Miracle-dz/UniFAR-FTUs huggingface dataset:
How Does Chat GPT Influence Human Capital Development Amongst Malaysian Undergraduate Students?

This study aims to explore the outcomes of Chat GPT on the education and learning process in the Malaysian undergraduate students. The influence of Chat GPT on the Malaysian undergraduate students predicted to impact the human capital development. This is a qualitative study conducting ethnography design approach, established an interview's questions and examined the reliability of the instruments, containing four interviewees from different four universities enrolled in undergraduate programs. The study resulted as there is a huge impact of Chat GPT on Malaysian undergraduate students, found that Chat GPT known by all the students and frequently used in their assignments and learning process. In addition, this research finds that Chat GPT impacts the undergraduate students all over the world positively by enforcing further monitoring and prepare a space for the students and lecturers to communicate and discuss the ideas to avoid the absence of innovation and creativity amongst the Malaysian undergraduate students.
Memristor Modeling with Homeostatic Threshold Variation for Simulation and Application

This paper proposes a novel approach to emulating biological neural components using memristors. The nanoscale, resistance variability, and non-volatility of the memristor make it a suitable candidate for this task. A homeostasis-type switching mechanism is induced into the memristor modeling, inspired by the homeostatic switching behavior of biological neural components. The proposed model is generalizable and adaptable to various threshold-type memristor models. A SPICE model is proposed, and the memristor model is utilized to implement a neuron circuit that follows the homeostasis-type mechanism. A spiking neural network and its verification platform are designed, where pattern recognition can be achieved. The simulation is carried out in Cadence PSPICE, and a test comparison between scenarios with homeostasis and non-homeostasis is proposed. This work demonstrates the potential of memristors in implementing neuromorphic computing systems.
DTR-Map: A Digital Twin-Enabled Real-Time Mapping System Based on Multi-View Stereo

Digital twins require efficient and cost-effective methods for updating virtual mappings to reflect changes in physical entities. In this paper, we propose DTR-Map, a realtime 3D reconstruction method based on multi-view stereo. Our method utilizes a moving monocular camera as an edge node to collect multi-view data and employs a publisher-subscriber communication framework. By leveraging deep learning-based monocular visual odometry and multi-view stereo matching network, we achieve accurate local camera motion estimation and high-quality output models. Our experimental results demonstrate the method's effectiveness, showcasing its high-quality realtime 3D reconstruction, reconstruction accuracy, and scalability.
Improve speech enhancement with Wave-USE-Net

At present, most deep learning speech enhancement models rely on spectrograms, and phase estimation is needed to restore the speech signal from spectrograms. In this paper, a speech enhancement model called Wave-USE-Net is proposed, which combines Squeeze-and-Excitation Networks(SENet) and Wave-U-Net. On the basis of Wave-U-Net directly estimating the waveform of the enhanced speech signal end-to-end, the SE block is used to suppress redundant features and effectively recover the enhanced speech signal. The experimental results show that compared with the baseline Wave-U-Net model, the proposed Wave-USE-Net model improves PESQ, CSIG, CBAK, COVL and SSNR by 7.5%,7.1%,1.2%,8.5% and 1.1% respectively on the VCTK dataset. The enhancement effect of the proposed model is better than that of other speech enhancement models based on Wave-U-Net, and the enhanced speech signal is closer to clean speech.
RISC-VR-Extension: Advancing Efficiency with Rented-Pipeline for Edge DNN Processing

The proliferation of edge devices necessitates efficient computational architectures for lightweight tasks, particu-larly deep neural network (DNN) inference. Traditional NPUs, though effective for such operations, face challenges in power, cost, and area when integrated into lightweight edge devices. The RISC-V architecture, known for its modularity and open-source nature, offers a viable alternative. This paper introduces the RISC-V R-extension, a novel approach to enhancing DNN process efficiency on edge devices. The extension features rented-pipeline stages and architectural pipeline registers (APR), which optimize critical operation execution, thereby reducing latency and mem-ory access frequency. Furthermore, this extension includes new custom instructions to support these architectural improvements. Through comprehensive analysis, this study demonstrates the boost of R-extension in edge device processing, setting the stage for more responsive and intelligent edge applications.
Breast Cancer Assessment Using a HybridANN-CNN Approach

