Miracle-dz/UniFAR-FTUs huggingface dataset:
Deep Reinforcement Learning Based Task Offloading for UAV-Assisted Edge Computing

Task offloading, underpinned by Unmanned Aerial Vehicle (UAV) relays within the realm of edge computing, has evolved as a vital mechanism to circumvent limitations associated with terminal computing capabilities. Contemporary offloading strategies predominantly prioritize either the minimization of energy consumption in individual UAVs or the adjustment of flight trajectories to accommodate a more expansive pool of mobile users. These strategies often overlook the mechanisms through which UAVs could effectively collaborate to optimize system resources. Given the energy constraints inherent to UAVs, adopting an appropriate cooperative offloading mechanism can ameliorate dilemmas tied to excessive loads or energy insufficiency in a single UAV. As a result, data offloading from the UAV swarm to edge servers can be maximized, mitigating resource wastage during inter-UAV task transfers. This paper introduces a Deep Q-Network-based Resource Allocation Policy (DRAP) tailored to manage task offloading upon the arrival of task data at the UAVs. This policy employs a Deep Reinforcement Learning (DRL) network for task data allocation, delegating suitable UAVs to perform offloading actions. The offloading strategy conducts a central assessment of the current state of all UAVs in the swarm and all pending task data, selecting the most suitable UAV for each task offloading operation. This method significantly alleviates load pressure in scenarios where a single UAV is overly burdened or energy-deprived. Given that local task data is not processed but merely transmitted, task offloading can be considered as a discrete action. This perception enables the transformation of the UAV task offloading challenge into a selection problem within a discrete action space, aimed at maximizing the utilization of UAV resources. Extensive simulations demonstrate the proposed solution beats the benchmark algorithms by providing higher task offload volume and lower communication resource consumption.
A Lightweight Network Traffic Prediction Method for SmartNICs

Network traffic patterns in high-performance computing systems can impact application performance by inducing costly re-transmissions or consuming memory bandwidth. Emerging SmartNIC technologies present new opportunities for addressing these issues and optimizing network performance by providing a platform for the intelligent utilization of network resources through machine learning models of network traffic. We analyze traffic data from scientific applications and proxies and propose lightweight approaches to modelling network traffic based on dynamic (i.e., rolling) linear regression and supplement it with random forest classifiers for additional accuracy, reaching 90% or higher.
Pest Detection and Identification Guided by Feature Maps

Effective pest detection and identification are of great significance for agricultural activities, and morden machine learning methods, especially the deep neural network, undoubtedly provide convenient and effective ways for this issue. Most of the mainstream methods for pest detection require annotation of manual bounding box. However, the public pest datasets usually lack the labeling information. In this work, we propose a method for detecting pests with weak supervision. We obtain the key regions of the pest targets by deeply mining the information in the feature maps of the convolutional neural networks (CNNs). First, using the class activation mapping technique to generate activation maps of the target class during forward propagation of the model, and then obtaining the key regions of the target based on the hotspots extracted from the activation maps. In addition, our proposed method can be deployed as a general strategy to other CNNs, and experimental results demonstrate that our method can steadily improve the classification accuracy of the model. By applying our method to the single Resnet50, we can achieve 74.27% classification accuracy on the publicly accessible IP102 dataset. Another attribution of this paper is that we propose a lightweight approach to find solutions for pest detection in agricultural automation applications.
Accelerating I/O in Distributed Data Processing Systems with Apache Arrow CHFS

In recent years, distributed data-processing frameworks have become popular for processing big data. However, in an HPC, where the computation and storage nodes are separated, the bandwidth between the computation and storage components is small, causing a reduction in data processing throughput. Therefore, in this paper, data were stored on the computation node to solve the data processing throughput degradation. We propose an I/O acceleration method that integrates Apache Arrow and CHFS. It leverages non-volatile memory, a state-of-the-art storage device, via CHFS and leverages CHFS from a distributed data processing framework via Apache Arrow's abstract file system API. The evaluation results showed that the system achieved up to 11.60 times higher bandwidth than when reading data from the parallel file system Lustre. This study also compared with Apache Arrow with BeeOND and UnifyFS, other ad hoc filesystems. The proposed Apache Arrow CHFS showed up to 1.67x/1.23x better write performance. The implementation is published at https://github.com/tsukuba-hpcs/arrow-chfs
Mind to Motion: EEG-Based Classification of Motor Imagery and Actual Hand Movements Using LSTM Models

