Reviewerly/exHarmony huggingface dataset:
Gesture Control of a Robotic Head using Kinect

Robots, from the simplest to the most complex, require remote control. Teleoperation requires contact, whether a joystick, a controller, or an HMI is used. In most cases, these devices do not offer the necessary comfort, nor the possibility for the teleoperator to be able to use his skills and abilities at an optimal level. Many studies have been done for sensors to solve this discomfort, some of which also offer wearable sensors such as: microwave multiple-input multiple-output (MIMO) arrays, IMU sensors, fisheye monocular camera for human posture detection. In this paper, he proposes a 3D posture estimation solution that uses a Kinect V1 device to fuse RGB and depth images. We developed a MatLab Simulink implementation algorithm to automatically compute angles of both shoulder joints, elbow joints, and wrist joints based on a skeleton model that allows us to estimate 20 joint positions. To validate our experiment, we offer a non-contact solution that can control the joints of a <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$3\mathrm{D}$</tex> printed robotic head based only on user right-hand movements.
JAVANESE PHONOLOGICAL INTERFERENCE IN ENGLISH PRONUNCIATION OF ENGLISH LITERATURE DEPARTMENT STUDENTS

The occurrence of phonological interference can be found if the native is able to acquire multilingually. The research aims to reveal the phonological aspects causing phonological interference and establish the strategies for avoiding interference. Students of the English literature department that take English Language Teaching (ELT) and speak Javanese, Indonesia, and English language become the primary sources of this research. The method used in this research is a descriptive qualitative as research design and the theory of Geoffry S. Nathan for the analysis. Meanwhile, Marsono’s theory is employed to compare it with vocal, diphthong, and consonant in Javanese language. The finding shows that phonological interference comes from a segmental aspect, totaling errors up to 99 and 189 in the suprasegmental aspect. In the segmental aspect, errors of English pronunciation were found on the assimilation of speech implementation based on vowel, diphthong, and consonant. Meanwhile, in the suprasegmental aspect, word stress on the second syllable with two, three, and four-syllable words become the most errors. Therefore, phonological interference can be avoided by doing more individual practice in looking for difficult vocabularies and using English as the main language in doing enjoyable activities such as listening to music and watching movies.
Unsupervised Machine Learning for Unusual Crowd Activity Detection

Unusual human crowd activity detection is the approach to identifying undesired human activities in a congested environment with a lot of objects in motion. This is accomplished by transforming input video into separate frames and then studying the behaviour of persons and the actions performed by them in the frames that are processed one by one. This paper puts forth a design for developing a system that can identify such human actions which are questionable. The performance of the proposed system compared with the existing model in terms of accuracy and F-score, which reflects higher than the existing model and decent enough for considering the activity as unusual in videos with complex graphics.
Blockchain-based Disaster Recovery Data Storage and Security Auditing Solution in Multi-cloud Environment

The utilization of cloud disaster recovery services has emerged as the current trend in data disaster recovery research and application due to the quick growth of virtualization technology and cloud applications. Single-cloud disaster recovery services fall short of users’ demands for data security backup due to technical flaws, managerial weaknesses, network attacks, natural calamities, etc. To implement cross-cloud disaster recovery and multi-cloud data storage, disaster recovery systems must be deployed in multi-cloud and heterogeneous cloud settings. To do this, problems with distributed cross-cloud storage’s data batch audit and problem resolution must also be resolved. In this paper, we propose a blockchain-based distributed storage and security audit solution for disaster recovery data in a multi-cloud environment, which addresses the issues of multi-copy storage management and security audit of multi-cloud data.
A Solution for A Disaster Recovery Service System in Multi-cloud Environment

The current enterprise information architecture is becoming more complex. As a result, user business data is lost or destroyed, services are interrupted, and systems crash, all of which harm users’ businesses. These factors include equipment failure, network attacks, and human error. Data disaster recovery has become an important method to ensure the sustainability of enterprise business. It is an inevitable trend toward disaster recovery services and technologies that “business in the cloud, cloud disaster recovery, and multi-cloud disaster recovery.” Traditional disaster recovery services systems and single-cloud disaster recovery services systems are unsatisfied with users’ current demands for business system deployment in multi-cloud environments, cloud resource sharing, and cross-cloud disaster recovery. We discuss the structure, composition, and functional ideas of a solution for a disaster recovery service system in multi-cloud environments. To provide cross-cloud disaster recovery management service in a multi-cloud environment with full-stack virtualization in response to user business requirements, it combines conventional disaster recovery technology, blockchain distributed multi-copy data storage and security audit, and system security state awareness.
Joint Hand and Object Pose Estimation from a Single RGB Image using High‐level 2D Constraints

