Reviewerly/exHarmony huggingface dataset:
A DISCOURSE HISTORICAL ANALYSIS ON LOCAL CULTURE IN THE FIRST BOOK TRILOGY “RONGGENG DUKUH PARUK” OF AHMAD TOHARI

Local culture is commonly used to characterize the experience of everyday life in specific, identifiable localities. It reflects ordinary people's feelings of appropriateness, comfort, and correctness—attributes that define personal preferences and changing tastes. The socio-cultural of Javanese especially in Jatilawang Banyumas. Through the trilogy of “Ronggeng Dukuh Paruk”. Through its discursive strategies, the text presents that the cultural life of Javanese people at that time is mostly influenced by Hinduism. In economic side, Javanese people generally work in farming field. Meanwhile, the cultural condition indicates the pluralistic life of Javanese community. The present research’s effort is to examine the representation of Javanese local culture in an old text entitled “Ronggeng Dukuh Paruk”. Framed generally by CDA (Critical Discourse Analysis) theory and method, and particularly DHA (Discourse Historical Approach), the examination is focused on observing the five discursive strategies in micro- analysis under DHA, which includes strategies of nomination, predication, argumentation, perspectivation, and mitigation. The discussion also highlights the socio-cultural background of Javanese people in that period to see its influence on the content of the text itself. Thus, the integration of two disciplinaries of historical study and linguistics is well-achieved.Keyword: historical discourse analysis, local culture, first book triogy “Ronggeng Dukuh Paruk”.
Spontaneous emission from nonhermitian perspective: complex scaling of the photon coordinates

Spontaneous emission (SE) is usually studied within the framework of quantum dissipation theory. In this paper we pursue a different view of SE. Namely, we take advantage of Nonhermitian Quantum Mechanics (NHQM) and interpret SE as the decay of a Feshbach type resonance. Correspondingly, we aim at calculating the SE decay rate Γ (resonance width) and the associated resonance wavefunction via the complex scaling (CS) method. Standard application of CS in NHQM is based on scaling the dissociative coordinates of the system, x↦xeiθ. In the case of SE, the decay consists in emitting photons. Therefore the CS must be applied on (suitably defined) position coordinates of the photons. Feasibility of such a programme is demonstrated explicitly through choosing the adequate photon coordinates and performing CS of the photon wavefunctions in the position representation. It is anticipated in this way that CS makes the full resonance wavefunction square integrable, and enables thus the calculation of Γ by proceeding in complete analogy with the well established NHQM approaches. As an illustration, the standard Golden Rule formula for Γ is rederived from the CS nonhermitian perturbation theory.
An Expert System Methodology for Planning IT Projects with Hesitant Fuzzy Effort: An Application

Delivering the projects on time and in accordance with the customer requirements is a crucial process for almost all software companies due to the budget and schedule constraints. Effective time planning provides optimum usage of all resources (i.e., people, time, budget, etc.). This study presents a new integrated decision support methodology for planning software projects. For this purpose, we identify the most important factors by expert judgments and literature review, find priorities of factors by Hesitant Fuzzy Linguistic Term Pairwise Comparison, and estimate time effort (duration) for the projects, respectively. Subsequently, we develop a hybrid metaheuristic by using the priorities of factors and estimated time efforts of the projects. As an experimental study, we apply this methodology to determine time planning of software projects in a Turkish company. We analyze that the proposed methodology gives very efficient plans with less delayed projects and higher award in comparison with the initial solutions.
Requirements elicitation for secure and interoperable cross‐border health data exchange: the KONFIDO study

In this study, the requirements elicitation approach employed in the context of the KONFIDO project is presented. KONFIDO introduces a technical paradigm for secure and interoperable cross-border health data exchange by leveraging novel approaches and cutting-edge technologies, such as homomorphic encryption and blockchains. Being a key part of the overall user requirements engineering methodology, requirements elicitation focused on producing high-level, end-user goals following a systematic procedure. First, the main business processes were identified based on the project's pilot scenarios. These business processes were the subject of a threat analysis, which identified the respective assets and a list of security risks/threats. Threats were further elaborated, considering the outcome of relevant projects and applicable best practices/standards. As a result, a set of user goals were identified and analysed in detail. Finally, a meta-analysis of the produced goals against the employed information sources was applied, highlighting the importance of standards as a guide for defining requirements, as well as the complexity concerning the interdependencies among the elaborated business processes, assets, threats, and user goals. As the deployment of the technical solution may be cloud-based, implications and challenges imposed by the adoption of cloud computing in this setting are also presented.
Towards an Architectural Approach to Supporting Collaborative Seamless Learning Experiences

