Reviewerly/exHarmony huggingface dataset:
Keynote Speaker: Advances in Edge-Based and In-Network Media Processing for Adaptive Video Streaming

Media traffic (mainly, video) on the Internet is constantly growing; networked multimedia applications consume a predominant share of the available Internet bandwidth. A major technical breakthrough and enabler in multimedia systems research was the HTTP Adaptive Streaming (HAS) technique. While this technique is widely used and works well in industrial networked multimedia services today, challenges exist for future multimedia systems, dealing with the trade-offs between (i) the ever-increasing content complexity, (ii) various requirements with respect to time (most importantly, low latency), and (iii) quality of experience (QoE). This situation sets the stage for our research work in the ATHENA Christian Doppler (CD) Laboratory (Adaptive Streaming over HTTP and Emerging Networked Multimedia Services; https://athena.itec.aau.at/), jointly funded by public sources and industry. In this talk, I'll explore one facet of the ATHENA research, namely how and with which benefits edge-based and in-network media processing can cope with adverse network conditions and/or improve media quality/perception. Content Delivery Networks (CDNs) are the classical example of supporting content distribution on today's Internet. In recent years, though, techniques like Multi-access Edge Computing (MEC), Software Defined Networking (SDN), Network Function Virtualization (NFV), Peer Assistance (PA) for CDNs, and Machine Learning (ML) have emerged that can additionally be leveraged to support adaptive video streaming services. In the talk, I'll present several approaches of edge-based and in-network media processing in support of adaptive streaming, in four groups: 1)Edge Computing (EC) support, for instance transcoding, content prefetching, and adaptive bitrate algorithms at the edge. 2)Virtualized Network Function (VNF) support for live video streaming. 3)Hybrid P2P, Edge and CDN support including content caching, transcoding, and super-resolution at various layers of the system. 4)Machine Learning (ML) techniques facilitating various (end-to-end) properties of an adaptive streaming system.
Students’ Difficulties in Speaking English as a Foreign Language: Undergraduate Students’ Voices

Even though there has been a wide range of research on students' English-speaking issues, there has been little research on concerns associated with perceived difficulties in speaking, particularly in post-pandemic. This study aims to investigate the difficulties experienced by EFL students in speaking. The method used is exploratory sequential mixed methods in which qualitative data support quantitative data. Two data collection techniques were deployed to triangulate the data. Questionnaires were distributed to 105 students to discover their perceptions of difficulties in speaking after the pandemic. Then, eight students were interviewed to dig deeper into the data from the completed questionnaire. This study revealed that students in English study programs still had problems communicating owing to psychological and linguistic reasons. Some of the explanations identified by this research include a lack of English vocabulary, a lack of confidence while speaking English, and nervousness about receiving negative criticism from colleagues or lecturers. The study’s findings are intended to add to the body of knowledge concerning students’ speaking issues. It is hoped that teaching practitioners will be able to identify the right approach to teaching speaking so that learning may be done more efficiently.
An Analytical Performance Evaluation on Multiview Clustering Approaches

The concept of machine learning encompasses a wide variety of different approaches, one of which is called clustering. The data points are grouped together in this approach to the problem. Using a clustering method, it is feasible, given a collection of data points, to classify each data point as belonging to a specific group. This can be done if the algorithm is given the collection of data points. In theory, data points that constitute the same group ought to have attributes and characteristics that are equivalent to one another, however data points that belong to other groups ought to have properties and characteristics that are very different from one another. The generation of multiview data is made possible by recent developments in information collecting technologies. The data were collected from à variety of sources and were analysed using a variety of perspectives. The data in question are what are known as multiview data. On a single view, the conventional clustering algorithms are applied. In spite of this, real-world data are complicated and can be clustered in a variety of different ways, depending on how the data are interpreted. In practise, the real-world data are messy. In recent years, Multiview Clustering, often known as MVC, has garnered an increasing amount of attention due to its goal of utilising complimentary and consensus information derived from different points of view. On the other hand, the vast majority of the systems that are currently available only enable the single-clustering scenario, whereby only makes utilization of a single cluster to split the data. This is the case since there is only one cluster accessible. In light of this, it is absolutely necessary to carry out investigation on the multiview data format. The study work is centred on multiview clustering and how well it performs compared to these other strategies.
Deep Learning Based Fine Grained Image Classification

