Reviewerly/exHarmony huggingface dataset:
Validation of Multiple Visitor Tracking with a Laser Rangefinder Using SMC Implementation of PHD Filter

In this study, the goal is to verify the accuracy of tracking multiple visitors using a laser rangefinder with a Sequential Monte Carlo (SMC) implementation of a Probability Hypothesis Density (PHD) filter. The feature of our method is using only a laser rangefinder. It is difficult to track multiple visitors using only a laser rangefinder for visitor measurement. However, this method is better to the method using cameras in viewpoint of privacy protection. The use of a PHD filter is also discussed to consider the variation of the number of visitors. In the results of the numerical experiment, the estimated number of visitors was compared with the actual number of visitors. From the comparison results, the PHD method suppressed the effect of occlusion compared with detection only. seems However, the errors at each time step slightly increased. The results of the experiment will be used to examine future challenges to improve accuracy.
Reducing computational cost in federated ensemble learning via rank-one matrix

As distributed environments have developed and data privacy has become more important, federated learning attracts more attentions. Federated learning is the method to train deep learning models without data exchange in distributed environments. However, in a Non-Independent and Identically Distributed data environment, performance degradation occurs in the federated learning environment. We want solve this problem by federated ensemble learning but computational cost problem occurs in edge devices. To solve this problem, we proposes FedRE. FedRE reduces computational cost on ensemble model via rank-one matrix and using shared weights to minimize performance degradation when Non-IID situations. As a result of the experiments, SVHN and CIFAR-10 image classification tasks showed high accuracy compared to the previous federated learning methods.
A spiking neural network that extracts frequent symbol patterns from time-series data with different symbol appearance intervals

In this research, we aim to extract frequent symbol patterns from symbol sequences. In the target time series data, the appearance timing of symbols is different. The purpose is to absorb the deviation of the appearance timing and extract frequent symbol patterns. This paper proposes a spiking neural network that extracts frequent symbol patterns. The structure of the proposed network grows automatically each time a symbol is given. By learning the network, the output unit will only fire when given a frequent symbol pattern, and the network will extract the frequent symbol patterns. As a result of a simple experiment, it was confirmed that the proposed network can extract frequent symbol patterns from the target time series data.
Proposal for High-speed Picking System Aimed at Shortening the Cycle Time of Bulk Picking Task

This paper addresses high-speed picking systems for bulk picking task. Proposed systems adopt parallelization of path planning and pick motion, and utilize multi-work picking to shorten the cycle time of bulk picking task. Our parallelization methods conduct the next picking path planning during the current picking motion to shorten the time from the end of the current picking motion to the start of the next picking motion. In addition, since bulk works can collapse during the current picking motion, we prepare multi path candidates in the next picking path planning and select the path against not collapsed works at the end of the current picking motion. We verify the validity of the proposed systems by comparing the cycle time with conventional picking systems.
Improving Computational Method of Dynamic Saliency Maps for Estimating Drivers’ Fixation Points Based on Issues Extracted via In-vehicle Camera Video Simulator

Currently, driving support technologies are making notable progress in reducing road accidents caused by drivers’ errors or inattention. To further develop the driving support technologies, we have been continuously working on a statistical-machine-learning-based computational method of dynamic saliency maps for estimating drivers’ fixation points, which enables us to quantify the degree of drivers focusing on specific objects as a probability. Simultaneously, we have also developed a prototype simulator to replicate in-vehicle camera videos under a variety of driving situations using 3-dimensional computer graphics to validate such a computational method of dynamic saliency maps. Moreover, in our previous study, we have extracted two issues of the above-mentioned computational method via validations with in-vehicle camera videos replicated using this simulator. In the present study, we improve the computational method by introducing techniques for overcoming these extracted issues. The computational method improved using these techniques is also tested with real videos captured using real in-vehicle cameras and replicated videos using our simulator. Based on these results, we discuss the effectiveness of the improved method.
Converging and Moving Detection Using an Outward Vector Rate for Adaptive Differential Evolution

The performance of Differential Evolution (DE) is affected by algorithm parameters, mutation strategies and so on. One of the most successful studies on controlling the parameters in DE is JADE (adaptive DE with optional external archive). In this study, we propose a method to detect that the population is converging or moving. The vectors from parent to child are classified into inward vectors and outward vectors, and the rate of the outward vectors is used to determine the search state. In order to improve search efficiency of JADE, if the population is converging, an algorithm parameter is adjusted to enhance the convergence. If the population is moving, the parameter is adjusted to enhance the movement. Also, the moving direction of the population is determined by the averaged outward vectors and the search in the direction is strengthened. The advantage of JADE with the proposed method is shown by solving thirteen benchmark problems.
Effects of Accuracy-based Single-Objective Optimization in Multiobjective Fuzzy Genetics-based Machine Learning

