Miracle-dz/UniFAR-FTUs huggingface dataset:
A High-Speed Computational Pipeline Single MAC-Based VLSI Architecture for Real-Time Signal and Image Processing

VLSI architectures play a vital role in most real-time applications because of their optimal performance. In this work, a single MAC (Multiply-Accumulate) based pipelined architecture is proposed for faster computation of the one-dimension (1-D) discrete wavelet transform (DWT). The main objective of this work is to reduce computational complexity by utilizing resource sharing and to enhance the speed of operation by employing a pipelining scheme. The designed single MAC-based architecture for computing DWT is suitable for infrasound data processing which employs a high-pass filter (HPF) and low-pass filter (LPF). The architecture efficiently implements a resource-sharing scheme by adopting only these two filters. In addition, in this realization, pipelining is utilized to achieve high-speed computation of DWT. Further, this design makes use of common elements several times which provide a convenient and suitable implementation. The proposed structure is coded using Verilog and implemented in FPGA (Field Programmable Gate Array) hardware with the Xilinx platform. Intending easily transported and real-time utilization, this architecture is realized using Xilinx Virtex-5 series FPGA. The area of the proposed architecture is reduced by about 44.8% when examining conventional parallel MAC architecture. Likewise, this pipelined design offers power conservation of about 31.97% over the parallel design. Further, the proposed architecture offers an operating speed of 276MHz with a power conservation of 200mW at 276MHz clock frequency.
Human Auditory and Adjacent Nonauditory Cerebral Cortices Are Hypermetabolic in Tinnitus as Measured by Functional Near-Infrared Spectroscopy (fNIRS)

Tinnitus is the phantom perception of sound in the absence of an acoustic stimulus. To date, the purported neural correlates of tinnitus from animal models have not been adequately characterized with translational technology in the human brain. The aim of the present study was to measure changes in oxy-hemoglobin concentration from regions of interest (ROI; auditory cortex) and non-ROI (adjacent nonauditory cortices) during auditory stimulation and silence in participants with subjective tinnitus appreciated equally in both ears and in nontinnitus controls using functional near-infrared spectroscopy (fNIRS). Control and tinnitus participants with normal/near-normal hearing were tested during a passive auditory task. Hemodynamic activity was monitored over ROI and non-ROI under episodic periods of auditory stimulation with 750 or 8000 Hz tones, broadband noise, and silence. During periods of silence, tinnitus participants maintained increased hemodynamic responses in ROI, while a significant deactivation was seen in controls. Interestingly, non-ROI activity was also increased in the tinnitus group as compared to controls during silence. The present results demonstrate that both auditory and select nonauditory cortices have elevated hemodynamic activity in participants with tinnitus in the absence of an external auditory stimulus, a finding that may reflect basic science neural correlates of tinnitus that ultimately contribute to phantom sound perception.
Detection of Mineral Deficiencies and Pests Symptoms in Coffee Crop Using Computer Vision

One of the contributions of computer vision is precision agriculture, which uses high technology to ensure good production and profitability in agribusiness. Its techniques can be applied to identify diseases and pests and diagnose the degree of leaf damage caused by them. In this context, this work seeks to identify the symptoms of mineral deficiencies and pests in coffee crops through computer vision. Therefore, the general objective of this work is to identify mineral deficiencies and pests in coffee plants using computer vision techniques and convolutional neural networks, using a pre-trained AlexNet architecture model available on the Matlab platform. This research is justified by the difficulty, in agriculture, in determining the damage caused by pests and mineral deficiencies accurately through visual analysis, preventing the taking of measures to minimize them. Therefore, the use of Convolutional Neural Networks can present significant results for the accurate classification of diseases in coffee plants, helping to minimize losses in productivity. 117 images were tested with the static classifier, among which the neural network managed to classify 98 correctly. Some images obtained correct classification, but without 100% indication of the real class.
Sentiment Analysis for Assessing Customer Satisfaction in Chatbot Service Encounters

The growing demand for chatbots in online customer service has led to an increased need for effective evaluation methods of service encounter satisfaction (CSES). Conventional post-interaction surveys, often incomplete and biased, pose challenges in accurately measuring CSES. This study explores the Incribo synthetic sample chatbot dataset, obtained from Kaggle, comprising more than 10,000 distinct messages. The dataset encompasses diverse conversational interactions, with each message annotated with User ID, User Utterance, Bot Response, User Feedback, Conversation Outcome, User Profile, Platform, Language, User Emotion/Sentiment, Location, and User Segment. The research aims to explore the sentiments methods, assess the influence of the mentioned variables, and evaluate the correlations between sentiment and conversational outcomes. Employing a hybrid approach, the study reveals widespread user participation, varied linguistic preferences, and equitable conversation outcomes. The correlation analysis underscores a notable negative association between neutral user sentiments and feedback, providing valuable insights into interactions within online customer service.
On Second-Order Derivatives of Rigid-Body Dynamics: Theory and Implementation