Image classification, specifically object classification is the focused research area in the computer vision and machine learning field in the past decade. In image classification a label or category is assigned to an input image based on its content. With breakthroughs in deep learning-based approaches, performance of image classification models' has improved significantly, particularly fine-grained image classification, which includes discriminating between items of the same category with slight changes. The object classification can be categorised as coarse grained object classification, which identifies highly diverse object categories, such as an elephant and a bus. One example of this type of object classification is a bus and an elephant. On the other hand, fine-grained image categorization seeks to recognise photos as belonging to distinct species of animals, birds, or plants, as well as distinct models of automobiles, versions of aircraft, and so on. The purpose of this study is to evaluate previously published research that investigates deep learning techniques for the classification of fine-grained images and to compare the effectiveness of these techniques using datasets that are open to the public.
Trimmed Non-Local Means Filtering for the Suppression of Mixed Noise in Color Images

In this paper, a novel approach to the problem of mixed Gaussian and impulsive noise reduction in color images is proposed. The elaborated denoising framework is based on the Non-Local Means technique, which has been shown to effectively suppress Gaussian noise. To overcome the inability of this filter to deal with impulsive distortions, a robust similarity measure between image patches, that is insensitive to the impact of impulsive distortions, was developed. First, the distorted pixels of the image are detected using the concept of a peer group. If the number of neighbors of a pixel at the center of a filtering window, whose Euclidean distance in the RGB color space does not exceed a threshold value, is smaller than a predefined value, then the considered pixel is treated as corrupted. Afterwards, the similarity between image patches are calculated in a standard way omitting the noisy pixels. In this way, only the average of distances between pairs of unaffected pixels are calculated. Thus, the closeness measure of the patches, which serves as the coefficient in weighted averaging of pixels in the processing block, is not affected by impulsive noise and is still able to cope with the Gaussian noise component. The proposed filtering design is capable of reducing even strong mixed noise while preserving image details and edges. Moreover, it is not computationally intensive, so it can be used in real-time image processing scenarios.
Enhancing Traffic Flow Using Computer Vision Based - Dynamic Traffic Light Control and Lane Management

Traffic congestion is a persistent problem in many metropolises worldwide. Despite the existence of traffic control systems, they are not always efficient enough to manage the ever-changing traffic density environment. The traditional approach of allocating specific times to each lane with the green light, regardless of the traffic situation, has not been very effective. In fact, it often can make the traffic congestion worse. Thus, the need for a more sophisticated system has emerged to simulate and optimize traffic control. This paper proposes the use of computer vision technology to develop a traffic control system that is based on periodic still photo feeds and compares different object detection models to find the best model for vehicle detection in our system . The system aims to enhance traffic flow by dynamically adjusting the traffic light cycles based on real-time traffic conditions.
Classification of Cyberbullying Detection in Social Networking with Audio using Machine Learning Approach

Every day, more people use the internet and social media, which leads to an increase in cyberbullying vulnerabilities. By transmitting, posting, and spreading damaging, false, and bad stuff online, it is taking place. For those impacted, it causes psychological and emotional issues. Therefore, the development of automated tools for cyberbullying identification and prevention is essential. Recent research on identifying cyberbullying has largely focused on text-based analysis. The two most significant media in cases of cyberbullying are text and audio. In this paper, a machine learning model for detecting cyberbullying in two types of social data, namely text and audio is presented. This paper is focused on detecting majors form of Cyberbullying: cyberbullying dataset on Twitter and classify them as containing Cyberbullying or not. This paper used datasets, namely, ‘cyberbullying Dataset’. In this paper, the implementation can be done with machine learning algorithms such as logistic regression, naive bayesian classifier, and support vector machine. Also, these three algorithms were compared and evaluated with performance metrics like accuracy, precision, recall, and f1 score. The main aim is to detecting cyberbullying messages on any type of social media platform.
An Evaluation of Machine Learning and Big Data Analytics Performance in Cloud Computing and Computer Vision

