Reviewerly/exHarmony huggingface dataset:
An Efficient Classification of Emotions in Students’ Feedback using Deep Neural Network

Background and Objective: In both the corporate and academic worlds, the collection and analysis of feedback (product evaluation, social media debate, and student input) has long been a significant topic. The traditional approaches to collect student feedback focused on data collection and analysis via questionnaires. However, the student makes comments on social media sites that need to be looked at to improve educational standards at schools.Methods: The purpose of this work is to construct a deep neural network-based system to assess students’ feedback and emotions found in the reviews. Our approach applies a Deep Learning-based Bi-LSTM Model to a benchmark student input dataset. It would categorize students’ feedback about their instructors according to their emotional states, such as love, happiness, fury, and disdain.Results: The experimental findings demonstrate that the proposed approach outperforms both benchmark studies and state-of-the-art machine learning classifiers.
A Fast and Efficient No-Reference Video Quality Assessment Algorithm Using Video Action Recognition Features

This work addresses the problem of efficient noreference video quality assessment (NR-VQA). The motivation for this work is that even the best and fastest VQA algorithms do not achieve real-time performance. The speed of quality evaluation is impeded primarily by the spatio-temporal feature extraction stage. This impediment is common to both traditional as well as deep learning models. To address this issue, we explore the efficacy of features used in the action recognition problem for NR- VQA. Specifically, we leverage the efficiency offered by Gate Shift Module (GSM) in extracting spatio-temporal features. A simple yet effective improvement to the GSM model is proposed by adding the self-attention module. We first show that GSM features are indeed effective for NR-VQA. We then demonstrate a speed-up that is orders of magnitude faster than the current state-of-the-art VQA algorithms, albeit at the cost of overall performance. We evaluate the efficacy of our algorithm on both Standard Dynamic Range (SDR) and High Dynamic Range (HDR) datasets like KoNViD-1K, LIVE VQC, HDR.
Economics of Immutability Preserving Streaming Healthcare Data Storage Using Aggregation in Blockchain Technology

The contemporary technology landscape is big data oriented and driven by analytics. The paradigm shift fueled by rapid data generation and higher storage capabilities has resulted in a massive transition across data storage technologies. Traditional database management systems being used in record-based transactional applications are paving the way for new rapid engagement-based data stores leading the newer applications. This transition is a result of a sharp growth trajectory witnessed across many parallel technology landscapes including cloud for storage and processing, Internet of things (IoT) for rapid data generation and transmission, support for unstructured, semi-structured and structured data by social media platforms, and polyglot persistence supported by NoSQL, to name a few. The emerging applications thrive on insights acquired by data analytics, and immutability of data storage is expected to be one of the key factors in growth of forward leaning enterprises. There has been a constant rise in application development requiring a full history of transactions, which is both trustworthy and traceable. Such applications mandate the append-only nature of the data storage to support analytics and trust. Immutability is also the underlying premise of popular cloud native storage systems. This paper explores blockchain technology as a solution for privacy and disclosure compliance in stored healthcare data. Blockchain as a data structure is primed to store only small amounts of data to maintain the properties of immutability, tamper proofing, security, and transparency in applications and is not suitable for storing big data. However, healthcare blockchain implementations have several possibilities for storage management, and the paper presents a comparative analysis of the aggregation-based storage with IoT-based streaming e-healthcare application data for the use case. The in-depth analysis for the potential blockchain storage in the paper includes the cost factors, in addition to immutability and privacy preservation, and the results show that costs may be saved up to an order of magnitude 300 with aggregation.
Subtitle Synthesis using Inter and Intra utterance Prosodic Alignment for Automatic Dubbing

Automatic dubbing or machine dubbing is the process of replacing the speech in the source video with the desired language speech, which is synthesized using a text-to-speech synthesis (TTS) system. The synthesized speech should align with the events in the source video to have a realistic experience. Most of the existing prosodic alignment processes operate on the synthesized speech by controlling the speaking rate. In this paper, we propose subtitle synthesis, a unified approach for the prosodic alignment that operates at the feature level. Modifying the prosodic parameters at the feature level will not degrade the naturalness of the synthesized speech. We use both inter and intra utterance alignment in the prosodic alignment process. We should have control over the duration of the phonemes to perform alignment at the feature level to achieve synchronization between the synthesized and the source speech. So, we use the Prosody-TTS system to synthesize the speech, which has the provision to control the duration of phonemes and fundamental frequency (f <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</inf> ) during the synthesis. The subjective evaluation of the translated audiovisual content (lecture videos) resulted in a mean opinion score (MOS) of 4.104 that indicates the effectiveness of the proposed prosodic alignment process.
Optimization of Multimodal Japanese Teaching Model Using Virtual Reality

