Reviewerly/exHarmony huggingface dataset:
Auto-generated Control Program in Mobile Robot using Grammatical Evolution

Complexity of robot control program is become a problem as variety of robot used today is dramatically increased due to demand of automation. Several studies has been made using GE to solve robot tasks but application in actual environment is still lack of proof. In this research, auto-generated control program using Grammatical Evolution (GE) is proposed to resolve the burden of constructing manually adjusted control program that consider both physical condition of robot and input from sensors. Grammatical Evolution (GE) is a genetic programming that map genotype to phenotype using predetermined grammar. As GE is subset to Genetic Programming which produce heuristics solution, the grammar-based algorithm enable the search range evolve in a set of rules. This search strategy can be applied to build the structure of control program which consists of multiple sets of program blocks with adjustable parameter. This system is evaluated using Santa Fe Trail problem with open world configuration. The results show the usability of the algorithm by manipulating crossover and mutation rates. Another important factors that effect the performance of the algorithm is the grammar that can govern the evolution of the control program. The proposed method able to generate control automatically with sets of rules determined using grammar.
Analyzing Security Vulnerability and Forensic Investigation of ROS2: A Case Study

For several years, robotics technology has used ROS (Robot Operating System) as a middleware application for operating robots. The adoption of robots in various domains such as defense, industrial, academia, healthcare, and many more are rising gradually. After the market demand rose, ROS was upgraded to ROS2 for better performance and security. However, researchers have identified many security issues and breaches in the ROS2 framework. However, security is the biggest issue in today's era. Thus the security issue is unrestricted in the robotics domains, which can cause human loss, injuries to humans and destruction of internal/external environments. In this paper, we demonstrate the security vulnerabilities of ROS2-based applications, such as the unauthorized injection of the data, unauthorized access to the data and denial of service attacks. Nevertheless, anti-forensic techniques on ROS2-based applications were performed to obstruct the forensics investigation. After that, we forensic investigated the attacks performed on the ROS2-based application. Our result shows that it is possible to compromise ROS2-based applications and attackers can crash the ROS2 system quickly. In a forensic investigation, it is possible to identify the attacker's IP address, mac address, network interface and the process of the ROS2 system running on the robot. The authors stated the limitations and future work of the ROS2-based application in forensic investigation.
Methodology for Weapon Detection in Social Media Profiles using an Adaptation of YOLO-V5 and Natural Language Processing Techniques

Weapon identification has been a hot topic in the area of Object Recognition in recent years. However, its appli-cation has been virtually explored in social media. This work focuses on the detection of weapons in profiles that explicitly advocate their procession, both graphically and textually. This is a challenge, since access to a dataset is difficult; and once the samples are obtained, the dimensions and attributes of the images can vary significantly. In addition, the possession of a weapon does not imply that any offense or crime is being committed. To tackle these challenges, this manuscript presents a regularized adaptation of a Fast-Convolutional Neural Network (F-CNN) based on YOLO-V5, to merge and improve the results of the algorithm, along with a textual fingerprinting technique, to first corroborate if the intent of the post contains red flags of crime and violence. The results demonstrate that regularized adaptive models, mainly using Data Image Augmentation techniques, along with text classification, can provide better performance on unstructured data, such as those found in social media.
iPaddle: An Intelligent Swimming Training System

This paper presents an implementation of an intel-ligent swimming rowing system which consists of a swimming paddle attached to a motor connected by an inextensible cable. This system consists of a combination of two elements to create a device that simulates an aquatic environment out of water The first element consists of: an inertial system measurement unit, a communication device and a battery; all placed inside the swimming paddle. The second element is composed of an odometric arrangement consisting of an effort returning motor that coils the cable to compute by odometry the distance from motor to the paddle. In combination with the acceleration and rotational speed provided by the IMU, the motor apply resistant to simulate an aquatic environment. This device has proved is uttility because as being out of the water, the coach has a better control to analyze and correct the swimming technique of the athletes. Moreover, position, speed, acceleration and effort done by swimmer can be analyzed later offline.
Astralis: A High-Fidelity Simulator for Heterogeneous Robot and Human-Robot Teaming