Fuzzy classifier design often requires the maximization of classification accuracy and minimization of model complexity. Multiobjective Fuzzy Genetics-Based Machine Learning (MoFGBML) can efficiently obtain a set of fuzzy classifiers considering the above-mentioned two objectives simultaneously using an evolutionary multiobjective optimization algorithm. However, the search by MoFGBML is biased toward minimizing the complexity, and it is easy to obtain classifiers with low complexity. At the same time, it is difficult to obtain classifiers with high classification accuracy. In this paper, we propose a two-stage MoFGBML, which first performs accuracy-oriented single-objective optimization to obtain a set of accurate classifiers with a large number of rules. Then, multiobjective optimization is performed to obtain a wide variety of classifiers, from highly-accurate ones to very simple ones.
The Relationship between Spatial Continuity and Sence of Ownership on Rubber Hand Illusion in Virtual Reality

Recently, research has been conducted on virtual hand illusion (VHI), which rubber hand illusion (RHI) is induced in a virtual space. In this study, the spatial continuity of the body was broken in VR, and the effect of the spatial relationship between the virtual hand and the real hand on the sense of ownership was analyzed. Introspective reports were strongly correlated with synchronization of brain wave in right and left frontal lobes, and scores on the introspective report questionnaire assessing experiential sense of ownership were high. The lower scores on the items that asked about a more sensory sense of ownership suggested that the experienced sense of ownership may have been generated by consideration rather than intuition.
Convolutional Neural Networks for Semantic Segmentation: A Recent Survey

Image segmentation is one of the most researched problems in computer vision. Various algorithms and techniques in artificial intelligence and machine learning were used and experimented with. Recently deep learning models have improved state-of-the-art performance on most of the well-known semantic segmentation datasets. In this work, we present a comprehensive study of 9 of the most recent models and methods in semantic segmentation. We experiment with these models on different datasets and draw our conclusions.
Pancreatic Tumor Detection by Convolutional Neural Networks

Artificial Intelligence and its sub-branches like Machine Learning (ML) and Deep Learning (DL) applications have the potential to have positive effects that can directly affect human life. Medical imaging provides a way for the internal structure of the human body to be visible with various methods. With DL models, cancer detection, which is one of the most lethal diseases in the world, from medical images can be made possible with high accuracy. The main objective of this paper is to detect Pancreatic Cancer, which is one of the cancer types with the highest fatality rate, from a dataset of Computed Tomography (CT) images, which is one of the medical imaging techniques and has an effective structure in Pancreatic Cancer imaging. The designed DL model is integrated into the Flask application to develop a web application. With this application, early diagnosis of pancreatic cancer can be achieved, which progresses insidiously and therefore does not spread to neighboring tissues and organs when the treatment process is started. Due to the abundance of medical images reviewed by medical professionals, this application can assist radiologists and other specialists in Pancreatic Tumor detection.
Depth Completion using Convolution Stage, Infinity Laplacian, and Positive Definite Metric Operator

Deep maps have many applications, such as video games, autonomous driving, 3D reconstruction, and many others. Frequently acquired depth maps present holes or lack of information. Interpolation of the available data seems to be a suitable strategy to tackle this problem. Different methods have been proposed to solve this problem, from simple interpolation to deep learning. We proposed a hybrid interpolation model based on the Infinity Laplacian and convolutional stages in this work. The interpolator uses the available depth data and a color reference image of the scene to guide the diffusion process. Given the image domain and a proposed anisotropic metric, we embedded the depth data in a manifold, and the infinity Laplacian was solved in that manifold. The convolutional stage was created with Gabor filters of variable size. We also Pre-filter the available data to detect and eliminate outliers. Obtained results in the publicly available data set for depth completion show that the proposed model outperforms not only the previous version of this model but also similar state-of-the-art methods.
Ranking of Bug Reports into Findbug Categories using the BM25 Function

