Reviewerly/exHarmony huggingface dataset:
Improved Metaheuristics with Deep Learning Enabled Movie Review Sentiment Analysis

Sentiment Analysis (SA) of natural language text is not only a challenging process but also gains significance in various Natural Language Processing (NLP) applications. The SA is utilized in various applications, namely, education, to improve the learning and teaching processes, marketing strategies, customer trend predictions, and the stock market. Various researchers have applied lexicon-related approaches, Machine Learning (ML) techniques and so on to conduct the SA for multiple languages, for instance, English and Chinese. Due to the increased popularity of the Deep Learning models, the current study used diverse configuration settings of the Convolution Neural Network (CNN) model and conducted SA for Hindi movie reviews. The current study introduces an Effective Improved Metaheuristics with Deep Learning (DL)-Enabled Sentiment Analysis for Movie Reviews (IMDLSA-MR) model. The presented IMDLSA-MR technique initially applies different levels of pre-processing to convert the input data into a compatible format. Besides, the Term Frequency-Inverse Document Frequency (TF-IDF) model is exploited to generate the word vectors from the pre-processed data. The Deep Belief Network (DBN) model is utilized to analyse and classify the sentiments. Finally, the improved Jellyfish Search Optimization (IJSO) algorithm is utilized for optimal fine-tuning of the hyperparameters related to the DBN model, which shows the novelty of the work. Different experimental analyses were conducted to validate the better performance of the proposed IMDLSA-MR model. The comparative study outcomes highlighted the enhanced performance of the proposed IMDLSA-MR model over recent DL models with a maximum accuracy of 98.92%.
Human Behavior Recognition and Detection Technology Based on Video Stream

Nowadays, human behavior recognition is a hot spot in computer vision research. As an influential branch of computer vision, human behavior recognition and detection techniques have been widely used in intelligent security, medical monitoring, traffic management, and other fields. The goal of human behavior recognition is to automatically analyze ongoing behaviors from unknown video or image sequences. In this paper, behavior recognition is defined as a fully supervised learning problem, and a multi-task deep neural network is proposed to solve it. Firstly, a 3d convolution network is used to obtain local spatiotemporal context features, and then a recursive convolution neural network is used to input local spatiotemporal context features. Human behavior types can be simultaneously calculated by multi-task neural networks based on global spatiotemporal context.
360 8K Viewport-Independent VR

Over the past few years 360 virtual reality (VR) has been deployed using different techniques. Viewport-independent technology has been used on 4K content for delivery to 4K-capable head-mounted displays (HMDs) and smartphone devices, resulting in a disappointing experience. The alternative is using viewport-dependent technology with 8K content on 4K-capable HMDs and smartphones devices, which enables a good experience, but with complexities and limitations in terms of the integration into existing over-the-top (OTT) workflows. The 8K viewport-independent technology presented uses off-the-shelf encoding techniques to compress 8K 360 VR content as a single file and to distribute it in a common media application framework (CMAF) low-latency Dynamic Adaptive Streaming over HTTP (DASH) mode to 8K-capable devices such as the Oculus Quest 2 or Galaxy S20. This article presents an end-to-end 8K VR workflow, which is entirely cloud-based and capable of delivering high-quality 8K VR digital rights management (DRM)-protected content compared with 4K content on different devices. We present the initial results of the trial performed by the VR Study Group of the Streaming Video Alliance (SVA) in collaboration with the VR Industry Forum (VRIF), where 8K VR content was encoded and streamed on different very-high-speed (fiber and data over cable service interface specification (DOCSIS)) networks.
A Hybrid Controller Enhancing Transient Performance for an Aerial Manipulator Extracting a Wedged Object

