Reviewerly/exHarmony huggingface dataset:
Content Steering

Authors:
raw_author_name: Yuriy Reznik
raw_affiliation_string: Brightcove, USA
raw_author_name: Daniel Silhavy
raw_affiliation_string: Fraunhofer FOKUS, Germany
raw_author_name: Stefan Pham
raw_affiliation_string: Fraunhofer FOKUS, Germany
raw_author_name: Alex Giladi
raw_affiliation_string: Comcast, USA
raw_author_name: Alex Balk
raw_affiliation_string: Comcast, USA
raw_author_name: Ali C. Begen
raw_affiliation_string: Ozyegin University, Turkiye
raw_author_name: Will Law
raw_affiliation_string: Akamai, Switzerland

Abstract: DASH-IF Content Steering [4] (to be soon published as ETSI TS 103 998) is a new standard developed by the DASH Industry Forum (DASH-IF), defining means for managing DASH [5] media delivery using multiple content delivery networks (CDNs). At the server-client interaction level, this standard is compatible with the CDN steering features of HTTP Live Streaming (HLS) [1, 6], effectively enabling the same content steering servers to control delivery for both HLS and DASH-based media distribution.
AWS Compute Video Super-Resolution powered by the Intel® Library for Video Super Resolution

Authors:
raw_author_name: Onur Barut
raw_affiliation_string: Intel Corp

Abstract: The rise of free ad-supported streaming TV (FAST) services has boosted personalized content, including classic movies and shows. Most of this content is available in lower-resolution formats (SD) and needs an enhanced viewing experience. Traditionally, low-complexity upscaling methods like Lanczos and bicubic have been used. However, they often introduce image artifacts such as blurring and pixelation. While deep learning (DL) techniques such as SRCNN [1], and EDSR [2] have shown remarkable results in terms of quality metrics (VMAF, SSIM, PSNR). However, they are computationally expensive, and can require GPUs to implement, making them unsuitable for low-cost channels,the most typical case in FAST services. Therefore, we (Intel®1 and AWS) propose a cost-effective architecture for video super-resolution that leverages the benefits of Amazon Spot instance to process video assets using the Intel® Library for Video Super Resolution (Intel® Library for VSR) on Intel® Processors balancing quality and performance for real world use cases.
Toward WebTransport Support in dash.js

Authors:
raw_author_name: Stefan Pham
raw_affiliation_string: Fraunhofer FOKUS, Germany
raw_author_name: Minh Nguyen
raw_affiliation_string: Fraunhofer FOKUS, Germany
raw_author_name: Daniel Silhavy
raw_affiliation_string: Fraunhofer FOKUS, Germany
raw_author_name: Ali C. Begen
raw_affiliation_string: Ozyegin University, Turkiye
raw_author_name: Stefan Arbanowski
raw_affiliation_string: Fraunhofer FOKUS, Germany
raw_author_name: Stephan Steglich
raw_affiliation_string: Fraunhofer FOKUS, Germany

Abstract: Over the recent years, video streaming has gained more and more popularity [14]. This not only puts much strain on the Internet infrastructure [4, 10] but also creates a drive to continue improving the viewer experience. One avenue to tackle both points is the new transport protocol--- QUIC [8], which has attracted enough attention to have led to the founding of the Media over QUIC (MoQ) working group in the Internet Engineering Task Force (IETF) [6]. This group is avidly working on creating a standard for transmitting delay-sensitive media using QUIC. QUIC can reduce initial latency thanks to the combination of its handshake and TLS handshake [8].
Ultra-low Latency Video Delivery Over WebRTC Data Channels

Authors:
raw_author_name: Nelson Francisco
raw_affiliation_string: MediaKind
raw_author_name: Olie Baumann
raw_affiliation_string: MediaKind
raw_author_name: Julien Le Tanou
raw_affiliation_string: MediaKind

