Reviewerly/exHarmony huggingface dataset:
Measuring and managing picture quality

Assessing perceptual quality is one of the most critical yet challenging tasks in image and video processing. Visual perception is highly complex, influenced by many confounding factors, not fully understood, and difficult to model. For these reasons, the characterization of compression algorithm performance has invariably been based on subjective assessments where a group of viewers are asked their opinions on quality under a range of test conditions. Methods for conducting such trials and for analyzing the results from them are described in Section 10.2. A discussion of the properties of some publicly available subjective test databases is given in Section 10.3. Objective measures of video quality have conventionally been computed using the absolute or squared difference between the coded version of a picture and its original version. It is however well known that the perceptual distortion experienced by the human viewer cannot be fully characterized using such simple mathematical differences. Because of the limitations of distortion-based measures, perception-based metrics have begun to replace them. These offer the potential for enhanced correlation with subjective opinions, thus enabling more accurate estimates of visual quality. However, many still have shortcomings. The most promising of these are reviewed in Section 10.4, including the structural similarity image metric (SSIM), multiscale SSIM (MS-SSIM), visual information fidelity (VIF), the video quality metric (VQM), the visual signal to noise ratio (VSNR), spatial and temporal most apparent distortion (MAD), motion-tuned spatio-temporal quality assessment (a.k.a. MOVIE), and the perception-based video metric (PVM). We also describe and compare a machine learning-based video quality metric, video multimethod assessment fusion (VMAF), which combines multiple conventional assessment methods using a regression model. Video metrics are not just used for comparing different coding algorithms. They also play an important role in the picture compression and delivery processes, for example enabling in-loop rate-quality optimization (RQO). Section 10.5 addresses this issue, describing some of the most common techniques that enable us to select the optimal coding parameters for each spatio-temporal region of a video. Finally, methods for controlling the bit rate generated by a codec in the context of time-varying channel conditions (rate control) are described in Section 10.6.
Horizontal Auto-Scaling in Edge Computing Environment using Online Machine Learning

A major challenge in edge computing platforms for container-based applications is dealing with the dynamic workload. At certain times, the resources previously allocated to a given container may not be adequate for a substantial increase in processing requests. This paper proposes an online machine learning auto-scaling approach for applications running at the network edge. The auto-scaling follows the MAPE-K control loop architectural style to dynamically adjust the number of containers in response to workload changes. The approach was designed for scenarios at the edge of the network where the behavior of the data is unknown beforehand. We designed a hybrid auto-scaling mechanism that behaves reactively while a prediction online machine learning model is continuously trained. When the prediction model reaches a desirable performance, the auto-scaling behaves proactively using predictions to anticipate scaling actions. The evaluations have demonstrated the feasibility of the architecture. Compared with proactive and reactive auto-scaling, it achieved the best results, decreasing the scaling actions, preventing insufficient provision situations, and assuring the meeting of QoS requirements.
Embedding Learning

Numerical embedding has become one standard technique for processing and analyzing unstructured data that cannot be expressed in a predefined fashion. It stores the main characteristics of data by mapping it onto a numerical vector. An embedding is often unsupervised and constructed by transfer learning from large-scale unannotated data. Given an embedding, a downstream learning method, referred to as a two-stage method, is applicable to unstructured data. In this article, we introduce a novel framework of embedding learning to deliver a higher learning accuracy than the two-stage method while identifying an optimal learning-adaptive embedding. In particular, we propose a concept of U-minimal sufficient learning-adaptive embeddings, based on which we seek an optimal one to maximize the learning accuracy subject to an embedding constraint. Moreover, when specializing the general framework to classification, we derive a graph embedding classifier based on a hyperlink tensor representing multiple hypergraphs, directed or undirected, characterizing multi-way relations of unstructured data. Numerically, we design algorithms based on blockwise coordinate descent and projected gradient descent to implement linear and feed-forward neural network classifiers, respectively. Theoretically, we establish a learning theory to quantify the generalization error of the proposed method. Moreover, we show, in linear regression, that the one-hot encoder is more preferable among two-stage methods, yet its dimension restriction hinders its predictive performance. For a graph embedding classifier, the generalization error matches up to the standard fast rate or the parametric rate for linear or nonlinear classification. Finally, we demonstrate the utility of the classifiers on two benchmarks in grammatical classification and sentiment analysis. Supplementary materials for this article are available online.
Deep Learning based Effective Identification of EU-GDPR Compliant Privacy Safeguards in Surveillance Videos