Autonomous aerial manipulation requires the capability to handle inevitable dynamic changes during physical interaction. Previously, very few studies have addressed the stability and transient performance of the scenarios involving abrupt changes in dynamics. This paper proposes a hybrid controller enhancing transient performance for an aerial manipulator extracting an object wedged in a static structure. This task incurs a significant jump in the interaction force on the end-effector so that the analysis using the concept of hybrid dynamical systems is required. To demonstrate the dynamic characteristics of the object-extracting aerial manipulator, we derive the dynamic equations for two flight modes, i.e., free-flight and object-extracting, and the rule of state jumps. Also, we design control strategies which enhance the transient performance during flight mode transition. Then, the stability of the proposed control law is proven, and the overshoot reduction after the object extraction is analyzed. To show the improved performance, we conduct plug-pulling experiments with a quadrotor-based aerial manipulator using the proposed controller and two different existing controllers. The comparative results confirm that our controller enables the aerial manipulator to maintain its stability after the flight mode transition and shows the best transient performance in overshoot minimization among three controllers. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —The motivation for this article is the desire to prevent unexpected collisions between obstacles and an aerial manipulator after the vehicle extracts a wedged object from a static structure. To resolve this problem, we present a hybrid control method for such tasks while avoiding an excessive overshoot after the extraction. This method can be utilized in tasks involving abrupt changes in the dynamic model such as retrieving a device attached to a tall structure, reclaiming an object in disaster recovery, or pulling a plug out of a socket. Unlike existing methods, the proposed method simultaneously considers the stability <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">and</i> the initial overshoot right after pulling the object out of the structure, by employing a disturbance observer (DOB) in a hybrid control structure. It can be utilized for the aerial manipulator to avoid collision in a narrow space or to keep it in a safe operation envelope even though the task requires producing a relatively large pulling force.
BFS-SVM Classifier for QoS and Resource Allocation in Cloud Environment

In cloud computing Resource allocation is a very complex task. Handling the customer demand makes the challenges of on-demand resource allocation. Many challenges are faced by conventional methods for resource allocation in order to meet the Quality of Service (QoS) requirements of users. For solving the about said problems a new method was implemented with the utility of machine learning framework of resource allocation by utilizing the cloud computing technique was taken in to an account in this research work. The accuracy in the machine learning algorithm can be improved by introducing Bat Algorithm with feature selection (BFS) in the proposed work, this further reduces the inappropriate features from the data. The similarities that were hidden can be demoralized by the Support Vector Machine (SVM) classifier which is also determine the subspace vector and then a new feature vector can be predicted by using SVM. For an unexpected circumstance SVM model can make a resource allocation decision. The efficiency of proposed SVM classifier of resource allocation can be highlighted by using a single-cell multiuser massive Multiple-Input Multiple Output (MIMO) system, with beam allocation problem as an example. The proposed resource allocation based on SVM performs efficiently than the existing conventional methods; this has been proven by analysing its results.
Threat-Driven Dynamic Security Policies for Cyber-Physical Infrastructures

How does one defend Cyber-Physical Systems (CPSs) in the face of changing security threats without continuous manual intervention? These systems form a critical part of national infrastructure and exist within an ever changing dynamic threat environment. The static security policies programmed into systems cannot react quickly to changing threats. We describe how a MAPE-K-based threat-centered dynamic policy framework could reconfigure CPSs in the face of attack. The framework encodes threats, their mitigations, and a threat posture as a mechanism for deciding how best to react. We describe our framework, a prototype implementation and show how it be integrated within a testbed CPS demonstrator. Threat centered dynamic policies allow us to harden a system as the threat assessment evolves, reconfiguring a system and how on the basis of policy and perceived threat.
Source Code Metrics for Software Defects Prediction

In current research, there are contrasting results about the applicability of software source code metrics as features for defect prediction models. The goal of the paper is to evaluate the adoption of software metrics in models for software defect prediction, identifying the impact of individual source code metrics. With an empirical study on 275 release versions of 39 Java projects mined from GitHub, we compute 12 software metrics and collect software defect information. We train and compare three defect classification models. The results across all projects indicate that Decision Tree (DT) and Random Forest (RF) classifiers show the best results. Among the highest-performing individual metrics are NOC, NPA, DIT, and LCOM5. While other metrics, such as CBO, do not bring significant improvements to the models.
Securing the Stakeholders’ Data Policy using Hybrid ECC Model with Mutual Authentication Model

The Internet of Things (IoT) has recently emerged as one of academia's top research priorities and growth areas. The scope of the Internet of Things includes the ecosystem that allows for the safe, reliable, and beneficial connecting of people, things, and devices. The security of the (IoT) and the underlying technologies is a key area of concern. The security measures are implemented after extensive research and practical use. This study's overarching goal is to help make the Internet of Things (IoT) a safer place by investigating the barriers to creating a trustworthy electronic environment and the technology already in use to combat those barriers. Stakeholders, including users, manufacturers, and service providers, are crucial to discussions on security concerns. Finally, in this literature, we have a mathematical representation of the difficulties involved in ensuring security (integration, costing, handling, and observation). Using the novel mutual authentication approach, Elliptic Curve Cryptography with Schnorr's signature system, and early finding security breaches, this study ensures secure communication between the participating parties. The experimental findings validate the superior performance of the projected model associated to the current best practices.
Audio-visual resources and learning improvement: an experimental analysis

