Reviewerly/exHarmony huggingface dataset:
A Robotic Hand: Controlled With Vision Based Hand Gesture Recognition System

Human-robot interaction (HRI) has become an important topic in today’s robotic world, especially in assistive robotics. Now pen, mouse, keyboard etc. are not sufficient due to the limitations of these devices for taking command. Human now wants direct use of hands as an input device for getting more natural human-robot interactions (HRI). Vision based hand recognition system has come with a solution for these type of demands of human. In this system, human need not to operate any kind of devices, instead a camera is used for recording the movements of hands and these recorded data are used as input for performing any kind of gesture control. The elderly people, patients and disabled can be benefitted from these kind of gesture control. Besides it can be used in extreme environments where human can’t go directly. Here, a vision based hand gesture system for controlling robotic hand has been developed. The main intension here, is to implement a vision based gesture recognition system that recognizes data gathered from hand movements through a camera.
On Post Selection Using Test Sets (PSUTS) in AI

This is a theory paper. It first raises a rarely reported but unethical practice in Artificial Intelligence (AI) called Post Selection Using Test Sets (PSUTS). Consequently, the popular error-backprop methodology in deep learning lacks an acceptable generalization power. All AI methods fall into two broad schools, connectionist and symbolic. PSUTS practices have two kinds, machine PSUTS and human PSUTS. The connectionist school received criticisms for its “scruffiness” due to a huge number of scruffy parameters and now the machine PSUTS; but the seemingly “clean” symbolic school seems more brittle than what is known because of using human PSUTS. This paper formally defines what PSUTS is, analyzes why error-backprop methods with random initial weights suffer from severe local minima, why PSUTS violates well-established research ethics, and how every paper that used PSUTS should have at least transparently reported PSUTS data. For improved transparency in future publications, this paper proposes a new standard for AI metrology, called developmental errors for all networks trained in a project that the selection of the luckiest network depends on, along with Three Conditions: (1) system restrictions, (2) training experience and (3) computational resources.
COVID-19 and rapid digitalization of learning and teaching: quality assurance issues and solutions in a Sino-foreign higher education institution

Purpose This paper aims to report and analyze the lessons learnt from a case study of the implementation of the move from onsite to online learning at a Sino-Foreign higher education institution. Design/methodology/approach This paper reports a case study based on an analysis of three elements of the move to digital education during the COVID-19 pandemic. In suggesting three elements, it argues that the first and most immediate move away from onsite teaching should be thought of as emergency response teaching, and it is the lessons learnt from this vital and necessary stage that have informed the subsequent implementation of a more digitalization process. Findings The case study identifies the journey that the university is going through to deliver digitalized education, and how the pandemic hastened what had already been started. It illustrates how the pandemic has been useful in highlighting where we are, what decisions have been made and what still needs to be done with regard to readiness for the disruption likely by Industry 4.0. Research limitations/implications The study was conducted at one institution, which along with eight other similar institutions is unique within China. Findings, especially the processes of making decisions, while relevant to the wider discussion on digitalization and Industry 4.0, should also be considered in this light. Practical implications While the focus of the paper is on COVID-19 and Rapid Digitalization of Learning and Teaching, the main implication is that universities need to better prepare their teaching staff for digitalized education, especially if they are wanting to engage with disruption brought about by Industry 4.0. Originality/value The paper is based on a single university case study of a rather unique university within China. The discussion illustrates the need for flexibility and decisiveness in making key decisions on managing disruption, but with strategic direction in mind, illustrated in the study by COVID-19, but with future digitalization initiatives also to the fore.
Evaluating Efficiency and Effectiveness of Federated Learning Approaches in Knowledge Extraction Tasks

Federated Learning is a valuable instrument for building AI-based systems that preserve the privacy and security of sensitive data, based on the main concept of shifting no more the data to the edges but moving computations to data, avoiding the collection, sharing, and use of such data by third parties. More robust federated learning systems should be able of preventing malicious inference over both data exchanged during training and the final trained model while ensuring the resulting model also has acceptable predictive accuracy. This study proposes a preliminary analysis to investigate and evaluate the effectiveness and efficiency of a federated approach to ensure valid classification accuracy and data security. A real case study from the ANDROIDS project, concerning the application of machine learning-based systems for supporting mental-health disorders detection, was considered. Large amounts of sensitive patient information are collected, which must be obfuscated or anonymized to provide a preliminary level of protection. Unfortunately, the real bottleneck lies in the difficulty of extracting all sensitive data for anonymization, due to a lot of data to handle as well as the considerable effort required. We propose a Natural Language Processing approach for sensitive knowledge detection and classification, performed by adopting a federated approach. Accuracy decay and latency introduced by applying a decentralized learning approach compared to the same task and data performed in a centralized way were evaluated. Preliminary results proved that effectiveness can be reached by a correct tuning of the federated algorithm and by choosing the right number of participants to the federation.
Would It Be Better if Instructors Technically Adjust Their Image or Voice in Online Courses? Impact of the Way of Instructor Presence on Online Learning