We introduce Astralis simulator, a high-fidelity robot simulation platform for the development of multi-robot and human-robot coordination algorithms which can be seamlessly translated to real-world environments. The simulator provides novel features of dynamically initializing the virtual environment with real-world 3D point cloud data and a uniformly random arrangement of static and dynamic obstacles in the environment. This allows the user to generate several variants of a base scenario for strategic planning and algorithm validation. The simulator can receive high-level command inputs to control a team of Unmanned Aerial Vehicles (UAVs), Unmanned Ground Vehicles (UGVs), and human avatars. The simulated robot models are built with high fidelity control and navigation capabilities which can be readily deployed on real robot platforms. We use Astralis simulator to analyze human-robot coordination algorithms for tracking, following and leading targets in a search and rescue mission. The algorithm is validated using a UAV and a UGV in simulation and on physical platforms. Our simulator provides comparable results to the real-world experiments in terms of the executed trajectories by the robots.
Simulation of Mobile Robots in Human Crowds Based on Automatically Generated Maps

Ahstract- Mobile robots are more and more used in human environments, which means they have to navigate near the walking path’ of humans. Navigation in crowds is difficult for autonomous mobile robots because humans are unpredictable mobile obstacles that make localization difficult by obscuring sensor fields of view. Especially when there are many dynamic obstacles around the robot, localization is disturbed and navigation may fail. Another challenge is that the robot has to pay special attention to humans for safety reasons. In order for mobile robots to be used safely and reliably in the vicinity of humans in the future, new algorithms need to be developed and extensively tested. In practice, these tests are very time-consuming and expensive, especially if they are done in many different environments with a large number of humans. To reduce this workload and enable extensive testing in many different environments, we present a new cosimulation in this paper. It allows to simulate crowds in the vicinity of navigating mobile robots. For this, 3D apartments are automatically generated from over 80k residential drawings, in which robots and humans can navigate. Thus, this simulation allows to perform tests in many generated environments and thus to make statements that are less dependent on the environment. In simulated experiments with up to 15 humans in an apartment, the influence of the number of humans on the localization error as well as on the navigation is investigated and the simulation results are evaluated.
Time delay detector in seismic signals using real-time multipoint radiofrequency links

Seismic waves are composed of a superposition of waves that differ in their frequencies, speed of propagation, and amplitude. In particular, primary seismic waves (P waves) travel faster than secondary seismic waves (S waves), which are more destructive. This property allows P waves to be detected first than the S waves, giving a chance to activate an alarm if considered so. This work presents a prototype to monitor the P waves of an earthquake at different geographical points. A radio communication link between a few locations is established, and the seismometers consist of Lehman pendulums. The signals from the seismographs are Fourier-transformed continuously and cross-correlated to discern between the presence of a seismic signal or noise. The system developed consisted of real-time multipoint communication in the UHF band. Three nodes were implemented to evaluate its performance. These consisted of two distant points transmitting data from seismic sensors, and a receiver located in the central node read those signals. This point also had its detector and performed signal processing and information visualization through a graphical interface. Experimentally, the multipoint communication permitted the continuous transmission of information between the different nodes in real-time, using test signals with programmed time delays, which agreed with those calculated by the algorithms, with an accuracy of tenths of milliseconds.
Hardware Design and Tests of Two-Wheeled Robot Platform for Searching Survivors in Debris Cones

Response and recovery are critical soon after or during a large-scale disaster such as earthquakes, floods, landslides, strong winds, explosions, structure failures, and so on. In the project called CURSOR, we have been developing a search and rescue kit to grasp the situation and find trapped victims efficiently under the debris while securing the safety of the first responders. In this paper, hardware design and tests of a small two-wheeled robot platform SMURF are shown. SMURF aims to search for victims under the rubble efficiently by large-scale deployment using transport drones. While descending the rubble pile, they search for victims using cameras and gas sensors. To evaluate the performance and verify the effectiveness of the mobility system in actual conditions, ruggedization tests, mobility tests, and field tests were conducted. The reliability and mobility performance results show the potential of the developed two-wheeled robots to carry out large-scale disaster responses.
Behavioral attributes in password reuse: Analysis of password practices in work and personal spaces

Passwords created by users are hard to do away with, albeit the presence of other authentication methods. With unsafe password practices still being a concern, we use a behavioral lens to understand password practices for work and personal accounts. Based on a survey of 730 employees of a technology firm, we were able to characterize reusers and non-reusers of passwords. We find behavioral attributes such as personality, protection motivation, consideration of future consequences, neutralization, and exposure to hacking to be significantly different for people who tend to reuse passwords and those who do not. Further, where password reuse is present, it is inferred to be more dominant in the personal space. Evidence showing that communication on good password practices at work has an effect on personal space is also found. Based on these findings, the need for designing interventions leveraging behavioral insights to promote desirable password practices is discussed.
Future of Intimate Artefacts: A Speculative Design Investigation

