Miracle-dz/UniFAR-FTUs huggingface dataset:
Vehicle and Pedestrian Fast Detection based on YOLO-FPN

An unbalanced accuracy and computational cost exist within the YOLO series object detection algorithms, as well as a lack of generalization in the models. To address these issues, we propose YOLO-Day Night and Fast, a highly accurate and fast vehicle and pedestrian detection model based on YOLO that is capable of detecting both vehicles and pedestrians across a wide range of lighting conditions. The purpose of this paper is to investigate the effects of convolutional structure and network depth on the ability of the Backbone to extract features and its calculation cost in comparison to mainstream detection models that use convolutional neural networks. In this paper, we propose a low-cost ACNet network that has a high feature extraction capability. We select Arrow-Blocks and CSP-Blocks to represent different levels of the network, and determine network depth by quantifying the computational cost of stacked units. A data enhancement strategy of HSV domain perturbation and luminance processing has also been developed to improve the detection accuracy of the model and to alleviate the problem of insufficient generalization of the model. Furthermore, differences in luminosity between daytime and nighttime images are taken into account in the experiments. The experimental results indicate that the proposed model achieves 32.8% detection accuracy of full time mAP at 24.36 frames per second after training using the SODA10M dataset containing only daytime images, which is higher than the current mainstream detection models in terms of detection accuracy and speed. The nighttime accuracy reached 27.7%, which improved the nighttime detection capability of the model and extended the detection application scenarios of the model.
Development and Validation of Customer Intention Factors for Live-Stream Shopping

Along with technological improvements, online shopping is currently developing quickly. Online shops started by selling electronics, clothing, food, and home appliances and continue to evolve, selling various things. Currently, digital marketing has a novel technique, namely live shopping, where sellers can present, promote and offer their products directly through live streaming on social media and e-commerce platforms. This research will focus on gaining insight into the factors influencing customers to watch and purchase through live streaming. This research is an early-stage study of exploring and searching for research models. The study used a literature review approach by reviewing similar previous research articles. Several variable components were used in this study. The findings of the factors include visibility capacity, meta-vocation capacity, purchase orientation capacity, social presence, price, perceived product quality, perceived enjoyment, purchase orientation, perceived usefulness, and intention to buy. These factors were tested and proved to be valid with Cronbach's Alpha value being more than 0.5 and reliable with the value of the loading factor a greater than 0.6. Criteria The factors of the findings for further analysis of the relationship between factors and other variables.
Risk Management Planning of Certification Authority Termination Based on ISO 31000:2018

Certification Authorities (CAs) are vital for ensuring the security of personal data and electronic transactions within Public Key Infrastructure (PKI). However, CA termination, whether due to compromised keys, financial troubles, or government policy changes, poses significant challenges, particularly in critical sectors like government, banking, healthcare, and education. This paper highlights the need for comprehensive risk management strategies to address CA termination, where existing guidelines are scarce. We propose a risk management framework based on ISO 31000:2018, focusing on the non-technical risk factors that go beyond technical considerations. ISO 31000:2018 offers a versatile approach applicable to various organizational levels and information systems. This research provides recommendations managing risks surrounding CA termination to minimize losses on the customer side.
Cyber Security and Data Mining: Detecting Malicious URL Using Data Mining Techniques

Increasing usage of data generation and utilization, the threat of data exploitation and loss also increases. So, the need for cyber security also increases. Using data mining techniques, we can protect our data from threats that come along with the extensive use of technology. In this paper, we present machine-learning and deep-learning approaches to identify the cyber-attacks that are in the form of malicious URLs (Uniform Resource Locators). The cyber-attackers use malicious URLs as a way of invading the system and propagating malware over the web. When a person accesses such URL, it initiates a predetermined harmful behavior which can result in various cybercrimes such as spamming, phishing, loss of confidentiality, denial-of-service, etc. To prevent suck attacks, we have analyzed the use of Deep Learning (DL) and Machine Learning (ML) algorithms for the detection and classification of malicious URLs. Machine learning algorithms implemented include Gaussian Naïve Bayes (NB), Multinomial Naïve Bayes (NB), Bernoulli Naïve Bayes, and Random Forest Classifier (RFC). Deep learning algorithms that were implemented include RNN, LTSM, GRU, Bidirectional LSTM, ANN, and 1D convolution neural network.
Computing for Mobile Ad Hoc Network in IoT Fuelled Smart Environments: Computer Integration in Smart Environment

