Reviewerly/exHarmony huggingface dataset:
Distributed video coding using interval overlapped arithmetic coding

Distributed video coding (DVC) is a flexible and novel video coding mode that is different from traditional video compression methods, which can shift the complex task such as motion estimation from encoder to decoder. Nowadays, most of the DVC architectures adopt channel coding such as low-density parity-check codes. It is found that arithmetic coding can be also extended to compress correlated sources and even has better performance than the channel coding for short to medium block length sources. Therefore, we propose a DVC scheme using interval overlapped arithmetic coding that is distributed arithmetic coding (DAC) without feedback channel, where the Key frames are compressed using traditional video coder while the Wyner–Ziv frames are compressed using DAC. We compare the rate–distortion performance of the proposed scheme with the state-of-the-arts, and the experimental results indicate that the proposed scheme is competitive. The source code for this scheme is available in GitHub.
"Pretty Close to a Must-Have"

We report on a qualitative inquiry among security-expert and non-expert mobile device users about the adoption of biometric authentication using semi-structured interviews(n=38, 19/19 expert/non-expert). Security experts more readily adopted biometrics than non-experts but also harbored greater distrust towards its use for sensitive transactions,feared biometric signature compromise, and in some cases distrusted newer facial recognition methods. Both groups harbored misconceptions, such as misunderstanding of the functional role of biometrics in authentication, and were about equally likely to have stopped using biometrics due to usability. Implications include the need for tailored training for security-informed advocates, better design for device sharing and co-registration, and consideration for usability needs in work environments. Refinement of these features would remove perceived obstacles to ubiquitous computing among the growing population of mobile technology users sensitized to security risk.
A Scenario-Based Methodology for Exploring Risks

In this paper we report on research exploring the privacy, security and safety implications of children being able to program Internet of Things devices. We present our methodology for understanding the contexts in which children may wish to use programmable IoT, identifying risks that emerge in such contexts, and creating a set of questions that might guide design of such technologies so that they are safe for child users. We evaluate the success of the methodology, discuss the limitations of the approach, and describe future work.
The Visibility of Authority Records, Researcher Identifiers, Academic Social Networking Profiles, and Related Faculty Publications in Search Engine Results

Information about faculty and their publications can be found in library databases such as the Library of Congress Name Authority File, VIAF, WorldCat, and institutional repositories; in identifier registries such as ORCID and ISNI; and on academic social networking sites such as Academia, Google Scholar, and ResearchGate, but the way search engines use such identifiers and profiles is unclear. Therefore, researchers at a large comprehensive university conducted several rounds of web searching before and after the creation and modification of faculty authority records. The sample consisted of 24 faculty and the 35 publications associated with their authorities. The researchers searched for the faculty and their publications on the social networking and identity websites directly, and then used Google, Bing, and Google Scholar to record which of the faculty members' profiles and publications were found within the top 50 results. Faculty with more profiles were more visible in search engine results, and faculty with authority records ranked more highly in Google. Results related to publication discovery and ranking were more inconclusive, but revealed clear differences between search tools. The implications of this exploratory research can support educational efforts about academic identities and scholarly profiles, begin a research agenda, and inform methodological development surrounding the influence of identity records and academic social networking profiles on web visibility.
Linear Searching Method for Solving Approximate Solution to System of Max-Min Fuzzy Relation Equations With Application in the Instructional Information Resources Allocation

Max-min fuzzy relation equations are introduced to describe the peer-to-peer (P2P) data transmission mechanism in instructional information resources' sharing system.In some cases, it is not able to satisfy the download requirements of the terminals completely.Hence, the max-min fuzzy relation equations system might be inconsistent.In order to avoid the unbalance of the dissatisfaction degree, we introduce a new definition of approximate solution for the inconsistent max-min fuzzy relation equations' system.Our defined approximate solution could minimize the biggest dissatisfaction degree among the equations.Based on an auxiliary system with the parameter, we propose a linear searching algorithm to find an approximate solution of an inconsistent max-min system.A detailed numerical example is provided to show the resolution processes and the validity of our proposed algorithm.
Virtual Environments Testing as a Cloud Service: A Methodology for Protecting and Securing Virtual Infrastructures

