Reviewerly/exHarmony huggingface dataset:
Low-Latency Collaborative Predictive Maintenance: Over-the-Air Federated Learning in Noisy Industrial Environments

The emergence of Industry 4.0 has revolutionized the industrial sector, enabling the development of compact, precise, and interconnected assets. This transformation has not only generated vast amounts of data but also facilitated the migration of learning and optimization processes to edge devices. Consequently, modern industries can effectively leverage this paradigm through distributed learning to define product quality and implement predictive maintenance (PM) strategies. While computing speeds continue to advance rapidly, the latency in communication has emerged as a bottleneck for fast edge learning, particularly in time-sensitive applications such as PM. To address this issue, we explore Federated Learning (FL), a privacy-preserving framework. FL entails updating a global AI model on a parameter server (PS) through aggregation of locally trained models from edge devices. We propose an innovative approach: analog aggregation over-the-air of updates transmitted concurrently over wireless channels. This leverages the waveform-superposition property in multi-access channels, significantly reducing communication latency compared to conventional methods. However, it is vulnerable to performance degradation due to channel properties like noise and fading. In this study, we introduce a method to mitigate the impact of channel noise in FL over-the-air communication and computation (FLOACC). We integrate a novel tracking-based stochastic approximation scheme into a standard federated stochastic variance reduced gradient (FSVRG). This effectively averages out channel noise's influence, ensuring robust FLOACC performance without increasing transmission power gain. Numerical results confirm our approach's superior communication efficiency and scalability in various FL scenarios, especially when dealing with noisy channels. Simulation experiments also highlight significant enhancements in prediction accuracy and loss function reduction for analog aggregation in over-the-air FL scenarios.
WORKSHOP PENYUSUNAN SKRIPSI, PUBLIKASI ILMIAH DAN PEMBUATAN POSTER ILMIAH BAGI MAHASISWA TINGKAT AKHIR JAKARTA GLOBAL UNIVERSITY

Enhancing the academic capabilities of senior-level students in higher education involves a series of pivotal tasks such as completing a thesis, publishing scholarly works, and creating scientific posters. However, the execution of these tasks still encounters several challenges, including limitations in academic writing skills, a restricted understanding of enhancing writing quality, as well as a deficiency in comprehending ethical writing aspects, such as avoiding plagiarism and employing proper citation methods. Moreover, there are challenges in embarking on the path toward publication and within the overall writing process. In this context, the purpose of organizing the workshop is to provide an in-depth understanding of essential elements within these three areas. This encompasses enhancing writing skills, enriching the understanding of ethical writing to mitigate instances of plagiarism, and ensuring accurate citation practices. Furthermore, it aids participants in grasping research requisites and guidelines, while elevating awareness of academic norms. Crafting a thesis necessitates an understanding of fundamental principles and established regulations, while scholarly writing involves formulating strategies and meticulously presenting research outcomes. The creation of scientific posters also plays a crucial role in effectively conveying research findings visually. This workshop offers practical guidance and training in these aspects, including elucidating formats, arranging layouts, and employing tools such as the Canva application. The outcomes of this workshop materialize in the form of attraction, enthusiasm, and a profound understanding among participants regarding thesis writing methods, scholarly work composition, and research poster presentations. Participants have mastered the use of the citation tool Mendeley, as well as the implementation of Canva in designing research posters. This workshop has been successfully conducted and serves as a platform to advance the academic careers of participants. The workshop is deemed successful to sharpen the skills and self-confidence of participants in thesis writing, precise citation utilization, and crafting scholarly writing effectively.
Clinical prediction tool pitfalls and considerations: Data and algorithms

In recent years, many surgical prediction models have been developed and published to augment surgeon decision-making, predict postoperative patient trajectories, and more. Collectively underlying all of these models is a wide variety of data sources and algorithms. Each data set and algorithm has its unique strengths, weaknesses, and type of prediction task for which it is best suited. The purpose of this piece is to highlight important characteristics of common data sources and algorithms used in surgical prediction model development so that future researchers interested in developing models of their own may be able to critically evaluate them and select the optimal ones for their study.
Two-stage coarse-to-fine image anomaly segmentation and detection model

