Reviewerly/exHarmony huggingface dataset:
ABR Quality Monitoring: State of the Art

With the move to increasingly complex adaptive bitrate technology for video transport delivery comes a corresponding increase in quality assurance complexity. Video service providers require more information than only that some number of viewers are unsatisfied with their viewing experience. More proactive warnings of deteriorating service and the descriptions of specific impairments, for example, are needed for rapid corrective actions. This paper describes both common industry approaches to quality monitoring for Hypertext Transfer Protocol (HTTP) Live Streaming (HLS) and dynamic adaptive streaming over HTTP (DASH) delivery methods as well as video content quality evaluation leveraging mean opinion score (MOS), bits per pixel, and other metrics aimed at evaluating if viewers have a satisfactory viewing experience. A comprehensive set of metrics including both those recommended by standards and trade groups as well as key supplemental metrics for both end user viewing and transport system analysis is discussed along with the tradeoffs in measurement implementation techniques gained from significant deployments for some past entertainment events. Recommendations on the selection of a mix of reactive and proactive metrics are provided as derived from synthetic clients, passive agent technologies, and virtual swarming agents. Both transport and content quality metrics are critical in assessing viewers’ experience and pinpointing impairment causes and viewer losses.
Solving a Tool-Based Interaction Task Using Deep Reinforcement Learning with Visual Attention

We propose a reinforcement learning approach that combines an asynchronous actor-critic model with a recurrent model of visual attention. Instead of using the full visual information of the scene, the resulting model accumulates the foveal information of controlled glimpses and is thus able to reduce the complexity of the network. Using the designed model, an artificial agent is able to solve a challenging “mediated interaction” task. In these tasks, the desired effects cannot be created through direct interaction, but instead require the learner to discover how to exert suitable effects on the target object through involving a “tool”. To learn the given mediated interaction task, the agent is “actively” searching for salient points within the environment by taking a limited number of fovea-like glimpses. It then uses the accumulated information to decide which action to take next.
Aspect-Oriented Modeling of Spatial Data Interpolation for Estimating Missing Data in Internet of Things (IoT) Service Discovery

Abstract The Internet of Things (IoT) is a model of future Internet and pervasive computing that has its own particular difficulties gained from the Internet as far as adaptability, vague topology, and so on are concerned. The proposed work means to determine the difficulties posed by IoT in the service discovery field. Most of the data analytics algorithms applied for data collected through sensors and actuators assume that the data are complete such that each property of the instances is filled with the appropriate value. These data have temporal and spatial correlation between them, and missing such data results in a significant decrease in accuracy and reliability of data analysis performed. Considering the importance of estimating the spatial data and the intricacies involved in estimating it using interpolation techniques, the proposed work bases its system development using an aspect-oriented programming improvement technique, thereby addressing the interpolation strategy as a cross-cutting aspect and reducing the complexity involved thereof. The proposal analyzes the situation of missing data and appropriately weaves the aspect and the application together, thereby decreasing the complexity in handling the interpolating data. The woven aspect estimates the missing data using an inverse distance weighting method and updates the information. Observation of the experimental results reveals significant improvement in response time compared with estimating the unknown value in a conventional manner.
Developing a Research Agenda to Support CCE in the Future

There is a solid CCE research based on which future research can be built. This chapter will argue, however, that a future research agenda needs to be expanded and it needs to be a priority. First, theoretical/philosophical/conceptual research will need to move beyond democratic theory so that the forces of democratic deconsolidation can be better understood. While democratic theory will always be important for understanding CCE, understanding the forces aligned against democracy will also be important. Second, the multiple sites for civic learning must also be better understood as well as the ways they interact. Clearly, social media is one of these sites and it will deserve special attention in the future. Civic learning itself also needs to be the subject of future research. Third, every effort will need to be made to draw on the broadest range of research in cognate disciples to ensure that there is a much better understanding of the multiple ways young people are inducted into civic learning. Finally, it is acknowledged that more needs to be known about how young people learn in order to assist with the development of CCE programs that will have an impact in supporting young people to become knowledgeable, participative and tolerant citizens in what can only be volatile times in the future.
Robot Simulation and Reinforcement Learning Training Platform Based on Distributed Architecture