Testing is a vital component of the system development life cycle.As information systems infrastructure move from native computing to cloud-based and virtualized platforms, it becomes necessary to evaluate their effectiveness to ensure completion of organizational goals.However, the complexity and scale of virtualized environments make this process difficult.Additionally, inherited and novel issues further complicate this process, while relatively high costs can be constraining.Enabling service-driven environments to provide this evaluation is therefore beneficial for both providers and users.No such complete service offering currently exists.This paper is therefore aimed to benefit industry and academia involved in areas involved with cloud-based testing of virtualized software and its environments.A review of current literature highlights a number of challenges in the domain.An analysis of the challenges aided in deriving requirements for developing a servitization framework for virtual infrastructure testing as a service.It is anticipated that this framework can further feedback into developing solutions to the aforementioned challenges.An evaluation of a real-world organization's servitization requirements case scenario indicates that the proposed framework provides potential solutions for associated use cases.
3D mineral potential modelling of gold distribution at the Tampia gold deposit

A 3D mineral potential model was developed for the Tampia Gold Project in Western Australia to help constrain resource estimation, understand the distribution of gold grades from the resource estimation techniques with respect to geological and physiochemical continuity, and predict the location of new gold mineralisation for future exploration drilling to expand the gold resource at Tampia. The 3D mineral potential model was generated using predictive maps based on a local granulite-facies orogenic gold mineral system model. These were generated from regional scale data and data collected during a 40 m by 40 m resource drilling programme, and included lithology, structure, rock property data and geochemical data. The predictive capacity of each map was tested for the spatial correlation with training data from high grade gold drill intersections, using the weights of evidence technique. There were 44 predictive maps created that can be used as proxies to map the physical and chemical processes active in the orogenic mineral system at Tampia. Of these, 11 were chosen for the final model that had the highest spatial correlation with the training data and did not duplicate map patterns. A closely spaced infill drilling programme was subsequently undertaken over an area where the post probability results indicated high and continuous probability for gold mineralisation, while the resource model estimated less continuous and lower grade gold mineralisation. This infill drilling aimed to compare the gold continuity at a 10 m by 10 m drill spacing with the resource estimate gold grades and post probability distribution developed from 40 m by 40 m spaced resource drilling. The results from the 10 m by 10 m spaced drilling were thereby used to test the performance of both the resource and prospectivity models, and assess the utility of mineral potential modelling for use in developing geological domains to constrain resource estimation. Results from the first phase of infill drilling, which only covers 4% of the total model area, confirm the continuity of the post probability values and suggests that the mineral potential model predicts the location and distribution of gold mineralisation within the area drilled. The results were also better and more continuous than predicted by the resource estimate. Importantly, these results confirm that geological and physiochemical controls on gold mineralisation can be numerically measured and mapped at the scale of an orebody. This allows mineral potential modelling to be considered as an option to constrain and help inform the results of geostatistical techniques used in resource estimation.
COSAC: COmpact and Scalable Arbitrary-Centered Discrete Gaussian Sampling over Integers

The arbitrary-centered discrete Gaussian sampler is a fundamental subroutine in implementing lattice trapdoor sampling algorithms. However, existing approaches typically rely on either a fast implementation of another discrete Gaussian sampler or pre-computations with regards to some specific discrete Gaussian distributions with fixed centers and standard deviations. These approaches may only support sampling from standard deviations within a limited range, or cannot efficiently sample from arbitrary standard deviations determined on-the-fly at run-time. In this paper, we propose a compact and scalable rejection sampling algorithm by sampling from a continuous normal distribution and performing rejection sampling on rounded samples. Our scheme does not require pre-computations related to any specific discrete Gaussian distributions. Our scheme can sample from both arbitrary centers and arbitrary standard deviations determined on-the-fly at run-time. In addition, we show that our scheme only requires a low number of trials close to 2 per sample on average, and our scheme maintains good performance when scaling up the standard deviation. We also provide a concrete error analysis of our scheme based on the Rényi divergence. We implement our sampler and analyse its performance in terms of storage and speed compared to previous results. Our sampler's running time is center-independent and is therefore applicable to implementation of convolution-style lattice trapdoor sampling and identity-based encryption resistant against timing side-channel attacks.
A 2nd Person Social Perspective on Bodily Play