Abstract: Over the past years, the video streaming industry has focused great interest in Low-Latency streaming protocols, targeting a sub-5-seconds end-to-end delay comparable to live broadcast TV. Such low delays are considered critical for streaming live sports, gaming, online learning, and interactive video applications, but are hard to achieve using traditional HTTP-based live streaming technologies. Media Over QUIC (MoQ) has been proposed as a promising alternative for ultra-low delay streaming, but limited network infrastructure compatibility and the lack of a mature and optimized custom application-layer protocol have been major hurdles for practical deployments. In this paper, we discuss the alternative use of WebRTC data channels to achieve latencies lower than two seconds on live streaming applications.
SSIM Revisited

Authors:
raw_author_name: Yuriy Reznik
raw_affiliation_string: Brightcove, Inc., Seattle, WA, USA

Abstract: We review the design of the SSIM quality metric and offer an alternative model of SSIM computation, utilizing subband decomposition and identical distance measures in each subband. Based on this model, we also propose an extension of this metric, using a finer-grain sub-band structure resembling the spatial channel model of contrast sensitivity function (CSF). We use a weighted combination of subband-specific scores into a single error visibility score for each spatial location within an image. The averaging is calibrated based on the characteristics of the viewing setup (translation from pixels to spatial frequencies). We test this proposed model using several subjective score datasets and show that it can outperform the original SSIM and MS-SSIM in the accuracy of fit to MOS scores. We discuss applications of this proposed model for the design and improvement of video encoding applications. Specifically, we show how to design pre-filters or inloop noise shaping filters improving SSIM scores of encoded video sequences.
The TDC profile of JPEG XS 3rd edition

Authors:
raw_author_name: Tim Bruylants
raw_affiliation_string: intoPIX, Mont-Saint-Guibert, Belgium

Abstract: The JPEG XS [1,2,3] activity started around 2015 to address the problem of the ever- increasing bandwidth requirements for transmitting video content in professional production environments. In such environments, ultra-low latency, low complexity, and visually lossless image quality are key features, ruling out the existing state of the art video compression technology. A paradigm shift was needed to find a solution where these three key features were prioritized over the pure compression performance.
Standards-Based Neural-Network Post-Filters for Improved Video Quality

Authors:
raw_author_name: Sachin Deshpande
raw_affiliation_string: Sharp Corporation, Camas, WA, USA

Abstract: In this paper we provide an overview of Versatile Supplemental Enhancement Information Messages for coded video bitstreams (VSEI) standard-based neural-network post-filter (NNPF) design. The paper provides details about the key aspects of neural-network post-filter characteristics (NNPFC) message which defines neural-network post-filter (NNPF) and its activation and deactivation via neural-network post-filter activation (NNPFA) messages. We also describe using NNPF SEI design to implement a picture rate upsampling NNPF based on an off-the-shelf neural network. Additionally, technologies under consideration for enhancements to the version 3 NNPF VSEI design are described.
Learning Label Correlations for Multi-Label Online Passive Aggressive Classification Algorithm

Authors:
raw_author_name: Yongwei ZHANG
raw_affiliation_string: Nanjing NARI Intelligent Transport Technology Co., Ltd., Nanjing210061, Jiangsu, China

Abstract: Label correlations are an essential technique for data mining that solves the possible correlation problem between different labels in multi-label classification. Although this technique is widely used in multi-label classification problems, batch learning deals with most issues, which consumes a lot of time and space resources. Unlike traditional batch learning methods, online learning represents a promising family of efficient and scalable machine learning algorithms for large-scale datasets. However, existing online learning research has done little to consider correlations between labels. On the basis of existing research, this paper proposes a multi-label online learning algorithm based on label correlations by maximizing the interval between related labels and unrelated labels in multi-label samples. We evaluate the performance of the proposed algorithm on several public datasets. Experiments show the effectiveness of our algorithm.
The Implementation Of Daily Journal To Improve Writing Skills For University Students

Authors:
raw_author_name: Nida Husna HR

Abstract: This study focused on implementing Daily Journal as a student’s writing activity. The study's objective is to impact the implementation of a daily journal on students' writing skills. The research method used in this study is quantitave. The subject of the study was 32 students majoring in English Education in one of Islamic University in Indonesia. Participants will take a pre and post-test and from there the researcher can compare their vocabulary and grammar skills in written form. The results of both tests will be subjected to a two-sample paired t-test analysis conducted using the Statistical Package for the Social Sciences (SPSS) software program. The test resulted in a significance value of 5%, providing sufficient evidence for a statistical change in post-treatment testing. The results of this study indicate that Daily Journal is an effective tool in enhancing English writing skills among the students in university level.
Optimal Asymmetric Quantum Codes from the Euclidean Sums of Linear Codes