Although cloud computing is receiving a lot of attention, security remains a significant barrier to its general adoption. Cloud service users frequently worry about data loss, security risks, and availability issues. Because of the accessibility and openness of the huge volume of data amassed by sensors and the web throughout recent years, computer applications have seen a remarkable change from straightforward data processing to machine learning. Two widely used technologies, Big Data and Cloud computing, are the focus of worry in the IT industry. Enormous data sets are put away, handled, and broke down under the possibility of "Big Data." Then again, cloud computing centres around giving the framework to make such systems conceivable in a period and cash saving way. The objective of the review is to survey the Big Data Analytics and Machine learning ideal models for use in cloud computing and computer vision. The programmed data examination of enormous data sets and the production of models for the wide connections between data are the centre highlights of machine learning (ML). The usefulness of machine learning-based strategies for identifying threats in a cloud computing environment is surveyed and compared in this research.
A Secure Storage Management &amp; Auditing Scheme for Cloud Storage

Cloud computing is an evolving domain that provides many on-demand services that are used by many businesses on daily basis. Massive growth in cloud storage results in new data centers which are hosted by a large number of servers. As number of data centers increases enormous amount of energy consumption also increases. Now cloud service providers are looking for environmental friendly alternatives to reduce energy consumption. Data storage requires huge amount of resources and management. Due to increasing amount of demand for data storage new frameworks needed to store and manage data at a low cost. Also to prevent data from unauthorized access cloud service provider must provide data access control. Data access control is an effective way to ensure data storage security within cloud. For data storage cost minimization we are using DCT compression technique to ensure data compression without compromising the quality of the data. For data access control and security asymmetric cryptographic algorithm RSA is used. For data auditing we have used MD5 with RSA to generate digital signatures, In proposed work we tried to cover all attributes in terms of efficiency, performance and security in cloud computing.
Deep Learning-based Recognition of Devanagari Handwritten Characters

Numerous techniques have been used over many years to study handwriting recognition. There are two methods for reading handwriting, one of which is online and the other offline. Image recognition is the main part of the handwriting recognition process. Image recognition gives careful consideration to the picture's dimensions, viewing angle, and image quality. Machine learning and deep learning techniques are the two areas of focus for developers looking to increase the intelligence of computers. A person may learn to perform a task by repeatedly exercising it until they recall how to do it. His brain's neurons begin to work automatically, enabling him to carry out the task he has quickly learned. This and deep learning are fairly similar. It uses a variety of neural network designs to address a range of problems. The convolution neural network (CNN) is a very effective technique for handwriting and picture detection.
On the action of a fuzzy group on a fuzzy set

In this paper, we develop fundamental concepts required to extend the concept of group action on a set to fuzzy domain. We define product of a fuzzy set and a fuzzy group by using the idea of cartesian products of sets. We construct examples to demonstrate the defined concepts. We also discuss properties of the defined product of a fuzzy set and a fuzzy group as requisite to study of fuzzy group actions on fuzzy sets. Mathematics Subject Classification (2020). 08A99, 08C05, 22F05
Increasing Understanding of Research and Development in the University of North Sumatra Student Organization

The low level of scientific publications in the academic environment is inseparable from limited interests and skills. Efforts are needed to strengthen insight, methodological knowledge, and skills. Students as an integral part in the development of knowledge, are expected to have high interest and motivation in conducting research. So far, the research produced by students has not shown a significant trend. Various causes of problems arise, starting from limitations in formulating ideas, preparing proposals, submitting proposals, determining publications and motivating students to carry out research. It is important for students who are members of the commissariat of the Islamic Student Association of the Faculty of Social and Political Sciences of the University of North Sumatra to be given training in strengthening research knowledge and training in organizational development. The objectives of the training activities include; First, strengthening research insights and assisting in strengthening research problems. Second, training on how to prepare research proposals. Third, assistance and guidance in finding trusted sources of publications. Overall, this training was carried out using participatory techniques, participants were actively involved in all stages of training activities. The output of the service recommends that the participants of the activity carry out various research trainings, in order to improve their abilities, and maintain a high motivational rhythm from students in conducting research. Furthermore, coaching and mentoring for the greatest students in student organizations must be carried out on an ongoing basis.
Interactive IoT Cloud Deep Learning Model for Research Development in Universities for the Educational Think Tank

