Once the clinician knows about the condition of the vitals, any complications post-surgery can be identified, and necessary medication can be taken quickly, with an improved response time as the app updates the vitals every second. The flexible body sensor also gives insight to hypothesize a similar system that could be carried out in an in vivo manner. The proposed system allows for remote monitoring, as readings can be seen through the mobile application, and any audio alerts can inform clinicians of any abnormality. The entire system can be implemented as a wearable.
Reduction of Toggling Activity Using Novel LFSR Driven Logic for ULSI Circuits

Testing is very essential in ULSI (Ultra-Large-Scale Integration) designs to identify structural defects in the chip and to fix hard defects before shipping the defective manufactured chip to customers to avoid failures at site. Toggling activity in the design is the main reason for defects being local or latent between lower cells and metal layers and is popped up at different PVT conditions. Identification of these defects require testing process to ensure the chip being manufactured at desired specifications with reduced cost and time for testing. Also, toggling activity shows impact on pattern inflation, power, and testability aspects. In today's ULSI world, maintaining lower power in limelight of energy crisis has been a captious issue while keeping in pace with ever increasing complexity of transistor integration onto a single silicon substrate. As the chip density increases, power dissipation during testing mode doubles than the normal mode affecting the chip's durability, performance, cost, and reliability. It became critical for test engineers to minimize the test power leveraging the techniques of Design for Testability (DFT) to incorporate low power management of a SoC (System on Chip). Traditional DFT approaches uses structural scan-based testing to obtain testability metrics. In this paper, a novel LFSR architecture is proposed for power reduction in test mode by replacing the conventional scan approach. This architecture is designed and tested using cadence tools on ISCAS89 benchmarking circuits. Experimental results are compared for different parameters like coverage, pattern count, WSA (Weighted Switching Activity) and runtime to prove that LFSR (Linear shift feedback Register) driven design shows better results than the traditional scan structured procedure. Adopting the novel LFSR architecture, toggling activity is reduced by factor of ~1.34X with 4K pattern reduction running almost 1hr faster than traditional scan-based design. Test Power is reduced by ~45% and is found to be showcasing better results as complexity of design is increased. Hence, it is found to be ideal approach for industrial designs racing to obtain low power with reduced pattern count, lower cost and with shorter run time. This novel approach is more helpful for 2.5D and 3D designs.
ESAF: An Efficient and Secure Authentication Framework for V2G Network

Electrical Vehicles (EVs) are integrated into the Smart Grid (SG) network to enhance energy flow and fulfill future energy demand using the Vehicle to Grid (V2G) technology. The V2G supports bidirectional information sharing among the power grid and EVs. The EVs can send a request to charge their batteries from the charging station and transfer excess energy to the power grid from their batteries. However, the communication is carried out over a public channel and that leads to several security and privacy concerns in the V2G network. Moreover, the IoT devices embedded in the EVs and charging stations are resource constrained in nature. So, there is a need to overcome the mentioned security and privacy issues with lightweight secure algorithms. In addition, EVs and charging stations are not protected by a human thus, it is necessary to protect them from physical attacks as well. To address these limitations, we propose Physically Unclonable Function (PUF) based an Efficient and Secure Authentication Framework (ESAF) to carry out secure communication in the V2G network. The ESAF is suitable for resource constrained networks as it uses lightweight basic concatenation, bitwise XoR, and one way hash operations. The correctness of the ESAF is validated through the security analysis using the BAN logic and AVISPA tool. In addition, the performance of the proposed ESAF illustrates that the computation and communication overheads are competitive with improved security.
A Hybrid Approach for Many-Objective Feature Selection in Intrusion Detection on Windows Operating Systems

The exponential increase in devices connected to the Internet has rendered them vulnerable to various types of cyberattacks, disrupting their proper functioning. As a result, it is crucial to have reliable Intrusion Detection Systems (IDS) that can identify malicious activities on the network. However, the unpredictable nature of network behavior and the large volume of data to be audited pose significant challenges. This work aims to address these challenges by eliminating redundant data features, identifying a subset of relevant and representative features that enhance the anomaly detection performance of an IDS using many-objective optimization algorithms. In this work, we have analyzed the recent TON IoT dataset, which collects monitoring data on devices using Windows and Linux operating systems. The NSGA-II, NSGA-III, RVEA, and MOEA/D were used as many-objective feature selection algorithms. Our computational simulations identified good subsets of features that showed better classification performance and accuracy in comparison to the complete set of features. Notably, the results obtained using the NSGA-II stood out from the others.
Polyimide (PI)- Thin Film Based Holographic Metasurface Leaky Wave Antenna

