Reviewerly/exHarmony huggingface dataset:
Multiplicity of voices in community development through radio in Fanteakwa District, Ghana

The study examines community radio as a channel for development through people's participation in development activities in the Fanteakwa District of Ghana.The study argues that participation of people is necessary for development.The study employed the concept of participatory development communication and the empowerment theory to help understand the role of community radio in development.Mixed method research design was used for the study.Data was collected through survey questionnaires and semi-structured interviews.Purposive sampling and multi-stage sampling techniques were used to select respondents.It was found that participation is highest amongst people whose preferred channel of contribution to development initiatives is Radio Listenership Clubs.Though community radio station is serving the community, more needs to be done to properly integrate it into the lives of the people.Another finding was that community radio is not necessarily owned by the community, but can be the result of an individual's entrepreneurial effort.It is concluded that the integration of participatory approaches in radio programming and ownership is relevant because they are more people-centred and can be sustainable.A legislation on community broadcasting is recommended in order to define the role of individual entrepreneurs and NGO's in establishing community radio stations.
Counting the frequency of time-constrained serial episodes in a streaming sequence

As a representative sequential pattern mining problem, counting the frequency of serial episodes from a streaming sequence has drawn continuous attention in academia due to its wide application in practice, e.g., telecommunication alarms, stock market, transaction logs, bioinformatics, etc. Although a number of serial episodes mining algorithms have been developed recently, most of them are neither stream-oriented, as they require processing the whole dataset multiple times, nor time-aware, as they fail to take into account the time constraint of serial episodes. In this paper, we propose two novel one-pass algorithms, ONCE and ONCE+, each of which can respectively compute two popular frequencies of given episodes satisfying predefined time-constraint as signals in a stream arrives one-after-another. ONCE is only used for non-overlapped frequency where the occurrences of a serial episode in sequence are not intersected. ONCE+ is designed for the distinct frequency where the occurrences of a serial episode do not share any event. Theoretical study proves that our algorithm can correctly mine the frequency of target time constraint serial episodes in a given stream. Experimental study over both real-world and synthetic datasets demonstrates that the proposed algorithm can work, with little time and space, in signal-intensive streams where millions of signals arrive within a single second. Moreover, the algorithm has been applied in a real stream processing system, where the efficacy and efficiency of this work are tested in practical applications.
Efficient Bayesian credible-region certification for quantum-state tomography

Standard Bayesian credible-region theory for constructing an error region on the unique estimator of an unknown state in general quantum-state tomography to calculate its size and credibility relies on heavy Monte Carlo sampling of the state space followed by filtering to obtain the correct region sample. This conventional methodology typically gives negligible yield for very small error regions originating from large data sets. In this article, we discuss at length the in-region sampling theory for computing both size and credibility from region-average quantities that avoids this general problem altogether. Among the many possible numerical choices, we study the performance and properties of accelerated hit-and-run Monte Carlo algorithm for in-region sampling and provide its complexity estimates for quantum states. Finally with our in-region concept, by alternatively quantifying the region capacity with the region-average distance between two states in the region (measured for instance with either the Hilbert-Schmidt, trace-class, or Bures distance), we derive approximation formulas to analytically estimate both region capacity and credibility without Monte Carlo computation.
The Provable Virtue of Laziness in Motion Planning

The Lazy Shortest Path (LazySP) class consists of motion-planning algorithms that only evaluate edges along candidate shortest paths between the source and target. These algorithms were designed to minimize the number of edge evaluations in settings where edge evaluation dominates the running time of the algorithm such as manipulation in cluttered environments and planning for robots in surgical settings; but how close to optimal are LazySP algorithms in terms of this objective? Our main result is an analytical upper bound, in a probabilistic model, on the number of edge evaluations required by LazySP algorithms; a matching lower bound shows that these algorithms are asymptotically optimal in the worst case.
Zero-shot Metric Learning

In this work, we tackle the zero-shot metric learning problem and propose a novel method abbreviated as ZSML, with the purpose to learn a distance metric that measures the similarity of unseen categories (even unseen datasets). ZSML achieves strong transferability by capturing multi-nonlinear yet continuous relation among data. It is motivated by two facts: 1) relations can be essentially described from various perspectives; and 2) traditional binary supervision is insufficient to represent continuous visual similarity. Specifically, we first reformulate a collection of specific-shaped convolutional kernels to combine data pairs and generate multiple relation vectors. Furthermore, we design a new cross-update regression loss to discover continuous similarity. Extensive experiments including intra-dataset transfer and inter-dataset transfer on four benchmark datasets demonstrate that ZSML can achieve state-of-the-art performance.
An Agile-DevOps Reference Architecture for Teaching Enterprise Agile

