Miracle-dz/UniFAR-FTUs huggingface dataset:
Architecture and Interface for Collaborating with a Group of Agents in an Adversarial Game

Despite the impressive advances in artificial intelligence (AI), close collaboration between people and AI systems is still hard to achieve. To overcome this problem, we have designed an architecture consisting of the Contract Net Protocol, the Behavior Tree, and a human–multiple agent interface that we call "human-swarm interaction. " Its effectiveness and feasibility were evaluated using team play in a simulated Tail Tag game. Matches with up to 19 AI agents and 1 person on one team and 20 people on the other team were played. The results indicate that our system is scalable and that there is room for improvement. We identified two key features for AI agents to achieve collaboration with people in a serious game as represented by Tail Tag game: the ability to at least remain alive in any situation, and active declaration of roles being attempted as well as sharing of roles.
Revolution of Electric Vehicle Charging Technologies Accelerated by Wide Bandgap Devices

This article presents the state-of-the-art electric vehicle (EV) charging technologies that benefit from the wide bandgap (WBG) devices, which is regarded as the most significant revolution in the power electronics industry in the past few decades. First, the recent WBG device technology evolution, performance comparison, and reliability issues are introduced. Then, topology development, efficiency and power density boost, and cost reduction brought by the WBG devices for EV charging equipment, such as onboard chargers, fast-charging stations, and wireless chargers, are discussed. A figure of merit (FOM) for evaluating the performance of wireless chargers is also proposed. Finally, the EV charging technology roadmap forecast is presented based on the WBG devices’ evolutionary trends.
An Internet of Things (IoT) Homecare Management System Using Cardiac Arrhythmia Classification

Due to the fast growing of population, the need for providing patient care while they are at home at anytime is important and the era of homecare get the attention. Internet of Things (IoT) is widely known and used by different fields. IoT with the assist of homecare will help in reducing the burden upon hospitals. The most important challenge in homecare system is the accuracy because those systems are dealing with human health which is sensitive and need high accuracy. The trusted homecare system by health experts should be able to detect abnormalities and make decisions in an accurate way. To overcome the accuracy challenge, this paper presents a Cardiac Arrhythmia monitoring framework. Cardiac Arrhythmia happen when the Electrocardiogram (ECG) signal contains irregularity in their features. The raw ECG Signal is passed through signal processing stage for denoising. Then the denoised signal is passed through the feature extraction stage to extract a number of features that are used in the classification stage. After that, a classification stage to detect Cardiac Arrhythmia is made using deep learning to improve the accuracy. Experiment results show that the proposed model is more accurate in the classification of Cardiac Arrhythmia and more reliant from caregivers point of view in comparison with other researches.
Performance Evaluation of Full-Duplex IAB Multi-Cell and Multi-User Network for FR2 Band

One of the approaches to support high data rates in beyond 5G cellular networks is the dense deployment of the small cell millimeter-wave (frequency range 2 (FR2)) ( $>24.5$ GHz) base stations. However, this dense deployment of base stations leads to high cost, because it requires fiber backhaul connection with the core network. One prominent solution is the integrated access and backhaul (IAB) network proposed in 3GPP Release 16, where some portion of wireless spectrum is used for backhaul to serve base stations instead of fiber, and the remaining portion of the spectrum is used for user equipment for communication. The partition of the spectrum into access and backhaul degrades the spectral efficiency of the IAB network. Thus, in this paper, we propose a full duplex (FD) enabled IAB network with large-scale array systems to enhance the spectral efficiency of the IAB network. In the FD IAB network, the IAB node is FD which transmits and receives at the same frequency/time resources. We consider a multi-cell multi-user IAB network with all the interferences due to FD transmission and evaluate the performance in terms of bit-error-rate and spectral efficiency. Moreover, an algorithm for choosing different users is proposed which is based on the cross-correlation of RF precoder weights. Further, identification of the optimal beam’s index is also proposed which is based on the index of synchronization signal block and half-frame bit of physical broadcast channel payload of 5G NR. Finally, successive interference cancellation is proposed for self-interference mitigation in the digital domain.
Route Planning and Tracking for Ships Based on the ECDIS Platform

Automatic route planning and tracking control are important for unmanned surface vehicles (USVs). This study presents a final-optimized A* (FO A*) algorithm for automatic route planning of USVs and designs a nonlinear tracking controller to ensure that the ship navigates on the route. Ship domain theory is employed in the FO A* algorithm to ensure that the planning route is far from shorelines and reefs. Meanwhile, water depth is used to determine the safety area in the search process. The nonlinear feedback algorithm is also applied to reduce the rudder angle when the ship tracks the planning route in wind and waves. Finally, the proposed algorithms are well validated on the ship-based Electronic Chart Display and Information System (ECDIS). The experiment shows that the FO A* algorithm and the nonlinear tracking controller are effective and deterministic for route planning offline. It can be put into navigation practice, which will make some contribution in accelerating the USV development.
Prospects for High-Efficiency Silicon and lll-V Power Amplifiers and Transmitters in 100-300 GHz Bands