Existing Convolutional Neural Network (CNN) based anomaly detection and segmentation approaches are overly sensitive or not sensitive enough to noise, resulting in anomaly patterns, partially detected in the testing stage. The previous methods may also differentiate normal and abnormal images, but they cannot identify the location of anomaly presented in test images with high accuracy. To address this issue, we propose a two-stage CNN model for coarse-to-fine anomaly segmentation and detection called (TASAD). In both stages of TASAD, we train our model on a mixture of normal and abnormal training samples. The abnormal images are obtained by inserting pseudo-anomaly patterns that are automatically generated from anomaly source images. We use a novel and sophisticated anomaly insertion technique to generate various anomalous samples. In the first stage, we design a coarse anomaly segmentation (CAS) model that takes a whole image as an input, while in the second stage, we train a fine anomaly segmentation (FAS) model on image patches. FAS model improves detection and segmentation performance by refining anomaly patterns partially detected by CAS model. We train our framework on MVTec dataset and compare it with state-of-the-art (SOTA) methods. The proposed architecture leads to a compact model size – four times smaller than the SOTA method, while exhibiting better pixel-level accuracy. TASAD can also be applied to SOTAs to further improve their anomaly detection performance. Our experiments demonstrate that when applied to the latest SOTAs, TASAD improves the average precision (AP) performance of previous methods by 6.2%. For reproducibility of the results, code is provided at https://github.com/RizwanAliQau/tasad.git.
Triumph over adversity: Unlocking optimal trauma outcomes during healthcare ransomware attacks

Healthcare ransomware attacks refer to a specific set of cyber-attacks perpetrated against healthcare institutions where malicious software infects the computer systems of a healthcare organization preventing members of the organization from accessing data unless a ransom is paid. This malware is often inadvertently introduced into systems because of intrusion methods such as phishing, where a user opens a file in a seemingly benign message giving the malware access. These attacks have the potential to significantly disrupt patient care, put patient safety at risk, and can prove extremely costly to healthcare institutions.
Visual-RRT: Integrating IBVS as a steering method in an RRT planner

In this paper, we present a new approach to robot motion planning that anticipates the use of vision-based feedback control during task execution. We accomplish this by incorporating an image-based visual servo (IBVS) controller directly into the steering function used by a Rapidly Exploring Random Tree (RRT) planner. Our approach requires a number of extensions to traditional RRT-style planning. First, we derive a new sampling strategy that augments the usual state information by including image features that will be used by the IBVS control law. These augmented samples are then used by our new IBVS steering function, which simulates an IBVS control law to generate local trajectories that extend the current tree. These trajectories must be validated to ensure that they are collision-free and that all image features remain unoccluded and within the camera field of view throughout the local trajectory. We also provide a formal proof showing that the proposed approach is probabilistically complete. We have applied our approach to the problem of planning trajectories for three different systems: a robotic arm, an unmanned aerial vehicle (UAV) and a car-like robot, which are equipped with an IBVS control law. We explore performance trade-offs in the control design via simulation studies and demonstrate real-world effectiveness via experiments in which a small-scale car-like robot uses IBVS to navigate a track that includes a number of obstacles and potential occlusions. By exploring performance trade-offs, we mean that several elements, such as the metric used to identify nearest neighbors in the RRT and the steering method used to generate nodes, are tested and compared.
CDDQN based efficient path planning for Aerial surveillance in high wind scenarios

Robotic surveillance, monitoring, and maintenance problem are open-for-research domains required by the military, industrial facilities, ports, airports, and various indoor and outdoor venues each having different needs. Recent work in path planning of aerial robotics is an emerging field of the surveillance problem, particularly for unstructured or unexplored areas. The nature of path planning problems with different foreign elements like wind, rain, and others escalate the cost of complex computation and power consumption. Due to constraints in payload and endurance, algorithms based on pose-graph, both from the run-time and solution point of view become inefficient when working with unstructured spaces. We propose a simple but effective Clipped Double Q-learning [1] based deep reinforcement learning algorithm (CDDQN)for efficient path planning under the influence of wind and with improved computational efficiency for surveillance in a port area. In the proposed algorithm we have formulated a dense reward structure in consideration of wind’s effect on power consumption and time to reach the destination which led to a robust path planning system for high wind scenarios.
Sonar Object Detection from Synthetic Sonar Data Generated by a Sonar Simulator and an Adversarial Neural Network