In recent years, reinforcement learning, which enables robots to learn previously missing abilities, plays an increasingly important role in robotics, such as learning hard-to-code behaviors or optimizing problems without an accepted closed solution. The main problem of RL in robotics is that it is expensive and takes a long time to learn and operate. Another problem: advanced robot simulators like Gazebo are inefficient and time-consuming. In order to cope with these problems, a hybrid computing platform based on traditional robot simulation architecture and distributed architecture (hereinafter referred to as RDTP) is proposed in this paper, which helps to save cost, shorten time and speed up simulation and training. Additionally, the platform is optimized to a certain extent in terms of ease of use and compatibility.
Building a Dataset Search for Institutions: Project Update&#x0D;

Most out-of-the-box institutional repository systems do not provide the workflows and metadata features required for research data. Consequently, many libraries now support two institutional repository systems—one for publications, and one for research data—even when there are nearly a thousand data repositories in the United States, many of which provide services and policies that ensure their trustworthiness and suitability for research data. Libraries are either increasing spending by purchasing data repository solutions from vendors, or replicating work by building, customizing, and managing individual instances of data repository software. This article gives an update on a potential solution to this issue: An in-progress prototype for an open source Dataset Search tool that promotes discovery and reuse of institutional research datasets through automatic metadata harvesting and search engine optimization. Once finished, the Dataset Search tool has the potential to support three key impacts: Increasing discovery, reuse, and citation of research data; reinforcing the idea that research data are a legitimate scholarly product; and promoting community-owned systems that require less resource expenditure.
Video-Based Person Re-identification by Region Quality Estimation and Attributes

Person re-identification (re-ID) has become a significant area in automated video surveillance. The main challenge of this task lies in changes of pedestrians’ appearance across different cameras due to occlusions and illumination variations. Video-based person re-ID provides more information about pedestrians, but how to aggregate useful information of all frames is still an open issue. Although using region quality estimation network (RQEN) can achieve relative good performance on standard datasets, it is limited by the alignment of all bounding boxes. If the position of person varies in bounding boxes, it will influence the region generating unit, which thus decreases the accuracy of re-ID. To solve this limitation, this paper proposes a network combining the quality estimation and attribute classification. While convolution neural network can effectively learn global features, attribute recognition focus more on details. Therefore, our method achieves comparable results on iLIDS-VID and PRID 2011 datasets with the help of attribute classification.
A Proposal for the Development of Lifelong Dialog Systems

In this paper we describe a proposal that employs Soft Computing techniques for developing intelligent dialog systems that can improve over time. To do this, our proposal merges statistical dialog management methodologies, intentional and emotional information in order to make dialog managers more efficient and adaptive. The prediction of the user intention and emotion is carried out for each user turn in the dialog by means of specific modules that are conceived as an intermediate phase between natural language understanding and dialog management in the architecture of these systems. We have applied and evaluated our method in the UAH system, for which the evaluation results show that merging both sources of information improves system performance as well as its perceived quality.
Paraconsistent Reasoning in Cops and Robber Game with Uncertain Information: A Simulation-Based Analysis

We apply a non-classical four-valued logic in the process of reasoning regarding strategies for cops in a modified game of “Cops and Robber” played on a graph. We extend the game by introducing uncertainty in a form of random failures of detecting devices. This is realized by allowing that a robber can be detected in a node only with the given probability P A . Additionally, with the probability P F , cops can be given a false-positive, i.e., they are informed that the robber is located at some node, whereas it is located somewhere else. Consequently, non-zero P F introduces a measurement noise into the system. All the cops have access to information provided by the detectors and can communicate with each other, so they can coordinate the search. By adjusting the number of detectors, P A , and P F we can achieve a smooth transition between the two well-known variants of the game: “with fully visible robber” and “with invisible robber”. We compare a simple probabilistic strategy for cops with the non-parametric strategy based on reasoning with a four-valued paraconsistent logic. It is shown that this novel approach leads to a good performance, as measured by the required mean catch-time. We conclude that this type of reasoning can be applied in real-world applications where there is no knowledge about the underlying source of errors which is particularly useful in robotics.
Linearization of Cryptographic Sequences

The generalized self-shrinking generator (or generalized generator) produces binary sequences (generalized sequences) with good cryptographic properties. On the other hand, the binomial sequences can be obtained considering infinite successions of binomial coefficients modulo 2. It is possible to see that the generalized sequences can be computed as a finite binary sum of binomial sequences. Besides, the cryptographic parameters of the generalized sequences can be studied in terms of the binomial sequences.
Practical Initialization of Recursive Mixture-Based Clustering for Non-negative Data