Breast cancer is the most prevalent kind of cancer that affects women globally. Effective treatment requires early detection, which will reduce the risk factor. In the present era of healthcare technology advancements, significant progress has been made in the timely identification and avoidance of breast cancer. Mammography is a substantial technique for screening breast cancer, but it has limitations, including notable inter-observer variability and false-positive rates. Recent advancements in artificial neural networks (ANNs) and convolutional neural networks (CNNs) have demonstrated encouraging outcomes, enhancing the accuracy and effectiveness of breast cancer detection. The paper presents an ANN-CNN method for early breast cancer identification. Using a publicly available dataset, we train the machine to categorize breast histology images as cancerous or not. Our approach is evaluated based on crucial metrics, including accuracy, support, f1-score, and recall, all of which achieve nearly 90% or higher values.
Dependency-based Semantic Role Labeling of PropBank

We present a PropBank semantic role labeling system for English that is integrated with a dependency parser. To tackle the problem of joint syntactic--semantic analysis, the system relies on a syntactic and a semantic subcomponent. The syntactic model is a projective parser using pseudo-projective transformations, and the semantic model uses global inference mechanisms on top of a pipeline of classifiers. The complete syntactic-semantic output is selected from a candidate pool generated by the subsystems. We evaluate the system on the CoNLL-2005 test sets using segment-based and dependency-based metrics. Using the segment-based CoNLL-2005 metric, our system achieves a near state-of-the-art F1 figure of 77.97 on the WSJ+Brown test set, or 78.84 if punctuation is treated consistently. Using a dependency-based metric, the F1 figure of our system is 84.29 on the test set from CoNLL-2008. Our system is the first dependency-based semantic role labeler for PropBank that rivals constituent-based systems in terms of performance.
Delay in Underwater Acoustic CSMA Networks

The paper considers the delay performance of an underwater acoustic multiple access network where bottom mounted sensor nodes transmit packets to an aggregation point. The focus is on Carrier Sense Multiple Access (CSMA) slotted Aloha. It is assumed that packets generated by the sensor nodes can be described by independent Poisson processes. The delay is investigated under the assumption that there is a pseudo-Bayesian stabilization of the considered CSMA network. Numerical examples are presented that illustrate the delay performance.
The Moderating Effect of Design-Engineering Capability on the Relationship between Firms' Innovation Activity and Financial Performance

The integration of design and technology innovations in the new product development (NPD) process is critical for successful product development and market success. Technology innovation serves as the backbone of the development side of NPD, empowering products with the necessary functionalities and capabilities. Design innovation, on the other hand, is instrumental in aligning the products with customer needs and preferences, serving as a key driver on the market side of NPD. The present study aims to examine the effects of firms' capability in effectively integrating design and technology throughout the NPD process, which we refer to as design-engineering capability, on financial performance, as well as its moderating effect on the relationship between firms' innovation activity and financial performance. Our main hypothesis is that firms' design-engineering capability positively moderates the effects of innovation activities on financial performance. Using patent data for 1,659 US-headquartered publicly-traded firms from 1980 to 2015, the results of panel generalized least square (GLS) regressions confirm our hypotheses on the direct effect of design-engineering capability on firm performance and its moderating effect on the relationship between innovation activity and firm performance, highlighting the importance of having a strong integration of design and technology expertise within the organization.
Segment Routing Traffic Engineering by Pruned Landmark Labeling