With video surveillance through Closed Circuit Television (CCTV) cameras becoming increasingly ubiquitous, maintaining the integrity of such videos assumes significance to guarantee compliance with privacy protection laws. Recently adopted European General Data Protection Regulation (EU-GDPR) suggests encryption as a reversible safeguard for all type of data protection. Traditionally, data encryption methods deal with naïve encryption of the videos which is not advisable if the intention is to provide efficient privacy to some important parts/areas in the video. This has resulted in the research being directed towards Features-of-Interest (FOI) based privacy protection schemes, wherein only selected parts of a video frame are encrypted/obfuscated. This research utilizes a deep learning (DL) model to identify GDPR compliant privacy-preserved (encrypted) videos among different types of obfuscated videos. For evaluation, two types of video obfuscation methods viz. selective encryption and data removal are applied on the extracted background and foreground FOIs/parts of video frames. Then the applicability of a modified version of the highly-optimized MobileNetV2 model for identification of privacy-preserving features is presented. Results show that the lightweight DL model is suitable for deployment on low-resource hardware (such as Raspberry Pi's) in Internet of Things (IoT) infrastructure in smart cities.
Mental Models and Conceptual Design

In this chapter, we discuss mental models and conceptual designs. A conceptual design is the part of UX design that serves as a kind of mapping to convey the designer’s mental model to users. A mental model is an explanation of someone’s thought process about how something works in the world. A designer’s mental model is the designer’s vision of how a system works, and a user’s mental model is user’s idea of how the product or system works and, therefore, how to use it. A clear and consistent conceptual design uses design patterns, analogies, and metaphors to assure the product or system design presents a unified and coherent appearance to the user. The UX design for the ecology, interaction, and emotional impact are all built around this model of understanding.
HDR and WCG Image Quality Assessment Using Color Difference Metrics

High Dynamic Range (HDR) and Wide Color Gamut (WCG) imagery is now mainstream across content creation. Thus, having a reliable way of evaluating HDR systems is essential. One common way of measuring the quality of an HDR system is measuring the color errors introduced along the imaging pipeline. Unfortunately, development of color difference metrics has been based on test patches, as opposed to natural imagery, with evaluation mostly limited to Standard Dynamic Range (SDR) databases. In this paper, we evaluate several color difference metrics on five publicly available HDR distortion databases consisting of natural images and subjective scores. Distortion types include lower frequency distortions such as those from tone-mapping as well as higher frequency distortions resulting from compression artifacts by the various compression schemes such as JPEG. We analyze these databases with the well-established CIE L*a*b* metrics (ΔE <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">00</inf> , ΔE <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">94</inf> ) as well as two HDR specific metrics: ΔE <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">z</inf> (J <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">z</inf> a <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">z</inf> b <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">z</inf> ) and ΔE <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ITP</inf> (IC <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">T</inf> C <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">P</inf> ). To quantify the performance, we use four standard performance evaluation procedures. We observe that ΔE <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ITP</inf> outperforms the other color difference metrics on four out of five databases. We also perform statistical analysis which demonstrates the effectiveness of using ΔE <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ITP</inf> to assess overall image quality of HDR/WCG content. Weaknesses of all metrics for one database suggests more advanced spatio-chromatic visual models are worth pursuing.
Analyzing Interoperability and Portability Concepts for Self-Sovereign Identity

The Self-Sovereign Identity (SSI) paradigm postulates global unique identities that are controlled by the user. To achieve a widespread applicability, the emphasized interoperability principle supports the proclaimed ambition. Furthermore, identity portability enables the transfer of the identity to another SSI solution. These axioms gain additional momentum due to the development of numerous implementations. In this paper, we examine interoperability and portability concepts for SSI. Initially, we define these principles regarding the blockchain-based SSI model. Subsequently, we outline assessment criteria considering functional scope, governance/ trust, scalability and further characteristics. For interoperability, we evaluate the concepts of protocol and standard, broker, hub and pairing. Besides that, we assess the transformer and auxiliary solutions for portability. We can conclude that all interoperability schemes provide the maximum functional level theoretically. In contrast, portability patterns are fragmented in this regard. Nonetheless, protocol and standards can only be applied in the design phase, whereas broker, hub, pairing, transformer and auxiliary solutions enable interoperability, respectively portability post-deployment of the SSI system.
Counting Technique versus Single-Time Test for Track-to-Track Association