In recent years, educational practitioners and learning technologists have been designing, developing and deploying a wide range of innovative environments to enhance meaningful, seamless and exciting learning experiences. In these learning environments, students may interact as individuals or in groups across several learning spaces, while utilizing various technologies that support different contexts of use. However, despite these efforts and progress in this field, there are still a number of technical challenges associated with software architecture issues related to how to implement tools that support seamless learning. In this chapter, we describe our ongoing research related to the design, development and deployment of different software solutions to support seamless learning activities across a variety of settings. Here, we examine and propose a candidate software architecture that is inspired by our implementation of seamless learning activities. In particular, we direct our efforts towards enhancing and facilitating these activities by supporting the reuse and remixing of digital content generated by learners and teachers. The features of the proposed architecture also support the reusability and interoperability of software components and micro-services for different interactions. We address architectural challenges identified in activities we have carried out during the last five years. Three different use cases of seamless learning activities in different subject areas are analysed and the supporting technical and software solutions examined, in order to identify and extract the salient features and functional requirements of the proposed architecture. In addition, we use these cases to examine, analyse and propose new and refined ways to cope with challenges related to the activities that need to be implemented for seamless learning flow. We present and discuss our results describing an architecture capable of supporting meaningful, appealing and seamless learning experiences that are independent of subject matter, context of use and supporting technological solutions.
Deploying Blockchains for a New Paradigm of Media Experience

In this paper, we demonstrate the multiple points of innovation when combining multimedia content with blockchain technology. As of today, content creators (authors, photographers, radio and video reporters, data visualizers etc.) are publishing and sharing content (articles, photos, audio, video and combinations) on media/social networks but without the effective control over who is going to reuse this content. To this direction, we introduce a blockchain based service which successfully blends different technologies to provide more transparency on how the content is further tracked, promote openness, trust and security between participants, allow direct monetization for the content creator and expose the benefits of using blockchain technology as: a database of multimedia content, a novel payment method while using a dedicated created cryptocurrency, an insurance of proof of ownership through the exploitation of smart contracts running on Ethereum blockchain platform and a means to implement new solutions to value content based on quality. Additionally, by integrating Hyperledger Projects (Fabric, Composer, Explorer), we examine how their functionalities such as private and permissioned blockchain improve our system. Highlighting the importance of applications exploiting blockchain technology to efficiently support, store and retrieve data we utilized, integrated with blockchain and compared different solutions among which InterPlanetary File System (IPFS) and traditional databases such as MongoDB with GridFS tool.
Theorizing lip reading as interface design

This article explores what lip reading can teach us about interface design. First, I define lip reading. Second, I challenge the idea that people can "read" lips---an idea that is deeply imbedded in the literate tradition described by Walter Ong (1982) in Orality and Literacy. Third, I frame lip reading as a complex rhetorical activity of filling in the "gaps" of communication. Fourth, I present a lip reading heuristic that can challenge those of us in communication related fields to remember how the invisible "gaps" of communication are sometimes more important than the visible "interfaces." And finally, I conclude with some reflections about how lip reading might "reimagine" disability studies for technical and professional communicators.
Regularized Landmark Detection with CAEs for Human Pose Estimation in the Operating Room

Robust estimation of the human pose is a critical requirement for the development of context aware assistance and monitoring systems in clinical settings. Environments like operating rooms or intensive care units pose different visual challenges for the problem of human pose estimation such as frequent occlusions, clutter and difficult lighting conditions. Moreover, privacy concerns play a major role in health care applications and make it necessary to use unidentifiable data, e.g. blurred RGB images or depth frames. Since, for this reason, the data basis is much smaller than for human pose estimation in common scenarios, pose priors could be beneficial for regularization to train robust estimation models. In this work, we investigate to what extent existing pose estimation methods are suitable for the challenges of clinical environments and propose a CAE based regularization method to correct estimated poses that are anatomically implausible. We show that our models trained solely on depth images reach similar results on the MVOR dataset [1] as RGB based pose estimators while intrinsically being non-identifiable. In further experiments we prove that our CAE regularization can cope with several pose perturbations, e.g. missing parts or left-right flips of joints.
Processing Quechua and Guarani Historical Texts Query Expansion at Character and Word Level for Information Retrieval