Intimacy is an important subdomain in emotional communication and has garnered widespread attention in the HCI community. However, the future of intimacy coupled with upcoming technologies remains under-explored. What could intimate artefacts look like in the future? We try to answer this by exploring intimacy from a speculative design perspective. In this paper, we discuss the process of artefact design, world building, audience interaction planning and engagement and conclude with the results of a participatory speculative design project, capturing participants through objects of intimacy. Our work aims to catalyse meaningful conversations about intimacy in the future, expanding to new HCI research avenues.
Evaluation of ASR Systems for Conversational Speech

Automatic speech recognition (ASR) meets more informal and free-form input data as voice user interfaces and conversational agents such as the voice assistants such as Alexa, Google Home, etc., gain popularity. Conversational speech is both the most difficult and environmentally relevant sort of data for speech recognition. In this paper, we take a linguistic perspective, and take the French language as a case study toward disambiguation of the French homophones. Our contribution aims to provide more insight into human speech transcription accuracy in conditions to reproduce those of state-of-the-art ASR systems, although in a much focused situation. We investigate a case study involving the most common errors encountered in the automatic transcription of French language.
Acute Lymphoblastic Leukemia Prediction from Microscopic Blood Smear Images Using Advanced Machine Learning

Overproduction of the immature white blood cells in the bone marrow leads to leukemia which is a dangerous disease. The probability of recovery can be increased if leukemia is predicted at an early stage. Typically, morphological analysis for the detection of leukemia is performed on blood cells manually by the experienced medical staff which results in various drawbacks like lack of medical staff, slow analysis, and prediction is dependent on the medical staff skills. Therefore, we proposed an automated system which can detect leukemia correctly and at low cost. The proposed methodology is based on the prediction of acute lymphoblastic leukemia by using microscopic blood smear images. For the detection of ‘ALL’, we have used three baseline architectures i.e. VGG-16, Inception-V3 and ResNet50 that are pre-trained on the ImageNet database. Data augmentation and pre-processing have been utilized to minimize the error of the network. Transfer learning is becoming a developing health image processing method because of its greater presentation in minor samples of images, unlike old-style DL methods. In this study, we have recommended a novel transfer-learning-based automatic ALL classification approach. Our proposed ResNet50 model achieved 100% accuracy on ALL_IDB dataset and achieved 97% accuracy on C_NMC-2019 dataset.
A Novel Approach to Find Accuracy in Credit Card Fraud Detection Using Improved K-Nearest Neighbor Classifier Method Comparing with Logistic Regression Algorithm

The aim of this research is to identify the credit card fraud detection model using an improved K-nearest neighbor classifier method to find the credit card fraudulent transactions with accuracy percentage compared with Logistic regression algorithm. Materials and Methods: Logistic regression algorithm with sample size is 26 and K-nearest neighbor classifier method with sample size is 26 was iterated for predicting accuracy percentage of credit card fraud detection. The study contains two groups where the K-nearest neighbor classifier method algorithm is analysed in the first group, the logistic regression algorithm is analysed in the second group. The G power test value is 0.8 and the accuracies of each algorithm are compared. Results: Logistic regression algorithm is significantly better accuracy 92% compared to K-nearest neighbor 90% with the power value of 80% The two algorithms KNN and logistic regression are statistically not satisfied with the independent sample T-Test <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">${(\alpha=.881)}$</tex> value <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">${(\mathrm{p}=0.05)}$</tex> with a confidence level of 95%. Conclusion: The outcome of the proposed algorithm was compared with the K-nearest neighbor classifier method algorithm and the proposed algorithm has significantly better accuracy of 92% compared with Logistic regression algorithm.
Towards Data-Efficient Continuous Learning for Edge Video Analytics via Smart Caching

Continuous learning (CL) has recently been adopted into edge video analytics, gaining huge success in maintaining high accuracy without constantly retraining DNN models by human intervention. Though existing solutions offer optimized processing pipelines, the cost brought by CL should not be neglected. This vision paper starts an investigation by exploring two kinds of cost, human labeling and edge storage. The former comes from the need for CL's automatically tuning, and the latter is due to an exemplar pool (including both drift and historical data) maintained to prevent catastrophic forgetting caused by naive retraining. To alleviate the costs, we propose a new CL-based edge video analytics system by incorporating an active learner mechanism. Specifically, we revisit the current CL video system design and develop an active CL pipeline atop them. The pipeline first accepts the drift data stored in drift pool and utilizes an active learner to sample a small partition of them for labeling. Then it mixes up both small labeled drifted data and some historical data to send them to an exemplar pool for CL. Our preliminary benchmark studies exhibit that the new system can achieve competitive accuracy by spending only 30% labeling and storage cost compared to other baselines, showing a promising research direction for future study.
The Application of HCI in Industry and IT