Recent HCI work on digital games highlighted the advantage for designers to take on a 1st person perspective on the human body (referring to the phenomenological "lived" body) and a 3rd person perspective (the material "fleshy" body, similar to looking in the mirror). This is useful when designing bodily play, however, we note that there is not much game design discussion on the 2nd person social perspective that highlights the unique interplay between human bodies. To guide designers interested in supporting players to experience their bodies as play, we describe how game designers can engage with the 2nd person social perspective through a set of design tactics based on four of our own play systems. With our work, we hope we can aid designers in embracing this 2nd person perspective so that more people can benefit from engaging their bodies through games and play.
The Random Neural Network with Deep Learning Clusters in Smart Search

This paper proposes a Neurocomputing application that reorders the Web results obtained from different Web Search Engines emulating the way our brain takes decisions. The proposed application is based on the Random Neural Network with Deep Learning Clusters that evaluates and adapts Web result relevance by associating independently each Deep Learning Cluster to a specific Web Search Engine. In addition, this paper presents a Deep Learning Cluster to perform as a Management Cluster that decides the final result relevance based on the inputs from each independent Deep Learning cluster. The performance of the proposed Management Cluster is evaluated when included as an additional layer to the Deep Learning Clusters. On average; the proposed Deep Learning cluster structure improves Smart Search performance.
A Complexity-Reduced Block Encryption Algorithm Suitable for Internet of Things

The Internet of Things (IoT) poses stringent requirements on service quality and security strength. A highly reliable encryption algorithm with low complexity is required to ensure information security. Therefore, this paper proposes a complexity-reduced secure and fast encryption routine (SAFER)-Fermat block encryption method that accounts for the confusion and diffusion principles. In particular, a novel diffusion layer that exploiting the Fermat number theory transform is proposed, while the confusion layer remains unchanged as the SAFER algorithm. We also propose two data record structures to accommodate the two layers and deal with the overflow issue. The proposed scheme only considers the integer operation, the inclusion of multipliers in our study is no longer necessary, thereby resulting in low complexity and lightweight encryption method. Therefore, the proposed encryption method is advantageous for power-limited IoT devices. The simulations demonstrate that the proposed encryption method maintains the security of the SAFER++ method but requires fewer computations.
Image Denoising Based on Nonlocal Bayesian Singular Value Thresholding and Stein’s Unbiased Risk Estimator

Singular value thresholding (SVT)- or nuclear norm minimization (NNM)-based nonlocal image denoising methods often rely on the precise estimation of the noise variance. However, most existing methods either assume that the noise variance is known or require an extra step to estimate it. Under the iterative regularization framework, the error in the noise variance estimate propagates and accumulates with each iteration, ultimately degrading the overall denoising performance. In addition, the essence of these methods is still least squares estimation, which can cause a very high mean-squared error (MSE) and is inadequate for handling missing data or outliers. In order to address these deficiencies, we present a hybrid denoising model based on variational Bayesian inference and Stein's unbiased risk estimator (SURE), which consists of two complementary steps. In the first step, the variational Bayesian SVT performs a low-rank approximation of the nonlocal image patch matrix to simultaneously remove the noise and estimate the noise variance. In the second step, we modify the conventional SURE full-rank SVT and its divergence formulas for rank-reduced eigen-triplets to remove the residual artifacts. The proposed hybrid BSSVT method achieves better performance in recovering the true image compared with state-of-the-art methods.
Design and Plural Heritages

We make theoretical and methodological contributions to the CHI community by introducing comparisons between contemporary Critical Heritage research and some forms of experimental design practice. Beginning by identifying three key approaches in contemporary heritage research: Critical Heritage, Plural Heritages and Future Heritage we introduce these in turn, while exploring their significance for thinking about design, knowledge and diversity. We discuss our efforts to apply ideas integrating Critical Heritage and design through the adoption of known Research through Design techniques in a research project in Istanbul, Turkey describing the design of our study and how this was productive of sensory and speculative reflection on the past. Finally, we reflect on the usefulness of such methods in developing new interactive technologies in heritage contexts and go on to propose a series of recommendations for a future Critical Heritage Design practice.
Text Entry Throughput

