Reviewerly/exHarmony huggingface dataset:
THE VALUE OF CHARACTER EDUCATION IN THE STORY OF THE GARUDA IN ADIPARWA TEXT

Character values in Hinduism contain many lessons about ethics, morals and manners. The educational system could be found, for example, in the Adiparwa text, which is a part of the Mahabharata that tells about the birth of The Garuda, how he saved his mother from slavery and eventually became Lord Vishnu's mount. In this Adiparwa, there is an interesting part of The Garuda's story. Through this literature study, a narrative about the Garuda was obtained which was then made into a short story in the form of a synopsis. Based on the research, the setting could be found in the story includes time and places where the story took place. By this literature search, the characters of and in The Garuda story could also be formulated. Based on the writing, five characters of education are found, which are reflected in the story that tells the students (The Kadru, The Winata, the dragons, The Garuda and Lord Vishnu). As for the character education in question, its botom lines are as follows: religious, honest, disciplined, hard working and responsible.
DOA Based Identification for Devices and Applications of IoT in Heterogeneous Networks

This paper proposes a method for recognizing Internet of Things devices and applications in heterogeneous networks based on the Digital Objects Architecture (DOA). Also, the work discusses techniques for integrating a DOA identifier into IoT devices that support various wireless data transfer technologies and presents a metadata structure. A description of the structure of a typical IoT device and the resolution process based on the DOA is presented. Typical examples of implementing the represented methods and the interaction of the IoT device with the components of the DOA are considered. The potential for implementing the DOA-based identification of Internet of Things devices and applications in heterogeneous communication networks are examined. It is shown that the proposed integration should contribute to a more open exchange of data between different IoT devices and applications. The proposed method allows for identifying IoT devices and applications on a global scale.
A mathematical approach for correcting distortions in a 2-D laser scanning system

This paper describes a novel mathematical approach for correcting image distortions created with an optical laser scanning system employing a two-dimensional rotatable plane mirror. The image distortions are created by the movement of the plane mirror during the scanning process. Scanning mirror systems are frequently used in a variety of fields, such as medical imaging, material processing, device measurement, and three-dimensional scanning. The case of a fast steering scanning mirror used for low energy material retro-reflection measurements is studied. The system described suffers from distortions. This paper presents the derivation of a novel mathematical model used to correct the distortions. The corrections obtained from the model are tested and verified by application to an experimentally acquired data set.
Capped <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <msub> <mrow> <mi>L</mi> </mrow> <mrow> <mn>1</mn> </mrow> </msub> </math>-Norm Proximal Support Vector Machine

Compared to the standard support vector machine, the generalized eigenvalue proximal support vector machine coped well with the “Xor” problem. However, it was based on the squared Frobenius norm and hence was sensitive to outliers and noise. To improve the robustness, this paper introduces capped <math xmlns="http://www.w3.org/1998/Math/MathML" id="M2"> <msub> <mrow> <mi>L</mi> </mrow> <mrow> <mn>1</mn> </mrow> </msub> </math> -norm into the generalized eigenvalue proximal support vector machine, which employs nonsquared <math xmlns="http://www.w3.org/1998/Math/MathML" id="M3"> <msub> <mrow> <mi>L</mi> </mrow> <mrow> <mn>1</mn> </mrow> </msub> </math> -norm and “capped” operation, and further proposes a novel capped <math xmlns="http://www.w3.org/1998/Math/MathML" id="M4"> <msub> <mrow> <mi>L</mi> </mrow> <mrow> <mn>1</mn> </mrow> </msub> </math> -norm proximal support vector machine, called CPSVM. Due to the use of capped <math xmlns="http://www.w3.org/1998/Math/MathML" id="M5"> <msub> <mrow> <mi>L</mi> </mrow> <mrow> <mn>1</mn> </mrow> </msub> </math> -norm, CPSVM can effectively remove extreme outliers and suppress the effect of noise data. CPSVM can also be viewed as a weighted generalized eigenvalue proximal support vector machine and is solved through a series of generalized eigenvalue problems. The experimental results on an artificial dataset, some UCI datasets, and an image dataset demonstrate the effectiveness of CPSVM.
Deep Reinforcement Learning Based Congestion Control for Delay-Sensitive Multimedia Streaming