The paper provides a practical guide on initialization of the recursive mixture-based clustering of non-negative data. For modeling the non-negative data, mixtures of uniform, exponential, gamma and other distributions can be used. Initialization is known to be an important task for a start of the mixture estimation algorithm. Within the considered recursive approach, the key point of initialization is a choice of initial statistics of the involved prior distributions. The paper describes several initialization techniques for the mentioned types of components that can be beneficial primarily from a practical point of view.
Improvement of Video Human Activity Recognization by using ANN Approach

In this work projected for watershed segmentation technique takes regarding 3 times the time taken by the k-means bunch technique. Though the HSV color area was found to convey higher results compared to the RGB color area, in our experiments the RGB and HSV color areas were found to convey virtually equivalent results. Eventually, it had been set to use the HSV color area as a result of it gave higher results than the RGB color area just in case of "difficult Queries". K-MEAN primarily {based} bunch rule has been projected and also the iterations taken was abundant less (sometimes forty times less) than that of K-MEAN and ANN based schemes. Moreover, K-MEAN based mostly schemes might discover all the peaks and thence, categories accurately. The impact of the configuration, migration policy, rate of migration, and kind of migration on the speed convergence has been studied and it had been discovered that the migration policy and rate of migration greatly influence the convergence rate.
Useful observational research

Watching people can be better than listening to them. Observational research can illuminate the unconscious behaviors that lead to breakthrough solutions to problems. This chapter is a manual on how to use observational research to solve problems in different contexts. The chapter shows how an active form of observational research called storytelling research can yield problem-solving solutions for further testing. Using illustrations ranging from Disney World attractions to fast food restaurants, this chapter focuses on three critical steps of useful, solution-based observational research. Specifically, this is an observational research manual on (1) how to find potential breakthrough insights, (2) how to confirm them (using quantitative coding), and (3) how to use the results to develop solutions to problems.
Blockchain Technologically Driven Innovation at the Gulf Cooperation Council Countries

This chapter delves into the innovation of blockchain technology and its benefits for advancing the economic growth in the Gulf Cooperation Council (GCC) countries. Blockchain technology provides the security and reliability that is required in the GCC region for enhancing the technology acceptance level of its population. Apart from providing security, blockchain technology offers financial efficiency, cost reduction, and the elimination of operational risks. Blockchain technology is equally beneficial for nonfinancial industries, such as the healthcare and education industry of the GCC region. The digital revolution based on blockchain technology is beneficial for GCC countries to shift their economic dependence from oil to innovations driven by technology. This will move the GCC to develop more smart cities as the GCC region has an ample amount of financial and human resources that are required for successful integration of blockchain technology in its numerous industries.
Post-Independence African Economies: 1960–2015

Ocran points out that the 1960s was momentous in the history of Africa. He intimates that the by the end of the 1960s, most African countries had attained political independence. The period was therefore characterised with the euphoria of independence after seventy-five years of Western European domination. Behind the veil of euphoria existed harsh realities of poignant developmental challenges: unskilled human resources, political fragility, inappropriate institutions, rapid population growth and the legacy of history. After a short spell of economic growth, the countries succumbed to the global economic difficulties following the oil crises of the 1970s. The World Bank and the International Monetary Fund, which came to the rescue of the economies made financial support conditional on the adoption of stabilisation and structural adjustment policies. Ocran argues that after two decades of reforms, by the year 2000, the policies had failed to engender either sustained economic growth or considerable poverty reduction. He concludes by providing evidence to show development challenges still persist, even though the past two decades have witnessed a rebound of economic growth in many African countries.
THE CULTURAL COMPETENCES FORMATION FOR YOUTH

The article deals with the issue of honoring Borys Grinchenko's pedagogical and artistic work by Canadian teachers; Borys Grinchenko's handwritten magazine, «What does our school prepare for?» about the effectiveness of his teaching, was put into circulation; the model of cultural competences formation for students of the University and high school, is drawn up. The principle of dialogism and creativity are the basis of the author's concept. According to this, on the basis of the heuristic method, the following dialog task was introduced: to simulate the imaginary dialogue between excursionists and participants of the master-class and the workshop with B. Grinchenko according to the question / answer scheme.In accordance with the principle of dialogism on the basis of the research method, the traditional method of problem issues was used: «What steps did B. Grinchenko rise to success? What are the ways to bring education to the people? What skills do I need to have to become a publisher?»; innovative techniques «Steps to success» and «Pyramid of Achievements». Accordingly, models of schemes are represented in the form of steps and pyramids. The innovative type of work – the search task was implemented: «Complete the scheme with a word that summarizes Grinchenko's contribution as a teacher and publisher to the education of his native people». Answers of the first-year students were divers via personal imagination skills, which were stimulated by B. Grinchenko’s many-sided talent and confirmed the using of «Steps to success» technique.The article analyzes the results of the cultural competences formation for students of the University during the interactive excursion «Smart Museum of Borys Grinchenko», master-class and workshop were held.It is recommended for students undergoing pedagogical practice, to share knowledge about the pedagogical and educational heritage of Borys Grinchenko.
SIRCS: Slope-intercept-residual Compression by Correlation Sequencing for Multi-stream High Variation Data