The LANGAS project provides an online database containing historical (16th–19th) texts in Quechua, Guarani and Tupi, for sociolinguistic studies. Querying texts for such low-resourced languages raises several questions, issues and challenges. Among them, our work addresses word variation (diacritization, typographic variations) as an optional query expansion mechanism of the search engine. For such processing, taking into account the peculiarities of considered languages is unavoidable. This paper describes the morphology of considered languages, collected linguistic resources, implemented modules (regular expressions, stemming, word clusters) and some preliminary evaluations. Our work will be an opportunity to release resources for those languages. We plan to deepen this work in the near future and hopefully expect it to be useful for other researchers interested in the matter.
Patterns in Style Evolution of Poets

This article investigates the problem of style stability and variation over time and presents the results of analysis of style evolution of American romantic poet H.W. Longfellow. The feature set includes 29 morphological and syntactic characteristics. The method of multivariate discriminant analysis is used. The study showed significant style changes over time and made it possible to single out the major tendencies of style evolution: the poet in his mature verses returns to the characteristics of his early lyrics. Besides, comparison of the variable and stable characteristics demonstrated that the initial part of the line is connected with style evolution, whereas the end of the line (which is the rhymed position) is less variable over time. The results may be useful for authorship attribution and manuscript dating research.
A Framework for Multiple Moving Objects Detection in Aerial Videos

Aerial videos captured using dynamic cameras commonly require background remodeling at every frame. In addition, camera motion and the movement of multiple objects present an unstable imaging environment with varying motion patterns. This makes detecting multiple moving objects a difficult task. In this chapter, a two-step framework, termed the motion differences of matched region-based features (MDMRBF), is presented. Firstly, each frame goes through super-pixel segmentation to produce regions where each frame is then represented as a region adjacency graph structure of visual appearance and geometric properties. This representation is important for correspondence discovery between consecutive frames based on multigraph matching. Ultimately, each region is labeled as either a background or foreground (object) using a proposed graph-coloring algorithm. Two datasets, namely (1) the DARPA-VIVID dataset and (2) self-captured videos using an unmanned aerial vehicle-mounted camera, have been used to validate the feasibility of MDMRBF. Comparison is also done with three existing detection algorithms where experiments show promising results with precision at 94%, and recall at 89%.
A Hybrid BitFunnel and Partitioned Elias-Fano Inverted Index

Search engines encounter a time vs. space trade-off: search responsiveness (i.e., a short query response time) comes at the cost of increased index storage. We propose a hybrid method which uses both (a) the recently published mapping-matrix-style index BitFunnel (BF) for search efficiency, and (b) the state-of-the-art Partitioned Elias-Fano (PEF) inverted-index compression method. We use this proposed hybrid method to minimize time while satisfying a fixed space constraint, and to minimize space while satisfying a fixed time constraint. Each document is stored using either BF or PEF, and we use a local search strategy to find an approximately optimal BF-PEF partition. Since performing full experiments on each candidate BF-PEF partition is impractically slow, we use a regression model to predict the time and space costs resulting from candidate partitions (space accuracy 97.6%; time accuracy 95.2%). Compared with a hybrid mathematical index (Ottaviano et al., 2015), the time cost is reduced by up to 47% without significantly exceeding its size. Compared with three mathematical encoding methods, the hybrid BF-PEF index allows performing list intersection between around 16% to 76% faster (without significantly increasing the index size). Compared with BF, the index size is reduced by 45% while maintaining an intersection time comparable to that of BF.
A Study of the Build-Versus-Buy Decision in Developing Embedded Systems

We studied parameters in making the build-versus-buy decision when developing embedded systems. Team expertise and available resources are most important, while partitioning tasks and reducing interdependence are next in importance. The quality and support of commercial units are less important while premiums and product lifecycles have the least affect on the cost of development.
Comprehensive evaluation of static and dynamic obstacle detection method for safe navigation of the visually impaired people

Among the most challenging difficulties that hinder the visually impaired is a safe navigation from one place to another. We will provide a comprehensive framework to evaluate our smartphone-based obstacle detection method for safe navigation for the visually impaired people. This method aims to provide a safe navigation by detecting static and dynamic obstacles in unknown environments while offering maximum flexibility to the user and using the most cost-effective equipment possible. Our obstacle detection method relies on a multiregion analysis technique to examine the results of analyzing different regions of video frames. The user is notified about the existence of an obstacle by receiving alert messages. We (1) introduce a rich dataset, which is available for research purposes and can be used for the future evaluation and development of computer vision-based applications pertinent to static and dynamic obstacle detection in the unknown environment via smartphone. (The dataset is made available by King Abdulaziz University for research purposes only. Please, contact the authors for further information.) In addition, (2) we demonstrate the efficiency of the proposed method through a comprehensive framework of testing and evaluation in various complex and unknown scenes. The usability of the method has been tested by allowing a set of visually impaired people to use our system in their navigation. The experimental results show that our obstacle detection method is easy to use, operates efficiently, can be deployed on an average range smartphone device, and has a high accuracy rate ranging from 85% to 96%.
Mixture Modeling of Global Shape Priors and Autoencoding Local Intensity Priors for Left Atrium Segmentation