Abstract Joint pose estimation of human hands and objects from a single RGB image is an important topic for AR/VR, robot manipulation, etc. It is common practice to determine both poses directly from the image; some recent methods attempt to improve the initial poses using a variety of contact‐based approaches. However, few methods take the real physical constraints conveyed by the image into consideration, leading to less realistic results than the initial estimates. To overcome this problem, we make use of a set of high‐level 2D features which can be directly extracted from the image in a new pipeline which combines contact approaches and these constraints during optimization. Our pipeline achieves better results than direct regression or contact‐based optimization: they are closer to the ground truth and provide high quality contact.
PENERAPAN ALGORITMA K-NEAREST NEIGHBOR DALAM KLASIFIKASI JUDUL BERITA HOAX

With the rapid development of information technology, especially in Indonesia, information is more easily obtained through online media. Therefore, the dissemination of information in online media becomes uncontrollable and a lot of information is not in accordance with the facts or can be said to be a hoax. Readers should be more careful when reading news headlines to avoid hoaxes. The purpose of this research is to find out how to apply the K-Nearest Neighbor (KNN) algorithm in classifying news including hoaxes or not hoaxes. In the process, the classification of hoaxes or non-hoaxes uses the KDD method in text mining and goes through several stages, namely preprocessing, word weighting with TF-IDF and classification using the KNN algorithm. There are 3 scenarios in the data split process, namely 90:10, 80:20, and 70:30. Evaluation is done by using a confusion matrix. The results of this study obtained the highest accuracy of 93.33% with a k value of 3 in the 90:10 scenario. So, the K-Nearest Neighbor algorithm is suitable for classifying hoax news titles.
How are the sense of presence and learning outcomes being investigated when using virtual reality? A 24 years systematic literature review

The sense of presence is an important aspect of virtual reality experiences, being increasingly researched in educational contexts for its potential association with learning outcomes. A panorama of how these investigations have been conducted could help researchers and practitioners to harness this potential and find new directions. A systematic literature review was conducted to contribute to this perspective, with a comprehensive analysis of 140 primary studies recovered from five worldwide databases. The results show an overview of 24 years of research, with a summarization of areas, factors, and methodological approaches that have been the focus of investigation when these three variables of interest (VR, sense of presence, and learning) are together. We conclude with a list of research gaps that need to be addressed and a research agenda, identifying current and emerging challenges.
Academic Culture of Students in the Assessments of Professors of Classical Universities

The subject of the research in the article is the academic culture of students. In-depth interviews were conducted with professors of classical universities in Russia (N=34). The goal is to objectify the content of academic culture as a value-semantic phenomenon. The emphasis is placed on the role of academic culture in the formation of the value world of students. It is established that academic culture is an element of academism, forms social patterns. The social models include professionalism, transmission of knowledge and general cultural competencies, and others. The assessments of the state of academic culture at universities formulated by professors are summarized. In such assessments, value-semantic reflection prevails, which makes it possible to identify academic culture as a system of interaction of subjects of the educational process in order to achieve joint goals for the formation of their value world.
Joint optimization for attention-based generation and recognition of chinese characters using tree position embedding

Despite the growing interest in Chinese character generation, creating a nonexistent character remains an open challenge. Radical-based Chinese character generation is still a novel task while radical-based Chinese character recognition is more technologically advanced. To fully utilize the knowledge of recognition task, we first propose an attention-based generator. The generator chooses the most relevant radical to generate each zone with an attention mechanism. Then, we present a joint optimization approach to training generation-recognition models, which can help the generator and recognizer learn from each other effectively. The joint optimization is implemented via contrastive learning and dual learning. Considering the symmetry of the generation and recognition, contrastive learning aims to strengthen the performance of the encoder of recognizer and the decoder of generator. Since the generation and recognition tasks can form a closed loop, dual learning feeds the output from one to another as input. Based on the feedback signals generated during the two tasks, we can iteratively update the two models until convergence. Finally, as our model ignores the order information of a sequence, we exploit position embedding to extend the image representation ability and propose tree position embedding to represent the positional information for tree structure captions of Chinese characters. The experimental results in printed and nature scenes show that the proposed method improves the quality of the generating images and increases the recognition accuracy for Chinese characters.
Comparison of Convolutional Neural Network (CNN) Models in Face Classification of Papuan and Other Ethnicities