As network usage is increasing recently, providers are faced with the dual challenge of enhancing network performance while also optimizing link utilization. In a provider’s network with a redundant structure, there may be multiple shortest routes. However, basic route control typically selects only one of these paths, leading to potential link congestion. For this reason, Traffic Engineering (TE) utilizing Multi-Protocol Label Switching (MPLS) and Segment Routing (SR) have been developed to enable the selection of routes other than the shortest path. Despite the various methods that have been proposed, the challenge of selecting the optimal route has not yet been resolved.In this paper, we focus on Segment Routing Traffic Engineering (SR-TE) in Software-Defined Networking (SDN) networks. We propose a heuristic Pruned Landmark Labeling (hPLL) method that dynamically selects the best Landmark as the intermediate node for each traffic flow. This reduces the Maximum Link Utilization (MLU) in the network with restricted Segment Identifiers (SIDs), making it more suitable for large-scale SR operator networks. Through a series of experiments, we demonstrate that our proposed algorithm outperforms traditional routing algorithms to a significant extent.
Collision Aware Energy Efficient UAV-assisted Heterogeneous Sensor Networks

Heterogeneous Sensor Networks (HSN) is the most trending technology which is used in lots of intelligent communication systems so that it greatly helps to increase the quality of life of people. Due to high speed and unpredictable mobility certain drawbacks are occurred in the network that reduces the communication quality. For that purpose and Unmanned Aerial Vehicles (UAVs) are created in the network that can able to improve the device monitoring and its mobility so that it increases the sustainability of the devices. The process of UAV deployment is highly flexible but proper UAV placement is the challenging issue in the network. For that purpose in this article Collision Aware Energy Efficient UAV (CEEUH) is concentrated which can able to increase the effectiveness of UAV utilization. The experimental demonstration is conducted using NS3, and the parameters analyzed in this study include energy efficiency and packet delivery ratio. The final output is compared to the findings of existing research, and the results demonstrate that the proposed model outperforms previous techniques
Dynamic Data Reconciliation for Improving the Forecasting Performance of Wind Power based on Long Short-term Memory

As one of the most promising power generation methods, effective forecast of wind power generation for uncertain environment contributes to the planning and operation of power systems. In this work, the long short-term memory (LSTM) network is developed for wind power forecasting. Then, a dynamic data reconciliation (DDR) method is proposed for suppressing sensor noise and improve the forecasting performance. LSTM extends the recurrent neural networks (RNN) through gate functions, which is a popular RNN variant with state memory. However, the modeling datasets with Gaussian (or non-Gaussian) sensor noise may degrade the expected performance of LSTM. The proposed DDR performs Bayesian estimation of the real information by extracting useful parts of the two process-related information, and the relatively accurate data obtained from DDR is used to improve the accuracy of modeling. Experimental results verify that the LSTM model with DDR is less affected by Gaussian or non-Gaussian sensor noise, showing a better wind power forecasting performance.
The effects of health state, hemoglobin, global symptom distress, mood disturbance, and treatment site on fatigue onset, duration, and distress in patients receiving radiation therapy.

PURPOSE/OBJECTIVES To describe the fatigue experience of patients with cancer receiving radiation therapy and determine to what extent diverse correlates of fatigue affect fatigue onset, duration, and distress. DESIGN Descriptive correlational study completed by secondary data analysis. SAMPLE/SETTING Data were obtained from 384 subjects recruited from two urban, university-affiliated, radiation oncology clinics located in a large, Midwestern city. METHODS The effects of health indicators and treatment site on fatigue onset, duration, and distress were examined using correlational analyses and analyses of variance. MAIN RESEARCH VARIABLES Hemoglobin, health status, global symptom distress, mood disturbance, treatment site, and fatigue onset, duration, and distress. FINDINGS Fatigue started near the middle of the second week of treatment, was moderately distressing, and lasted approximately 32 days. Higher levels of health and hemoglobin at the start of therapy were associated with a delayed onset, shorter duration, and lower levels of fatigue distress. In contrast, higher pretreatment levels of global symptom distress and mood disturbance were associated with an earlier onset, longer duration, and greater severity of fatigue distress. CONCLUSION The fatigue experience in patients undergoing radiation therapy is highly individualized. Variations in the health states of patients as well as the area of the body being treated can influence fatigue onset, duration, and distress. IMPLICATIONS FOR NURSING Pretreatment screening for fatigue and its correlates is needed to identify patients at risk for an earlier onset, longer duration, and more distressing levels of fatigue.
Detection Of Image Forgery Using Error Level Analysis

