Reviewerly/exHarmony huggingface dataset:
Exploiting the layer correlation to improve DASH scheduling with scalable video coding

DASH is a promising video streaming solution that gracefully deals with network throughput fluctuations as well as terminal heterogeneity. In DASH, an MPD file describes the available video qualities and their average bitrates. However, the coarse-grained average bitrate is insufficient for an accurate client-side scheduler. The bitrate overestimate or underestimate of bandwidth requirements for segments to be fetched could result in suboptimal scheduling decisions and possibly drain the playback buffer. In this paper, we first made the observation that with SVC, video segments across different layers exhibit high size correlation. We then showed that enhancement layer segment size can be predicted by the corresponding base layer segment with relatively high accuracy. Based on this observation, we enhanced existing rate-based and buffered-based adaptive bitrate (ABR) algorithms with a size predictor, which is orthogonal to existing adaptation logics. Results show that augmented by the size predictor, the average playback bitrate can be improved by up to 20% and playback stalls can also be significantly reduced. Our results demonstrated that similar to the bandwidth predictor, a size predictor should also be added to SVC-based ABR algorithms to increase the performance gain.
Book Studies Communication in Selected Communication Systems and Their Influence on the Location of the Discipline in the Current Obligatory Classification of Fields and Scientific Disciplines – an Outline of the Research Problem

Objective - The aim of the article is to discuss the individual components of book studies communication and show its place in contemporary communication systems and changes occurring in this area of research. Research methods – A method of analysis and criticism of the literature was used. Results and conclusions – The subject of book studies communication is not often the subject of theoretical considerations. The directions of the development of book studies communication from the mid-twentieth century, when it was based on print, to the changes brought about by the digital revolution were followed. Existing models of book science communication are discussed, links to the literary communication system and its place in the social and media communication system are shown. The communication role of book studies was highlighted through the prism of subdisciplines related to book production (editing, digital publishing), its individual and collective circulation (bookselling, librarianship), and to its use and reading behaviour (reading). Originality and cognitive value – The article organizes knowledge about the current development of book science communication and defines its place within various communication models. It shows the duality of the book studies field, situating this science both in the humanities and social sciences, and resulting from its historical connections with various scientific disciplines and various communication models.
Supervised machine learning models based on support vector regression

A vast majority of practically utilized machine methods in fact involve supervised learning techniques. In supervised learning, an algorithm is used to learn an approximate mapping function from the input variable x to the output y=f(x) so that when there is new input data, we can predict the output for that data. Support vector regression, which is derived from its parent version, is based on an optimization problem. The weights corresponding to each input sample in a training set are obtained. Variants such as least square support vector regression, twin support vector regression, and ε-twin support vector regression are presented with a case study for each. In this chapter, such regression models are described in detail.
Integrating Context to Preferences and Goals for Goal-oriented Adaptability of Software Systems

Abstract Modern software systems are continuously seeking for adaptability realizations, to generate better fit behaviours in response to domain changes. Requirements variability motivates adaptability; hence, understanding the influence of the domain changes, i.e. context variability, to requirements variability is necessary. In this paper, we propose an approach for context-based requirements variability analysis in the goal-oriented requirements modelling. We define contextual goals and contextual preferences to specify the relationships of contexts with requirements and preferences, respectively. Given a requirements problem represented through a goal model, we use the contextual goals to derive applicable solutions at a given situation. Then, from those applicable solutions, we use the contextual preferences as criteria for evaluating and selecting the ones that would best satisfy stakeholder priorities. To support our variability analysis, we develop a tool to automate the derivation and evaluation of the solutions. We further demonstrate the use of our approach in detecting modelling errors and validating the impact of prioritizations, leading to improvements in the requirements specifications. Our approach broadens the scope of requirements variability by weaving context variability with both stakeholder goals and preferences, in order to sufficiently represent the adaptability needs of software systems where contextual changes are commonplace.
Edge Enhancement for Image Segmentation Using a RKHS Method

Image segmentation has many important applications, particularly in medical imaging. Often medical images such as CTs have little contrast in them, and segmentation in such cases poses a great challenge to existing models without further user interaction. In this paper we propose an edge enhancement method based on the theory of reproducing kernel Hilbert spaces (RKHS) to model smooth components of an image, while separating the edges using approximated Heaviside functions. By modelling using this decomposition method, the approximated Heaviside function is capable of picking up more details than the usual method of using the image gradient. Further using this as an edge detector in a segmentation model can allow us to pick up a region of interest when low contrast between two objects is present and other models fail.
Quasi real-time energy use estimation using Google's Popular Times data