Difficult image segmentation problems, e.g., left atrium in MRI, can be addressed by incorporating shape priors to find solutions that are consistent with known objects. Nonetheless, a single multivariate Gaussian is not an adequate model in cases with significant nonlinear shape variation or where the prior distribution is multimodal. Nonparametric density estimation is more general, but has a ravenous appetite for training samples and poses serious challenges in optimization, especially in high dimensional spaces. Here, we propose a maximum-a-posteriori formulation that relies on a generative image model by incorporating both local intensity and global shape priors. We use deep autoencoders to capture the complex intensity distribution while avoiding the careful selection of hand-crafted features. We formulate the shape prior as a mixture of Gaussians and learn the corresponding parameters in a high-dimensional shape space rather than pre-projecting onto a low-dimensional subspace. In segmentation, we treat the identity of the mixture component as a latent variable and marginalize it within a generalized expectation-maximization framework. We present a conditional maximization-based scheme that alternates between a closed-form solution for component-specific shape parameters that provides a global update-based optimization strategy, and an intensity-based energy minimization that translates the global notion of a nonlinear shape prior into a set of local penalties. We demonstrate our approach on the left atrial segmentation from gadolinium-enhanced MRI, which is useful in quantifying the atrial geometry in patients with atrial fibrillation.
Demosaicking algorithm for white‐RGB CFA images

WRGB colour filter array (CFA) has attracted much attention because it is structurally advantageous to improve image quality in low-light environment using high sensitivity of W channel. However, the demosaicking techniques for WRGB CFA image sensors developed so far have suffered from blurring at edges and deterioration in image quality due to the lack of correlation between W and RGB channels. In order to overcome the above problems, this paper proposes a correlation error compensation in W channel and a G channel restoration to mitigate blurring via edge adaptive filtering. In addition, the authors propose a brightness enhancement method utilising W channel, while avoiding noise boosting. Experimental results show that the proposed demosaicking algorithm not only shows better subjective visual quality than the existing technique but also has about 15% higher signal-to-noise ratio (SNR) than conventional Bayer CFA image.
Smart Video Surveillance Systems and Identification of Human Behavior Analysis

Smart surveillance cameras are placed in many places such as bank, hospital, toll gates, airports, etc. To take advantage of the video in real time, a human must monitor the system continuously in order to alert security officers if there is an emergency. Besides, for event detection a person can observe four cameras with good accuracy at a time. Therefore, this requires expensive human resources for real-time video surveillance using current technology. The framework of ATM video surveillance system encompassing various factors, such as image acquisition, background estimation, background subtraction, store, and further process like segmentation, people counting, and tracking are done in cloud environment briefly discussed in this chapter.
Interpreting Information in Smart Environments with Social Patterns

Smart Environments (SEs) work in close interaction with their users. To perform properly, these systems need to process their information (both from sensing and to act) considering its meaning for people. For instance, when they manage workflows that represent users’ activities or consider the tradeoffs between alternative actions. Introducing social knowledge about the human context helps SEs to better interpret information, and thus people’ needs and actions. However, working with this knowledge faces several difficulties. Its level of abstraction is different from that directly related to system components. Moreover, it belongs to a background that is not frequent among engineers. In order to address these issues, this paper proposes the use of Social Context-Aware Assistants (SCAAs). These Multi-Agent Systems (MASs) manage explicitly social information using specifications conform to a domain-specific Modelling Language (ML). The ML aims at describing human aspects and their changes in a given context related to a SE. Social properties describe reusable knowledge using a template with these specifications and textual explanations. Working with the ML facilitates the semi-automated transformation of specifications to integrate social and other system information, derive new one, and check properties. Specific agents are responsible for managing information, and translating data from sensors to the ML, and from this to data for actuators. A case study on an alert system to monitor group activities, extended with social knowledge to interpret people’ behaviour, illustrates the approach.