The lack of suitable sonar datasets has made it difficult to train robust deep-learning models for object detection with sonar. Using synthetic sonar data to expand existing or create new data sets can facilitate robust training of machine learning based detectors for underwater sonar applications. In this work a pipeline is presented that enables the generation of synthetic sonar images from 3D models for both forward-looking sonar (FLS) and side scan sonar (SSS) by combining 3D renderings with the style transfer capabilities of generative adversarial neural networks. Images generated by this pipeline are used to train object detectors which are evaluated on two data sets, one with shipwrecks and the other with subsea infrastructure collected with a synthetic-aperture sonar and forward-looking sonar respectively. The results of this study show that an object detector trained purely on the synthetic FLS data generated by this approach, was able to detect and correctly classify the structures observed in the majority of the subsea infrastructure images. A detector trained on real images of the same structures performed better with a recall of 87.4% against 64.8% . The object detector trained purely on synthetic side scan sonar data yielded a recall of 83% on our shipwreck dataset at 80% confidence, but yielded a high rate of false positives when the confidence threshold was lowered. The results indicate that an object detector trained on synthetic data of specific objects generated with this approach is able to learn features that translate to detection in real images.
Modelling Differential Drive Mobile Robot for High-Resolution Multi-View Image Capturing

This paper presents a specialized differential drive robot with a high-resolution camera that captures detailed 3D images from multiple perspectives. To make 3D models of an object using different viewpoints, this robotic has two DC motors to propel itself and caster wheels supporting its flank for better balance. The creative mechanical design includes a DSLR camera attached through a tripod-mounted fixture that rotates along with vertical axis relative to capturing focused views of middle-direction objects against varied points of view surrounding them. Essentially integrated into operation concepts within major planning modules, our mathematical model algorithm combines straight-line or curved movement to permit coordinated control over position and motion while ensuring continued accuracy during image acquisition, thus allowing undisturbed focus when maneuvering around an object. Further application of this model is seen in determining motor drive speeds and camera angles while keeping steady motion on the path toward achieving specific robotic goals. Our research shows how advanced differential drive robots can accurately carry out detailed imaging from multiple vantage points. Mathematical modeling advances like those in this paper have furnished a sound foundation for upcoming specialized robot developments.
Versatile Vision: Innovation of a Node-RED-based Smart Surveillance System

Our study focuses on creating an adaptable surveillance system through visual programming in Node-RED. We have used a combination of Raspberry Pi 3B+, a camera module, and two mini servo actuators for our hardware framework, which provides flexible camera movement. The pan-tilt mechanism operated by mini servos gives greater control over camera positioning, resulting in a broader visibility range than traditional fixed-mount cameras, increasing versatility. Our research integrates computer vision techniques like contour-based object detection into the program blocks. We provide users to develop their programs independently. Through these methods, we aim to count objects within an area accurately. It is worth noting that our Node-RED-based adaptable surveillance solution has extensive applicability across various environments. To monitor object presence or quantity efficiently while considering all possible variations. One noteworthy aspect of this study is our proposed method’s inherent ability to ease deployment. Our innovative solution combines adaptability with efficiency through automated monitoring mechanisms which activate alarms if necessary. This research highlights how Node-RED can be an essential aspect of future programmable surveillance system development that is key to enhancing security and efficient use of spaces.
Blockchain Integration for Mixed Reality Based Smart Lab Systems

Smart lab is a system for controlling electronic equipment in a laboratory room that is possible to be operated remotely via the internet network and integrate mixed reality for a more realistic user interface. In the smart lab system, it is important to know the activity history of each device, particularly in case of malfunctions or misuse. Therefore, the usage of devices in laboratories needs to be traceable. Blockchain is one type of technology that able to address traceability issues. Blockchain’s distributed and transparent nature makes it simpler to track each transaction occurrence. In a smart lab system, we are integrating Blockchain as a data storage system for historical smart device usage data. Blockchain has successfully recorded smart devices history data. Based on testing, the transaction throughput on the blockchain is an average of 1 transaction per second with a transaction delay of 1.2 seconds. The REST API developed for the smart lab system able to handle more than 20 requests from 5 users simultaneously with an average response time of 3.75 seconds at a bandwidth ranging from 8 Mbps to 0.5 Mbps. However, there are already failed requests when 10 users use it simultaneously at 8 Mbps. Traceability in the smart lab has been enabled by preserving historical records of smart device usage on the blockchain system.
A Versatile Fourier Domain Filtering Library for Diverse Applications

Fourier domain filtering is a commonly applied data post processing technique used in many engineering applications, e.g., time domain gating is often used to remove or isolate responses in a multiple reflective environment. Many commercial vector network analyzers (VNAs) include the gating algorithm as a “black box“ function. A user has little recourse to reprocess or further process the data once the data has been downloaded from a VNA. A versatile Fourier domain filtering library has been developed recently, giving users much more flexibility and convenience over the VNA-dependent process. It is well known that the gated results can exhibit significant “band edge effects“ due to the band limited measurement data. The new library toolset incorporates the standard edge treatment techniques often found in commercial VNAs. In addition, it includes a newly developed spectrum extension technique which improves the gated data accuracy significantly for many practical applications. The library works equally well for spatial and spectrum applications, such as for image processing and spectrum analyses in antenna measurement applications.
Fundamental Study on the Effect of the Duty Ratio of Clock Signal on Side-Channel Leakage