DevOps emerged as an important extension to support the Agile development for frequent and continuous software delivery.The adoption of Agile-DevOps for large scale enterprise agility depends on the most important human capability such as people competency and experience.Hence, academic education and professional training is key to the successful adoption of Agile-DevOps approach.Thus, education and training providers need to teach Agile-DevOps.However, the challenge is: how to establish and simulate an effective Agile-DevOps technology environment for teaching Enterprise Agile?This paper introduces the integrated Adaptive Enterprise Project Management (AEPM) and DevOps Reference Architecture (DRA) approach for adopting and teaching the Agile-DevOps with the help of a teaching case study from the University of Technology -Sydney (UTS), Australia.These learnings can be utilised by educators to develop and teach practice-oriented Agile-DevOps for software engineering courses.Furthermore, the experience and observations can be employed by researchers and practitioners aiming to integrate Agile-DevOps at the large enterprise scale.
Salient video object detection using a virtual border and guided filter

In this paper, we present a novel method for salient object detection in videos. Salient object detection methods based on background prior may miss salient region when the salient object touches the frame borders. To solve this problem, we propose to detect the whole salient object via the adjunction of virtual borders. A guided filter is then applied on the temporal output to integrate the spatial edge information for a better detection of the salient object edges. At last, a global spatio-temporal saliency map is obtained by combining the spatial saliency map and the temporal saliency map together according to the entropy. The proposed method is assessed on three popular datasets (Fukuchi, FBMS and VOS) and compared to several state-of-the-art methods. The experimental results show that the proposed approach outperforms the tested methods.
Diversity-Inducing Policy Gradient: Using Maximum Mean Discrepancy to Find a Set of Diverse Policies

Standard reinforcement learning methods aim to master one way of solving a task whereas there may exist multiple near-optimal policies. Being able to identify this collection of near-optimal policies can allow a domain expert to efficiently explore the space of reasonable solutions. Unfortunately, existing approaches that quantify uncertainty over policies are not ultimately relevant to finding policies with qualitatively distinct behaviors. In this work, we formalize the difference between policies as a difference between the distribution of trajectories induced by each policy, which encourages diversity with respect to both state visitation and action choices. We derive a gradient-based optimization technique that can be combined with existing policy gradient methods to now identify diverse collections of well-performing policies. We demonstrate our approach on benchmarks and a healthcare task.
Design of Interactions for Handheld Augmented Reality Devices Using Wearable Smart Textiles: Findings from a User Elicitation Study

Advanced developments in handheld devices’ interactive 3D graphics capabilities, processing power, and cloud computing have provided great potential for handheld augmented reality (HAR) applications, which allow users to access digital information anytime, anywhere. Nevertheless, existing interaction methods are still confined to the touch display, device camera, and built-in sensors of these handheld devices, which suffer from obtrusive interactions with AR content. Wearable fabric-based interfaces promote subtle, natural, and eyes-free interactions which are needed when performing interactions in dynamic environments. Prior studies explored the possibilities of using fabric-based wearable interfaces for head-mounted AR display (HMD) devices. The interface metaphors of HMD AR devices are inadequate for handheld AR devices as a typical HAR application require users to use only one hand to perform interactions. In this paper, we aim to investigate the use of a fabric-based wearable device as an alternative interface option for performing interactions with HAR applications. We elicited user-preferred gestures which are socially acceptable and comfortable to use for HAR devices. We also derived an interaction vocabulary of the wrist and thumb-to-index touch gestures, and present broader design guidelines for fabric-based wearable interfaces for handheld augmented reality applications.
Design and optimization of VLC enabled data center network

Visible-Light Communication (VLC) has the potential to provide dense and fast connectivity at low cost. In this paper we propose SFNet, a novel VLC-enabled hybrid data center network that extends the design of wireless Data Center Networks (DCNs) into three further dimensions: (1) fully wireless at the inter-rack level; (2) no need for a centralized control mechanism on wireless links; and (3) no need for any infrastructure-level alterations to data centers. Previous proposals typically cannot realize these three rationales simultaneously. To achieve this vision, the proposed SFNet augments fat-tree by organizing all racks into a wireless small-world network via VLC links. The use of VLC links eliminates hierarchical switches and cables in the wireless network, and thus reduces hardware investment and maintenance costs. To fully exploit the benefits of the topology of SFNet, we further propose its topology design and optimization method, routing scheme, and online flow scheduling algorithm. Comprehensive experiments indicate that SFNet exhibits good topological properties and network performance.
New Approach based on Machine Learning for Short-Term Mortality Prediction in Neonatal Intensive Care Unit