Present Information Technology scenario is engulfed with exponential rise in mobile computing devices mostly embedded in diverse equipments of our daily life. This has led to evolutionary demand of new solutions to mobile computing for smart environments equipped with Internet of Things (IoT). A plethora of challenges are emerging to ensure effective internet connectivity for such mobile devices. These devices are mostly equipped with sensors and demand continuous computing and data transfers. The globe is evolving to humancentred society, i.e., Society 5.0 loaded with innovations of Big Data, IoT, Artificial Intelligence and much more to make social life comfortable and sustainable. Hence, this trend is the need of the hour but has brought a range of new challenges to MANETs for mobile computing in smart environments. This paper explores the MANET-IoT computing trends and provides a detailed analysis of the challenges for computing in the smart environment having MANET-IoT as backbone for mobile communications.
Modeling Emotion Analysis of Indonesian Tweets

Emotion analysis in social media plays a pivotal role in deciphering the underlying feelings and attitudes of users towards topics, brands, or events. It helps businesses and researchers to understand public opinion, predict trends, and inform decision making. In this study, we are conducting an emotion analysis task by using 6 different types of deep learning model architecture to see which model produced the best result for a relatively low resource data (less than 7000 data) with imbalance class. In our experiment, we use the context of the homecoming ban during COVID-19 pandemic in Indonesia. The dataset is taken during Eid al-Fitr holiday in 2021. The dataset shows the diversity in emotion shown in Twitter. Hence, we use the dataset and the emotions that arise from opinions circulating on Twitter. We used 6,990 manually labelled data from Twitter that has different forms of emotions, such as tender, scared, angry, excited, sad, happy, and wonder. From our experiments, it turns out that Bidirectional Long Short-Term Memory with Attention layer got the best accuracy of 91.40%. It is also proven that the addition of an attention layer to the model architecture can increase the accuracy.
Performance Assessment of Multi-Controller in Software Defined Network

In a network design, the control plane and the data plane are separated by the architectural concept of software defined networking (SDN). A centralised controller that serves as the only point of control for the whole network is introduced by the SDN architecture. The controller serves as a logically centralized entity in charge of communicating the appropriate rules to a switch's flow table. Using a single controller to handle a lot of flows becomes insufficient. A lot of large-scale networks have employed multi-controller SDN to meet their objectives. In this research, we use a multi-controller infinite buffer waiting queue model to analyze the system performance metrics. Both numerical and simulated assessments are used to illustrate the proposed model.
Test Platform for Blueberry Inspection System, Based on UR3e Robot With camera in the End Effector

En este trabajo, se describe la implementación de una plataforma de pruebas para un sistema de inspección de plantas de arándanos basado en un dron con cámaras multiespectrales. El objetivo principal es implementar, en un robot UR3e, el seguimiento dinámico de trayectorias comandadas por un sistema de visión incorporado en su efector final, que permita probar y evaluar diversas alternativas de escaneo, detección e inspección de los racimos de arándanos en las plantas, que serían implementadas posteriormente en el dron. Para realizar experimentos, se colocaron imágenes de racimos de arándanos, en pequeñnos postes de madera, que se ubicaron en el espacio accesible por el UR3e. Cuando el robot se acciona, comienza a mover la cámara hasta detectar los frutos, guarda un video de la inspección del racimo, y continúa su trayectoria en busca de más frutos. Los resultados de estas pruebas permitieron establecer el rango de movimientos necesarios para capturar imágenes representativas de las plantas de arándanos, establecer y validar la comunicación robusta entre los dispositivos involucrados en el sistema de inspección y corroborar la efectividad del modelo de detección de arándanos.
Blockchain-Enabled Private and Secure Task Allocation Framework

In the realm of wireless communications and task allocations, where agents need to travel to perform tasks, a new challenge has emerged. This challenge involves efficiently distributing tasks among agents while considering their preferences and priorities. Unfortunately, many existing solutions rely on centralized systems, making them susceptible to various vulnerabilities, including privacy concerns and security threats. To address these issues, this paper introduces a novel solution named SEPP-TAP-a secure and privacy-preserving decentralized framework for multi-agent task allocation problems, harnessing the power of blockchain technology. SEPP-TAP not only protects agents' privacy and location information but also strengthens network security by mitigating threats like the Sybil attack and network flooding. Our approach employs innovative anonymization techniques to ensure that agents' identities remain concealed throughout successive task requests, breaking any associations between tasks performed by a particular agent.
Feed-Animal Balance Control:Approach Based on Feed Availability in Critical Period.