This chapter illustrates a geostatistic model (Chapter 4) application to estimate quasi real-time energy consumption in each building using Google's Popular Times data. After the introduction in Section 11.1 where our research motivations are explained, Section 11.2 describes the empirical methods, and we adopt compositional kriging approach for this analysis. Section 11.3 shows the estimation results of estimating building-level energy consumption. Effective visualization is attempted with the results. Section 11.4 concludes this chapter with a discussion.
Towards an Architecture of Collaborative Objects

Towards an Architecture of Collaborative Objects, explores the potential of playing with Collaborative Objects in real, augmented and mixed realities.A multi-player game platform App: VoxelCO, developed by the author, provides a speculative playground to research, the interaction with objects, things and people, as well as provokes new opportunities to engage deeply with its content and context.Furthermore, VoxelCO, reveals new modes of participation, to design and collaborate in real-time with augmented reality, using millennial tools: mobile devices.A case study project, the VoxelStage, offered an opportunity to apply VoxelCO to design a stage together with a group of students.To merge the collaboratively aggregated virtual objects of VoxelCO with reality, real WireCubes were augmented and assembled, proposing an architecture of socially augmented fuzzy formations.
Design of Istitaah Classification System Based on Machine Learning Using Imbalanced Dataset

Hajj is an obligatory activity for every Muslim who fulfills the requirements. One of the capable (istitaah) criteria is the health of a prospective pilgrim. Consequently, a health assessment is critical to ensure such a pilgrim can worship without any obstacles and comply with the sharia law. This study aims to design a classification system for istitaah based on machine learning technique by utilizing Electronic Health Records (EHR) of the prospective pilgrims. The EHR data was collected from prospective pilgrims in Purworejo District and used for training and testing purposes. The supervised machine learning method was leveraged for data analysis. This study describes how to handle the preprocessing stage of data imbalance among the classes to improve the accuracy of results. Predictive analysis from imbalanced data might generate bias decisions on majority or minority classes during the learning process if data are not properly handled. This paper studied two approaches to handling imbalanced data distribution including Synthetic Minority Over-sampling Technique (SMOTE) based on data approach and algorithmic approach by comparing AdaBoost, Neural Network, Random Forest, and Decision Tree algorithm. The combination of SMOTE and Neural Network showed the result more accurate in this case.
Observing the oceans in real time—need for affordable technology and capacity development

The observations from the oceans are extremely important for climate science, weather forecasts, maritime industry, tourism, etc. Ships, satellites, Argo floats, etc. have been the backbone of ocean-observation systems for the past many years. Technology for ocean science research covers a wide spectrum, ranging from ship sensors, satellites, autonomous tools, underwater vehicles, and buoys, to sophisticated laboratory instrumentation. Moored buoys are one of the long-term sustaining ocean observatories. The moored buoy technology is also used for early detection and real-time reporting of tsunamis in the open ocean. Building a network that is truly global requires expanding participation beyond scientists from well-resourced countries to a far broader representation of the global community. New approaches are required to provide appropriate training, and resources and technology should follow to enable the application of this training to engage meaningfully in global observing networks and in the use of the data. This paper explores the ocean science, early warning improvements as well as societal and technological elements of realizing such a global network while describing success of sustained ocean observation network of India for more than two decades.
A Reasoning Approach of Human Activity Based on Ontology Model in Home Intelligent Space

To solve the problem of lack of representation and reasoning of knowledge in human activity understanding in home environment, the activity reasoning method based on ontology model in intelligent space is presented. Firstly, a knowledge model of human activity domain based on the ontology technology is established. Then, the data-concept conversion mechanism centered on the human-object interaction relationship is applied to realize the conceptual representation of activity knowledge in intelligent space. On this basis, in order to make the best of activity domain knowledge, we use the semantic network rule language to build a knowledge rule base of human activity domain. The complex human activities in the current state are inferred by matching the real-time ontology in home intelligent space and the knowledge in rule base. Finally, the experimental results show that the method can effectively realize the reasoning of human activity.
Simulation analysis of 3D medical image reconstruction based on ant colony optimization algorithm