Frequency bands between 100-300GHz, known as upper millimeterwave (UmmW), offer an opportunity to converge communication and sensing systems to support future high-throughput backhaul and radar applications [1]. In particular, frequency bands located at 140 and 220 GHz feature O2 and H2 O absorption windows for low propagation loss in outdoor channel environments. A challenge to operating in UmmW bands is the lack of mature electronic and packaging technologies that has resulted to date in poor power efficiency and higher costs when compared to lower millimeter-wave (LmmW) bands (28/39/60 GHz). While the 60 GHz bands offer substantial bandwidth, the high absorption at this band prohibits energy efficient operation over more than a kilometer.
15–40 GHz and 40–110 GHz Double-Ridge Open-Ended Waveguide Antennas for Ultra-Wideband Medical Imaging Applications

This paper presents the design, fabrication, and measurement of two ultra-wideband doubleridge open-ended waveguide antennas (OEWAs) for emerging high-resolution millimeter-wave (mm-wave) medical imaging applications. The proposed antennas can collectively provide a 95-GHz synthetic imaging bandwidth in the mm-wave regime. The antennas feature the smallest aperture sizes while providing the widest bandwidths amongst all OEWAs reported in the literature. The proposed antennas consist of coaxial to double-ridge waveguide (DRW) transitions and free-space matching sections. Two DRWs with 8:1 single-mode bandwidth in the mm-wave regime are designed. In the transition structures, coaxial flange launchers are connected to the DRWs through customized <inline-formula> <tex-math notation="LaTeX">$50-\Omega$ </tex-math></inline-formula> coaxial lines. To improve the impedance matching while meeting the fabrication requirements, an effective tuning technique is applied to adjust the input impedance of the transitions. Finally, to achieve a smooth transition and a well-matched impedance to the free space while maintaining small aperture sizes, piecewise tapered-ridge matching sections with the same aperture size as that of the designed DRWs are employed. The antennas are fabricated using the computer numerical control (CNC) milling process. Each antenna demonstrates <inline-formula> <tex-math notation="LaTeX">$\left|\mathrm{S}_{11}\right|\lt -10 \mathrm{~dB}$ </tex-math></inline-formula> and stable and symmetric radiation patterns across <inline-formula> <tex-math notation="LaTeX">$15-40 \mathrm{GHz}$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$40-110 \mathrm{GHz}$ </tex-math></inline-formula> with aperture sizes of <inline-formula> <tex-math notation="LaTeX">$9.47 \times 5.79 \mathrm{~mm}^{2}$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$3.78 \times 2.27 \mathrm{~mm}^{2}$ </tex-math></inline-formula>, respectively, reporting state-of-the-art results.
Antenna Element Space Interference Cancelling Radar for Angle Estimations of Multiple Targets

This paper focuses on estimating the angle and size of different-sized targets using a radar system that estimates the angles of trucks, vehicles, bicycles, and pedestrians. The problem is that the presence of a sidelobe from the larger target degrades the accuracy of estimating the angle and size of the smaller target in multiple target detection. In this paper, a novel scheme, called antenna element space interference cancelling (AIC), is proposed for reducing the influence of the sidelobe. The performance of the proposed AIC radar system was evaluated through both computer simulations and experiments. The simulation results show that the proposed AIC radar system can estimate the angles of smaller targets within a 1-degree error, and the size estimation error is 1 dB. The experimental results also show that the proposed AIC radar is effective in detecting smaller targets that cannot be detected with traditional methods due to the sidelobes of larger targets.
System Identification of Lightly Damped Systems Using Recursive Kautz Functions-Case Study With Coriolis Mass Flow Meter

This manuscript deals with the system identification of lightly (under) damped processes to find a suitable model structure. To identify such processes, the Kautz model, which is a two-parameter representation of orthogonal basis functions (OBF) in state space format, is used. The process parameters are obtained through the subspace method by sequencing the states obtained from the Kautz model. The obtained states are transformed into convex optimization in which the eigen values poles) to be estimated are confined to lie within user-defined regions in the convex plane. To derive the optimal states, an efficient control scheme based on the state observer design is implemented. The viability of the proposed work has been verified on real-time Coriolis Mass Flow Meter (CMFM) under no flow condition and the corresponding responses are plotted using MATLAB.
What Do Good Integrated Reports Tell Us?: An Empirical Study of Japanese Companies Using Text-Mining