With the rapid development of the Internet and the dawn of the 5G age, more and more creative network services and applications are pouring out. Some of these applications are delay-sensitive, requiring the data to arrive within the specific acceptable end-to-end latency (i.e., meeting deadline), such as Metaverse, cloud VR gaming, and autonomous driving. These applications have brought new challenges to ensuring users' QoE(quality-of-experience). To achieve high bandwidth and acceptable end-to-end latency and improve the QoE of these delay-sensitive applications. We proposed SAC-CC (Soft Actor-Critic based Congestion Control), which combines a deep reinforcement learning method to solve the poor performance of the heuristic congestion control algorithms when delay-sensitive multimedia streaming is transmitted. SAC-CC can also adapt to the complex network state. We also design a new framework based on the SAC framework and utilize GRU and self-attention networks for SAC-CC. The new framework can extract more historical information to improve the robustness and stability of SAC-CC. Experimental results show that SAC-CC performs well in various scenarios and outperforms other baseline algorithms.
Investigating the Process of Teaching the Creation of Interactive Art in a Collaborative Virtual Environmental Context

Due to the outbreak of COVID-19 pandemic, higher education institutes quickly turn to the use of online tools, radically transforming the modes of teaching and communication with students. This educational shift significantly affected the conduct of artistic laboratory courses, where physical presence is essential for students and teaching staff. In order to address this shift, the teaching staff for the laboratory course “Digital Artistic Creation 2”, of the Department of Digital Arts and Cinema of the National and Kapodistrian University of Athens adopted a project-based learning methodology and used a combination of teleconferencing tools and multiuser 3D Social Virtual Environments, to teach the creation of interactive and possibly dynamically evolving 3D assemblages and spatial compositions. This paper presents a research study which aims at investigating the result of this teaching course with regards to the educational impact and the experience of the students. The study was conducted at the end of the semester with the use of questionnaires delivered to the students in order to explore the learning experiences, outcomes and improvisation suggestions concerning this novel, combined form of teaching, as well as to detect the emerging collaborative and self-regulated learning patterns that emerged throughout the course.
Image classification and training with severe data loss

Image classification forms an important class of problems in machine learning and is widely used in many realworld applications, such as medicine, ecology, astronomy, and defense. Convolutional neural networks (CNNs) are machine learning techniques designed for inputs with grid structures, e.g., images, whose features are spatially correlated. As such, CNNs have been demonstrated to be highly effective approaches for many image classification problems and have consistently outperformed other approaches in many image classification and object detection competitions. A particular challenge involved in using machine learning for classifying images is measurement data loss in the form of missing pixels, which occurs in settings where scene occlusions are present or where the photodetectors in the imaging system are partially damaged. In such cases, the performance of CNN models tends to deteriorate or becomes unreliable even when the perturbations to the input image are small. In this work, we investigate techniques for improving the performance of CNN models for image classification with missing data. In particular, we explore training on a variety of data alterations that mimic data loss for producing more robust classifiers. By optimizing the categorical cross-entropy loss function, we demonstrate through numerical experiments on the MNIST dataset that training with these synthetic alterations can enhance the classification accuracy of our CNN models.
Content dependent secret hiding in visual steganography

In this paper we’ll describe new techniques of information hiding in visual containers. Presented methods will be connected with the carrier features, so secret distribution will be dependent from the content and pixel values. In traditional secret hiding solutions the way of information distribution is not dependent on the content or container’s features. Such approaches simply modify spatial or frequency parameters, without consideration of local carrier parameters. Such parameters can also determine the way of secret hiding and allow to split information over container in different manner, especially when we consider containers with different graphical features.
A Decision Support System Model for Middle School Education Management Based on Sparse Clustering Algorithm

With the development of China’s economy, education has also been greatly improved, and the enrollment scale has been continuously expanded. It has become increasingly difficult to manage student performance. The traditional manual management of student grades has shortcomings such as low efficiency, poor privacy, difficulty to find and maintain, and incomplete knowledge mining of student grade data resources, so it is impossible to fully test and utilize related technologies. Based on this background, this paper designs and develops a set of decision support systems for middle school education management combined with a sparse clustering algorithm according to the actual needs of middle school education. The article first introduces the concept of sparse clustering algorithm and the four levels of the decision support system for middle school education management, data layer, model layer, application layer, and display layer, and then introduces based on five system models, namely, database design, online analysis process module design, data mining design, model library design, function design, and finally, the system model of educational decision management support is constructed, and the model test is completed. The practice shows that the system proposed in this paper can manage the current educational information of students scientifically, extract the information from the changes in students’ achievements in time, and make certain assistance for teachers’ educational decisions. This paper designs an effective decision support system by studying the sparse clustering algorithm and introducing it into the field of middle school education management.
Blockchain Adherence to System Reliability and Security

