Reviewerly/exHarmony huggingface dataset:
The Security Concerns On Cyber-Physical Systems And Potential Risks Analysis Using Machine Learning

The use of engineering to drive down costs and improve productivity has been an ongoing business exercise since the first Industrial Revolution. The term Cyber-Physical System is a wide range of different computing technologies embedded with the next-generation engineered systems into the physical world. Connected Cyber-Physical Systems (CPS) improve the lives of people and increase industry and manufacturing efficiency. It is affecting many branches of life such as transportation, healthcare and medicine, the environment, and energy. Industry 4.0 integrates humans, machines, and data to provide a holistic and interlinked approach to manufacturing, hence, increasing privacy concerns. For example, Autonomous Vehicles (AV) can be driven without a pilot and those systems can be hacked if there is a breach in the system. Nowadays, most of the systems are interconnected to the internet and nothing can be considered fully safe. Therefore, with this increase of security threats and privacy concerns, there is a need to assess and evaluate the trade-off between enhancements and improvements in manufacturing and the possible threats and security risks in the context of Cyber-Physical Systems. We need to bridge the gaps and overcome some of these limitations. In this work, we studied the security concerns emerging from interconnected Cyber-Physical systems, devices, and services in Industry 4.0. To identify security vulnerabilities, we have chosen the energy dataset because energy is the key point of every Cyber-Physical system so aimed to show the importance of energy, and the K-Means algorithm implemented which is an advanced Machine Learning and potential risks detected.
Blockchain Education Concept 4.0: Student-Centered iLearning Blockchain Framework

Technological developments encourage digitization in the learning process. Inversely proportional to the results of competency evaluation which are still centralized. Cause competitiveness and competence of students that are not measured quantitatively. The application of micro-teaching can be an option in solving existing problems. Combining gamification and blockchain makes the learning process more enjoyable with game techniques, where activities such as learning, lecture assignments, and assessments are documented transparently and have reliable security. This student-centered learning process is called GamiChain (Gamification Blockchain). This blockchain gamification-based learning application is also an important activity that can be a brilliant breakthrough in education. This application aims to encourage the student creativity ecosystem, to make students have a more competitive spirit to compete with maximum capabilities. And can display learning outcomes in the form of certificates that are accurate in authenticity and can be used in the industrial world. GamiChain can form an official standard of learning by documenting lectures permanently, transparently, and distributed formally and informally into the Blockchain network with the help of smart contracts. The Gamification method in education is used in this research. Results Evaluation of blockchain application in education through GamiChain (Gamification Blockchain) can benefit universities to create official certifications and increase confidence in student scores transparently and sustainably that they can control.
Effects of the COVID-19 pandemic in higher education: A data driven analysis for the knowledge acquisition process

The COVID-19 pandemic abruptly changed the classroom context and presented enormous challenges for all actors in the educational process, who had to overcome multiple difficulties and incorporate new strategies and tools to construct new knowledge. In this work we analyze how student performance was affected, for a particular case of higher education in La Plata, Argentina. We developed an analytical model for the knowledge acquisition process, based on a series of surveys and information on academic performance in both contexts: face-to-face (before the onset of the pandemic) and virtual (during confinement) with 173 students during 2019 and 2020. The information collected allowed us to construct an adequate representation of the process that takes into account the main contributions common to all individuals. We analyzed the significance of the model by means of Artificial Neural Networks and a Multiple Linear Regression Method. We found that the virtual context produced a decrease in motivation to learn. Moreover, the emerging network of contacts built from the interaction between peers reveals different structures in both contexts. In all cases, interaction with teachers turned out to be of the utmost importance in the process of acquiring knowledge. Our results indicate that this process was also strongly influenced by the availability of resources of each student. This reflects the reality of a developing country, which experienced prolonged isolation, giving way to a particular learning context in which we were able to identify key factors that could guide the design of strategies in similar scenarios.
Object Detection and Segmentation Using Deeplabv3 Deep Neural Network for a Portable X-Ray Source Model

The primary purpose of this research is to implement Deeplabv3 architecture’s deep neural network in detecting and segmenting portable X-ray source model parts such as body, handle, and aperture in the same color scheme scenario. Similarly, the aperture is smaller with lower resolution making deep convolutional neural networks more difficult to segment. As the input feature map diminishes as the net progresses, information about the aperture or the object on a smaller scale may be lost. It recommends using Deeplabv3 architecture to overcome this issue, as it is successful for semantic segmentation. Based on the experiment conducted, the average precision of the body, handle, and aperture of the portable X-ray source model are 91.75%, 20.41%, and 6.25%, respectively. Moreover, it indicates that detecting the “body” part has the highest average precision. In contrast, the detection of the “aperture” part has the lowest average precision. Likewise, the study found that using Deeplabv3 deep neural network architecture, detection, and segmentation of the portable X-ray source model was successful but needed improvement to increase the overall mean AP of 39.47%.
Physical human locomotion prediction using manifold regularization