In this paper, a metasurface based on the holographic principle is implemented to design a leaky wave antenna. The thin film polyimide (PI) having a thickness of 50μm (Dk = 3.05, Df = 0.008) is employed to develop the holographic metasurface. The proposed antenna is a multilayered structure, below the metasurface layer there is an adhesive layer of 40 μm (Dk = 6, Df = 0.004), then the bottom layer is the Rogers RT 5880 (Dk = 2.20, Df = 0.0009) grounded substrate of 1.6 mm. By the implementation of the proposed metasurface, the omnidirectional monopole is converted to the directional leaky wave antenna with beam steering at ɵ = 90o in addition to enhanced gain and S11 values for the 15 GHz band.
Generating Concept Effect Relationship Based On Frequency Of Concept's Co-Occurrence Using Concept-Rank Algorithms

Understanding a topic in a course mainly consists of many concepts, which is challenging for students. It is because, usually, those concepts need to be conveyed with information on the relationships between concepts to solve the problem. Students' different limits, backgrounds, and goals have made research regarding concepts become even more challenging. Researchers have applied various learning path personalization strategies employing multiple methods and techniques, including depicting the connections between topic concepts. However, the strategy has several problems to solve, one of which is the main and exciting is the process of generating connections between concepts which is seen as a weighted-directed graph. This research offers a new practical and adaptive method for constructing Concept Effect Relationship (CER), a more specific concept map, by identifying the co-occurrence frequency of concepts hidden in a set of questions. It is proven that it is possible to extract the hidden concept when it appears simultaneously in the question item. It can be used to determine the weight and direction of each concept's node to generate CERs.
Glaucoma Detection Using Deep Learning

A damage in optic nerve which causes vision loss is known as Glaucoma and it is a chronic and progressive eye disease. Retina is a sensitive region which consists of millions of cells in which visual information is received and organized. This disease generally occurs in elder people, which is known as Glaucomatous Optic Neuropathy where nerve Fiber layer and vision field is defected. Deep learning is a solution combining with convolutional neural networks gives high accuracy in detecting Glaucoma. Colours and lesion texture is controlled by neural networks for diagnosis similar to human decision making. This method is tested with various Glaucoma symptoms as training data which is useful for detection purpose.
A Holistic Review on Detection of Malicious Browser Extensions and Links using Deep Learning

The growth of the Internet has aroused people’s attention toward network security. A secure network environment is fundamental for the expeditious and impeccable development of the Internet. The majority of internet-based tasks can be completed with the help of a web browser. Although many web applications add browser extensions to improve their functionality, some of these extensions are malicious and can access sensitive data without the user’s knowledge. Browser extensions with malicious intent present a growing security concern and have quickly become one of the most prevalent methods used to compromise Internet security. This is largely due to their widespread usage and the extensive privileges they possess. After being installed, these malicious extensions are executed and make an attempt to compromise the victim’s browser. This makes them particularly elusive and challenging to combat. It is crucial to promptly develop an effective strategy to address the threats posed by these extensions. A comprehensive review of the research on browser extension vulnerabilities is presented in this paper. The role of malicious links in web browser extensions are examined for several attacks. Detection of malicious browser extension on various aspects are represented namely Intrusion malicious web browser extensions detection using Intrusion detection, Machine learning based detection methods and Deep learning based techniques to mitigate malicious web browser extensions are examined. This study investigates the critical function of malicious detection in protecting web browsers, looking at the changing threats and risk-reduction tactics. A robust cybersecurity frameworks can be created that not only respond to known threats but also anticipate and thwart the strategies of future cyber adversaries by realizing the significance of proactive detection. Thus this survey provides a detailed comparison of various solutions for malicious browser extension.