The potential of virtual reality technology to aid education is well acknowledged. It has already been implemented in at least 20 public schools and institutions, with many more participating in assessment and research initiatives. The proposed methodology is to use virtual reality technology in Japanese teaching in order to create an immersive virtual teaching environment for the learners. They can see and hear Japanese teaching through visual, auditory, tactile, and other senses. They can also interact through virtual reality. The objects simulated by this technology interact with each other, thus creating a feeling of living in a super world. The products of virtual reality technology can be used as a new tool for learning the Japanese language. On the one hand, the advantages of virtual reality technology are assisting Japanese teaching and training due to its immersive, interactive, and systematic characteristics. On the other hand, we can use multimodal data fusion to make the data sources more diversified and optimize the Japanese teaching model through virtual reality technology. In addition, more effective and interesting teaching methods provide references for others. The experimental results show that our multimodal fusion BP (backpropagation) neural network algorithm provides a better result than the single decision tree algorithm and support vector machine algorithm.
Making across space: Mapping creative information creation in the everyday environment

Information creation can be a varied and changing process. In information science, an increasing amount of research is conducted to explore the maker product as information creation. However, what is left relatively unknown is how space may associate with information creation in a private everyday space. Therefore, this qualitative research applied diary studies and semi-structured individual interviews with 25 arts and crafts hobbyists to investigate the potential relationship between space and information creation in the context of making at home. The findings reveal that making can occur across the house and be facilitated and hampered by space. Also, this research shows that space is malleable in an information-creating episode where people can display cunning to reconfigure the environment. This study demonstrates a novel perspective to analyzing information creation, highlighting that information creation is a complex landscape shaped by the spatial property, bodily experience, and often the presence of others.
A Dynamic Scheduling Algorithm with Time Varying Resource Constraints in Colocation Data Centers

The colocation of online services and offline tasks has been widely used in data centers, which improves the resource utilization. Due to the interference between different types of workloads, scheduling co-located offline tasks becomes very complex and challenging. Existing colocation scheduling algorithms try to find the optimal combination of different workloads to avoid performance interference and maximize the utilization. Different from existing works, which usually assume the amount of resources for offline tasks is predefined and keeps unchanged, this paper takes the time varying resource constraints caused by dynamic changes of the workload of online services into consideration. We propose a dynamic scheduling algorithm based on Monte Carlo Tree Search(MCTS) and use the simulation of subsequent scheduling to obtain the best scheduling action at each moment. In order to handle the dynamics of resource constraints, we firstly extend MCTS by adding a slide window mechanism, and get a new search tree named Monte Carlo Tree with Slide Windows(MCTSW). With time changing, MCTSW reconstructs the scheduling tree to include new resource constraints, and the reconstruction can reuse subtrees so as to reduce overhead. The experiment results show that, compared existing works, our algorithm can obtain better scheduling plan and improve the resource utilization of the colocation system.
A Systematic Survey on Photorealistic Computer Graphic and Photographic Image Discrimination

The advent in graphic rendering software and technological progress in hardware can generate or modify photorealistic computer graphic (CG) images that are difficult to identify by human observers. Computer-generated images are used in magazines, film and advertisement industry, medical and insurance agencies, social media, and law agencies as an information carrier. The forged computer-generated image created by the malicious user may distort social stability and impacts on public opinion. Hence, the precise identification of computer graphic and photographic image (PG) is a significant and challenging task. In the last two decades, several researchers have proposed different algorithms with impressive accuracy rate, including a recent addition of deep learning methods. This comprehensive survey presents techniques dealing with CG and PG image classification using machine learning and deep learning. In the beginning, broad classification of all the methods in to five categories is discussed in addition to generalized framework of CG detection. Subsequently, all the significant works are surveyed and are grouped into five types: image statistics methods, acquisition device properties-based techniques, color, texture, and geometry-based methods, hybrid methods, and deep learning methods. The advantages and limitations of CG detection methods are also presented. Finally, major challenges and future trends in the CG and PG image identification field are discussed.
English Used by the Teachers of Cheeky Monkeys Learning School in Sanur