The HCI in the industry, and IT has increased the feasibility of data exchange. Multiple transactions are carried out online, which has led to overwhelming increasing shopping behavior. As a result of which the personal information of people like account details, and others are made available to the digital medium. However, that exposure of information is triggering data privacy issues, which is a major problem associated with HCI in the IT and industries. The proposed solution to this problem is to spread awareness among people about managing data online and make them conscious about setting strong passwords for making the accounts secure.
AutoMatch

Traffic camera is one of the most ubiquitous traffic facilities, providing high coverage of complex, accident-prone road sections such as intersections. This work leverages traffic cameras to improve the perception and localization performance of autonomous vehicles at intersections. In particular, vehicles can expand their range of perception by matching the images captured by both the traffic cameras and on-vehicle cameras. Moreover, a traffic camera can match its images to an existing high-definition map (HD map) to derive centimeter-level location of the vehicles in its field of view. To this end, we propose AutoMatch - a novel system for real-time image registration, which is a key enabling technology for traffic camera-assisted perception and localization of autonomous vehicles. Our key idea is to leverage landmark keypoints of distinctive structures such as ground signs at intersections to facilitate image registration between traffic cameras and HD maps or vehicles. By leveraging the strong structural characteristics of ground signs, AutoMatch can extract very few but precise landmark keypoints for registration, which effectively reduces the communication/compute overhead. We implement AutoMatch on a testbed consisting of a self-built autonomous car, drones for surveying and mapping, and real traffic cameras. In addition, we collect two new multi-view traffic image datasets at intersections, which contain images from 220 real operational traffic cameras in 22 cities. Experimental results show that AutoMatch achieves pixel-level image registration accuracy within 88 milliseconds, and delivers an 11.7× improvement in accuracy, 1.4× speedup in compute time, and 17.1× data transmission saving over existing approaches.
Smart Contracts Vulnerability Classification through Deep Learning

We investigate the use of deep learning to classify smart contract code vulnerabilities. We use different variants of Convolutional Neural Networks (CNNs) and a Long Short-Term Memory (LSTM) neural network. Five classes of vulnerabilities were employed. Our results suggest that the CNNs are able to provide a good level of accuracy, thus showing the viability of the proposed approach.
High dimensional Bio medical images denoising using wavelet transform and modified bilateral filter

Biomedical images consist of salient information that helps in diagnosis of diseases. Significant amount of information is lost during process of transmission due to addition of noisy components especially for Computed Tomography (CT) scan, Ultrasound imaging and Magnetic Resonance Imaging (MRI). Image denoising is an important and necessary pre-processing phase in image applications. Numerous techniques have been developed for denoising process over decades. The purpose is to remove noise from images. Wavelet transform is current state of the art addition to analyze image denoising; able to achieve improved experimental results than from existing techniques. Generalized Gaussian distribution is considered to model noise. In this article, we introduce mix adaptive thresholding with modified bilateral filter. Simulation results demonstrate that attributes of images are improved. The Qualitative and quantitative assessment proves better denoised results for proposed method using parameters like Peak Signal to Noise Ratio (PSNR), Mean Opinion Score (MOS) and Structural Similarity Index (SSIM) between the true and estimated biomedical images.
PRO-UX: System Redesign Process for Improving the UX

System redesign is a software modernization strategy that consists of restructuring a solution to provide a new graphical interface, improving the user experience, and using modern technologies while preserving relevant system functionalities. When it comes to enterprise applications, the challenge for redesign becomes greater. It is necessary to ensure that the system can operate according to existing business rules and that its operation will not compromise the integrity of the enterprise. This paper presents a process for redesigning systems to improve the user experience. We also describe a case study where we applied this process to redesign an enterprise application and share some lessons learned during the application redesign.
Investigating Human and Social Factors in Requirements Engineering in Software Ecosystems

In software ecosystems (SECO), requirements engineering (RE) activities and process require the collaboration of several stakeholders such as requirements engineers, users, third-party developers, collaborators etc. Therefore, it is a highly human-dependent process. Identifying how human and social factors can influence RE-related activities and process would improve RE in SECO. In this context, this study aims to identify which human and social factors influence RE activities in SECO. To do, we conducted a rapid review, supplemented with the snowballing technique to retrieve published studies. A total of 673 studies were returned, of which 12 were included and analyzed from three research questions that provided us with an understanding of the human and social factors of RE in SECO. The results indicate that decision making, multiple actors, geographic distance/time, and negotiation skills are important human and social factors when involved in RE activities in SECO. Finally, the results of this study can help practitioners and researchers define mechanisms to enhance the positive and decrease the negative influence of these factors.