Currently, the massive use of remote equipment and applications at different levels of modern society is an unprecedented reality. This trend is due to an increasing decentralization and popularization of computing and communication devices for personal use. Due to the demands imposed by environmental and professional factors, work on the virtual model, in which employees are somehow connected and interacting in a network and in a data cloud, has dazzlingly benefited from this infrastructure. The financial market is one of the sectors that favored this context, mainly providing its operations in the hands of its clients. And in this trend, electronic coins also germinated, incorporating a cryptographic procedure with key validation in linked blocks, the so-called Blockchain. But, on the one hand, the systems technology tools allow the benefit of the prompt interaction between people and institutions; on the other hand, they hide the possibility of the improper use of information. Two pillars of the configuration of current solutions are based on the Security and Reliability of Systems: either by characterizing it by the probability of its operation or failure in a safe way or by determining its functioning according to its specification. As it is an essential subject of scientific research, this paper proposes and aims to verify the adherence of Blockchain to the Reliability and Security of Systems using the Markov Model, with the specific outline of the attack strategy, the states of possible attacks, and the observation of this interaction. Due to the evidence of the data presented and its consistent result, the method proved reliable. When addressing security, he crossed the probabilities of an attack versus an honest node finding the next block. The security sensitivity is perceived as a function of a brief period between each attack attempt.
Research on Animated GIFs Emotion Recognition Based on ResNet-ConvGRU

Animated Graphics Interchange Format (GIF) images have become an important part of network information interaction, and are one of the main characteristics of analyzing social media emotions. At present, most of the research on GIF affection recognition fails to make full use of spatial-temporal characteristics of GIF images, which limits the performance of model recognition to a certain extent. A GIF emotion recognition algorithm based on ResNet-ConvGRU is proposed in this paper. First, GIF data is preprocessed, converting its image sequences to static image format for saving. Then, the spatial features of images and the temporal features of static image sequences are extracted with ResNet and ConvGRU networks, respectively. At last, the animated GIFs data features are synthesized and the seven emotional intensities of GIF data are calculated. The GIFGIF dataset is used to verify the experiment. From the experimental results, the proposed animated GIFs emotion recognition model based on ResNet-ConvGRU, compared with the classical emotion recognition algorithms such as VGGNet-ConvGRU, ResNet3D, CNN-LSTM, and C3D, has a stronger feature extraction ability, and sentiment classification performance. This method provides a finer-grained analysis for the study of public opinion trends and a new idea for affection recognition of GIF data in social media.
Contrastive Learning-Based Haze Visibility Enhancement in Intelligent Maritime Transportation System

With the rapid development of artificial intelligence and big traffic data, the data-driven intelligent maritime transportation has received significant attention in both industry and academia. It is capable of improving traffic efficiency and reducing traffic accidents in maritime applications. However, video cameras often suffer from severe haze weather, leading to degraded visual data and ineffective maritime surveillance. It is thus necessary to restore the visually degraded images and to guarantee maritime transportation efficiency and safety under hazy imaging conditions. In this work, a contrastive learning framework is proposed for haze visibility enhancement in intelligent maritime transportation systems. In particular, the proposed learning method could fully learn both local and global image features, which are beneficial for visual quality improvement. A total of 100 clean images containing water traffic scenes were selected as the synthetic test dataset, and good dehazing results were achieved on both visual and indexing results (e.g., peak signal to noise ratio (PSNR): <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mn>23.95</mn> <mo>±</mo> <mn>3.48</mn> </math> and structural similarity index (SSIM): <math xmlns="http://www.w3.org/1998/Math/MathML" id="M2"> <mn>0.924</mn> <mo>±</mo> <mn>0.065</mn> </math> for different transmittance and atmospheric light values). In addition, extensive experiments on real-world 100 water hazy images demonstrate the effectiveness of the proposed method (e.g., natural image quality evaluator (NIQE): <math xmlns="http://www.w3.org/1998/Math/MathML" id="M3"> <mn>4.800</mn> <mo>±</mo> <mn>0.634</mn> </math> and perception-based image quality evaluator (PIQE): <math xmlns="http://www.w3.org/1998/Math/MathML" id="M4"> <mn>46.320</mn> <mo>±</mo> <mn>10.253</mn> </math> ). The enhanced images could be effectively exploited for promoting the accuracy and robustness of ship detection. The maritime traffic supervision and management could be accordingly improved in the intelligent transportation system.
Computation of financial risk using principal component analysis