Bug reporting is a mechanism that is used by software companies to audit and track product quality. The developers and the users can log bug reports via a dedicated system. The quality of the bug reports affects the developers' endeavors to fix and close the bug. The reports include important information for the developers such as severity and priority. However, previous research found that most bugs are left without changing the severity level. Therefore, the developers need to know more information about the reported bug. In this research, we propose to improve the bug report by adding linking bug reports with a pre-specified bug description that was prepared by experts in bug types. i.e. the Findbug tool. The Findbug adds more information to the bug such as the bug type and bug category. The bugs are reclassified using a similarity function, BM25, to match it with more necessary information about the type of reported bugs. Text preprocessing was applied to remove unnecessary terms and to improve the search query in BM25. The proposed methodology could assign extra information to bug reports that help categorize them into different types such as correctness, security, performance, and others.
Information Privacy Protection during Remote Trials

In response to the recently increasing trend towards the use of technology in all sectors, courts, in most countries that possess high technological potentials and suitable electronic infrastructure, have started to use technology in the field of litigation. That is to say, this newly introduced use of technology has culminated in light of the adoption of an integrated legal regulation for remote trials in both civil and criminal lawsuits. In this regard, the application of remote trials requires the use of certain electronic systems and software at the court, with regard to processing the personal data of litigants, defendants, victims, witnesses, etc.; as well as processing other data concerning the disputed matter, including all secret and confidential details of the litigants, defendants and victims; and that is whether these secrets are personal, domestic, commercial or professional. Therefore, it is necessary to adopt and apply the rules of Information Security, while using the new legal system of remote trials. In other words, it is of the utmost importance to take all necessary precautions, in order to protect the information privacy of litigants, defendants, victims, witnesses and all involved parties against any potential hacking or tampering. This paper will focus on three major issues as follows: a) the requirements that shall be duly fulfilled, in order to guarantee the success of the remote trials system; b) the required protection for the Right to Information Privacy; and c) the application mechanism of the remote trial system, as well as protecting the Right to Information Privacy.
Effect of Timers on the Keystroke Pattern of the Student in a Computer Based Exam

This research studies the effect of a countdown timer and a count-up timer on the keystroke pattern of the student and finds out whether changing the timer type changes the keystroke pattern. It also points out which timer affects more students in a timer environment during exams. We used two hypothesis testing statistical Algorithms, namely, the Two-Sample T-Test and One-way ANOVA Test, for analysis to identify the effect of different times our whether significant differences were found in the keystroke pattern or not when different timers were used. The supporting results have been found with determines that timer change can change the keystroke pattern of the student and from the study of hypothesis testing, different students result from different types of stress when they are under different timer environments.
Storing URL Shortener Links in The Decentralized Network

This paper presents a model which delivers the prototype of an application that will store the URL shorten links in the decentralized network. Ethereum Blockchain is used for storing the links and also the proposed model overcomes the issues with earlier URL shorteners like Bitly and short URL. In this paper our research focuses on the two perspectives, viz: i) making a system design to handle traffic easily and ii) making such a system, which will remove the third party system. As a result, the proposed URL shortening service will give the users more trust to use this service with confidentiality, availability, and security consideration compared to the available used services.
A Differential Privacy-based Secure Data Sharing Model in Cloud Environment

With the large variety of cloud services, including on-demand service, least cost, and maximum scalability, it also provides machine learning services that are utilized in various applications, such as medical diagnosis, risk analysis, and image recognition. Due to numerous benefits, most organizations transfer their data to the cloud platform for storage, analysis, and sharing purposes. As the cloud is run by a third party; therefore, data owners cannot fully rely on this environment. Thus, maintaining owners' data protection while sharing it with other parties has become a major challenge. This paper proposes a secure data sharing model in the cloud environment, which protects the owners' data by encryption, noise addition, and performing the classification task by employing access control, differential privacy, and machine learning mechanisms, respectively. The proposed model's performance (in terms of accuracy, precision, recall, and F1-score) is assessed over Dermatology, Ecoli, Yeast, and Cryotherapy datasets. The experimental results exhibit that the proposed model has high accuracy, precision, recall, and F1-score up to 97.29%, 97.50%, 96.52%, and 97.25%, respectively.
Evaluation of File Carving Tools for Forensic Investigation in Docker Containers

Container Technology has attracted a lot of attention and is increasingly utilized to deploy the industrial applications. Containers are executable units of software which encapsulate the code along with the libraries and other dependencies in order to facilitate the code to run anywhere. They take advantage of operating system virtualization and can run anything from a small microservice or software process to a larger application. Containers are very lightweight as compared to Virtual Machines. They offer high portability with very less overhead. This emerging field of container technology has attracted a lot of attention from the community of researchers. In this paper, we have performed digital forensics on the Docker containers using file carving techniques in order to test whether the lost data from the deleted containers can be retrieved or not. For this purpose, we have used three popular file carving tools and compared their performance. The results of the experiments show that the file carving is an effective way to recover the lost data from the deleted containers.
Investigating Student Perceptions of Augmented Reality Utilizing Technology Acceptance Model (TAM)