In recent years, ESG investment has been attracting a lot of attention both practice and academia. In the midst of these ESG investment trends, corporate non-financial information has become more important, and integrated reports that include both financial and non-financial information have been attracting attention. However, unlike financial statements, there are no standard forms in integrated reports. The content and amount of them vary, and the ingenuity of each company can be seen. In addition, while financial information can be shown as numerical values and graphs, non-financial information tends to be qualitative information. Since it is difficult to read and compare multiple integrated reports consisting of tens to hundreds of pages, the need to read more important non-financial information efficiently and objectively is increasing. Therefore, in this study, we analyze these integrated reports quantitatively by using natural language processing techniques to tell the differences between "good" and "not good" integrated reports. Here, as "good" integrated reports, we use a list of excellent integrated reports and significant improved integrated reports from the GPIF website. For the good integrated reports, we found that (1) they describe more sustainable words that create long-term value, (2) describe more about their services and customers, and (3) are proactive in sustainability and corporate governance.
Patient Drugs and Data Management by Mobile Application

The main purpose of this paper to design a mobile application that manages and saves patient drugs, examinations, and data that can be added by the health staff while the patient is at the hospital, then the patient can log in to the application on his mobile phone without using internet. The application includes empty templates dedicated to receive the specific data distributed over five main lists: Patient information list contains personal information about the patient, List of drugs that is electronic prescription, Medical Examinations list, Recommendations list, and Doctor’s profile list that contains doctor’s contacts to contact him if required. The application was designed using MIT App inventor website, then tested and evaluated by 7 patients as volunteers at Al Jawda Hospital in Khartoum, and the provided benefits by the application were evaluated by the volunteers at 90%. The application works on mobiles that powered by Android system.
Real-Time Multi-User Video Optical Wireless Transmission Based on a Parallel Micro-LEDs Bulb

In this work, we experimentally achieved a real-time video optical wireless transmission which can broadcast two users in different positions, simultaneously. Firstly, a single-pixel 50-μm micro-LED is designed and fabricated on a GaN-based wafer and then two micro-LED chips are connected as parallel form in a single bulb. The transceiver based on two FPGA boards is used to generate quadrature amplitude modulation-orthogonal frequency division multiplexed (QAM-OFDM) signals and measure the communication performance of the VLC system. The low-power micro-LEDs bulb emits 0.8 mW optical power through two optical paths at 20 mA driving current, and the modulation bandwidths for two users’ access are 90 MHz and 73 MHz, respectively. In this two-user real-time video transmission system, the total data rate exceeds 105.54 Mbps without naked eye discernable distortion over 2-m free-space distance. Different from traditional point-to-point links, this work demonstrates the possibility of flexible high-speed multi-user real-time video optical wireless transmission based on a parallel micro-LEDs bulb.
Accuracy Comparison of Home Security Face Recognition Model in The Several Lighting Condition Using Some Kinect Produced Image

As technology advances, security systems and the development of artificial intelligence systems around the world continue to be developed. Artificial intelligence is very often used to automate parts of a system. In terms of data security and physical security, the use of artificial intelligence to be able to recognize a person’s face plays a very important role in increasing the efficiency and management of knowledge. This paper was written with the aim of proposing a comprehensive comparison of accuracy, in recognizing faces using the features found on the Kinect camera (depth images, 3-dimensional images, and infrared images), compared to features found on ordinary cameras (RGB images), using techniques digital image processing in several different light conditions (dark room conditions and bright / standard room lighting). This research was conducted by comparing how reliably the facial recognition system classifies images that are part of the data class, as well as fake class images in the home security system. From this research, the Support Vector Machine (SVM) algorithm is used for classification, resulting in 20% better accuracy for images produced by Kinect cameras than for standard RGB cameras.
Application of Fuzzy PI control for driving DC Motor using Complexity Reduction Method

This paper presents a comparison of PI and simplified fuzzy PI controllers for Permanent Magnet DC (PMDC) motor speed control. The aim is to make fuzzy PI controller design simpler. Reduced fuzzy rule is proposed for the fuzzy PI, while Ziegler-Nichols method is used for the standard PI controller. This less complicated design rule leads to only one input needed for the fuzzy PI; an absolute error is used. Moreover, this rule base gives further reduction of the complex calculation. Simulation and experimental tests were conducted. Results show that the proposed fuzzy PI performs better than the conventional PI controller. Also, from the comparison, it has been found that this fuzzy PI controller can make the whole drive system higher efficiency than that of the conventional PI controller; low power consumption is considered.
A Multi-Kernel Mode Using a Local Binary Pattern and Random Patch Convolution for Hyperspectral Image Classification