Three-dimensional reconstruction technology can display the three-dimensional graphical data of medical images to diagnostic personnel, so as to facilitate multi-dimensional and multi-level observation of patient data, and assist doctors in qualitative and quantitative analysis of pathological tiss ue. A surface reconstruction algorithm for three-dimensional medical images based on segmentation is proposed. It combines image segmentation with MC (marching cubes) algorithm organically, which can be based on the characteristics of different medical images. An appropriate segmentation method is used to segment different tissues accurately, and the result of segmentation is used to extract the isosurface accurately, which avoids the limitation that MC is only suitable for threshold segmentation. After the medical image is segmented by combining threshold and region growth, the segmentation results are input as the reconstructed data, and the improved algorithm of three-dimensional reconstruction is realized. The medical image is rendered on three-dimensional surface, and the debugging results of the software are displayed. At the same time, a cube detection method based on region growing is adopted to improve the efficiency of surface tracking. Experiments show that this algorithm can improve the reconstruction speed and display effect.
Development of Mobile Learning System Based on WeChat Public Platform

Mobile Learning System (MLS) plays an important role in supporting mobile learning and how to develop the MLS effectively has attracted extensive attentions from researchers and engineers. However, existing works often have the problems of high development costs and difficult promotion. Aiming at these problems, we propose to develop the MLS based on WeChat public platform, which is an emerging development platform used for developing mobile business application system. We present the design scheme of the MLS based on WeChat public platform in detail, including system architecture, functions framework and database model. Besides, we also give the corresponding implementations of some core functions of the MLS. By developing the MLS using WeChat public platform, we find that this way can reducing the development cost of the MLS effectively. Moreover, the MLS can also be promoted easily among a large number of WeChat users.
Towards a Knapsack Model for optimizing e-Training services delivery: Application to Hybrid Intelligent Tutoring Systems

Nowadays, there exist many alternative training approaches to that of traditional “one size fits all” one do exist. The main aim of these approaches is to adapt learning content, activities and paths to support trainers especially in professional and non-formal contexts. In order to achieve a given e-Training objective, several pedagogical approaches, e-Training paths, activities and resources could be combined in different more or less efficient ways. Measuring efficiency and providing the most relevant combination or orchestration of e-Training activities is still a challenging problem, especially for decisions makers that have to face several constraints as time and budget. Depending on the ultimate goal of a given company and its context, this problem could be considered as a combinatorial optimization decision making one. The main aim of this paper is to provide an e-Training model optimization model and to solve it. of the model is based on knapsack. A numerical experimentation is curried on through an authentic case study.
FrUtEG

The user experience is the form in which a user feels when using a product or service, for this reason the UX has become a fundamental factor in the success or failure of a product or service [12]. To achieve a good UX, a product must be useful to allow users to perform their tasks, and easy to use by the user (product usability). Both aspects affect the desirability of use that the product produces in the user, as well as the way in which a user experiences a certain brand. Different frameworks, methods and instruments to evaluate the software UX have been proposed. Groupware is a software that provide a group of users with means for communication, collaboration, coordination and regulation their activity in order to achieve a common goal [18]. In this context, we are interested in UX evaluation of groupware from the utility aspect. Because a product can be easy to use and even look attractive, however, if it does not allow the user to achieve their goals, the user will not want to continue using the product. From this perspective, many of the current evaluation proposals focus on assessing how easy groupware is to use, but neglecting to evaluate how useful groupware is. This article introduces a conceptual framework to evaluate utility of groupwares.
Authenticated Key Distribution: When the Coupon Collector is Your Enemy

We introduce new authenticated key exchange protocols which on one hand do not resort to standard public key setups with corresponding assumptions of computationally hard problems, but on the other hand are more efficient than distributing symmetric keys among the participants. To this end, we rely on a trusted central authority distributing key material which size is independent of the total number of users, and which allows the users to obtain shared secret keys. We analyze the security of our construction taking into account various attack models. Importantly, only symmetric primitives are needed in the protocol making it an alternative to quantum-safe key exchange protocols which rely on hardness assumptions.
Toward Donut‐Centered Design: A Design Research Toolkit for the 21st Century