This study investigated the effects of the changes in the image and voice of instructors in online video courses on online learner's learning achievement, social presence, learning satisfaction, and academic emotion. Two simultaneous online experiments were conducted with 122 college students in the image experiment, where the course videos varied in terms of the instructor's image (original image, face-beautified image, virtual image, and no image), and 93 college students in the voice experiment, where the course videos varied in terms of the instructor's voice (original voice, mutated voice, computer-synthesized voice). The results showed that learners viewing videos without instructor images had better learning achievements and less academic boredom relative to those who viewed videos with instructor images. However, the real instructor images were able to promote learners' learning satisfaction of instructor-student interaction more than no image and virtual image and promote satisfaction of instructor teaching more than virtual image. Meanwhile, learners' evaluation of the real instructor images was better than that of the virtual instructor image, and their evaluation of the face-beautified instructor image was better than the original image. Moreover, learners evaluated real instructor voices better than the computer-synthesized voice. In addition, the linear regression analysis revealed that the evaluations of both instructor's image and voice had a positive relationship with learners' social presence, learning satisfaction, and enjoyment, whereas they had a negative relationship with learner's boredom. And the evaluation of the instructor's image positively predicted student's transfer learning achievement. Thus, we suggested that the way of instructor presence should be well-designed and integrated with the course's instructional design and image and voice processing technology can be applied to assist online video course development.
Road Image Segmentation using Unmanned Aerial Vehicle Images and DeepLab V3+ Semantic Segmentation Model

Road image segmentation is critical in a variety of applications, including road maintenance, intelligent transportation systems, and urban planning. Numerous image segmentation techniques, including popular neural network approaches, have been proposed for unmanned aerial vehicle (UAV) images recently. However, since these images include complex backgrounds, high-precision road segmentation from UAV images remains challenging. To address this issue, this study proposes a deep learning method called DeepLab V3+ semantic segmentation. Road images are captured and collected from several roads in Kedah and Selangor, Malaysia using a UAV. To segment the road from the background, the DeepLab V3+ with Resnet-50 backbone is utilised. Then, the performance is assessed by comparing segmented images by deep learning to manually segment images. Three metrics are used for the assessment; pixel accuracy (PA), mean area intersection by union (mIoU), and mean F1-score (MeanF1). The study also compares the segmentation performance with the DeepLab V3+ with mobile NetV2 for benchmarking purposes. Simulation results show that the DeepLab V3+ with Resnet-50 has performed better than the DeepLab V3+ with mobile NetV2 methods. The findings indicate that the DeepLab V3+ with Resnet-50 outperformed the DeepLab V3+ with mobile NetV2 for PA, mIoU, and MeanF1 by 1.39 %, 4.92 %, and 9.71 %, respectively.
Small files’ problem in Hadoop: A systematic literature review

Apache Hadoop is an open-source software library which integrates a wide variety of software tools and utilities to facilitate the distributed batch processing of big data sets. Hadoop ecosystem comprises two major components - Hadoop Distributed File System (HDFS), which is primarily used for storage and MapReduce, which is primarily used for processing of the files. The performance of Hadoop holds back when it comes to storage and processing of small files. Small files are essentially the files that are significantly smaller in size when compared to the default block size of HDFS. This is because each small file consumes a block individually leading to excessive memory requirement, access time and processing time. Scaling the memory, allowing access latencies and processing delays beyond a limit is not an option. Henceforth, in this paper, a Systematic Literature Review has been performed to provide a comprehensive and exhaustive gestalt of the small files' problem in Hadoop. The paper defines a comprehensive taxonomy of Hadoop ecosystem and its’ existent small files problem. Further, the study also attempts to critically analyze the solutions that have been proposed to overcome this problem. These solutions have been analytically studied to identify the set of parameters that should be considered in impending while proposing solutions pertaining to this problem.
COSMOS on Steroids