Mortality remains one of the most important outcomes to predict in Intensive Care Units (ICUs).In fact, the sooner mortality is predicted, the better critical decisions are made by doctors based on patient's illness severity.In this paper, a new approach based on Machine Learning (ML) techniques for short-term mortality prediction in Neonatal Intensive Care Unit (NICU) is proposed.This approach relies on many steps.At first, relevant features are selected from available data upon neonates' admission and from the time-series variables collected within the two first hours of stay in the NICU from the Medical Information Mart for Intensive Care III (MIMIC-III).After that, to predict mortality, many classifiers were tested which are Linear Discriminant Analysis (LDA), K-Nearest Neighbors (KNN), Classification and Regression Trees (CART), Logistic Regression (LR), Support Vector Machine (SVM), Naïve Bayes (NB) and Random Forest (RF).The experimental results showed that LDA was the best performing classifier with an accuracy equal to 0.947 and AUROC equal to 0.97 with 31 features.The third step of this approach is mortality time prediction using the Galaxy-Random Forest method achieving an f-score equal to 0.871.The proposed approach compared favorably in terms of time, accuracy and AUROC with existing scoring systems and ML techniques.It is the first work predicting neonates mortality based on ML techniques and time-series data after only two hours of admission to the NICU.
Multi-Margin based Decorrelation Learning for Heterogeneous Face Recognition

Heterogeneous face recognition (HFR) refers to matching face images acquired from different domains with wide applications in security scenarios. However, HFR is still a challenging problem due to the significant cross-domain discrepancy and the lacking of sufficient training data in different domains. This paper presents a deep neural network approach namely Multi-Margin based Decorrelation Learning (MMDL) to extract decorrelation representations in a hyperspherical space for cross-domain face images. The proposed framework can be divided into two components: heterogeneous representation network and decorrelation representation learning. First, we employ a large scale of accessible visual face images to train heterogeneous representation network. The decorrelation layer projects the output of the first component into decorrelation latent subspace and obtain decorrelation representation. In addition, we design a multi-margin loss (MML), which consists of tetradmargin loss (TML) and heterogeneous angular margin loss (HAML), to constrain the proposed framework. Experimental results on two challenging heterogeneous face databases show that our approach achieves superior performance on both verification and recognition tasks, comparing with state-of-the-art methods.
A Watermark Algorithm Based on Space-domain and Transform-domain

An algorithm for watermark based on space-domain and transform-domain is described in the paper. Making use of the different characteristic of spatial domain and transform domain, the conflict between invisibility and robust in the algorithm for watermark can be prevented, and the density of embedded watermark can be increased. In the first stage of the proposed method, the unessential parts of the watermark are embedded to the host image by LSB algorithm in spatial domain. After that, the image is converted to frequency domain with wavelet packet transform and the significant parts of the watermark are embedded to the significant coefficients. With this scheme, more watermark data can be embedded to the host image and the watermark data won't be easily distorted. From the simulation results, we can see that the approach has good performance and robust to the crop and noise attack.
Circular Proofs as Session-Typed Processes: A Local Validity Condition

Proof theory provides a foundation for studying and reasoning about programming languages, most directly based on the well-known Curry-Howard isomorphism between intuitionistic logic and the typed lambda-calculus. More recently, a correspondence between intuitionistic linear logic and the session-typed pi-calculus has been discovered. In this paper, we establish an extension of the latter correspondence for a fragment of substructural logic with least and greatest fixed points. We describe the computational interpretation of the resulting infinitary proof system as session-typed processes, and provide an effectively decidable local criterion to recognize mutually recursive processes corresponding to valid circular proofs as introduced by Fortier and Santocanale. We show that our algorithm imposes a stricter requirement than Fortier and Santocanale's guard condition, but is local and compositional and therefore more suitable as the basis for a programming language.
Affine Equivariant Autoencoder

Existing deep neural networks mainly focus on learning transformation invariant features. However, it is the equivariant features that are more adequate for general purpose tasks. Unfortunately, few work has been devoted to learning equivariant features. To fill this gap, in this paper, we propose an affine equivariant autoencoder to learn features that are equivariant to the affine transformation in an unsupervised manner. The objective consists of the self-reconstruction of the original example and affine transformed example, and the approximation of the affine transformation function, where the reconstruction makes the encoder a valid feature extractor and the approximation encourages the equivariance. Extensive experiments are conducted to validate the equivariance and discriminative ability of the features learned by our affine equivariant autoencoder.
360-Degree Photo-realistic VR Conferencing