A review was made on the currently practiced feed-animal balance management system. The weakness and problems existed in the system were analyzed. According to the seasonal fluctuations of primary productivity of the grasslands, a new approach for the feed-animal balance management system was proposed. The proposed system is mainly based on restrain of grazing during critical period of grassland sensitive to grazing, together with the consideration of grassland forage production capacity and economical adjustment. Principle and operating procedures were described.
Comprehensive Comparison of Current Mode Control Schemes for LLC Resonant Converter

This paper discusses some comparative aspects of three current control schemes for resonant converter under abnormal conditions. The current control schemes are designed specifically for circumstances where one or more system parameters fluctuate dramatically to the point where a conventional current controller cannot keep the system stable. A comprehensive analysis and comparison is presented for three current control schemes to enhance the power quality under abnormal conditions. For comparison, a small signal equivalent circuit model of each scheme is also simulated. Furthermore, the comparison and analysis of these schemes for the design of highbandwidth voltage loops, adapting converters to broad input voltage ranges, audio susceptibility and adapting voltage placement is also performed. Moreover, this paper provides a guideline for feedback circuit design and selection of best current control scheme.
Forex Daily Price Prediction Using Gated Recurrent Unit

The foreign exchange (Forex) market is globally recognized as one of the most prominent financial markets. In this paper, we focus on three major currency pairs: EUR/USD, GBP/USD, and USD/CHF, spanning from January 2007 to July 2022. We employ a range of techniques, including technical indicators, feature scaling, and Gated Recurrent Unit (GRU) network, to predict the closing price one day ahead of the current day. Our method demonstrates superior performance compared to other state-of-the-art approaches, achieving remarkably low Mean Absolute Errors (MAE) of 0.0046, 0.0063, and 0.0039 for the respective currency pairs: EUR/USD, GBP/USD, and USD/CHF.
A Single-Fed Dual-Band Fabry-Perot Antenna for Millimeter-Wave Communications

A single-fed Fabry-Perot antenna with high gains in $K$ and Ka bands is presented. The antenna is composed of a printed magneto-electric (ME) dipole, an artificial magnetic conductor (AMC) reflector and a partially reflective surface (PRS). The AMC unit cells, which are periodically arranged surround the ME dipole, can not only enhance the dual-band operation, but also adjust the cavity thickness in both operation bands. The final antenna is designed to have its impedance bandwidth of22.1 ~ 23.1 GHz and 30.2 ~ 31.2 GHz, with the peak gains of 14.5 and 14.5 dBi, respectively.
Optimization Method for Dynamic Performance of Dual Active Bridge DC-DC Converter

Dynamic characteristic is an important indicator of converters and energy systems. In order to speed up the response of dual active bridge (DAB) DC-DC converter when the load changes suddenly, this paper takes pulse width plus phase shift (PPS) modulation as an example to introduce the derivation of the small-signal modeling of DAB DC-DC converter and the implementation of the corresponding controller on DSP in detail. This method requires the participation of circuit parameters such as inductance in control, which increases the accuracy of the control method. Through experimental verification, the optimization method provides highly accurate and fast response speed for the converter. At the same time, the proposed modeling method can also be applied to other phase shift modulation strategies of DAB converters or resonant converters with arbitrary unidirectional/bidirectional power transmission.
Dilated Convolutional Neural Network for Scenic Text Detection and Recognition on a Synthetic Dataset

Scenic text detection and recognition in natural images pose significant challenges due to complex backgrounds, varying fonts, and irregular layouts. In this research, we propose a novel method utilizing Dilated Convolutional Neural Networks (DCNN) to address these challenges and achieve accurate text detection and recognition on a synthetic dataset The proposed DCNN architecture leverages dilated convolutions to broaden the receptive field of the model while preserving computational efficiency. We generate a large-scale synthetic dataset comprising diverse scenes with synthetic text, annotated with ground-truth bounding boxes and corresponding text labels. We create a sizable synthetic dataset with a variety of sceneries and artificial text that is tagged with real-world bounding boxes and text labels. For text detection, our DCNN model is designed to predict the bounding boxes encompassing text regions within an image. Through extensive experiments, we demonstrate the efficacy of our approach by comparing it with state-of-the-art text detection methods, achieving superior performance in terms of precision, recall, and F1-score. Experimental results on our synthetic dataset and benchmark real-world datasets demonstrate the robustness and generalizability of our proposed method for scenic text detection and recognition. Additionally, qualitative results highlight its capability to handle challenging scenarios, such as curved text, occlusions, and text in various orientations. The proposed model is tested on two standard datasets MNIST and ICDAR03 along with our synthetic dataset. The recognition is evaluated based on the accuracy and our proposed model yields 99.7 percentage of accuracy which is higher when compared to other state of art methods.
Cooperative Control Strategy of Load Shedding and Frequency Modulation of Doubly-fed Induction Generator considering Speed Recovery