Human locomotion is an imperative topic to be conversed among researchers. Predicting the human motion using multiple techniques and algorithms has always been a motivating subject matter. For this, different methods have shown the ability of recognizing simple motion patterns. However, predicting the dynamics for complex locomotion patterns is still immature. Therefore, this article proposes unique methods including the calibration-based filter algorithm and kinematic-static patterns identification for predicting those complex activities from fused signals. Different types of signals are extracted from benchmarked datasets and pre-processed using a novel calibration-based filter for inertial signals along with a Bessel filter for physiological signals. Next, sliding overlapped windows are utilized to get motion patterns defined over time. Then, polynomial probability distribution is suggested to decide the motion patterns natures. For features extraction based kinematic-static patterns, time and probability domain features are extracted over physical action dataset (PAD) and growing old together validation (GOTOV) dataset. Further, the features are optimized using quadratic discriminant analysis and orthogonal fuzzy neighborhood discriminant analysis techniques. Manifold regularization algorithms have also been applied to assess the performance of proposed prediction system. For the physical action dataset, we achieved an accuracy rate of 82.50% for patterned signals. While, the GOTOV dataset, we achieved an accuracy rate of 81.90%. As a result, the proposed system outdid when compared to the other state-of-the-art models in literature.
Estimation of yoga postures using machine learning techniques

Yoga is a traditional Indian way of keeping the mind and body fit, through physical postures (asanas), voluntarily regulated breathing (pranayama), meditation, and relaxation techniques. The recent pandemic has seen a huge surge in numbers of yoga practitioners, many practicing without proper guidance. This study was proposed to ease the work of such practitioners by implementing deep learning-based methods, which can estimate the correct pose performed by a practitioner. The study implemented this approach using four different deep learning architectures: EpipolarPose, OpenPose, PoseNet, and MediaPipe. These architectures were separately trained using the images obtained from S-VYASA Deemed to be University. This database had images for five commonly practiced yoga postures: tree pose, triangle pose, half-moon pose, mountain pose, and warrior pose. The use of this authentic database for training paved the way for the deployment of this model in real-time applications. The study also compared the estimation accuracy of all architectures and concluded that the MediaPipe architecture provides the best estimation accuracy.
Validation of Angle Estimation Based on Body Tracking Data from RGB-D and RGB Cameras for Biomechanical Assessment

Motion analysis is an area with several applications for health, sports, and entertainment. The high cost of state-of-the-art equipment in the health field makes it unfeasible to apply this technique in the clinics’ routines. In this vein, RGB-D and RGB equipment, which have joint tracking tools, are tested with portable and low-cost solutions to enable computational motion analysis. The recent release of Google MediaPipe, a joint inference tracking technique that uses conventional RGB cameras, can be considered a milestone due to its ability to estimate depth coordinates in planar images. In light of this, this work aims to evaluate the measurement of angular variation from RGB-D and RGB sensor data against the Qualisys Tracking Manager gold standard. A total of 60 recordings were performed for each upper and lower limb movement in two different position configurations concerning the sensors. Google’s MediaPipe usage obtained close results compared to Kinect V2 sensor in the inherent aspects of absolute error, RMS, and correlation to the gold standard, presenting lower dispersion values and error metrics, which is more positive. In the comparison with equipment commonly used in physical evaluations, MediaPipe had an error within the error range of short- and long-arm goniometers.
Towards Sequence-Level Training for Visual Tracking

Despite the extensive adoption of machine learning on the task of visual object tracking, recent learning-based approaches have largely overlooked the fact that visual tracking is a sequence-level task in its nature; they rely heavily on frame-level training, which inevitably induces inconsistency between training and testing in terms of both data distributions and task objectives. This work introduces a sequence-level training strategy for visual tracking based on reinforcement learning and discusses how a sequence-level design of data sampling, learning objectives, and data augmentation can improve the accuracy and robustness of tracking algorithms. Our experiments on standard benchmarks including LaSOT, TrackingNet, and GOT-10k demonstrate that four representative tracking models, SiamRPN++, SiamAttn, TransT, and TrDiMP, consistently improve by incorporating the proposed methods in training without modifying architectures.
Digital Education as an Integral Part of a Smart and Intelligent City: A Short Review