Language is an integral part of the human condition; without it, communication is impossible. Someone must communicate with others using language. Language is a type of communication that can be written or spoken to express something. The purpose of this study was to determine the type of language function, expressions used, and the grammatical aspects used by teachers. The research method used to write this paper was broken down into three steps. Data collection, data analysis, and data source. The researcher concluded after analyzing all the data that there are ten language functions that appear in conversations with their students. The ten functions were: greeting, giving information, receiving information, requesting, suggesting, offering something, refusing, apologizing, thanking, and saying goodbye in English. When teaching in the classroom, the teachers used formal language expressions and a polite demeanor. The majority of them expressed the English function grammatically. They did not use many neutral, less formal, or impolite expressions when communicating with the students.&#x0D; Keywords: teacher, language function, school
Research on Edge Computing and Caching Resource Allocation Mechanism for Multi-view Video

Multi-View Video (MVV) is an emerging video technology that allows users to freely change their viewing angle when watching. Compared with traditional video transmission, multi-view video transmission requires large bandwidth and high computing power, which brings great challenges to multi-view video transmission under wireless networks. With the rapid development of Mobile Edge Computing (MEC) technology, this technology has become one of the potential solutions to the problem of multi-view video transmission in wireless networks by using edge caching and computing technology. This paper firstly establishes a communication model for multi-view video transmission, models different transmission paths in the process of multi-view video transmission, and jointly optimizes the design of edge computing and storage resources to maximize the hit rate of edge caching and computing. Further, a deep reinforcement learning algorithm is designed for the resource allocation mechanism of edge computing and storage. Finally, the simulation results verify that the algorithm can significantly improve the hit rate of edge computing and storage.
In-Depth Research and Analysis of Multilabel Learning Algorithm

Multilabel learning (MLL), as a hot topic in the field of machine learning, has attracted wide attention from many scholars due to its ability to express output space polysemy. In recent years, a large number of achievements about MLL have emerged. Among these achievements, there are several typical issues worthy of attention. Firstly, the correlation among labels plays a key role in improving MLL model training process. Many MLL algorithms try to fully and effectively use the correlation among labels to improve the performance. Secondly, existing MLL evaluation metrics, which is different from those in binary classification, often reflects the generalization performance of MLL classifiers in some aspects. How to choose metrics in algorithms to improve their generalization performance and fairness is another issue that should be concerned. Thirdly, in many practical MLL applications, there are many unlabeled instances due to their labeling cost in training datasets. How to use the wealth information contained in the correlation among unlabeled instances may contribute to reducing of the labeling cost in MLL and improving performance. Fourthly, labels assigned to instances may not be equally descriptive in many applications. How to describe the importance of each label in output space to an instance has become one of research points that many scholars have paid attention to in recent years. This paper reviews the MLL-related research results of correlation among labels, evaluation metric, multilabel semisupervised learning, and label distribution learning (LDL) from a theoretical and algorithmic perspective. Finally, the related research work on MLL is summarized and discussed.
An Analysis on the Process of Expansive Learning in Namu School, Local-Based Teacher Learning Community

The purpose of this study is to analyze the expansive learning process of Namu School, which is local-based teacher learning community. The activity system of Namu School was analyzed with the framework of Engeström’s(1987) expansive learning theory, which is called the third-generation cultural-historical activity theory(CHAT). We try to explore how the activity system is expanding and what the meaning of expansive learning is. The results of this study are as follows. Namu School created an activity system called Growth Classroom that enables teachers to improve instruction. However, while facing various contradictions that exist in the Growth Classroom activity system, Namu School has transformed its community organization to expand the community and maintain its identity, and has expanded to class festival and small group activities oriented toward changes in local education and school. In the extended learning process of Namu School, first, there were expansive learning cycles based on multi-voicedness, democratic reflection and discussion for better practice. Second, the expansive learning of Namu School was forming a teacher platform that aims to change local schools. In this process, teachers create virtuous circles of teacher growth. Lastly, the expansive learning process of Namu School is meaningful as a bottom-up movement for school innovation that interacts with contexts surrounding the school. This study is able to illuminate the context of the expansive learning process and school innovation that is based on teacher learning community. In addition, it is possible to reveal the possibility of learning by overcoming contradictions and continuing new attempts based on a sense of community.&#x0D;
Yet Efficient Study for Evaluating the Quality of Service of Cloud Gaming Systems