With the deepening of the peak carbon dioxide emissions and carbon neutrality, renewable energy has become a strategic requirement. As a typical example, wind power generation accounts for an increasing share of the power system. When DFIGs adopt additional inertial control, the rotor speed may not be restored, which eventually causes the fan to deviate from the optimal operating point. Therefore, this paper proposes an additional inertial speed recovery strategy for DFIGs. First, additional inertial control is applied to the DFIG. It sequentially exits the additional inertial control and switches to the rotor speed recovery mode. The additional inertial tasks are distributed by the rest of the fans according to the amount of kinetic energy. In response to the problem of insufficient fan support capacity when the system fails, a cooperative control strategy for DFIG load reduction and frequency modulation is proposed, adjusts the pitch angle in real time, and calculates the wind energy utilization coefficient by comparing it given by sagging control. Finally, it is verified through simulation that the control strategy can reduce the magnitude of system frequency fluctuations and the level of frequency drop after failure, so as to effectively participate in system frequency modulation.
Novel Objective Function and Expectation Value Estimation Method for the Variational Quantum Singular Value Decomposition Algorithm

In this work, we discuss two modifications that can be made to a known variational quantum singular value decomposition algorithm popular in the literature. The first is a change to the objective function which hints at improved performance of the algorithm and decreases the depth of the circuits. The second modification introduces a new way of computing expectation values of general matrices, which is a key step in the algorithm. We then benchmark this modified algorithm and compare the performance of our new objective function with the existing one.
Performance Investigation of 24-lot/10-pole Synchronous Reluctance Machine in Integrated Onboard Battery Charger for EV Applications

This paper proposes an integrated onboard battery charger (IOBC) for electric vehicles (EVs) employing a six-phase synchronous reluctance motor (SynRm) equipped with fractional slot concentrated winding (FSCW) as the traction motor of the drivetrain. IOBC is more preferable than traditional onboard battery chargers (OBC) as they reduce the weight and cost of the EV through making use of the powertrain elements in the charging process. Only a few FSCW slot/pole combinations show satisfactory performance for the SynRm, among which 24-slot/10-pole is considered. As a result, this paper presents a preliminary design for a six-phase SynRm with the proposed slot/pole combination. Then, the machine is analyzed under propulsion and charging modes through finite element analysis (FEA) to validate its performance.
Centering Policy and Practice: Research Gaps Around Usable Differential Privacy

Differential privacy is seen by many experts as the ‘gold standard’ for privacy-preserving data analysis. Others argue that while differential privacy is a clean formulation in theory, it is not very workable in practice. Both perspectives are, in our view, valid and important. To bridge the gaps, researchers and practitioners must work together to advance policy and practice of differential privacy. In this paper, we outline the pressing open questions and offer recommendations around making differential privacy more usable in practice.
Evaluating Autistic Children's Dynamic Balance Ability by an Azure Kinect Sensor

We suggested using an Azure Kinect sensor to evaluate the dynamic balance ability of children with autism spectrum disorder (ASD). Two metrics, i.e., body mass center fluctuation (BMCF) and upper limb swing (ULS), were extracted from the trajectories of 32 body joints' movement during the balance beam test. Findings showed that ASD children had lower BMCF and ULS than typically developing (TD) children. Furthermore, the prediction using BMCF achieved a maximal accuracy of 79% and an AUC value of 0.69.
Plant Leaf Disease Classification using Deep learning based Hybrid Approach

Plant diseases lower crop quality and quantity, making them crucial. Thus, early disease identification and diagnosis are essential. To automate Maize leaf disease classification, we propose a deep learning-based approach. Convolutional Neural networks identify picture databases and perform best in image classification. We customized the Logistics Regression and applied the Convolutional Neural Network(hybrid) method to extract deep image characteristics. This research uses the maize leaves dataset from the Plant Village and PlantDoc datasets. The proposed method yields 93.2% accuracy in experiments.
Dynamic Beam Manipulation Based on Space-Time-Coding Metasurfaces

Metasurfaces are artificially designed ultra-thin structures that can be regarded as two-dimensional forms of metamaterials. Space-time-coding metasurface has been developed. This concept has aroused the research interests since it was proposed, and its great flexibility to beam manipulation has become one of the hot research directions. Here, we design a dynamic tunable metasurface combined with vanadium dioxide, and study properties of space-time coding metausurface in the THz band. In addition, by using the space-time coding theory, the vortex and its angle deflection are successfully generated in the 2- bit encoding, which shows the powerful ability of the space-time- coding metasurface to manipulate the beam.