This article uses Principal Component Analysis to compute and extract the main factors for the financial risk of a portfolio, to determine the most dominating stock for each risk factor and for each portfolio and finally to compute the total risk of the portfolio. Firstly, each dataset is standardized and yields a new datasets. For each obtained dataset a covariance matrix is constructed from which the eigenvalues and eigenvectors are computed. The eigenvectors are linearly independent one to another and span a real vector space where the dimension is equal to the number of the original variables. They are also orthogonal and yield the principal risk components (pcs) also called principal risk axis, principal risk directions or main risk factors for the risk of the portfolios. They capture the maximum variance (risk) of the original dataset. Their number may even be reduced with minimum (negligible) loss of information and they constitute the new system of coordinates. Every principal component is a linear combination of the original variables (stock rate of returns). For each dataset, each financial transaction can be written as a linear combination of the eigenvectors. Since they are mutually orthogonal and linearly independent and that they capture the maximum variance of the original data, the risk of the portfolio is calculated by using the principal components, then they have been used to calculate the total risk of the portfolio which is a weighted sum of the variance explained by the principal components.
IC-SAFE:Intelligent Connected Sensing Approaches for the Elderly

Senior citizens, young children, and people with age-related diseases, often find it hard to express themselves. They are not fully aware of their need for help, or how to ask for assistance. This lack of awareness decreases the quality of life, and even endangers those individuals.IC-SAFE (Intelligent Connected Sensing Approaches for the Elderly) tracks the safety of the elderly by using various connected smart wearable sensors. IC-SAFE collects motion data, including walking gaits, arm and leg tremors, and long lounging positions, from many lightweight body sensors to identify the safety status (both physical and emotional) of dementia patients. Feasibility tests have been performed using IMU (Inertial Measurement Unit) sensors in various positions and data from these experiments has been gathered. We have proposed efficient real-time algorithms using analytical learning methods and identified several safety target scenarios by analyzing the corresponding gait data.
Application of IoT in Wearable Technology

Internet of Things (IoT) wearable era is among the expert improvements that have adapted to the digital age. Many people who wish to measure a healthy modus vivendi use wearable technology. The Internet of Things generation is accountable for the wearable era’s hi-tech designs. Smart attire collects and examines data and modify it as per the function. In a few situations, it makes smart selections and offers a reaction to the person. Wearables are finding a wider range of applications in our everyday lifestyles. These potentials have been examined in this study using wearables and the Internet. A wide range of digital gadgets referred to as “wearables” include sensors that offer the thrilling possibility for features used in tracking, sending alert signs. Wearables are grouped into four dominating bunches: (i) wellness, (ii) sports exercises and ordinary distraction, (iii) observing and confinement, and (iv) security. It illustrates how different sensors may provide a wide range of readings that could be useful for a range of methods. Wearables come in a variety of shapes and sizes; they all have a certain purpose to perform in different sectors. With each passing year, wearables get more advanced. Through integrating generation, users are amassing extra facts about themselves. However, the wearable era is not mature in the times of tool security or the privacy attractiveness of users. There exist a few security lacks that make such wearable devices susceptible to assault. One of the important attacks on the wearable era is the authentication among other troubles viz-a-viz erroneous sensors, battery/energy problems, limiting the users in monitoring location/space, and lack of interoperability. This article furthermore addresses the chances and applications and requesting circumstances identified with executing IoT-empowered wearables.
Text-Based Prediction of Heart Disease Doctor Chatbot Using Machine Learning

Health care is important to live a happy and healthy life [1]. So Chatbots can be helpful in monitoring current health status before visiting the doctors physically. Doctor Chatbot is a means to communicate with man and machines. A Doctor Chatbot allows a user to ask queries about a health issue in a manner that they would consult a doctor. Doctor Chatbot interprets human input and responds back using Artificial Intelligence [2] and Natural Language Processing. Therapeutic, Educational, Agricultural areas are important domains to consider. Chatbots can now be utilized everywhere and at any time. Every day, a large number of people, both young and elderly, suffer as a result of a heart attack. In the proposed study the main objective is to predict whether a person has heart related issues using Deep Learning Techniques of Machine Learning. As a result accuracy of the prediction is measured. Comparative analysis of SVM, Naive Bayes, KNN algorithm is carried out and SVM outperforms other two algorithms.
A Future to the Blockchain Technology and Its Concepts

We can learn a lot of things from blockchain technology. Then, let’s consider the different areas where this technology may prove useful in the future. The first use of blockchain technology for implementation is electronic funds transfer with bitcoin in 2008. Actually, there are many things that blockchain technology can be applied to. It also poses some real problems to existing financial systems. Firstly, some of the predictions about blockchain are that it will radically disrupt the banking world; the opinion of every possible observer is that the challenge is still endless. Additionally, the most important difficulty will have a direct relationship with the components of blockchain—trust and incentives. So by the medium of the blockchain which is least controlled by technical scientists, people can basically negotiate or choose a “good” solution to the issue without the technical effects of their choice being visible.