Conventional education is now changing rapidly in this digital edge. Association of Information and Communication Technology ICT in education acts as catalyst to mold the traditional education into digital education. The integration of education and digital technology develops smarter education system. Pupil and educators get benefited from the digital education. The digitalized services of education system conduct teaching and learning in smart ways. Vast technological tools are used in education that makes learning system effective. The learner can learn anything from place and get many facilities for learning. People are staying in the city to gain the benefits from different perspectives. Nowadays, cities are growing due to fulfilment of demand and basic need of the resident. The infrastructure of the cities is newly enhanced with the support of latest innovative technologies. Therefore, smart city is formed by the application of recent emerged technology for the enhancement of infrastructure and operational services of the city. The digital education is one of the enhanced education systems of the smart city and also smart education is a part of the smart city. The various aspects of smart city and digital education are highlights in this paper. The ways to implement various potential technologies are analyzed in this paper. The scenario of digital education in smart world is focused in this article. Various cases are studied on some digital education and smart city. So, this paper analyzes how the digital education acts as an integral part of a smart and intelligent city.
Classifying histopathological images of oral squamous cell carcinoma using deep transfer learning

Oral cancer is a prevalent malignancy that affects the oral cavity in the region of head and neck. The study of oral malignant lesions is an essential step for the clinicians to provide a better treatment plan at an early stage for oral cancer. Deep learning based computer-aided diagnostic system has achieved success in many applications and can provide an accurate and timely diagnosis of oral malignant lesions. In biomedical image classification, getting large training dataset is a challenge, which can be efficiently handled by transfer learning as it retrieves the general features from a dataset of natural images and adapted directly to new image dataset. In this work, to achieve an effective deep learning based computer-aided system, the classifications of Oral Squamous Cell Carcinoma (OSCC) histopathology images are performed using two proposed approaches. In the first approach, to identify the best appropriate model to differentiate between benign and malignant cancers, transfer learning assisted deep convolutional neural networks (DCNNs), are considered. To handle the challenge of small dataset and further increase the training efficiency of the proposed model, the pretrained VGG16, VGG19, ResNet50, InceptionV3, and MobileNet, are fine-tuned by training half of the layers and leaving others frozen. In the second approach, a baseline DCNN architecture, trained from scratch with 10 convolution layers is proposed. In addition, a comparative analysis of these models is carried out in terms of classification accuracy and other performance measures. The experimental results demonstrate that ResNet50 obtains substantially superior performance than selected fine-tuned DCNN models as well as the proposed baseline model with an accuracy of 96.6%, precision and recall values are 97% and 96%, respectively.
CNN-BiLSTM-Attention: A multi-label neural classifier for short texts with a small set of labels

We propose a CNN-BiLSTM-Attention classifier to classify online short messages in Chinese posted by users on government web portals, so that a message can be directed to one or more government offices. Our model leverages every bit of information to carry out multi-label classification, to make use of different hierarchical text features and the labels information. In particular, our designed method extracts label meaning, the CNN layer extracts local semantic features of the texts, the BiLSTM layer fuses the contextual features of the texts and the local semantic features, and the attention layer selects the most relevant features for each label. We evaluate our model on two public large corpuses, and our high-quality handcraft e-government multi-label dataset, which is constructed by the text annotation tool doccano and consists of 29920 data points. Experimental results show that our proposed method is effective under common multi-label evaluation metrics, achieving micro-f1 of 77.22%, 84.42%, 87.52%, and marco-f1 of 77.68%, 73.37%, 83.57% on these three datasets respectively, confirming that our classifier is robust. We conduct ablation study to evaluate our label embedding method and attention mechanism. Moreover, case study on our handcraft e-government multi-label dataset verifies that our model integrates all types of semantic information of short messages based on different labels to achieve text classification.
A flexible and visually meaningful multi-image compression, encryption and hiding scheme based on 2D compressive sensing