The face of the Papuan people is unique when compared to other ethnic groups in Indonesia, even in Papua itself there are tribes whose differences can also be identified in general, namely coastal people and mountainous people. determined to identify Papuan faces. The faces of Papuans can be identified with the architectural models in Convolutional Neural Networks (CNN) such as VGG16, VGG-19, ResNet-50 and MobileNet v1 and Mobilenet v2. The best model is the Mobile Net v1 architectural model for Papuan and Non-Papuan Face Recognition with 0.95 or 95% accuracy.
Dynamic Asset Allocation with Expected Shortfall via Quantum Annealing

Recent advances in quantum hardware offer new approaches to solve various optimization problems that can be computationally expensive when classical algorithms are employed. We propose a hybrid quantum-classical algorithm to solve a dynamic asset allocation problem where a target return and a target risk metric (expected shortfall) are specified. We propose an iterative algorithm that treats the target return as a constraint in a Markowitz portfolio optimization model, and dynamically adjusts the target return to satisfy the targeted expected shortfall. The Markowitz optimization is formulated as a Quadratic Unconstrained Binary Optimization (QUBO) problem. The use of the expected shortfall risk metric enables the modeling of extreme market events. We compare the results from D-Wave’s 2000Q and Advantage quantum annealers using real-world financial data. Both quantum annealers are able to generate portfolios with more than 80% of the return of the classical optimal solutions, while satisfying the expected shortfall. We observe that experiments on assets with higher correlations tend to perform better, which may help to design practical quantum applications in the near term.
Application of the Internet of Things for Quality Service Delivery in Nigerian University Libraries

The study examined the application of the Internet of Things (IoT) for quality service delivery in Nigerian university libraries. The study’s objectives were toidentify the Internet of Things (IoT) technologies that are available in Nigerian University Libraries; identify the areas of use of Internet of Things (IoT) technologies in Nigerian University Libraries; examine the functions of Internet of Things (IoT) technologies in Nigerian University Libraries; and identify the benefits of the Internet of Things (IoT) technologies in Nigerian University Libraries. The study adopted a descriptive survey research design, and the population comprises 37 professional librarians in three university libraries in Kwara State. A self-designed questionnaire was used for data collection, while data were analyzed using descriptive statistics of frequency counts and percentages. The study’s findings revealed the available IoT technologies, including Wireless Sensor Networks, Cloud Computing, Smoke or Heat sensor, and RFID. The study also revealed that the aforementioned are used in technical services, readers services, theft management, and alerting services for quality service delivery in university libraries. The study recommended that Nigerian University libraries should increase awareness of existing IoT technologies available, put appropriate measures in place for its application to various library routines and departments and employ it to transform into innovative libraries.
Fusion of Ensembled UNET and Ensembled FPN for Semantic Segmentation

Image segmentation is an annotation method used to gain a deeper understanding of the images.Semantic segmentation involves constructing a pixel-by-pixel mask of an image by training a neural network.The accuracy of the semantic segmentation algorithms can be improved by eliminating background noise, and computational efficiency can be improved by using the pre-trained networks.This paper proposes a new architecture that ensemble inceptionV3, DenseNet, Resnet34 in the encoder part of UNET and ensemble inceptionV3, Resnet34, and VGG16 in the encoder part of FPN.The ensemble results are fused based on the weighted average and the predictions of the pixels are made on the fused features to perform semantic segmentation.The proposed architecture is implemented on Oxford-IIIT Pet Dataset, created by the visual geometry group, and on the SD saliency 900 dataset.The F1 score, IOU score, and Loss are used to evaluate segmentation model results.The results of the study show that the proposed architecture formed by the fusion of ensembled architectures is more accurate and efficient in segmenting oxford-IIIT pet dataset with the IoU score of 98.68% and segmenting the SD saliency 900 dataset with the IoU score of 66.78%.
Deep Learning and Fuzzy Logic Based Intelligent Technique for the Image Enhancement and Edge Detection Framework

Medical imaging is the promising area in digital image processing.Medical images are useful for all types of medical treatment and diagnostics.Medical images are captured through the medical devices, consists some kind of noises and it requires efficient enhancement techniques.Medical imaging also useful in the image segmentation and object detection purposes.Various researcher proposed several types of enhancement techniques and edge detection techniques, but still accuracy and noise are challenge for the enhanced image.So, it is the need of some intelligent techniques to address these issues.In this work we proposed deep learning-based convolution neural network for the image denoising and image enhancement and for the edge detection fuzzy logic-based approach used.The model of DnCNN used here for the image denoising and image enhancement, this model comprises several convolution layers along with input and output layer, this model learns according to the weights and bias.Also, fuzzy logic technique implemented fuzzy inference rules which can give more accurate edges of the image.The result obtained through this hybrid approach is very interesting and effective as compare with previous approaches like histogram-based approach and linear filtering approach.Proposed methods give the promising results as compare with existing methods.All types of simulation performed in MATLAB 2020.
Virtual reality as a transformed form