Model-based control for robots has increasingly depended on optimization-based methods, such as differential dynamic programming (DDP) and iterative LQR (iLQR). These methods can form the basis of model-predictive control, which is commonly used for controlling legged robots. Computing the partial derivatives of the robot dynamics is often the most expensive part of these algorithms, regardless of whether analytical methods, finite difference, automatic differentiation (AD), or chain-rule accumulation is used. Since the second-order derivatives of the robot dynamics result in tensor computations, they are often ignored, leading to the use of iLQR, instead of the full second-order DDP method. In this article, we present analytical methods to compute the second-order derivatives of inverse and forward dynamics for open-chain rigid-body systems with multi-DoF joints and fixed/floating bases. An extensive comparison of accuracy and run-time performance with AD and other methods is provided, including the consideration of code-generation techniques in C/C++ to speed up the computations. For the 36 DoF ATLAS humanoid, the second-order inverse and forward dynamics derivatives take <inline-formula><tex-math notation="LaTeX">$\approx 200 \,\mu \text{s}$</tex-math></inline-formula>, and <inline-formula><tex-math notation="LaTeX">$\approx \text{2.1}\,\text{ms}$</tex-math></inline-formula>, respectively, on a 12th Gen Intel i5-12400 processor with 2.5 GHz clock-speed, resulting in a <inline-formula><tex-math notation="LaTeX">$\approx 3.2 \times$</tex-math></inline-formula> and <inline-formula><tex-math notation="LaTeX">$\approx 3.8 \times$</tex-math></inline-formula> speedup, respectively, over the AD approach.
Projections of Future Trends in Biogeochemical Conditions in the Northwest Atlantic Using CMIP5 Earth System Models

ABSTRACT We use the results from eight of the Earth System Models (ESMs) made available for the Fifth Assessment Report of the Intergovernmental Panel on Climate Change to analyze the projected changes in biogeochemical conditions over the next 50 years in the northwest Atlantic. We looked at the projected changes using the Representative Concentration Pathway 8.5 scenario in the 100–400 m depth range over a large region and at more specific locations to assess the relevance of using these outputs to force a regional climate downscaling model of the Gulf of St. Lawrence. The projected trends for dissolved oxygen (decrease), pH (decrease), and nitrate (variable although negative in general) represent a continuation of the recently observed trends in the area. For primary production, no firm conclusions can be drawn because of large differences in the trends from one model to another. The consistency of the trends near the regional model lateral boundaries leads us to conclude that the ESM trends can be used to set up future boundary conditions to evaluate regional impacts of climate change although the uncertainty of the results for the Scotian Shelf will be greater than for the Gulf of St. Lawrence.
On Voting and Facility Location

We study mechanisms for candidate selection that seek to minimize the social cost, where voters and candidates are associated with points in some underlying metric space. The social cost of a candidate is the sum of its distances to each voter. Some of our work assumes that these points can be modeled on the real line, but other results of ours are more general. A question closely related to candidate selection is that of minimizing the sum of distances for facility location. The difference is that in our setting there is a fixed set of candidates, whereas the large body of work on facility location considers every point in the metric space to be a possible candidate. This setting gives rise to three types of candidate selection mechanisms which differ in the granularity of their input space (single candidate, ranking and location mechanisms). We study the relationships between these three classes of mechanisms. While it may seem that Black's 1948 median algorithm is optimal for candidate selection on the line, this is not the case. We give matching upper and lower bounds for a variety of settings. In particular, when candidates and voters are on the line, our universally truthful spike mechanism gives a [tight] approximation of two. When assessing candidate selection mechanisms, we seek several desirable properties: (a) efficiency (minimizing the social cost) (b) truthfulness (dominant strategy incentive compatibility) and (c) simplicity (a smaller input space). We quantify the effect that truthfulness and simplicity impose on the efficiency.
Analysis and Implementation of Machine Learning Approaches for Adaptive Learning Using Personalized Assessment