Authors:
raw_author_name: Peng XU
raw_affiliation_string: School of Science and Technology, College of Arts and Science of Hubei Normal University, Huangshi435109, Hubei, China
raw_author_name: Xiusheng LIU
raw_affiliation_string: School of Science and Technology, College of Arts and Science of Hubei Normal University, Huangshi435109, Hubei, China

Abstract: In this paper, we first give the definition of the Euclidean sums of linear codes, and prove that the Euclidean sums of linear codes are Euclidean dual-containing. Then we construct two new classes of optimal asymmetric quantum error-correcting codes based on Euclidean sums of the Reed-Solomon codes, and two new classes of optimal asymmetric quantum error-correcting codes based on Euclidean sums of linear codes generated by Vandermonde matrices over finite fields. Moreover, these optimal asymmetric quantum error-correcting codes constructed in this paper are different from the ones in the literature.
Three New Classes of Subsystem Codes

Authors:
raw_author_name: Hui LI
raw_affiliation_string: School of Mathematics and Physics, Hubei Polytechnic University, Huangshi435003, Hubei, China
raw_author_name: Xiusheng LIU
raw_affiliation_string: College of Arts and Science, Hubei Normal University, Huangshi435003, Hubei, China; School of Mathematics and Physics, Hubei Polytechnic University, Huangshi435003, Hubei, China
raw_author_name: Peng HU
raw_affiliation_string: School of Mathematics and Physics, Hubei Polytechnic University, Huangshi435003, Hubei, China

Abstract: In this paper, we construct three classes of Clifford subsystem maximum distance separable (MDS) codes based on Reed-Solomon codes and extended generalized Reed-Solomon codes over finite fields [see formula in PDF] for specific code lengths. Moreover, our Clifford subsystem MDS codes are new because their parameters differ from the previously known ones.
NAVIGATING THE COMPLEXITY: PSEUDO-CHAOTIC SYSTEMS AND MACHINE LEARNING

Authors:
raw_author_name: Tymoteusz Miller

Abstract: This discourse elucidates the intricate interplay between pseudo-chaotic systems and machine learning, highlighting a frontier where complexity meets computational prowess. Pseudo-chaotic systems, characterized by their deterministic yet intricately unpredictable behavior, present a unique challenge and opportunity for scientific exploration. Machine learning, with its robust pattern recognition and predictive capabilities, offers a promising toolkit for deciphering the nuanced dynamics of these systems. The text delves into the essence of pseudo-chaotic systems, explores the transformative potential of machine learning, and examines the synergistic fusion of these domains. Through this exploration, we uncover the profound implications of this synergy across various disciplines, from environmental science to healthcare and engineering. The discourse also addresses the challenges and ethical considerations inherent in this interdisciplinary pursuit, advocating for a future where the complexity of pseudo-chaotic systems is not merely understood but harnessed for advancement and innovation.
Comprehensive Analysis of Attacks and Defenses in IoT Sensory Big Data Analysis

Authors:
raw_author_name: Mohammad Ishrat
raw_affiliation_string: Koneru Lakshmaiah Education Foundation, India
raw_author_name: Wasim Khan
raw_affiliation_string: Koneru Lakshmaiah Education Foundation, India
raw_author_name: Faheem Ahmad
raw_affiliation_string: University of Technology and Applied Science, Al Musanna, Oman
raw_author_name: Shafiqul Abidin
raw_affiliation_string: Department of Computer Science, Aligarh Muslim University, India