The social and ecological challenges of the 21st century require a design research process that contributes to viable economic solutions. This paper proposes donut centered design as a hybrid of service design and ecological design that works within the donut economic model. It describes how private and public sector ethnographers can weld the best of these two processes by providing a holistic, empirical research foundation that seeks to provide distributed service innovation value to all within the limits of the planet. Donut‐centered design addresses lacunae of the current innovation models by advocating multi‐site assessments, multi‐species ethnography, ecosocial blueprints and holistics metrics as important components of a regenerative design research practice.
Design of Elderly Fall Detection Based on XGBoost

Aging has become a serious problem facing the whole world. Falling is the leading cause of injury and death in the elderly. This paper proposes a fall detection algorithm based on machine learning XGBoost and full-field positioning. Using the data of gyroscope and acceleration sensor, we exploit the “full-field positioning” to increase the dimension of input data and propose a method “maximum satisfaction rate” to mark and train the threshold of data. The experimental results show that this design has obtained high accuracy on falling detection and perfect balance between sensitivity and specificity.
Research Information Management in the Context of Open Science

Purpose/Thesis: The aim of this paper is to discuss the mutual relationship between open science and theory and practice in RIM (Research Information Management), especially apparent in CRISs (Current Research Information Systems), with an emphasis on the context of research assessment requirements, open science policies and CRIS interoperability.Approach/Methods: The study has been based on the critical review of the newest literature (2015–2019) presenting international studies of chosen aspects. Institution-centric and researcher-centric approaches has been presented.Results and conclusions: The emerging new ecosystem of open science changes the way research is done and modifies the monitoring and evaluating of it. CRIS must take into account the new trans­formation processes and research evaluation measures. This allows more transparency and more interaction with the individual researcher, as well as with the institutional, national and international stakeholders. Full interoperability and open standards are desired to improve the discoverability and reusability of research outputs and metadata for different purposes.Originality/Value: The study shows the significance of new tendencies in RIM/CRIS for researchers and research-performing organizations at institutional, national and international level.
Power index-based semantics for ranking arguments in abstract argumentation frameworks

Ranking-based semantics for Abstract Argumentation Frameworks represent a well-established concept used for sorting a group of arguments from the most to the least acceptable. This paper describes a ranking-based semantics that makes use of power indexes such as the Shapley Value and the Banzhaf P ower Index. This power index-based semantics is parametric to a chosen Dung semantics and inherits the properties of the index that is used for evaluating the arguments. We highlight the characteristics of the rankings obtained through different evaluation functions and we verify which of the properties from the literature are satisfied. Finally, we study the relation between the (skeptical/credulous) acceptability of an argument and its position in the ranking, thus designing new properties.
A Model for Successful Adoption of Cloud-Based Services in Indian SMEs

The aim of this research is to develop a model for successful adoption of Cloud based services in Indian SMEs. This research paper investigates the key factors determining the adoption of Cloud-based services by Indian SMEs. Cloud computing has been a priority for consideration, especially with regard to IT-based organisational strategy management techniques. Small and medium-sized enterprises are considered the most critical aspect of economic growth in the developing world because they improve national competition. This research paper explains how developed model would help Indian SMEs to understand the importance of organisational and technological factors for Cloud-centric service adoption. By discerning the importance of Cloud-based services among business organisations, this study will also be contributing to the business community in India. It could also act as a basis for future researchers, decision-makers and others to improve their competitive advantages gained by using Cloud-based services.
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For each non-constant Boolean function q, Klapper introduced the notion of q-transforms of Boolean functions. The q-transform of a Boolean function f is related to the Hamming distances from f to the functions obtainable from q by nonsingular linear change of basis. In this work, we discuss the existence of q-nearly bent functions, a new family of Boolean functions characterized by the q-transform. We prove that any balanced Boolean functions (linear or non-linear) are q-nearly bent if q has weight one, which gives a positive answer to an open question (whether there exist non-affine q-nearly bent functions) proposed by Klapper. We also prove a necessary condition for checking when a function is not q-nearly bent.
Sequential agglomerative procedure for sorting a production batch of electronic radio devices into homogeneous groups

Abstract Authors proposed a clustering algorithm for sorting a batch of electronic radio devices by homogeneous groups according to the results of non-destructive tests. Algorithm is based on searching for the best silhouette criterion value of clusters using the sequential agglomerative procedure (SAP). It merges groups with the smallest distance between their centers one by one. New procedure was tested on dataset of microcircuits for space applications in a specialized test center.