The construction of university education think tanks using the interactive service platform enables the sharing of research resources, encourages cross-disciplinary research collaboration, and fosters innovation in education. It also helps to build a stronger relationship between academia and industry by enabling practitioners to participate in research activities. The Internet of Things (IoT) can be used to collect and analyze data from various sources, including sensors and other connected devices, to provide insights into education-related issues. The integration of these technologies in university education thinks tanks can help to enhance the efficiency and effectiveness of research, decision-making, and collaboration processes. Hence, this paper constructed an Interactive IoT Cloud Computing Platform (IIoTCC). With IIoTCC model the innovative idea about research and other ideas are collected and stored in a Cloud environment. Within the environment, information collected is stored in the stacked architecture model with the voting-based model. Through the stacked model, information is processed and evaluated for academic activities. The IoT environment is implemented through IIoTCC for the information process in a deep learning environment for academic-related issues. Simulation analysis expressed that IIoTCC model achieves a higher accuracy rate of 99.34% which is significantly higher than conventional classifiers.
A New Cloud with IoT-Enabled Innovation and Skill Requirement of College English Teachers on Blended Teaching Model

The blended teaching model is a type of educational approach that combines traditional classroom-based instruction with online learning experiences. In this model, students are given access to digital content, resources, and tools, which they can use to supplement their in-person classroom instruction. The blended teaching model is also sometimes referred to as the hybrid learning model. IoT-SDNCT (IoT enabled SDN reinforcement learning with Cloud Technological Innovation and Skill Requirement of College English Teachers on Blended Teaching Model) is a proposed system that aims to revolutionize blended teaching models by leveraging the power of IoT, SDN, and cloud computing technologies. This system incorporates intelligent reinforcement learning algorithms and real-time data analysis to optimize the learning process and improve student engagement and outcomes. In the IoT-SDNCT system, IoT devices such as sensors and wearable technologies are deployed to collect real-time data on student engagement and performance. This data is then transmitted to an SDN controller, which dynamically manages the network infrastructure and optimizes learning pathways. The collected data is also stored and processed in cloud computing platforms, allowing for advanced analytics and personalized feedback for both students and teachers. The key contribution of IoT-SDNCT lies in its ability to adapt the learning process in real-time based on the collected data and intelligent algorithms. This adaptive learning approach enables personalized learning experiences, adjusts the difficulty level of learning tasks, and provides timely feedback to students. Moreover, it empowers teachers with valuable insights and analytics to enhance their teaching strategies and address individual student needs effectively. The proposed system addresses the technological innovation and skill requirements of college English teachers by integrating IoT, SDN, and cloud computing technologies. By utilizing IoT devices, SDN controllers, and cloud platforms, teachers can optimize their teaching methods and create dynamic and interactive learning environments. This not only enhances student engagement but also improves learning outcomes and fosters skill development in both teachers and students. The system's adaptive learning capabilities and real-time data analysis contribute to an enhanced learning experience, increased student engagement, and improved teaching effectiveness.
An Intelligent Knowledge Graph-Based Directional Data Clustering and Feature Selection for Improved Education

With advancements in technology and the increasing availability of data, there is a growing interest in leveraging intelligent learning models to enhance the educational experience and improve learning outcomes. The construction of intelligent learning models, supported by knowledge graphs, has emerged as a promising approach to revolutionizing the field of education. With the vast number of educational resources and data available, knowledge graphs provide a structured and interconnected representation of knowledge, enabling intelligent systems to leverage this wealth of information. This paper aimed to construct an effective automated Intelligent Learning Model with the integration of Knowledge Graphs. The automated intelligent model comprises the directional data clustering (DDC) integrated with the Voting based Integrated effective Feature Selection model through the LSTM-integrated Grasshopper Algorithm (LSTM_GOA). The data for analysis is collected from educational institutions in China. Through the framed LSTM_GOA model the performance is evaluated fro the analysis of the student educational performance. The simulation analysis expressed that the developed model exhibits a higher classification performance compared with the conventional technique in terms of accuracy and Mean Square Error (MSE).
Designing A Permissioned Blockchain Network For The Insurance Claim Process Using Hyperledger Fabric And Composer

This research aims to examine the benefits of blockchain technology (BCT) in the vehicle insurance process. The article addresses a number of benefits offered by BCT, including automating the identity verification and doing away with the need for numerous parties to manually certify the legitimacy of transactions. India's financial and business networks can be anticipated to change the accounting process to a different extreme with the introduction of BCT. Unfortunately, they are having some difficulties implementing and acclimating to this modern technology. Here is the solution, blockchain technology may help us solve the existing problem. Insurance firms and car owners can benefit from blockchain technology since it can effectively open up communication channels, encourage industry integration, and improve insurance provider’s ability to access record. Using BCT, banking operations will be more efficient, quicker, and less expensive because to the removal of middlemen. Decentralization, transparency, and secure transactions will be the main advantages.