A comparison of machine learning models developed for an adaptive learning platform is done, aiming to revolutionize educational methodologies in response to the diverse learning needs of individuals. The model dynamically adjusts the difficulty of test questions based on user performance, creating a personalized learning experience. The study evaluates and compares the model's performance across various machine learning algorithms, including Random Forest, Decision Tree, Naive Bayes and Logistic Regression. The findings reveal the model's high accuracy, ranging from 66% to 92%, showcasing its efficacy in adapting to individual capabilities. By generating user performance profiles, the model successfully tailors challenges to meet diverse learning needs, contributing to an optimized educational experience. Moreover, this research contributes to the field by demonstrating that the proposed model's simplicity, particularly when using the Decision Tree algorithm, yields comparable accuracy results to more complex models. Through extensive testing, the model consistently performed with accuracy ranging from 66% to 92%, even with a small preliminary test size of 21 questions. This simplicity, coupled with its ability to dynamically adjust difficulty levels, positions the model as a pragmatic and effective solution for adaptive learning. The study concludes by underlining the model's effectiveness in providing personalized learning experiences, ultimately enhancing user engagement and educational outcomes.
Statistical Analysis for Evaluation and Improvement of Computer Science Education

Developing well-prepared and competent graduates is one of the main goals of all university programs globally. Recently, Computer Science (CS) has achieved tremendous success among all career fields driven by the strong competence in the market and the rapid changes in technology. Our goal is to develop an automated framework that provides efficient management, evaluation and improvement of the CS students education, as well as a profound establishment of a successful study tree for CS university programs. Such a challenging goal comprises major factors that should be inclusively considered. High school (HS) students are expected to join CS university programs with different educational backgrounds and learning capabilities. The strength of association among several performance-related factors including the academic performance of students in HS is evaluated to gain insights and infer indicators in CS programs. The automatic correlational analysis of the prerequisites for each course is also investigated to assess the program structure and dependencies among several CS courses. In this comprehensive study, all these factors are efficiently analyzed in order to investigate the valid causes of low and high performance of both CS university students and programs. Experimental results have concluded several major findings with validated associations that assure and prioritize the importance of evaluation and improvement of CS education.
Gesture Therapy: An Upper Limb Virtual Reality-Based Motor Rehabilitation Platform

Virtual reality platforms capable of assisting rehabilitation must provide support for rehabilitation principles: promote repetition, task oriented training, appropriate feedback, and a motivating environment. As such, development of these platforms is a complex process which has not yet reached maturity. This paper presents our efforts to contribute to this field, presenting Gesture Therapy, a virtual reality-based platform for rehabilitation of the upper limb. We describe the system architecture and main features of the platform and provide preliminary evidence of the feasibility of the platform in its current status.
Benchmarking SVM Variants for Unsupervised Intrusion Detection System

With the growing complexity of the computer network, novel cyber-attacks have been emerging rapidly, increasing the demand for unsupervised learning-based intrusion detection systems (IDS). While SVM-based algorithms are a popular solutions for anomaly IDSs, it is lacking studies that comprehensively evaluating the performance of these algorithms. Therefore, this paper aims to address the gap by quantitatively benchmarking 11 variants of SVM algorithms within four popular network intrusion datasets, including BoT-IoT, N-BaIoT, CIC-IDS-2017, and CIC-DDoS-2019. Our comprehensive analysis, involving over 400 model-attack pairs with thousands of experiment trials, provides invaluable insights into the capabilities and limitations of these algorithms. The findings offer guidance for the practical application of SVM-based techniques in IDS, enhance cybersecurity, and foster more secure, resilient network systems.
Number Plate Detection and Recognition Using OpenCV

Number Plate Recognition using image processing and Optical Character Recognition (OCR) techniques aims to create an efficient automatic system for authorized vehicle identification. The proposed method involves a comprehensive iterative waterfall model for software development, emphasizing flexibility and risk management Challenges include handling diverse fonts, languages, and image variations, such as dear, blurred, and skewed plates. The system employs OpenCV for image manipulation, Tesseract OCR for text extraction, and Pandas for database matching. The objectives encompass accurate license plate detection, character recognition, and extraction of vehicle information from a dataset. The model demonstrates an 83% accuracy rate and offers potential applications in traffic management, surveillance, and toll collection. Future enhancements include extending the system to detect international plates, various languages, real-time applications, and integration into traffic infrastructure for enhanced functionality.
Laser-Plasma Ion Acceleration and Its Applications

Laser plasma ion acceleration has excellent properties that make it used in various applications like inertial confinement fusion, cancer therapy, proton radiography, etc. The duration of picosecond stimulation can be obtained using ion accelerators resulting from laser plasma interactions. Therefore, it is possible to dispense with traditional accelerators and rely on laser plasma ion acceleration. In this paper, a research overview of laser plasma depending on ions acceleration is given. The experimental designs are briefed and the last advances are discussed. A comprehensive review of basic acceleration has been performed with an overview of hybrid mechanisms and new concepts. Finally, we focused on engineering target advances to improve all-optical and acceleration approaches to control and acceleration beams.
An adaptive enhanced windowed correlation filter for visual tracking