Image forging is the manipulation of digital images using methods like copy-move, splicing, image removal etc. This paper provides an overview of the system’s architecture, highlighting the integration of digital image processing, OpenCV, and machine learning algorithms Image forgery detection is the process of identifying a modified image from the original. In the realm of digital image processing, image forgery detection is a critical task. The amount of altered and faked photographs has increased as digital imaging has become more prevalent and is used in a variety of fields, including forensics, media, and scientific study. They must guard the veracity of photos and stop the dissemination of false information and fake news. The detection of picture forgeries, however, is fraught with technological difficulties, including the requirement for reliable and accurate image features, the capacity to differentiate between various types of image alteration, and effective algorithms that can analyze enormous quantities of digital images. To meet these problems, the error level analysis image processing technique and convolutional neural network are used to create a model that can predict the input images as forged ad unforged. The proposed model shows a better performance by giving an accuracy of 87%.
A Systematic Analysis of Deep Learning Based Twitter Sentiment Analysis: Emerging Trends and Challenges

Sentiment analysis is a powerful area of research in the realm of natural-language-processing(NLP), with several practical applications. Businesses and governments can gain valuable insights from the immense amounts of user-generated data available through diverse social media platforms. However, it is now challenging to efficiently mine pertinent information from the available data. Readers, product vendors, and legislators all pay close attention to the opinions offered by internet users. Sentiment-analysis(SA) has received a lot of interest as a means of analyzing and organizing the unstructured data that comes through social media. Sentiment analysis is a method of text organization used to categorize the author's attitude or feelings as positive, negative, neutral, or otherwise. Deep learning models are useful because of their automated learning potential, this problem has been solved by combining sentiment analysis with deep-learning(DL). In this study, we undertake a thorough literature evaluation of DL strategies to document-based sentiment analysis, with the goal of extracting various features from social media content, namely from Twitter data. In addition, it offers a concise overview, evaluation, and improvement of deep learning's latest breakthroughs in the areas of sentiment analysis approaches and document applications. This paper investigates the difficulties that researchers have run into and other issues that may arise when attempting to analyze social media sentiment. It explains why and how SA is performed, what problems it solves, and what kinds of problems it solves. It also identifies significant difficulties with the datasets, text languages, investigation methods, and assessment measures, and compares the results of several investigations. This study aids the state of the art in SA and can guide practitioners toward the most useful approach.
Adept Guide and Guard Reinforcement Learning for Safe Driving in Lane-Keeping Scenarios

As a critical technology for achieving autonomous driving, reinforcement learning has demonstrated its effectiveness in enabling vehicles to learn decision-making and adapt their actions to the surrounding environment. Nevertheless, the safety vulnerabilities inherent in reinforcement learning present substantial hazards to highway safety. An AG2RL(Adept Guide and Guard Reinforcement Learning) is proposed to address this issue in this study. The AG2RL algorithm introduces a guardian to safeguard the exploration of the learning agent. The guardian prevents the agent from running out of lane line boundaries, which is good to achieve faster convergence. The simulation experiments are conducted to evaluate the proposed algorithm, and results show that the proposed algorithm has a low accident rate and a faster convergence rate compared with Soft Actor-Critic algorithm.
Macular choroidal thickness and volume in normal subjects measured by swept-source optical coherence tomography.