As technology is getting more prevalent in the classroom environment recently, it is evitable to explore the extent to which end-users, i.e. the students accept the implementation of technology, in order to verify its effectiveness in achieving educational goals. Therefore, the purpose of this study is to investigate social science students' perceptions of a specific type of technology, namely, Augmented Reality (AR) technology and whether variables such as gender, specialization or their program level may influence their attitude toward (AR). To this end, a Technology Acceptance Model (TAM) was adopted to elicit the students' degree of acceptance through a questionnaire addressing three domains: perceived usefulness, perceived ease of use and behavioral intention. The findings of the current study show that the students have a significantly positive attitude toward the integration of (AR), and that gender, specialization and program level have almost no impact on the students' perceptions. The implications of these findings are important for policies and practices related to instructional technology in higher education.
Detection and Recognition of Drones using Deep Convolution Neural Networks

Drones are gaining popularity in various fields, including delivering products, disaster management, entertainment, airport security, and more. As a result, the odds of a drone being utilized for malevolent purposes are increasing, prompting an unsettling concern about physical infrastructure security, safety, and monitoring at airports. There have been numerous allegations in recent years of unauthorized usage of various types of drones at airports, causing airline operations to be disrupted. To solve this issue, this study proposed two deep learning-based approaches named YOLOv4 and Faster RCNN. The offered methodologies were compared to find the optimal model. Drones are also frequently mistaken for birds due to their morphological and behavioral similarities. Not only can the proposed algorithms identify the presence of drones in a provided image, but they can also detect the presence of birds and point to their positions. The proposed YOLOv4 model obtained better results compared to Faster RCNN, with mAP values of 75% and 72%, respectively.
Text-Dependent Speaker Verification Under Practical Scenarios Using DTW-GMM Based Two-tier Authentication

This work proposes a two-level authentication process to improve the performance of the Text-Dependent Speaker Verification (TDSV) system under degraded and practical testing scenarios. For this purpose, in the first level, conventional Dynamic Time Warping (DTW) is used to generate scores by matching the utterances of the testing phase with that of the enrolment phase. The DTW scores generated are then passed as features to a 32-mixture Gaussian Mixture Model (GMM) learned from the DTW scores obtained by intra-matching the enrolment data. To evaluate the performance of this two-tier system, two databases - the RSR2015 database, and the IITG database - are used. It is ensured that the testing data of both the databases contain significant variabilities with respect to the enrollment data. It is observed that the DTW-GMM two-tier authentication system improves the performance of the TDSV system by ~ 1.75%, for both the databases, compared to the baseline DTW system alone.
A Robust Approach for Real Time Human Detection on Railway Track

Detection of Human is a staple praxis in the field of computer vision. Object recognition where both object and camera are moving can be utilized in transport frameworks (like railways) to lessen human dependency. This paper enunciates a potent human identification strategy in condition where both camera and object are moving. Technique has been proposed for railway track identification and human location for savvy application that railways can utilized. The Hough transformation is utilized for railroad tracks identification. Ultimately, the single shot detector (SSD) has been used for detection so that even an individual standing exceptionally distant can be distinguished and trains can be alerted in advance with sufficient time to keep away from accidents.
Parallel Training of a Set of Online Sequential Extreme Learning Machines

Size databases have constantly increased from advances in technology and the Internet, so processing this vast amount of information has been a great challenge. The neural network Extreme Learning Machine (ELM) have been widely accepted in the scientific community due to their simplicity and good generalization capacity. This model consists of randomly assigning the weights of the hidden layer, and analytically calculating the weights of the output layer through the Moore- Penrose generalized inverse. High-Performance Computing has emerged as an excellent alternative for tackling problems involving large-scale databases and reducing processing times. The use of parallel computing tools in Extreme Learning Machines and their variants, especially the Online Sequential Extreme Learning Machine (OS-ELM), has proven to be a good alternative to tackle regression and classification problems with largescale databases. In this paper, we present a parallel training methodology consisting of several Online Sequential Extreme Learning Machines running on different cores of the Central Processing Unit, with a balanced fingerprint database having 2,000,000 samples distributed in five classes. The results show that training and validation times decrease as the number of processes increases since the number of samples to train in each process decreases. In addition, by having several Online Sequential Extreme Learning Machines trained, new samples can beclassified on any of them.