With the development of deep learning technology, more and more scholars have applied it to hyperspectral image (HSI) classification to improve classification accuracy. However, these deep-learning methods not only take a lot of time in the pre-training phase, but also have relatively limited classification performance when there are fewer labeled samples. In order to improve classification performance while reducing costs, this article proposes a multikernel method based on a local binary pattern and random patches (LBPRP-MK), which integrates a local binary pattern (LBP) and deep learning into a multiple-kernel framework. First, we use LBP and hierarchical convolutional neural networks to extract local textural features and multilayer convolutional features, respectively. The convolution kernel for the convolution operation is obtained from the original image using a random strategy without training. Then, we input local textural features, multilayer convolutional features, and spectral features obtained from the original image into the radial basis function to obtain three kernel functions. Finally, the three kernel functions are merged into a multikernel function according to their optimal weights under the composite kernel strategy. This multikernel function is used as the input for the support vector machine to obtain the classification result map. Experiments show that compared with other HSI classification methods, the proposed method achieves better classification performance on three HSI datasets.
Social Media Emotion Analysis in Indonesian Using Fine-Tuning BERT Model

Social media is the leading platform were users’ express opinions and emotions. Emotion Analysis aims to identify emotions: happy, sad, angry, fear, disgust, shame, and guilt. InaMoodMeter framework takes the unstructured status of a Facebook user. It processes it to extract emotion from teenage users for self-assessment and to observe emotion related to online customer satisfaction. We also created an Indonesian Dataset for Emotion Analysis from Facebook. The dataset can be utilized as a valuable benchmark for emotion classification in Indonesian. With this dataset, many state-of-the-art approaches are evaluated. We also experimented on the ISEAR Indonesian translated dataset. This research consists of two stages: obtaining training data to build the dataset and performing classification. Our proposed method can achieve the highest accuracy of 79% with the Fine-Tuning Bert Model.
CloudSimHypervisor: Modeling and Simulating Network Slicing in Software-Defined Cloud Networks

Software-Defined Networking (SDN) is an innovative technology which provides a programmable network control which is decoupled from the physical infrastructure. Network Virtualization (NV) is the phenomenon where a given physical network infrastructure and its resources are abstracted to create multiple logical virtual network slices of the underlying substrate. NV enables independent virtual networks to co-exist on one or more shared physical network infrastructure. Edge computing makes use of the edge resources in close proximity to end-users to reduce service delay and the network traffic volume in the end-to-end networks. Similarly, network slicing which is a key enabling technology for 5G networks is designed to support different services from different platforms at different scales enables sharing of physical network infrastructure on many different virtual network layers. These innovative technologies and strategies have gained significant attention from both academia and industry as they have the potential to maximize network resource utilization and optimize end–to–end network service delivery in 5G solutions deployment. To enable continuous simulation and development of applicable 5G networking concepts using these technologies, there is a need for an accessible and easy-to-learn testbed which is able to efficiently measure the performance of physical and virtual network capacities, provisioning approaches and management of multiple architectural models using large-scale network slicing configurations in a repeatable and controllable manner. These tools and toolkits provide scalable, lightweight and controlled cloud simulation environments necessary to analyse network traffic flows, allocation capacities and policies and the behaviour of multiple heterogeneous networks at an extremely low cost as compared to the huge financial commitments involved in conducting similar experiments in a real-life event. Existing solutions do not support Network Slicing and end- to -end heterogenous network automation which are key enablers of 5G network implementation. Hence in this paper, the CloudSimHypervisor framework is developed in this based on CloudSimSDN-NFV. The complete architecture and features of the CloudSimHypervisor framework and some used cases are presented in this paper. We validate the CloudSimHypervisor framework with two use case experiments in the cloud computing environment: Joint compute and network resource utilization and network traffic prioritization. Results from these experiments display the efficiency of the CloudSimHypervisor in estimating and measuring processing speed, transmission speed, compute and network usage efficiency and energy consumption.
Designing an Escape Room Educational Game for Cardiopulmonary Resuscitation Training: The Evaluation of Learning Achievement and Flow State

The study aimed to develop an escape room educational game for learning cardiopulmonary resuscitation (CPR) procedure. To evaluate the game, thirty-eight junior high students from northern Taiwan participated in a learning activity using the game. The results revealed that learners this game enhanced students’ learning achievement of CPR procedure. Meanwhile, learners generally enjoyed playing the game in terms of their flow state, and perceived the game as useful and easy to use.
Speeding up CSIDH using parallel computation of isogeny

Isogeny-based cryptography is a promising candidate for post-quantum cryptography. CSIDH is a Diffie-Hellman key exchange protocol based on isogeny with a small public key size and is secure against a quantum computer. However, one of the problems is that it takes a long time to compute the group action corresponding to the secret key. Several efficiency improvements have already been studied, such as the SIMBA technique by Meyer, Campos, and Reith, and the two-point method by Onuki, Aikawa, Yamazaki, and Takagi. In this paper, we propose parallel computation method for the isogeny in CSIDH, which speeds up the computation of the isogeny from $\mathcal{O}(d)$ to $\mathcal{O}(\log d)$, where $d=(\ell-1)/2$ and $\ell$ is the degree of the isogeny.