In this paper, we firstly evaluate the effect of power supply noise duet to clock signal on information acquisition to accurately acquire side-channel information from the cryptographic devices that radiates in the same frequency band as the clock signal. The result shows that the information acquisition property changes according to the duty ratio of the clock signal.
A Multi-level Feature Extraction Method for Heterogeneous Sample Enhancement

The challenge of dealing with small sample sizes is a significant issue in data-driven modeling. To address this challenge, domain adaptation (DA) techniques have been developed to transfer samples from a source domain to a target domain to enhance the sample size. However, in real-world applications, the existing DA methods have limited applicability due to the lack of effective feature extraction approaches. To overcome these limitations, this paper introduces a novel multi-level feature extraction method for enhancing heterogeneous samples. The proposed method decomposes sample features into three orthogonal dimensions to more accurately express sample information. The results demonstrate that our method outperforms representative methods, highlighting the effectiveness of our approach in addressing the challenges of small sample sizes and domain adaptation.
Classifying user interaction activity in task mining

In this work, we have developed a solution to facilitate Digital Process Management and Task Mining by categorizing User Interaction Activities in User Interface. To achieve this goal, we have created an internal dataset of User Interaction Activities. The user interactions are determined by screenshots that have been taken by an internal Task Mining recording software. To categorize the User Interaction Activities of various applications and interaction types, we used the modern Computer Vision approach. To develop the dataset we took leverage of classical image process methods. We have compared successful and promising Computer Vision methods in our dataset considering its traits. Classifying such user interactions in a human-understandable way opens a vast range of possibilities in the field of digital Task Mining and Analysis.
Enhancing Document Authenticity with QR Codes and ECC-Based Digital Signatures

Handwritten signatures are commonly used to validate the authenticity of documents. In the digital realm, cryptographic signatures, known as digital signatures, provide integrity, authentication, and non-repudiation. This paper explores the utilization of the Edwards-Curve Digital Signature Algorithm (EdDSA) and Quick Response (QR) code technology for document authentication. The proposed system replaces conventional signatures with QR codes containing digital signatures, enabling the verification and validation of documents. Performance evaluation of EdDSA and the more commonly used Elliptic Curve Digital Signature Algorithm (ECDSA) reveals that EdDSA surpasses ECDSA in key generation, signature generation, and signature verification. EdDSA demonstrates better efficiency, shorter execution times, and lower sensitivity to the execution count, resulting in an average improvement in speed of approximately 98.17% compared to ECDSA. The findings highlight the practical feasibility and superiority of EdDSA for secure and efficient document authentication using QR codes.
Goalpost Detection and Recognition on Robot Soccer ERSOW Using SSD MobileNet-V2 FPNlite

In the Indonesian Robot Contest, EEPIS Robot Soccer on Wheeled (ERSOW) plays soccer on wheels. Based on the rules, the victory of a team is taken from the highest score of the robot in scoring goals against the goalpost. However, it’s not as simple as it may be thought to score goals against the goalpost. The robot has to maneuver during the game without stopping, causing the robot’s position data generated by the odometry and IMU sensors to experience an accumulation of errors. The goal position data, which will be a reference for the robot’s kick against the goalpost, will be impacted by the accumulated errors. This paper presents a goalpost detection system that can be used on robots. Deep Learning Neural Network (DNN) is used by the detecting system to teach itself the crucial visual characteristics of the goalpost in order to make detections. The deep learning model makes use of the Single Snapshot Detector (SSD) MobileNet V2 FPNLite 640x640. An individual image dataset of the goal point from the omnidirectional cameras. SSD MobileNetV2 FPNLite 640x640 model with 1% classification loss, 0.1% localization loss, 9% total loss and confidence score is 96-100%. The accuracy of goalpost detection and recognition is 93% with the frame rate average is 49 FPS.
Drone-to-drone Collision Detection Algorithm in Flight Traffic Management

This paper presents an algorithm for detecting collisions in flight traffic management for drones. The algorithm is simple to implement and can be used to effectively manage the flow of drones in a safe and efficient manner. The algorithm has been tested in simulation and has been shown to be effective in detecting collisions between drones. The algorithm is also simple to implement and can be used with existing drone traffic management systems. As the number of drones in the sky continues to grow, this algorithm will become increasingly important for ensuring the safety of drone operations.