Standard hypothesis tests for track-to-track association depend on the state estimates and covariances of the individual tracks. Unfortunately, covariances are not always available from the individual tracking systems. An alternative approach that can be used in such cases is a counting technique, where the number of good matches is used as a test statistic. In this paper, we compare the counting technique with a conventional hypothesis test in simulations for a fusion system designed to fuse maritime radar tracks with tracks from the automatic identification system. Since the data association of the radar tracking system inevitably makes it nontrivial to decide on a ground truth, we also propose a ground truth assessment scheme using a sliding window approach. The results indicate that the counting technique performs at par with the hypothesis test under certain tracking conditions. If an initialization time of several seconds is allowed, the counting technique may under certain conditions outperform the hypothesis test in terms of true-positive rate and false-positive rate.
Underwater Image Restoration and Target Detection Based on Monocular Depth Estimation

The underwater image was distorted due to scattering, absorption, colour loss, diffraction, polarization and attenuation with light frequency, which led to the distortion of the underwater image and poor target detection effect. An underwater image restoration algorithm based on monodepth2 monocular depth estimation was proposed, and the target detection algorithm based on depth learning was completed. The depth map obtained by monodepth2 monocular depth estimation was used, and the image depth was integrated into the sea-thru the underwater image restoration method. The experimental results showed that the proposed algorithm achieves a good image restoration effect, which laid a foundation for target detection; The underwater image target detection based on yolov5 was realized, and the experiments verify the effectiveness of the proposed algorithm.
Blockchain

There is a huge amount of interest around blockchain technology with a lot of enthusiasm and skepticism; nowadays “blockchain” is an inflated term but there is little research, as far as we know, on the “real” impact of this technology on management systems. Blockchain is sometimes considered to be far away from practical diffusion; however, this chapter examines the implementation of technology innovation in a case study.
A model for posttransliteration suggestion for balinese palm leaf manuscript with text generation and lstm model

Abstract The main challenge found in building an automatic transliteration system for Balinese palm leaf manuscript (Lontar) collections is that the recognition error in a small portion of glyphs of Balinese script can affect the results of transliteration widely. This is due to the fundamental nature of Balinese script which is a complex alphasyllabic script. This paper presents an initial proposition for a general scheme and model for suggesting several possible transliteration with text generation and LSTM for Lontar collection. The Edit-Insert-Replace model was proposed to be applied on the existing word collection dataset and a Bidirectional LSTM model with a specific feature extraction method was built for the training process of post transliteration suggestion module. This module will help in suggesting several possible transliterations based on the initial transliteration from the previous system.
Learning in Parametric Modeling: Basic Concepts and Directions

The present chapter presents an overview of basic directions in machine learning and the basic notions related to parametric modeling are introduced. The tasks of regression and classification are defined and basic concepts related to parameter estimation, such as estimator efficiency, Cramér–Rao bound, and sufficient statistic, are outlined. The least-squares estimator and some of its properties are discussed. The notions of inverse problems, overfitting, bias–variance dilemma, and regularization are presented. The methods of maximum likelihood, maximum a posteriori, and Bayesian inference are introduced. Finally, the curse of dimensionality and the cross-validation technique are provided. The chapter closes with a brief discussion and some formal definitions of notions such as expected loss and empirical risk functions and the interplay between approximation and estimation errors.
Telemedicine in Low Resource Settings

Telemedicine is a means to support health-care provision utilizing information and communication technology (ICT) tools and telecommunication services. This chapter focuses on telemedicine practices in low resource settings, referencing key telemedicine initiatives in Botswana. Telemedicine is highly practiced in the developed world, and recently there is an increasing interest in the developing world. Current literature suggests telemedicine as an important tool for improving healthcare delivery for low resource settings. Hence the authors' interest in exploring the current status of telemedicine practices with reference to telemedicine projects from low resource settings such as Botswana. The chapter reveals that telemedicine in such settings is mainly implemented through mobile phones, also known as mobile health (mHealth). In this chapter, the authors discuss factors influencing successful implementation of telemedicine solutions in Botswana. Furthermore, the chapter discusses telemedicine implementation challenges in each of the projects and presents possible mitigation strategies. The chapter concludes by affirming the feasibility of successfully practicing telemedicine in low resource settings; notwithstanding challenges such as lack of legal and eHealth frameworks in most developing countries.
Image Steganography: Analysis &amp;amp; Evaluation of Secret Communication