Background: Encrypting plain images into noise-like cipher images is a common method in image encryption. However, when noise-like images appear in public networks, they are more likely to attract attention and suffer more cryptanalysis. To solve this problem, researchers propose the concept of visually meaningful image encryption scheme, which encrypts a plain image into a visually meaningful cipher image. Objective: In order to realize the visual security of cipher image and increase information capacity, this paper proposes a flexible visually secure multi-image compression, encryption and hiding scheme based on two-dimensional compressive sensing (2DCS), which can flexibly complete the compression and encryption of multiple plain images without increasing the amount of ciphertext data. Methods: The scheme is divided into encryption process and embedding process. In the encryption process, the plain image is randomly scrambled and non-linear gray value transformed to obtain a pre-encrypted integer matrix, then 2DCS is used to compress the pre-encrypted integer matrix to get the secret image. Repeat this process for multiple plain images to obtain multiple secret images. In the embedding process, integer wavelet transform and bit-plane decomposition are used to embed multiple secret images into the quantized coefficient matrix of host image to get the modified coefficient matrix, and then the inverse integer wavelet transform is used to transform the modified coefficient matrix into spatial space to get the visually meaningful cipher image. Result: The simulation experiment verifies the feasibility and effectiveness of the visually meaningful multi-image encryption scheme, and users can choose to improve the system's encryption capacity or cipher image's visual security according to their own needs.
How Avatar Identification Affects Enjoyment in the Metaverse: The Roles of Avatar Customization and Social Engagement

Despite growing interest in studying the metaverse, a theory-driven investigation into the factors that contribute to an enjoyable metaverse experience remains scarce. The current study examined the impacts of avatar identification, social engagement, and avatar customization on users' enjoyment of the metaverse based on the impression management theory. A total of 301 metaverse users were recruited for the study. The hypothesized models of mediation effect of social engagement and moderation effect of avatar customization were tested between avatar identification and enjoyment. Results indicated that social engagement mediates the relationship between avatar identification and enjoyment in metaverse participation. Furthermore, the significant moderation effect implied that the degree of avatar customization affected the relationship between avatar identification and enjoyment in the metaverse. Empirical evidence from this study contributes to theorizing the transformative role of social engagement and conditional effects based on avatar customization in shaping a positive emotion (i.e., enjoyment) from a metaverse experience. The findings allow researchers and practitioners to gain an initial understanding of the factors contributing to a favorable metaverse experience.
Ultrafast dynamic machine vision with spatiotemporal photonic computing

Ultrafast dynamic machine vision in the optical domain can provide unprecedented perspectives for high-performance computing. However, owing to the limited degrees of freedom, existing photonic computing approaches rely on the memory’s slow read/write operations to implement dynamic processing. Here, we propose a spatiotemporal photonic computing architecture to match the highly parallel spatial computing with high-speed temporal computing and achieve a three-dimensional spatiotemporal plane. A unified training framework is devised to optimize the physical system and the network model. The photonic processing speed of the benchmark video dataset is increased by 40-fold on a space-multiplexed system with 35-fold fewer parameters. A wavelength-multiplexed system realizes all-optical nonlinear computing of dynamic light field with a frame time of 3.57 nanoseconds. The proposed architecture paves the way for ultrafast advanced machine vision free from the limits of memory wall and will find applications in unmanned systems, autonomous driving, ultrafast science, etc.
Subjective and Objective Audio-Visual Quality Assessment for User Generated Content

In recent years, User Generated Content (UGC) has grown dramatically in video sharing applications. It is necessary for service-providers to use video quality assessment (VQA) to monitor and control users’ Quality of Experience when watching UGC videos. However, most existing UGC VQA studies only focus on the visual distortions of videos, ignoring that the perceptual quality also depends on the accompanying audio signals. In this paper, we conduct a comprehensive study on UGC audio-visual quality assessment (AVQA) from both subjective and objective perspectives. Specially, we construct the first UGC AVQA database named SJTU-UAV database, which includes 520 in-the-wild UGC audio and video (A/V) sequences collected from the YFCC100m database. A subjective AVQA experiment is conducted on the database to obtain the mean opinion scores (MOSs) of the A/V sequences. To demonstrate the content diversity of the SJTU-UAV database, we give a detailed analysis of the SJTU-UAV database as well as other two synthetically-distorted AVQA databases and one authentically-distorted VQA database, from both the audio and video aspects. Then, to facilitate the development of AVQA fields, we construct a benchmark of AVQA models on the proposed SJTU-UAV database and other two AVQA databases, of which the benchmark models consist of AVQA models designed for synthetically distorted A/V sequences and AVQA models built through combining the popular VQA methods and audio features via support vector regressor (SVR). Finally, considering benchmark AVQA models perform poorly in assessing in-the-wild UGC videos, we further propose an effective AVQA model via jointly learning quality-aware audio and visual feature representations in the temporal domain, which is seldom investigated by existing AVQA models. Our proposed model outperforms the aforementioned benchmark AVQA models on the SJTU-UAV database and two synthetically distorted AVQA databases. The SJTU-UAV database and the code of the proposed model will be released to facilitate further research.