The general objective of this research is to analyse the students' learning performance on the use of audio-visual resources in the subject of didactics of the business and its environment, taught in master's degree in Teacher Training of High School, Vocational Training and Language Teaching (specialty in Economics, Business, and Commerce) at the University of Nebrija. This study applied the experimental method from two randomly generated samples of the two groups (virtual classrooms) into which the enrolled students were administratively divided. The results obtained by the experiment point to a significant improvement in the students' learning performance, and their perception about the use of this didactic tool turned out to be very positive.
Research on User Experience Model Based on Mixed Reality Interactive Games

This paper proposes a user experience model based on mixed reality interactive games. The model combines the characteristics of user experience and mixed reality games, and through user surveys and the establishment of a user experience model, it verifies that sensory experience, interactive experience, emotional experience, and behavioral experience are the main factors in mixed reality games, which can provide guidance for the design of mixed reality games.
Review and Suggestions of Grammar Items in Korean Textbooks for Elementary School Students in North America

The purpose of this study is to review and critically discuss the grammatical content of two textbooks developed for elementary school students in North America. Firstly, it confirms that “Korean 3” covers Level 1 and 2 grammar, while “Korean 4” covers Level 2 and 3 grammar. Secondly, the textbooks present many grammar items in chunks for efficient teaching and learning. Thirdly, the arrangement of grammar items based on difficulty was not taken into consideration. Fourthly, it was found that many grammar items are used in previous chapters, which students will learn later on. Finally, the presentation of grammar items in both textbooks was analyzed, and suggestions were made for improving teaching and correcting errors in the textbooks.
Hybrid Optimization-Based Neural Network Classifier for Software Defect Prediction

The software is applied in various areas so the quality of the software is very important. The software defect prediction (SDP) is used to solve the software issues and enhance the quality. The robustness and reliability are the major concerns in the existing SDP approaches. Hence, in this paper, the hybrid optimization-based neural network (Optimized NN) is developed for the effective detection of the defects in the software. The two main steps involved in the Optimized NN-based SDP are feature selection and SDP utilizing Optimized NN. The data is fed forwarded to the feature selection module, where relief algorithm selects the significant features relating to the defect and no-defects. The features are fed to the SDP module, and the optimal tuning of NN classifier is obtained by the hybrid optimization developed by the integration of the social spider algorithm (SSA) and gray wolf optimizer (GWO). The comparative analysis of the developed prediction model reveals the effectiveness of the proposed method that attained the maximum accuracy of 93.64%, maximum sensitivity of 95.14%, maximum specificity of 99%, maximum [Formula: see text]-score of 93.53%, and maximum precision of 99% by considering the [Formula: see text]-fold.
Adaptive spatial and temporal aggregation for table tennis shot recognition

Action recognition is one of the challenging video understanding tasks in computer vision. Although there has been extensive research in the task of classifying coarse-grained actions, existing methods are still limited in differentiating actions with low inter-class and high intra-class variation. Particularly, the table tennis sport that involves shots of high inter-class similarity, subtle variations, occlusion, and view-point variations. While a few datasets have been available for event spotting and shot recognition, these benchmarks are mostly recorded in a constrained environment with a clear view/perception of shots executed by players. In this paper, we introduce a Table tennis shots 1.0 dataset consisting of 9000 videos of 6 fine-grained actions collected in an unconstrained manner to analyze the performance of both players. To effectively recognise these different types of table tennis shots, we propose an adaptive spatial and temporal aggregation method that can handle the spatial and temporal interactions concerning the subtle variations among shots and low inter-class variations. Our method consists of three components, namely, (i) feature extraction module, (ii) spatial aggregation network, and (iii) temporal aggregation network. The feature extraction module is a 3D convolutional neural network (3D-CNN) that captures the spatial and temporal characteristics of table tennis shots. In order to capture the interaction among the elements of the extracted 3D-CNN feature maps efficiently, we employ spatial aggregation network to obtain the compact spatial representation. Later, we propose to replace the final global average pooling layer (GAP) with the temporal aggregation network to overcome the loss of motion information due to averaging of temporal features. This temporal aggregation network utilizes the attention mechanism of bidirectional encoder representations from Transformers (BERT) to model the significant temporal interactions among the shots effectively. We demonstrate that our proposed approach improves the performance of existing 3D-CNN methods by ~10% on the Table tennis shots 1.0 dataset.We also show the performance of our approach on other action recognition datasets, namely, UCF-101 and HMDB-51.
Human fall detection using spatial temporal graph convolution