For the sake of fulfilling the ever-increasing demands on the Internet of Things (IoT) applications and supporting the maximum number of users and elastic service with the minimum resources, researchers are implementing cloud computing technology in many applications. The main principle of this emerging technology is to provide the users and enterprises with the computation and storage resources they require while allowing the customers to pay only for the amount they use. Lately, a newly emerging application based on cloud computing called cloud gaming is introduced. This powerful application aims effectively to deliver a high-quality gaming experience over the internet to gamers anywhere and anytime. The advantages of such a mix in the gaming industry are revolving around lower costs for the users given the potential to play on thin client devices and easier development for developers compared to traditional gaming. Accordingly, we are considering key research challenging factor facing cloud computing systems which is the impact of latency, generally called lag on Quality of Experience (QoE). Interestingly, the main concentration of this work is to help the research community in understanding the big picture of the cloud gaming field, starting from the very basics of this promising application. Then going through the major difficulties facing this field that must be addressed to provide users with good performance. The performance of our systems is studied using an exclusive simulator, built-in Java language named by CloudSim. The results show that the response and execution time are performed in a cloud system on the same cloudlet number with different allocation policies: the space shared and time shared. The cloudlet response and execution time of the shared time policy perform less than of the space shared policy. Also, by increasing the number of cloudlets, the throughput is increased. In addition, it is shown that the closer users from the data centre get the service faster than other far and far away from users. Also, by increasing the number of cloudlets, the throughput is increased. In addition, it is shown that the closer users from the data center get the service faster than other far and far away users.
One-Class Classification by Ensembles of Random Planes (OCCERPs)

One-class classification (OCC) deals with the classification problem in which the training data have data points belonging only to the target class. In this paper, we present a one-class classification algorithm, One-Class Classification by Ensembles of Random Plane (OCCERP), that uses random planes to address OCC problems. OCCERP creates many random planes. There is a pivot point in each random plane. A data point is projected in a random plane and a distance from a pivot point is used to compute the outlier score of the data point. Outlier scores of a point computed using many random planes are combined to get the final outlier score of the point. An extensive comparison of the OCCERP algorithm with state-of-the-art OCC algorithms on several datasets was conducted to show the effectiveness of the proposed approach. The effect of the ensemble size on the performance of the OCCERP algorithm is also studied.
Sequential Nonparametric K-Medoid Clustering of Data Streams

We study a sequential nonparametric clustering problem to group a finite set of S data streams into K clusters. The data streams are real-valued i.i.d data sequences generated from unknown continuous distributions. The distributions them-selves are organized into clusters according to their proximity to each other based on a certain distance metric. We propose a universal sequential nonparametric clustering test for the case when K is known. We show that the proposed test stops in finite time almost surely and is universally exponentially consistent. We also bound the asymptotic growth rate of the expected stopping time as probability of error goes to zero. Our results generalize earlier work on sequential nonparametric anomaly detection to the more general sequential nonparametric clustering problem, thereby providing a new test for case of anomaly detection where the anomalous data streams can follow distinct probability distributions. Simulations show that our proposed sequential clustering test outperforms the corresponding fixed sample size test.
Signature Recognition and Detection of Skilled Forgeries Using Image Transformation and Multistream CNN

Person identification through their credentials such as biometrics or signature is very important for one&#x2019;s privacy and has become the most integral part for recognition. Prevention of forgeries in handwritten signatures has gain prominence in recent times. To serve this target this paper carried out Image Transformation techniques and an Artificial Intelligence model to effectively notice the differences of genuine and forged signature. Grass-fire transformations and optical flow captures the disparity in signatures. Proposed system uses Deep learning framework with ResNet 50 along with Convolutional Neural network (CNN). Comparative studies have been done using SVC2004 and SUSIG benchmark with the existing literature.
Robust and efficient optimization scheme leading to KL transform

The Karhunen-Lo&#x00E8;ve transform (KLT), as a component of block scalar quanti-zation, is optimal among linear orthonormal transforms and allows to obtain the smallest value of mean squared error (MSE) for a given rate of data representation. In this paper we propose a novel and robust optimization scheme designed for arti-ficial neural networks that implies possibly minimal constraints and allows to obtain the KLT up to the permutation of basis vectors. The proposed scheme involves two optimization criteria: (i) minimization of the MSE of signal reconstruction and (ii) minimization of the entropy related criterion, see Fig. 1(a).