VR experiences are becoming more social, but many social VR systems represent users as artificial avatars. For use cases such as VR conferencing, photo-realistic representations may be preferred. In this paper, we present ongoing research into social VR experiences with photo-realistic representations of participants and present a web-based social VR framework that extends current video conferencing capabilities with new VR functionalities. We explain the underlying design concepts of our framework and discuss user studies to evaluate the framework in three different scenarios. We show that people are able to use VR communication in real meeting situations and outline our future research to better understand the actual benefits and limitations of our approach, to fully understand the technological gaps that need to be bridged and to better understand the user experience.
Secure and Efficient Hybrid Random Number Generator Based on Sponge Constructions for Cryptographic Applications

Random numbers constitute the most important part of many applications and have a vital importance in the security of these applications, especially in cryptography. Therefore, there is a need for secure random numbers to provide their security. This study is concerned with the development of a secure and efficient random number generator that is primarily intended for cryptographic applications. The generator consists of two subsystems. The first is algorithmic structure, Keccak, which is the latest standard for hash functions. The structure provides to generate secure random numbers. The second is additional input that generates with ring oscillators that are implemented on the FPGA. The additional inputs prevent reproduction and prediction of the subsequent random numbers. It is shown that the proposed generator is satisfies security requirements for cryptographic applications. In addition, NIST 800-22 test suite and autocorrelation test are used to demonstrate that generated random numbers have no statistical weaknesses and relationship among itself, respectively. Successful results from these tests show that generated numbers have no statistical weaknesses. Moreover, important advantage of the proposed generator is that it is more efficient than existing RNGs in the literature.
The Tale of Greenlandite: Commemorating the Two-Hundredth Anniversary of Eudialyte (1819–2019)

Twenty years before the discovery of the mineral eudialyte, a red garnet-like mineral from Greenland was distributed among mineralogists and chemists in Europe. Furthermore, the first chemical analyses of the Greenlandic mineral reported 10 percent by weight of zirconium oxide. It was given the name greenlandite, and after the discovery of eudialyte many have wondered whether greenlandite could actually be eudialyte. Two-hundred years after the original definition of eudialyte I have tried to find out.
Usability of Central Library Websites of Iranian Universities of Medical Sciences an Evaluation

&#x0D; &#x0D; &#x0D; With regard to the potential of huge knowledge dissemination by academic library websites, their usability is considered as a matter of great interest to university administrators as well as users. In the present study, a descriptive survey was designed aimed at evaluating usability of central library websites of type-1 universities of medical sciences in Iran in order to identify their usability issues and to provide inputs for possible remediation efforts to improve the design of similar websites in the future. The research procedure involved a library study to identify the tasks typically needed on library websites, use of the identified tasks to design a checklist for measuring three dimensions of usability (effectiveness, efficiency, and satisfaction), and finally a usability evaluation by two specialists of medical informatics and two librarianship experts. The given websites were rated “good” (60 % - 80 %) in terms of usability and no statistically significant difference was found between evaluations by different experts. It was concluded that more attention to website usability could improve users’ interest in services and facilitate realisation of goals in parent organisations. The results of this study could provide a basic framework for website design and improvement through giving proper attention to usability dimensions.&#x0D; &#x0D; &#x0D;
Improving Human Pose Estimation with Self-Attention Generative Adversarial Networks

Human pose estimation in images is challenging and important for many computer vision applications. Large improvements have been achieved with the development of convolutional neural networks. However, when encountered some difficult cases, even the state-of-the-art models may still fail to predict all the body joints correctly. Some recent works try to refine the pose estimator. GAN (Generative Adversarial Networks) has been proved to be efficient to learn local body joints structural constrains. In this paper, we propose to apply Self-Attention GAN to further improve the performance of human pose estimation. With attention mechanism in the discriminator, we can learn long-range body joints dependencies, therefore enforce the entire body joints structural constrains to make all the body joints to be consistent. Experiments on two standard benchmarks demonstrate the effectiveness of our method.
Approximate Manifold Regularization: Scalable Algorithm and Generalization Analysis

Graph-based semi-supervised learning is one of the most popular and successful semi-supervised learning approaches. Unfortunately, it suffers from high time and space complexity, at least quadratic with the number of training samples. In this paper, we propose an efficient graph-based semi-supervised algorithm with a sound theoretical guarantee. The proposed method combines Nystrom subsampling and preconditioned conjugate gradient descent, substantially improving computational efficiency and reducing memory requirements. Extensive empirical results reveal that our method achieves the state-of-the-art performance in a short time even with limited computing resources.