PURPOSE To study the choroidal thickness in healthy subjects by swept-source optical coherence tomography (SS-OCT) at longer wavelength. METHODS The macular area of 31 eyes (31 healthy volunteers) was studied with an SS-OCT prototype system, which uses a tunable laser as a light source operated at 100,000 Hz A scan repetition rate in the 1-μm wavelength region. Three-dimensional volumetric measurement comprised of 512 × 128 A scans was acquired in 0.8 second. From a series of OCT images, a chroidal thickness map of the macular area was created by manual segmentation. To evaluate interoperator reproducibility, the choroidal thickness in each section from 10 subjects was determined independently by two observers. RESULTS SS-OCT at the 1-μm wavelength region allowed visualization of the fine structure of the choroid as well as that of the retina. Mean choroidal thickness and volume in the macula area were, respectively, 191.5 ± 74.2 μm and 5.411 ± 2.097 mm(3). The mean choroidal thickness of the outer nasal area was significantly thinner than that of all other areas (P < 0.05). The measurements by the two independent observers were significantly identical; the intraclass correlation coefficient in mean choroidal thickness was between 0.945 and 0.980 in each area. The macular choroidal thickness was significantly correlated with axial length after adjustment for age (P < 0.001), and with age after adjustment for axial length (P < 0.001). CONCLUSIONS SS-OCT system at 1 μm provides macular choroidal thickness maps and allows one to evaluate the choroidal thickness more accurately.
Deep Learning-Powered Multiclass Classification of DDoS Attacks on 6G-Connected IoT Devices

The rapid expansion of the 6G network and the widespread deployment of IoT devices resulted in challenging the effective detection and mitigation of distributed denial of service (DDoS) attacks originating from Internet of Things (IoT) sources. This paper presents an approach to handle this difficulty using machine learning and deep learning models. The approach uses Convolutional Neural Network (CNN) and Random Forest classifiers for binary classification, and Artificial Neural Network (ANN) model for determining the precise type of attack among nine classes. Fisher's Score and Recursive Feature Elimination with Cross Validation (RFECV) feature selection techniques are employed in the proposed approach for increasing the effectiveness of the system. The proposed approach is validated on the Canadian Institute for Cybersecurity-2019 dataset and the model achieves an accuracy rate of 99.5% for binary classification and more than 90% for different class classification.
Underwater Biological Detection Method Based on Improved YOLOv5

Aiming at the current problems of low underwater image quality in underwater biological target detection, low detection accuracy and high leakage rate caused by underwater biological occlusion and overlapping, an underwater biological target detection method based on improved YOLOv5s is proposed. Firstly, the SE attention mechanism is introduced at the end of the backbone network to enhance the feature proposing capability of the network. Secondly, BiFPN network is introduced in the neck network to replace the original structure, and different weights are assigned to different scale features for better feature fusion. Finally, the prediction frames are screened using the Soft-NMS strategy to minimize the missed detections. The experimental results show that compared with the original YOLOv5s algorithm, the improved algorithm improves the mean accuracy precision (mAP) by 3.2% on the URPC dataset, which validates the effectiveness of the proposed algorithm in underwater biological target detection.
The Task of Localizing the Source of Air Pollution by a Group of Drones Using Multi-Agent Approach

The article discusses the solution to the problem of localizing a single source of air pollution using a group of drones using a multi-agent approach. A description of the algorithm for searching and localizing pollution in a given area is given. A special software platform has been developed to implement the algorithm and evaluate the results of its work.
Vibration modes in a pendulums ring: Analogy with gas microbubbles surface modes

When subjected to ultrasound activation, microbubbles (1-8 μm) present a fairly complex dynamics. Their behavior can be well modeled by coupled oscillators system. In this study, microbubble surface modes have been investigated through an acousto-mechanical analogy base on coupled pendulums. Observation of parametric vibration modes up to n = 17 for a ring of pendulums excited at frequencies between 1Hz and 4 Hz is presented. The relevance of the use of the pendulums ring for studying microbubble dynamics is discussed.