Reviewerly/exHarmony huggingface dataset:
E-WISH

HTTP Adaptive Streaming (HAS) is the de-facto solution for delivering video content over the Internet. The climate crisis has highlighted the environmental impact of information and communication technologies (ICT) solutions and the need for green solutions to reduce ICT's carbon footprint. As video streaming dominates Internet traffic, research in this direction is vital now more than ever. HAS relies on Adaptive BitRate (ABR) algorithms, which dynamically choose suitable video representations to accommodate device characteristics and network conditions. ABR algorithms typically prioritize video quality, ignoring the energy impact of their decisions. Consequently, they often select the video representation with the highest bitrate under good network conditions, thereby increasing energy consumption. This is problematic, especially for energy-limited devices, because it affects the device's battery life and the user experience. To address the aforementioned issues, we propose E-WISH, a novel energy-aware ABR algorithm, which extends the already-existing WISH algorithm to consider energy consumption while selecting the quality for the next video segment. According to the experimental findings, E-WISH shows the ability to improve Quality of Experience (QoE) by up to 52% according to the ITU-T P.1203 model (mode 0) while simultaneously reducing energy consumption by up to 12% with respect to state-of-the-art approaches.
H/W Accelerated Unsupervised Codec Modernization

Following a decade where AVC/H.264 was the dominant codec in many video applications, the last years have finally seen more modern video codecs gain traction. This raises the need for cost effective, fast, fully automatic transcoding into modern codec formats which will preserve the input perceptual quality, at the lowest possible bitrate. The availability of such a solution makes the switchover to newer encoders a smooth, easy, and low-cost process accelerating adoption of HEVC, AVI or VVC.
Combining Deep Learning and Feature Engineering for No-Reference Video Quality Assessment

With the ever-growing demand for high-quality video content, methods for evaluating the aesthetic quality of digital videos have become invaluable tools to ensure a satisfying viewing experience for the end-user. Over the years, many accurate and reliable Full-Reference (FR) video quality assessment (VQA) metrics [4, 5, 10, 16, 17] have been developed that assess quality degradation by comparing the processed video to its original source. However, in scenarios where the reference video is unavailable or impractical to obtain, No-Reference (NR) metrics have to be used, which assess quality solely on the distorted video. Despite intensive research, no NR method so far has been found accurate enough to be widely adopted by industry [9].
Inferring Video Streaming Quality of Experience at Scale using Incremental Statistics from CDN Logs

With video streaming applications, end-user Quality of Experience (QoE) is generally estimated using metrics provided by the video players. While QoE monitoring is critical for Content Delivery Networks (CDNs), player metrics are not always provided due to technical, contractual or regulatory limitations. We propose to leverage CDN access logs to infer five common QoE player metrics, with the help of machine learning models. Our approach is based on constant-memory statistic accumulators, allowing large-scale analysis of video streams. We evaluated our implementation with more than 100 000 concurrent streaming sessions on a single CPU core, showing good correlation with QoE ground truth (ρ > 0.7, R2 > 0.5).
A New Blockchain-Based Medical Model for Smart Hospital Integrity Management of the Drug Supply Chain

Counterfeit medications are currently one of the biggest issues in pharmacology. According to a research from the Health Research Funding organization, between 10–30% of pharmaceuticals in underdeveloped nations are counterfeit. The primary problem is not in counterfeiting per such, but rather in the fact that these pharmaceuticals have distinct adverse consequences on human health when compared to regular drugs. The WHO estimates that over 30% of all medications sold in Latin America, Asia, and Africa are fake. This is the main issue facing the entire world, and things become worse in underdeveloped nations where one in ten medications are either counterfeit or do not adhere to drug standards. It is becoming increasingly challenging to standardize drug safety due to the growth of online pharmacies. It is challenging to identify fakes. The safety of the pharmaceutical supply chain is becoming a serious public health concern, a process that affects all of us. In this work, we present a new approach to medication supply chain management that handles safe drug supply chain data using Hyperledger Fabric, a blockchain-based platform. By implementing drug record transactions on a blockchain, a drug supply chain and smart healthcare ecosystem may be constructed. The suggested method addresses this issue. To allow for time-limited access to patient health information and electronic prescription records, a smart contract has been introduced.
Content Steering

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

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

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

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

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

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

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.
Building a Digit Classifier with MNIST

Students use feature engineering to build a classifier that can accurately recognize digits from images.
Learning Label Correlations for Multi-Label Online Passive Aggressive Classification Algorithm

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

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.
Implementation of TikTok as Medium for Learning of English Vocabulary: The Perspective of Department English Education Students

This research aims to examine students' perspectives on implementing the TikTok application as a medium for learning English in the modern era. Especially with the latest technological advances, in the post-pandemic COVID-19 social media can be used as a learning media such as YouTube, Twitter, Instagram, Facebook, and TikTok applications. TikTok can help students improve their English language proficiency, as well as what problems and difficulties arise when using the application as a vocabulary teaching tool and what difficulties students may encounter when using TikTok to learn new vocabulary. This research uses a qualitative design with a survey method where students fill out an online questionnaire using Google Forms and interviews. Questionnaires were given to students, and then students were interviewed. The participants were 30 students who took part in the English language learning program. The research results show that the students had a positive response to the use of TikTok in learning English vocabulary. TikTok ease of use and various functions, the TikTok application can be applied to learning activities to increase English vocabulary. This interactive learning media can provide much educational content about English vocabulary and make the learning process more efficient and effective for students. The implementation of This research makes it possible for educators. Content creator educators can easily create a learning process that attracts students' attention. It has been known that the TikTok application is efficient in increasing English vocabulary for students. Through the TikTok application, students can gain more vocabulary and continue to have fun learning by presenting information through interesting content, audio and visuals. Teachers can also process and develop various sources of interactive, interesting and unique TikTok content. So that it makes students interested in learning English and considers TikTok to be an effective medium for learning.
Optimal Asymmetric Quantum Codes from the Euclidean Sums of Linear Codes

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

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

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.
BRIEF APPROACH ON TEACHING HYDROGRAPHY IN ELEMENTARY SCHOOL II

This work is a possibility for us to think about rivers.The objective of the work is to show that rivers have a history of formation.Rivers are part of the entire systemic dynamics of terrestrial evolution, making up life in the system; as well as the lives of many traditional peoples.In this sense, the work data is constituted through scenes from the Landsat 5 and 8 satellites, and the National Common Curricular Base.As an example, there is a stretch of the Amambaí-MS river, close to the mouth, aiming to analyze the dynamics of evolution aimed at elementary school II.This way, the work demonstrates a way of learning about rivers that is more attractive to students than just memorizing nomenclatures.In this context, the work addressed the dynamics of the channel in the years 2006, 2013 and 2020, identifying the morphological change in the Amambaí river channel, mainly between the years 2013 and 2020, with the abandonment of the meander.From this perspective, the student understands that the dynamics of nature are constant, and, in the case of river dynamics, 14 years is a period in which there is the possibility of changing the channel of a meandering river.
Comprehensive Analysis of Attacks and Defenses in IoT Sensory Big Data Analysis

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

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

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

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