Falls are a growing social problem and have become a hot topic in healthcare. Thanks to recent advances in deep convolutional neural networks, the accuracy of video-based fall detection has been greatly improved. However, these methods are affected by illumination and complex backgrounds. Video angles and other influencing factors reduce the accuracy and generalization ability of these methods. In this paper, a video-based human fall detection method is proposed. First, a 2D joint point sequence in the video is extracted using a pose estimator, and then a 2D joint point pose sequence is extracted. It is elevated to a 3D joint point pose sequence and then recognized whether it is a fall action by our improved multi-scale unified spatial-temporal graph convolutional network (MS-G3D). The system proves its effectiveness and robustness in the field of action recognition, achieving 99.84% accuracy on the large benchmark action recognition dataset NTU RGB+D, and 95.72% accuracy on the LE2I fall dataset.
A lightweight network with multi-scale information interaction attention for real-time semantic segmentation

Real-time semantic segmentation is an important field in computer vision. It is widely employed in real-world scenarios such as mobile devices and autonomous driving, requiring networks to achieve a trade-off between efficiency, performance, and model size. This paper proposes a lightweight network with multi-scale information interaction attention (MSIANet) to solve this issue. Specifically, we designed a multi-scale information interaction module (MSI) is the main component of the encoder and is used to densely encode contextual semantic features. Moreover, we designed the multi-channel attention fusion module (MAF) in the decoder part, thereby realizing multi-scale information fusion through channel attention mechanism and spatial attention mechanism. We verify our method through numerous experiments and prove that our network possesses fewer parameters and faster inference speed compared to most of the existing real-time semantic segmentation methods in multiple datasets.
Designing a Modified Diffie-Hellman Model for Protecting the Privacy of Stakeholders’ Data

There is a growing awareness of the need for a system that facilitates the secure transfer of sensitive information across various parties as businesses around the world accelerate their efforts to digitise their operations. Data sharing presents difficulties in terms of privacy and accessibility, especially when disputes arise between parties who have a common interest, such as in a supply chain. At now, auditors must rely on the cooperation of stakeholders to confirm information. It's possible that malicious actors tampered with the data before giving it to the auditor. We use supply chains as a case study to provide a revised encryption protocol that can assist overcome these obstacles and make it easier for members in the supply chain to share data with one another. In addition, the Diffie-Hellman method is used in existing systems for key generation and exchange, such as in safe efficient encryption algorithms; however, this method is extremely susceptible to the man-in-the-middle attack. This article presents a Diffie-Hellman variant that can be used to secure data with faster reaction times and reduced computing overhead. By creating a hash of each value transmitted over the network, the Diffie-Hellman has been hardened against attacks. The proposed method's robustness against a variety of attacks has been thoroughly investigated. It has also been examined for various data quantities in terms of encryption time. The proposed method outperforms the state-of-the-art methods in the vast majority of tests. Last but not least, the architecture ensures that auditors will always have access to all relevant data and permits participants to provide their data in near-real-time without fear of information leakage or the possibility of data falsification.
The Rich Facets of Digital Trace Data

Increasing digitalization means that many of our daily interactions happen within digital environments where they leave digital footprints in the form of trace data. Such digital trace data is often thought to generate insights by virtue of its immense scale. This focus on ‘big data’ tends to overlook the richness and complex characteristics of digital traces that opens new vistas for a multitude of computational analyses that generate new and high-resolution insights to digital environments. As such, paying attention to the characteristics of trace data allows for deep investigations of social-technical interactions in unprecedented detail.
Analysis of WIUM Online Education Management System User Satisfaction Using PIECES Framework

WIOEM is a web and android application designed to quickly share information with users and help solve problems at the Salemba Adventist College. This information system is used by users for various purposes that are able to support the performance of teachers and parents in improving services to students. The various functions in this information system will affect user satisfaction or dissatisfaction with the system. So that there is a need for improvement and development of the system in the future. WIOEM information system analysis was carried out to determine the advantages and disadvantages that exist in the system based on the PIECES method. The purpose of this research is to get an overall picture of system performance, information, level of economic value, security, efficiency, and system services. From each aspect that is analyzed will be used as a recommendation for improvement of the WIOEM information system. The method used in this research is descriptive quantitative. The data collection technique uses a questionnaire distributed through the Google Forms platform. The results of this study indicate that the WIOEM system is in the good category, with an average total satisfaction level of 4.18. With each Performance indicator achieving a value of (4.31), Information (4.23), Economic (4.04), Control (4.11), Efficiency (4.25) and Service achieving a value of (4.17).