The growing prevalence of visual disinformation has become an important problem to solve nowadays. Cheapfake is a new term used for the altered media generated by non-AI techniques. In their recent COSMOS work, the authors developed a self-supervised training strategy that detected whether different captions for a given image were out-of-context, meaning that even though pointing to the same object(s) in the image, the captions implied different meanings. In this paper, we propose four methods to improve the detection accuracy of COSMOS. These methods range from differential sensing and fake-or-fact checking that detect contradicting or fake captions to object-caption matching and threshold adjustment that modify the baseline algorithm for improved accuracy.
Human Behavior Analysis: A Survey on Action Recognition

The visual recognition and understanding of human actions remain an active research domain of computer vision, being the scope of various research works over the last two decades. The problem is challenging due to its many interpersonal variations in appearance and motion dynamics between humans, without forgetting the environmental heterogeneity between different video images. This complexity splits the problem into two major categories: action classification, recognising the action being performed in the scene, and spatiotemporal action localisation, concerning recognising multiple localised human actions present in the scene. Previous surveys mainly focus on the evolution of this field, from handcrafted features to deep learning architectures. However, this survey presents an overview of both categories and respective evolution within each one, the guidelines that should be followed and the current benchmarks employed for performance comparison between the state-of-the-art methods.
Applying Thermal Side-Channel Attacks on Asymmetric Cryptography

Side-channel attacks (SCAs) are powerful attacks that could be used to retrieve keys from electronic devices.Several physical leakage sources can be exploited in SCAs, such as power, time, heat, and so on.Heat is one of the side-channels that is not frequently analyzed by attackers in the literature due to the high noise associated with thermal traces.This article investigates the practicality of adapting power-based SCAs [i.e., correlation power analysis (CPA) and deep-learning-based power attacks (DL-based PA)] for thermal attacks and refer to them as correlation thermal attack (CTA) and DL-based thermal attack (DL-based TA).In addition, we introduce a new attack called progressive CTA (PCTA).We evaluate the different thermal SCAs against an unprotected and protected software implementation of Rivest-Shamir-Adleman (RSA).Our results show the practicality of the three attacks (i.e.CTA, DL-based TA, and PCTA) as a 100% key recovery is realized.
Facial emotion recognition based on LDA and Facial Landmark Detection

Emotion recognition in the field of human-computer interaction refers to that the computer has the corresponding perceptual ability to predict the emotional state of human beings in advance by observing human expressions, behaviors and emotions, so as to ensure that computers can communicate emotionally with humans. The main research work of this paper is to extract facial image features by using Linear Discriminant Analysis (LDA) and Facial Landmark Detection after grayscale processing and cropping, and then compare the accuracy after emotion recognition and classification to determine which feature extraction method is more effective. The test results show that the accuracy rate of emotion recognition in face images can reach 73.9% by using LDA method, and 84.5% by using Facial Landmark Detection method. Therefore, facial landmarks can be used to identify emotion in face images more accurately.
Denoising of an ultraviolet light received signal based on improved wavelet transform threshold and threshold function

In this paper, the wavelet transform algorithm is used to reduce the noise of ultraviolet (UV) light received signals. An improved calculation method of the wavelet thresholds and a new threshold function are proposed. The new threshold function avoids the discontinuity of the traditional hard threshold function. It can also avoid the constant deviation caused by the traditional soft threshold function. The improved threshold calculation method takes into account the effect of the wavelet decomposition level, and the simulation results show the effectiveness of the proposed method. Compared with other methods, the method proposed in this paper can obtain a better denoising effect.
Blockchain-Based Public Key Infrastructure: A Transparent Digital Certification Mechanism for Secure Communication

Public Key Infrastructure (PKI) has been considered to be an enabler of secure communication, while, due to its complex and centralized design, there have been instances in the past for Certification Authority's (CA) misbehaving and publishing rogue certificates for targeted attacks. This research aims to present a blockchain-based mechanism that lays down a concrete foundation for creating a transparent and secure block-chain-based mechanism for the issuance and management of digital certificates that enables prevention against CA misbehaving. A prototype is deployed and tested on the Ethereum test network, and the results are made publicly available for verification and validation. As a result, the proposed Ethereum blockchain-based PKI mechanism enables secure, transparent, and auditable issuance and management of digital certificates together with the solution of Sybil, Spoofing, and Man-in-the-Middle (MITM) attacks.
RFID-based 3D human pose tracking: A subject generalization approach