Abstract: This chapter takes a deep dive into the security issues surrounding IoT sensory big data analysis, which is becoming increasingly important in today's world of ubiquitous connectivity and big data analysis. Starting with an introduction to this crucial juncture, the chapter proceeds to guide the reader through the complex terrain of IoT systems, with a focus on the far-reaching consequences of security precautions. From simple denial of service (DoS) attacks to complex man-in-the-middle (MitM) incursions, data manipulation, injection exploits, and unauthorised access are all under the microscope. Readers will obtain a sophisticated understanding of the many methods used by bad actors to exploit vulnerabilities in IoT systems as they are systematically explored. In addition, the chapter details a wide variety of defence measures, providing a versatile toolkit for shoring up IoT ecosystems. Intrusion detection systems (IDS), secure communication protocols, secure boot procedures, and strong data encryption are all part of these multi-layered plans of action.
Elevating IoT Sensor Data Management and Security Through Blockchain Solutions

Authors:
raw_author_name: Narayan Vyas
raw_affiliation_string: Chandigarh University, India

Abstract: The internet of things, security, and blockchain all come together to deal with the influx of sensor data. The autonomous vehicle industry, for example, relies heavily on effective data management. IoT applications in supply chains, healthcare, and smart cities can all benefit from the immutability, decentralization, and transparency offered by blockchain technology. The research gives recommendations for optimizing blockchain integration and managing scalability concerns. The methodology is supported by real-world case studies, which highlight the potential of blockchain technology to improve efficiency and security in dynamic environments.
Unveiling the Depths of Explainable AI

Authors:
raw_author_name: Wasim Khan
raw_affiliation_string: Koneru Lakshmaiah Education Foundation, India
raw_author_name: Mohammad Ishrat
raw_affiliation_string: Koneru Lakshmaiah Education Foundation, India

Abstract: Explainable AI (XAI) has become increasingly important in the fast-evolving field of AI and ML. The complexity and obscurity of AI, especially in the context of deep learning, provide unique issues that are explored in this chapter. While deep learning has shown impressive performance, it has been criticised for its opaque reasoning. The fundamental motivation behind this research was to compile a comprehensive and cutting-edge survey of XAI methods applicable to a wide variety of fields. This review is achieved through a meticulous examination and analysis of the various methodologies and techniques employed in XAI, along with their ramifications within specific application contexts. In addition to highlighting the existing state of XAI, the authors recognize the imperative for continuous advancement by delving into a meticulous examination of the limitations inherent in current methods. Furthermore, they offer a succinct glimpse into the future trajectory of XAI research, emphasizing emerging avenues and promising directions poised for significant progress.
SISTEM INFORMASI PENERIMAAN PESERTA DIDIK BARU DI SMA NUR EL-FALAH KUBANG MENGGUNAKAN FRAMEWORK CODEIGNITER

Authors:
raw_author_name: Yul Hendra
raw_affiliation_string: Universitas Banten Jaya
raw_author_name: Andi Usri Usman
raw_affiliation_string: Universitas Banten Jaya
raw_author_name: Dedi Juniansha
raw_affiliation_string: Universitas Banten Jaya
raw_author_name: Irma Yunita Ruhiawati
raw_affiliation_string: Universitas Banten Jaya

Abstract: Nur-Elfalah High School currently registers new prospective students manually, namely by coming directly to the school and filling out the registration form. In this way, sometimes queues often occur during registration, and when finding out initial information about parents or prospective students. having to spend time coming in person, this is considered less effective. Nur-Elfalah's New Student Admission (PPDB) is designed to be web-based using an internet network, prospective new students can view information related to the school online, can register online, making it easier to register and view information. Apart from that, by implementing the PPDB system, all registration data will be stored neatly in a database so that if necessary it is easy to search and display in report format. In designing the Information System for accepting new students (PPDB), the waterfall development method is used, where the system is developed in stages and continues to be refined on an ongoing basis. With the new student admission information system (PPDB), it is hoped that it will make it easier for prospective new students to view information and register at the Nur-Elfalah school. It is hoped that prospective new students can register easily anywhere and at any time, as long as the prospective students Just able to access the internet. By implementing this system, it is hoped that it will be able to help Nur-Elfalah school improve its services which will indirectly have an impact on increasing the number of prospective students
Identification of Social Media Posts Containing Self-reported COVID-19 Symptoms using Triple Word Embeddings and Long Short-Term Memory