This study presents an Electroencephalogram (EEG) based classification model tailored to discern between motor imagery and real motor actions. Additionally, the study investigates the efficacy of employing Long Short-Term Memory (LSTM) deep learning models for EEG signal analysis. The EEG analysis comprised two distinct phases, aimed at validating the hypothesis of distinguishing motor action from motor imagery. The proposed LSTM-based classification model exhibited a notable accuracy of 62.5% in discriminating motor action from motor imagery, and a promising 72.5% accuracy in distinguishing between resting state and motor action. These findings highlight the potential of EEG-based approaches in motor-related applications, thus providing auspicious avenues for the future development of brain-computer interfaces (BCIs) and motor rehabilitation technologies.
Int-BEB: Enhanced Binary Exponential Backoff using Q-learning

Because of their development and success, MANETs are now preferred alternatives in many fields of study and application. The IEEE 802.11 standard is the basis of most wireless technologies. To treat collisions in wireless networks, IEEE 802.11 Distributed Coordination Function (DCF) MAC layer employs Binary Exponential Backoff (BEB) algorithm, that is helpful in decreasing the potential of collision, but comes at the expense of several network performance metrics, such as medium access delay, bandwidth management, power use, and fairness. A Deep Learning technique called Deep Reinforcement Learning (DRL) allows an agent to communicate with the environment in order to accomplish a goal. In the present paper, we suggest Q-learning (QL), one of the DRL approaches to improve the Binary Exponential Backoff (BEB) in MANETs. The intelligently designed Int-BEB aims for better CW selection; this selection strikes to reduce collisions as much as possible and to give more chances to nodes that make collisions to access the channel by taking into consideration their residual energy. In-depth simulations are performed to evaluate the performance of the proposed mechanism. The performance of Int-BEB is evaluated in diverse networks with several criteria such as packet successful transmission, packet loss and energy.
Development of Optimal Fuzzy-PID Controller for an Assistant Human Knee Exoskeleton System

In this paper, an optimal fuzzy-PID controller is proposed for a knee exoskeleton model for patients with knee problems caused by strokes, post-polio, osteoarthritis, etc. For this purpose, a fuzzy control logic based proportional integral derivative (Fuzzy-PID) controller is considered. The fuzzy con-troller in the proposed control structure is used to adjust the PID controller parameters for the knee exoskeleton model. The error between the targeted angle and actual angle of knee exoskeleton system and the change of this error are taken as the input variables of the fuzzy controller. The output variables are chosen as the parameters of PID controller. The membership functions of the fuzzy controller are optimized with a well-known optimization algorithm called class topper optimization. The performance of the proposed controller is examined under different scenarios and plotted on a graph.
Model Predictive Control for Industrial Drive Applications

Over the last two decades, model predictive control (MPC) has gained popularity in the published research on power electronics. It is argued in the literature that MPC is well-suited for industrial variable speed drive (VSD) applications, due to its flexible and multi-objective nature. However, the adoption of MPC by industry has been slow. This paper reviews the VSD control strategies currently accepted by industry. It formulates expectations and determines the existing barriers for using MPC in VSD applications. Following that, the paper proposes a number of novel modifications to enhance the MPC performance and to offer new benefits to industry, compared to the existing solutions. The results of the paper are supported by simulations and validated by experiment.
Digital Twin Simulations for Connected and Automated Vehicles: A Comprehensive Study

The concept of "Digital Twin" is a relatively new term that involves emulating artificial or physical systems. It employs a digital representation and modeling technique to extend and improve the life cycle of complex objects, systems, and processes. This study focuses on developing Digital Twin simulations for both connected vehicles, utilizing the Unity game engine, and automated vehicles, employing Ros, Rviz, and Gazebo. The physical environment and the digital world, which has three layers for the linked car digital twin, are the two main components of the proposed Digital Twin simulation architecture. First, the "hardware" component is emulated using Unity game objects, and the "software" aspect is simulated using the Unity scripting API. Additionally, external tools such as Python are integrated to enhance simulation functionalities. On the other hand, the digital world for the automated vehicle digital twin comprises two layers. Gazebo game objects are employed to simulate the "hardware," and Rviz is used to simulate the "software. " The automated vehicle is equipped with Lidar, Camera, and Raspberry Pi. The simulation results demonstrate the effectiveness of the proposed system, as it accurately predicts and compares actual data and predicted data, such as Voltage and Speed. The Digital Twin approach presents promising possibilities for the realistic representation and analysis of both connected and automated vehicles.
Offline Collaboration Tool utilizing WebRTC in Ad Hoc Peer-to-Peer Networks