The article provides an analysis of one problem related to the discussion of the ontologi­cal status of virtual reality. The author proposes to discuss the problem of the reality of virtual worlds in terms of transformed forms. In this regard, an analysis is given of how this concept was introduced by K. Marx and how it was discussed further in the scientific literature. It is proposed to perceive the transformed form not as a perverted or false real­ity, but as a separate form of life presented in its transformation. An example is the meta­morphosis of the cost of labor into the monetary form of a commodity, as was shown in Capital. On this example, a proposal is made, according to which virtual reality is the same transformed form of reality, and not an illusion of consciousness, and not an imaginary world. The qualities of the transformed form of reality are introduced: 1) a separate, but not independent form of life; 2) playing a role in the process of trans­formation as an indirect form; 3) representation in the form of a copy of the original; 4) sign-symbolic mode of existence; 5) dependence on the original, generic form of real­ity, not independence. It is shown that virtual reality is, as it were, an unembodied form of reality, existence in its potency. Such a representation was presented in the history of philosophy in the form of the idea of potential being. But this idea did not subse­quently become the basis for the development of an independent ontology of virtual real­ity. A distinction is made between the concept of virtual reality as a reality, not an illusion (the reality of digital objects) in the concept of D. Chalmers, and reality in the form of its transformed form. It is shown that virtual reality is not reduced only to the reality of imaginary worlds, to the reality of consciousness, the reality of special psychological states. The rationale is given that virtual reality is the continuation of a natural-artificial process of metamorphosis of human activity, only embodied in its transformed form. In this regard, the author proposes to interpret virtual reality as a form of life in its trans­formation and non-incarnation, which a person must use as a new resource for their de­velopment in order to overcome alienation from themselves.
Punctured Syndrome Decoding Problem

Among the fourth round finalists of the NIST post-quantum cryptography standardization process for public-key encryption algorithms and key encapsulation mechanisms, three rely on hard problems from coding theory. Key encapsulation mechanisms are frequently used in hybrid cryptographic systems: a public-key algorithm for key exchange and a secret key algorithm for communication. A major point is thus the initial key exchange that is performed thanks to a key encapsulation mechanism. In this paper, we analyze side-channel vulnerabilities of the key encapsulation mechanism implemented by the Classic McEliece cryptosystem, whose security is based on the syndrome decoding problem. We use side-channel leakages to reduce the complexity of the syndrome decoding problem by reducing the length of the code considered. The columns punctured from the original code reduce the complexity of a hard problem from coding theory. This approach leads to efficient profiled side-channel attacks that recover the session key with high success rates, even in noisy scenarios.
CTLE-Ising:A 1440-Spin Continuous-Time Latch-Based isling Machine with One-Shot Fully-Parallel Spin Updates Featuring Equalization of Spin States

The Ising machine is a hardware accelerator that finds solutions to combinatorial optimization problems (COPs) using the natural convergence behavior of the Ising model, which comprises artificial-spin network. A previous Ising machine, which is based on the quantum interactions between superconducting quantum bits (qubits) [1], implemented 2048 qubits, but it requires an extremely-low operating temperature (15-20mK) and has significant power consumption (10–1 00kW). Recently, low-power Ising machines [2]–[4] operating at room temperatures have been developed using low-cost CMOS technologies. They implement discrete-time Ising machines using digital [2], [3] or mixed-signal arithmetic circuits [4] to compute the spin operations sequentially. Hence, the performance of these Ising machines is limited by the number of spin-updates, which exponentially increase with the problem size (i.e., the number of binary COP variables). Moreover, the serial nature of the discrete-time Ising machine impedes a fast time-to-solution (TTS) since the connected spins cannot be updated in parallel, which would otherwise lead to oscillations in the spin states. The significant hardware overhead of prior discrete-time Ising machines limits the number of integrated spins feasible. Moreover, integrating bulky random number generators, required for generating randomness for the annealing operations essential for the conventional Ising machines to escape from local minimum solutions, adds hardware overhead.