Authors:
raw_author_name: Raisa Amalia
raw_author_name: Mohammad Reza Faisal
raw_author_name: Fatma Indriani
raw_author_name: Irwan Budiman
raw_author_name: Muhammad Itqan Mazdadi
raw_author_name: Friska Abadi

Abstract: The COVID-19 pandemic has permeated the global sphere and influenced nearly all nations and regions. Common symptoms of this pandemic include fever, cough, fatigue, and loss of sense of smell. The impact of COVID-19 on public health and the economy has made it a significant global concern. It has caused economic contraction in Indonesia, particularly in face-to-face interaction and mobility sectors, such as transportation, warehousing, construction, and food and beverages. Since the pandemic began, Twitter users have shared symptoms in their tweets. However, they couldn't confirm their concerns due to testing limitations, reporting delays, and pre-registration requirements in healthcare. The classification of text from Twitter data about COVID-19 topics has predominantly focused on sentiment analysis regarding the pandemic or vaccination. Research on identifying COVID-19 symptoms through social media messages is limited in the literature. The main objective of this study is to identify symptoms using word embedding techniques and the LSTM algorithm. Various techniques such as Word2Vec, GloVe, FastText, and a composite approach are used. LSTM is used for classification, improving upon the RNN technique. Evaluation criteria include accuracy, precision, and recall. The model with an input dimension of 147x100 achieves the highest accuracy at 89%. This study aims to find the best LSTM model for detecting COVID-19 symptoms in social media tweets. It evaluates LSTM models with different word embedding techniques and input dimensions, providing insights into the optimal text-based method for COVID-19 detection through social media texts.
Data Theft

Authors:
raw_author_name: Dominik Brodowski

Abstract: The Elgar Encyclopedia of Crime and Criminal Justice stands apart as the most comprehensive global reference title in its field. New entries will be added every month and PDF downloads will be available once the Encyclopedia is complete.
Fine-Grained Image Generation Network With Radar Range Profiles Using Cross-Modal Visual Supervision

Authors:
raw_author_name: Shiyong Li
raw_affiliation_string: Beijing Key Laboratory of Millimeter Wave and Terahertz Techniques, School of Integrated Circuits and Electronics, Beijing Institute of Technology, Beijing, China
raw_author_name: Guoqiang Zhao
raw_affiliation_string: Beijing Key Laboratory of Millimeter Wave and Terahertz Techniques, School of Integrated Circuits and Electronics, Beijing Institute of Technology, Beijing, China
raw_author_name: Houjun Sun
raw_affiliation_string: Beijing Key Laboratory of Millimeter Wave and Terahertz Techniques, School of Integrated Circuits and Electronics, Beijing Institute of Technology, Beijing, China
raw_author_name: Yi Zhang
raw_affiliation_string: Beijing Key Laboratory of Millimeter Wave and Terahertz Techniques, School of Integrated Circuits and Electronics, Beijing Institute of Technology, Beijing, China

Abstract: Electromagnetic imaging methods mainly utilize converted sampling, dimensional transformation, and coherent processing to obtain spatial images of targets, which often suffer from accuracy and efficiency problems. Deep neural network (DNN)-based high-resolution imaging methods have achieved impressive results in improving resolution and reducing computational costs. However, previous works exploit single modality information from electromagnetic data; thus, the performances are limited. In this article, we propose an electromagnetic image generation network (EMIG-Net), which translates electromagnetic data of multiview 1-D range profiles (1DRPs), directly into bird-view 2-D high-resolution images under cross-modal supervision. We construct an adversarial generative framework with visual images as supervision to significantly improve the imaging accuracy. Moreover, the network structure is carefully designed to optimize computational efficiency. Experiments on self-built synthetic data and experimental data in the anechoic chamber show that our network has the ability to generate high-resolution images, whose visual quality is superior to that of traditional imaging methods and DNN-based methods, while consuming less computational cost. Compared with the backprojection (BP) algorithm, the EMIG-Net gains a significant improvement in entropy (72%), peak signal-to-noise ratio (PSNR; 150%), and structural similarity (SSIM; 153%). Our work shows the broad prospects of deep learning in radar data representation and high-resolution imaging and provides a path for researching electromagnetic imaging based on learning theory.