Advancement in technology introduced us to various platforms that changed our way of communicating and collaborating. With the sudden demand for technological dependence, the COVID-19 situation exposed the limited internet infrastructure and costly internet service of countries like the Philippines. In this study, the researchers explored the option of real-time offline collaboration in an ad hoc network as a solution to unimpeded collaboration in low bandwidth and poor connectivity environments. The researchers developed on top of CONCLAVE, an open-source collaborative text editor that utilizes WebRTC. Offline functionality was enabled in this collaborative platform along with the implementation of offline peer discovery using Websockets and a peer handling technique inspired by Scalable Content Addressable Networks (CAN). These modifications were evaluated on an ad hoc network using different metrics such as packet delay, jitter, data rate, and peer disconnection resolution time. Results of the testing was favorable to the modified version of CONCLAVE in an ad hoc network. With these results, the project has successfully enabled offline use and continued operation in the event of a disconnected peer, and has successfully implemented a peer handling technique that has improved the P2P operation of the original application.
Mappings and patterns to improve the triangular matrix product on distributed systems

Matrix multiplication is one of the most costly linear algebra operations, very often present in scientific computational applications. Current generic linear algebra libraries, such as ScaLAPACK and its recent evolution SLATE, include functionalities for generic and triangular matrix multiplication. They generally rely on block-cyclic partitioning, which has two main advantages. First, it provides good interoperability with other functionalities of the libraries. Second, it provides a good balance of computation and inter-process communications. The focus of these libraries is performance and scalability, targeting even huge number of processes. Nevertheless, many enterprises and computing centers work with commodity clusters or small partitions with a reduced amount of nodes.In this paper, we propose and evaluate a combination of data distributions and communication patterns intending to optimize the triangular matrix product in distributed memory systems when targeting commodity clusters (up to approximately 36 nodes). The main four ideas are: Use panels (horizontal or vertical band partitions) instead of tiling; avoid zero-elements in communication buffers; balance the number of elements in communicated buffers; and evaluate the performance when combined with both pipeline and broadcast communication strategies. We compare our implementation performance against the state-of-the-art implementations provided by ScaLAPACK and SLATE. The results show that we outperform both of them. Our proposal is up to 41% faster than ScaLAPACK, and up to 6.7% faster than SLATE.
Transmission Line Outage Detection with Limited Information Using Machine Learning

Transmission line outage detection plays an important role in maintaining the reliability of electric power systems. Most existing methods rely on optimization models to estimate the outage of transmission lines, and the process is computationally burdensome. In this study, we propose a transmission line outage detection method using machine learning. Using this method, we could monitor the power flow of one line and estimate whether another line is in service or not, despite the load fluctuations in the system. The study also investigates the principles for observation point selection and the effectiveness of this method in detecting the outage of transmission lines with different levels of power flows. The method was implemented on an IEEE 118-bus system, and results show that the method is effective for transmission lines with all levels of power flows, and line outage distribution factors (LODF) are good indicators in observation point selection.
Low-input-power Sub-GHz RF Energy Harvester for Powering Ultra-low-power Devices

Using RF energy to power ultra-low-power devices is an appropriate solution to reduce the dependency on conventional sources (e.g. batteries); however, providing a regulated DC voltage from low RF power is a challenging task. This work presents a low-input-power RF harvester designed with off-the-shelf components and composed of an RF rectifier and a power management integrated circuit (PMIC). In the rectifier, an inductive-matching technique is employed which consists of an inductive branch composed of a lumped inductor together with a short-circuited stub due to sub-GHz frequency. The rectifier is designed to present an optimal DC load in function of the PMIC operation at low powers. Measured RF-to-DC conversion efficiency of 32% and DC output voltage of 186 mV with an input power of −20 dBm at 888.7 MHz are achieved in the rectifier. Measured peak efficiency is 52% at −4 dBm. At −20 dBm, a relatively high efficiency and output DC voltage are obtained compared to other discrete rectifiers. For this power level, this performance is sufficient to maintain the operation of the PMIC that delivers a power of 324 nW aiming to supply an ultra-low-power wake-up receiver. The required RF power to deliver a regulated DC voltage is so far the lowest reported in the literature for an RF harvester using off-the-shelf components and a continuous wave (CW) signal to date.
Institutional Values and CDR for Green ITC: Transforming E-waste to Ethical Gems

The growing use of electronic devices significantly impacts the environment and society by depleting resources, causing pollution, and raising health concerns. Information and communication technology (ICT) must properly manage waste electrical and electronic equipment (WEEE) to promote more environmentally friendly and healthier business operations. This study examines how institutional values can contribute to corporate digital responsibility (CDR) in ITC companies and proposes institutional value propositions to solve WEEE issues at multiple levels. The study suggests four institutional value propositions, including establishing multilateral institutional norm networks, implementing public-private participatory strategies, collaborating with NGOs to understand social legitimacy better, and promoting CDR for effective e-waste management as self-regulation. The results suggested that addressing WEEE issues can be advanced by leveraging a range of institutional value propositions in different dimensions to promote societal progress.
Influences of Rotor Design on Air-Gap Field Modulation Effect in Spoke-Type Permanent Magnet Machine for Traction Applications