In the present scenario, the networked multimedia system has given rise to various security related issues which create a need for the security of digital data content. Therefore, security is the primary concern for any multimedia system to maintain the confidentiality, integrity, and authenticity of the data content. Currently, there exist several multimedia security techniques like cryptography, watermarking, steganography etc. The technique of hiding secret information in digital media i.e. audio, video and image are jointly known as Steganography. The detection of the presence of steganography referred to as Steganalysis. This paper provides an image steganography technique including numerous aspects related to data hiding capacity and enhanced security with the criteria of performance evaluation. The final outcomes show that the proposed technique provides data hiding capacity and also achieves a high level of security.
Executing, Comparing, and Reusing Linked-Data-Based Recommendation Algorithms With the Allied Framework

Data published on the web following the principles of linked data has resulted in a global data space called the Web of Data. These principles led to semantically interlink and connect different resources at data level regardless their structure, authoring, location, etc. The tremendous and continuous growth of the Web of Data also implies that now it is more likely to find resources that describe real-life concepts. However, discovering and recommending relevant related resources is still an open research area. This chapter studies recommender systems that use linked data as a source containing a significant amount of available resources and their relationships useful to produce recommendations. Furthermore, it also presents a framework to deploy and execute state-of-the-art algorithms for linked data that have been re-implemented to measure and benchmark them in different application domains and without being bound to a unique dataset.
Event detection based on open information extraction and ontology

Most of the information is available in the form of unstructured textual documents due to the growth of information sources (the Web for example). In this respect, to extract a set of events from texts written in natural language in the management change event, we have been introduced an open information extraction (OIE) system. For instance, in the management change event, a PERSON might be either the new coming person to the company or the leaving one. As a result, the Adaptive CRF approach (A-CRF) has shown good performance results. However, it requires a lot of expert intervention during the construction of classifiers, which is time consuming. To palpate such a downside, we introduce an approach that reduces the expert intervention during the relation extraction. Also, the named entity recognition and the reasoning, which are automatic and based on techniques of adaptation and correspondence, were implemented. Carried out experiments show the encouraging results of the main approaches of the literature.
GIS for Earth Sciences

Geographic Information Systems (GIS) supports data collection, geospatial data analysis, visualization, scientific communication and research collaboration. GIS has implications for many fields of the Earth Sciences, which are above and beyond one's imagination. Since the development of the first computerized GIS in the 1960s, the need by professionals for geospatial technology in fields that utilize geospatial data has never stopped expanding. As noted by a market analysis in August 2017: “The GIS Market was valued at USD 5.33 Billion in 2016 and is expected to reach USD 10.12 Billion by 2023, growing at a compound annual growth rate of 9.6% between 2017 and 2023” (marketsandmarkets.com, August 2017). Earth Sciences encompasses a broad and diverse array of technical areas, such as geology, geomorphology, geography, geophysics, hydrology, hydrogeology, environmental sciences, oceanography, meteorology, and atmospheric sciences. All of these fields use geospatial data to solve complex problems related to the planet Earth. Some of these problems are near impossible to solve without the use of GIS. This article presents a brief introduction to GIS and examples of its applications to the Earth sciences. Three case studies highlight the utility of GIS applications in compiling, integrating, analyzing and visualizing geospatial data.
Towards Intelligent Human Behavior Detection for Video Surveillance

Computer vision techniques are capable of detecting human behavior from video sequences. Several state-of-the-art techniques have been proposed for human behavior detection and analysis. However, a collective framework is always required for intelligent human behavior analysis. Therefore, in this chapter, the authors provide a comprehensive understanding towards human behavior detection approaches. The framework of this chapter is based on human detection, human tracking, and human activity recognition, as these are the basic steps of human behavior detection process. The authors provide a detailed discussion over the human behavior detection framework and discuss the feature-descriptor-based approach. Furthermore, they have provided qualitative and quantitative analysis for the detection framework and demonstrate the results for human detection, human tracking, and human activity recognition.