Three-dimensional (3D) human pose tracking has recently attracted more and more attention in the computer vision field. Real-time pose tracking is highly useful in various domains such as video surveillance, somatosensory games, and human-computer interaction. However, vision-based pose tracking techniques usually raise privacy concerns, making human pose tracking without vision data usage an important problem. Thus, we propose using Radio Frequency Identification (RFID) as a pose tracking technique via a low-cost wearable sensing device. Although our prior work illustrated how deep learning could transfer RFID data into real-time human poses, generalization for different subjects remains challenging. This paper proposes a subject-adaptive technique to address this generalization problem. In the proposed system, termed Cycle-Pose, we leverage a cross-skeleton learning structure to improve the adaptability of the deep learning model to different human skeletons. Moreover, our novel cycle kinematic network is proposed for unpaired RFID and labeled pose data from different subjects. The Cycle-Pose system is implemented and evaluated by comparing its prototype with a traditional RFID pose tracking system. The experimental results demonstrate that Cycle-Pose can achieve lower estimation error and better subject generalization than the traditional system.
The Abstract of Thesis Classifier by Using Naive Bayes Method

The thesis is a requirement for graduation from Bumigora university. The final year student’s problem is determining the research topic because the undergraduate thesis collection of Computer Science is not grouped or classified based on student competencies. The purpose of this study was to compare the performance of the naïve Bayes method with TF-IDF weighting and without TF-IDF weighting for the classification of thesis topics based on the abstract. The stages of this research are data collection, text pre-processing, term weighting with TF-IDF and without TF-IDF, Naïve Bayes method implementation, and result evaluation. Based on the results of the tests that have been done, the naïve Bayes method with TF-IDF has an accuracy of 81.74%, a precision of 86.1%, and a sensitivity of 80.15%. While the naïve Bayes method without TF-IDF weighting produces 88.69% accuracy, 89.76% precision, and 90.49% sensitivity. Thus, the naïve Bayes method without TF-IDF weighting has better performance than TF-IDF weighting for the classification of thesis topics based on the abstract.
Defect Classification and Detection Using a Multitask Deep One-Class CNN

Defect classification and detection have been explored using convolutional neural networks (CNNs). Normally, a large set of training images containing defects and the associated annotation data are required by these approaches. However, such a large set of images is usually difficult to collect because defects are rare and annotation is time-consuming and expensive. To address these issues, we propose to use a multitask deep one-class CNN for defect classification. Compared with supervised classification methods, this CNN does not require abnormal images and annotated data for training. Specifically, we build a stacked encoder–decoder autoencoder for learning feature representation from normal images. The encoder is used as a feature extractor based on the hard sharing scheme of multitask learning. A one-class classification (OCC) objective learned as a hypersphere using minimum volume estimation is appended to it. Together the encoder and the OCC objective lead to a deep one-class classifier. To train both the autoencoder and one-class classifier end-to-end, a multitask loss function is built. Given an unknown sample, the distance between its feature representation and the center of the hypersphere is used as the anomaly score. Furthermore, defect detection is implemented using a moving-window scanning method on top of the deep one-class classifier. The proposed approach achieves better performance than its counterparts trained using a two-stage method. For defect detection, our approach achieves results almost as good as the supervised method even without using any annotated data. We attribute the promising results to the advantages of multitask learning. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Building and evaluating vision-based nondestructive testing (NDT) techniques require many examples of abnormal images, which may not be easy to acquire. This article describes a method that does not require abnormal images for training a convolutional neural network (CNN) in order to perform one-class defect classification (outlier detection). We also applied the method to defect detection with promising results. We include results of experiments demonstrating that better performance can be obtained using our method compared to a set of baselines. Although the proposed method does not use abnormal images for training, it still produces results that are almost as good as the supervised learning-based CNN approaches. This study provides a solution to the challenge encountered by the industrial inspection community when enough abnormal samples are hard to obtain.
The Degree of Practicing Organizational Justice by Teachers of Learning Disabilities in English Language from Their Point of View

The study aimed at identifying the degree of practicing organizational justice by teachers of learning disabilities in English language from their point of view. The study used the descriptive approach and developed a questionnaire, which was administered to a sample of (30) teachers of LDs in English language in the Irbid governorate. The questionnaire was used to collect data that included demographic information about the respondent and to measure the use of organizational justice among teachers of LDs in English language. The validity of the study instrument was verified by presenting it to specialized judges, and their reliability was verified using the test and re-test method. The results of the study showed a high practice of organizational justice among teachers of LDs in English language in general and in all the dimensions of the instrument. In light of these results, the study recommended increasing the practice of organizational justice in educational institutions to increase teachers' performance and holding more training on the topic