This article investigates the influences of the rotor design on air-gap field modulation (AGFM) theory in spoke-type permanent magnet (STPM) machines for traction applications. Due to flux-concentrated effect, STPM machines exhibit abundant air-gap field density harmonics and relatively high torque density. The Maxwell stress tensor (MST) method is adopted to identify and quantify the torque contributions of the various air-gap field harmonics in STPM machines. It reveals a fact that, except the main-order working harmonic is responsible for average torque generation, abundant modulated air-gap field harmonics contribute a non-negligible torque component. Besides, the influences of rotor open slot and PM arrangements on AGFM effects in STPM machines are evaluated and compared by finite element (FE) analysis, which identifies that the rotor slot and PM arrangement designs show a more conspicuous effects on their torque components and AGFM effect. Finally, the experimental results on a STPM prototype with rotor closed slot design are carried out to validate the theoretical and FE analyses.
NLDDPG Based Joint Optimization Decision Scheme for Vehicular Network Offloading and Resource Allocation

In response to the explosive growth of data computation in vehicular terminals, computation offloading has emerged as a viable solution to mitigate the limitations of resources. Efficient offloading decisions not only meet the demanding requirements of complex vehicular tasks in terms of time, energy consumption, and computational performance but also minimize competition and resource consumption in the network. However, existing work on task offloading in vehicular networks often exhibits certain limitations, such as incomplete consideration of relevant factors or suboptimal utilization of available resources. This research presents the construction of a three-layer vehicular network environment, which is based on cloud and edge computing paradigms. The design entails the formulation of real-time vehicle location tracking and task priority metrics, while also considering the challenges posed by time-varying channels and signal blockage prevalent in vehicular network environments. In this paper, a novel variant of the Deep Deterministic Policy Gradient (DDPG) algorithm NLDDPG is proposed to iteratively train the model, aiming to optimize a weighted objective function. Simulation results show that this algorithm can improve the efficiency and optimize the task average utility.
Design of a Non-Invasive Egg Sexing Device Utilizing Artificial Intelligence for Duck Species

This study introduces a non-invasive egg sexing device that combines artificial intelligence (AI), spectroscopy, and computer vision technology to accurately determine the sex of duck embryos inside eggs. The device utilizes the plumage color as a reliable indicator of sex, employing a hybrid Convolutional Neural Network (CNN) and Gated Recurrent Unit (GRU) model alongside spectroscopic analysis. Extensive simulations and experiments validate the proposed algorithm, achieving an impressive 98.68% accuracy rate in sex determination, with an average processing time of 37.46 milliseconds, significantly enhancing farming efficiency. Additionally, the research assesses the impact of spectroscopy on egg hatchability, demonstrating a higher hatchability rate of 74.80% within a population of 500 eggs. This finding indicates that spectroscopy does not adversely affect egg viability. Overall, this study presents a sustainable solution for effectively managing male ducklings in the industry, optimizing resource utilization, and mitigating wastage.
Extended km-Anonymity for Randomization Applied to Binary Data

Various models for evaluating anonymity have been proposed so far. Among them, k-anonymity is widely known as a typical anonymity measure, which guarantees that at least k individuals in a database have the same values. However, it is difficult to create highly useful anonymized data satisfying k-anonymity for high-dimensional data because of the curse of dimensionality. To overcome the problem, several approaches relaxing k-anonymity have been proposed, such as km-anonymity and σ-km-anonymity. Unfortunately, they can only evaluate deterministic anonymization methods.We propose Pkm-anonymity, a variant of km-anonymity, and prove that km-anonymity and Pkm-anonymity are equivalent in a deterministic privacy mechanism. This suggests that our Pkm-anonymity is an extension of km anonymity. Also, we propose a km-anonymization method for binary data, unlike the previous approaches for non-binary data. The success probability and utility of the proposed method are examined with the number of attributes as a parameter. Our experiments show that the "curse of dimensionality" does not occur up to a dimensionality of 45 and that usefulness does not deteriorate in the range of dimensionality from 10 to 40.
Audio Deepfake Detection by using Machine and Deep Learning

Fake voices are one of the hottest topics in cyber security, forensics, and social media. There are a variety of usage scenarios, from speech disorders to fake news to telephone and financial fraud. With products using artificial intelligence technology such as Google Audio LM, it is possible to produce realistic, well-structured, and consistent sound sequences. These products, although synthetic, can accurately replicate intonation, accents, and other unique features by mimicking the human voice. A solution using machine and deep learning methods to recognize fake voices is proposed. In the feature extraction stage, Mel-frequency cepstral coefficients (MFCCs) are used. Then these features are classified using machine and deep learning-based models. According to the results obtained, the sound is judged to be real or fake.