Multi-stream data with high variation is ubiquitous in the modern network systems. With the development of telecommunication technologies, robust data compression techniques are urged to be developed. In this paper, we humbly introduce a novel technique specifically for high variation signal data: SIRCS, which applies linear regression model for slope, intercept and residual decomposition of the multi data stream and combines the advanced tree mapping techniques. SIRCS inherits the advantages from the existing grouping compression algorithms, like GAMPS. With the newly invented correlation sorting techniques: the correlation tree mapping, SIRCS can practically improve the compression ratio by 13% from the traditional clustering mapping scheme. The application of the linear model decomposition can further facilitate the improvement of the algorithm performance from the state-of-art algorithms, with the RMSE decrease 4% and the compression time dramatically drop compared to the GAMPS. With the wide range of the error tolerance from 1% to 27%, SIRCS performs consistently better than all evaluated state-of-art algorithms regarding compression efficiency and accuracy.
Motion Analysis and Object Tracking

The goal of this chapter is to cover motion analysis and the tracking of objects. You will learn how to get information about different types of objects in motion, understand techniques to remove background and foreground information, and see real-time tracking options with hands-on implementation steps. The topics in this chapter are an extension of the object detection and recognition techniques you learned about in Chapter 5 and hence require a thorough understanding of that chapter.
Nonnegative Spectral Clustering for Large-Scale Semi-supervised Learning

This paper proposes a novel clustering approach called Scalable Nonnegative Spectral Clustering (SNSC). Specifically, SNSC preserves the original nonnegative characteristic of the indicator matrix, which leads to a more tractable optimization problem with an accurate solution. Due to the nonnegativity, SNSC offers high interpretability to the indicator matrix, that is, the final cluster labels can be directly obtained without post-processing. SNSC also scales linearly with the data size, thus it can be easily applied to large-scale problems. In addition, limited label information can be naturally incorporated into SNSC for improving clustering performance. Extensive experiments demonstrate the superiority of SNSC as compared to the state-of-the-art methods.
Dynamic Detection and Tracking Based on Human Body Component Modeling

Focus on the problem of dynamic human detection and tracking in complex scenes, a physical structure based Convolutional Nerual Network is proposed. Firstly, aiming at the modeling and analysis of the human body and its components, the human body detection algorithm adapted to complex scenes is proposed, and the convolutional neural network is designed to realize the model. Secondly, the human body tracking model based on convolutional neural network and off-line training is designed, and the human body tracking algorithm is optimized to realize fast and accurate tracking of human body. Using IOU, Euclidean distance and other algorithms, the relationship between the targets detected by the detection algorithms in two adjacent frames is established. Multi-modal fusion of multiple models using a state machine or the like, so that multiple models can work effectively at the same time. This experiment carried out simulation experiments on the bus video dataset. The experimental results show that the algorithm can effectively track the passengers who are obscured by each other on the bus, and the accuracy exceeds the current best algorithms, which proves the effectiveness of the algorithm.
Evaluating the Choice of Tags in CQA Sites

Tags play a crucial role in CQA sites by facilitating organization, indexing and categorization of the entire post in a few words. The choice of tags determines the audience that is elicited upon to seek a response for any particular post. This could either lead to receiving an accurate response for the question or result in receiving no answers. The choice of tags, thus, directly determines the quality of the post as well as to a large extent the success of the CQA site itself. In this paper, we a present a novel approach to evaluate the choice of tags in any post. We perform tag network analysis to find relationship between tags. We then find the anomalous combination of tags by performing anomaly detection. We demonstrate the robustness of our approach by showing high AUC, in the range of 0.95 to 0.98, on four datasets from Stack Exchange, namely Ask Ubuntu, Server Fault, Super User and Software Engineering.