Boundary effect, as an inherent drawback of discriminative correlation filter (DCF) trackers, cannot be handled well in most existing studies. This paper proposes an adaptive enhanced windowed correlation filter tracker (AEWCF), which can alleviate boundary effect adaptively and capture more reliable target information effectively. Firstly, a target enhanced likelihood map is introduced to extract reliable target information from searching window. Then, this paper further proposes an adaptive parted searching window including two sub-windows: the object region window depended on object likelihood maximization strategy and the background window generated by background suppression scheme. The extensive evaluation on OTB2015 benchmarks demonstrate that the AWCF tracker performs favorably against some popular trackers and keeps real-time speed.
A visual attention estimator applied to image subject enhancement and colour and grey level compression

Image segmentation technology has immediate application to compression where image regions can be identified and economically coded for storage or transmission. Normally segmentation identifies regions that are uniform and homogeneous with respect to some characteristic such as colour or texture and ideally these regions coincide with image objects and thereby offer the potential of huge compression ratios. This paper proposes a technique for colour variability reduction that only affects the background material and leaves perceptually important areas unchanged.
Shared Autonomous Electric Vehicle Dispatch with Mobility and Distribution System Resilience Benefits

Shared autonomous electric vehicles (SAEVs) can provide on demand transportation for passengers while also interacting extensively with the electric distribution system. This interaction is especially beneficial after a disaster when the large battery capacity of the fleet can be used to restore critical electric loads. This study develops a dispatch policy that balances the need to continue serving passengers (especially critical workers) and the ability to transfer energy across the network. Power flows are modeled using the LinDistFlow model, while transportation network is incorporated through a queuing model. The resulting model predictive control algorithm is examined in simulation to demonstrate the ability of vehicles to balance these competing objectives with benefits to both systems.
Federated Learning-based Framework for Cross-Environment Human Action Recognition Using Wi-Fi Signal

Wi-Fi-based Human action recognition (HAR), as significant support for the loT applications, such as human-computer interaction, healthcare, etc. is attracting the attention of more and more researchers. With the rapid development of deep learning (DL), the DL-based HAR methods achieve excellent performance. Even though, the generalization performance of cross-environment HAR is still a challenge. Previous work relies on collecting sufficient data in different environments, which is time-consuming and labor-constraint. To address this problem, in this paper, we proposed a cloud-edge paradigm-based framework named WiFed-Sensing. In this framework, a personalized federated learning strategy is proposed to learn the general human action knowledge that cross-environment, which can make the HAR in new environments benefit from it and realize reliable HAR performance even with only a few action samples, thus improving the overall cross-environment HAR accuracy. Extensive experiments are conducted to evaluate the effectiveness of our framework, and the results demonstrate that our method achieves 89.52% cross-environment HAR accuracy, which outperforms the state-of-the-art method.
Smart Traffic Monitoring through Drone Images via Yolov5 and Kalman Filter

Monitoring traffic is of vital relevance in our modern scenario. Over the years, standard ways of capturing data, such as induction loops and camcorders, were applied for this aim. Nevertheless, the introduction of unmanned aerial vehicles (UAVs) has opened new possibilities in this subject, leading to extensive study in computer vision. However, there are still issues in object detection and tracking because of the complexities that come from a high number of objects, shifting heights of unmanned aerial vehicles (UAVs), and variable lighting conditions. Our study provides a new approach that combines centroid tracking with the YOLOv5 algorithm to successfully monitor and identify vehicles. To acquire an accurate analysis, we began our method by aligning and referencing aerial images. These basic measurements assisted the advancement to next phases covering feature extraction, segmentation of the region of interest, and tasks connected to detection and tracking. The effectiveness of our technique was tested by applying it to the VAID dataset, resulting in amazing levels of accuracy demonstrated by our recommended solution. Our system exhibited an unparalleled accuracy of 98.5% for object detection and a reasonable 90.9% for tracking tests. The validation indicates the robustness and efficacy of our designed system in solving the obstacles given by sophisticated aerial surveillance conditions.
The Role of Social Media before and during a Holiday Travel: Generational and Gender Differences

This article discusses the use of social media by different generations in destination marketing. The study analyses the use of social media by travelers at all stages of a trip. It considers Kimolos, a small Aegean Sea Island as a case study. This research aimed to examine the degree of social media influence by travelers before and during their trip to the island. Other elements such as the most popular social media, the reason for their use by travelers and their role as a source of information were also analyzed.