Human-computer input performance inherently involves speed-accuracy tradeoffs---the faster users act, the more inaccurate those actions are. Therefore, comparing speeds and accuracies separately can result in ambiguous outcomes: Does a fast but inaccurate technique perform better or worse overall than a slow but accurate one? For pointing, speed and accuracy has been unified for over 60 years as throughput (bits/s) (Crossman 1957, Welford 1968), but to date, no similar metric has been established for text entry. In this paper, we introduce a text entry method-independent throughput metric based on Shannon information theory (1948). To explore the practical usability of the metric, we conducted an experiment in which 16 participants typed with a laptop keyboard using different cognitive sets, i.e., speed-accuracy biases. Our results show that as a performance metric, text entry throughput remains relatively stable under different speed-accuracy conditions. We also evaluated a smartphone keyboard with 12 participants, finding that throughput varied least compared to other text entry metrics. This work allows researchers to characterize text entry performance with a single unified measure of input efficiency.
A Movie Recommender System Using Modified Cuckoo Search

Recommender systems (RS) are data sieving method that recommends on the basis of liking of users and category of products from big data. In the same way, videos and movie recommender systems give some technique which helps the users to classify movie according to user’s similar interest. This mechanism process makes recommender systems very important tool for Web site and various digital marketing applications. This paper focuses on the movie recommender system by using data clustering and nature-inspired algorithm. K-means algorithm is widely used algorithm for clustering due to its simple nature, handling large amount of data and running time is low. But it falls into local optima due to its randomly generated initial centroids. This algorithm can achieve global optimum solution if it is integrated with nature-inspired algorithm. This paper integrates k-means with nature-inspired algorithms (bat, firefly, cuckoo, modified cuckoo search) on data set (movielens). Outcomes are compared on the basis of objective function, less the objective function better the results. Our proposed system (k-mean modified cuckoo search) gives improved outcome than other algorithms.
Multifractal characterization of healing process after bone loss

In this work we have proposed to use multifractal analysis to evaluate the effectiveness of the healing process in postresectal and postcystal bone loss using the guided bone regeneration (GBR). The material of the study consists of 19 radiographic images obtained from patients (13 females and 6 males), observed within 1-year-long period, who had undergone bone augmentation with xenogenic material. Using radiographic images (RVG) made with digital radiography set Kodak RVG 6100 on the day of the procedure (Group A) and 12 months later (Group B), the degree of reconstruction of intraosseous defects were compared. A visual subjective evaluation of the healing process based on RVG images may not be sufficient. For this purpose, modern computer image analysis methods can be used. Pre-processing of radiographic images consisted of separating areas of interest (ROI) and binarization. The resulting images were analyzed using multifractals. The properties of biological structures cannot be sufficiently described in the Euclidean space, but they can be characterized by a parameter called the fractal dimension. Significant increase (p < 0.0001) of multifractal features were found in patients from group B. Individual parameters may characterize bone structure, which had undergone bone augmentation with xenogenic material. Finally, we showed that the properly selected multifractal quantificators obtained for those images could be used to assign those images to the appropriate group. The proposed analysis based on RVG images could help to evaluate the healing process after bone loss. The analysis and interpretation of the healing process after bone loss using monofractal and multifractal analysis as a multi-steps process quantify a stable (good) clinical result.
Investigating the dynamics of ontological reasoning across contexts in quantum physics

The ontologies students use—their conceptions about the nature of entities—impact the way they learn physics and reason through physics problems. We investigate students' capacities for flexible use of ontologies in a modern physics context, focusing on students' reasoning around two quantum entities (photons and electrons) for three canonical topics in introductory quantum physics (double slit experiment, Mach-Zehnder interferometer, and quantum tunneling). We present a description of a full framework to describe and distinguish between different kinds of dynamic ontologies. The framework identifies possible ontological structures in individual reasoning episodes, three of which we identify in our data set: unitary (applying a single stable ontology), parallel (switching back and forth between multiple ontologies), and blended (constructing a novel ontology by blending two or more input ontologies). These different ontological structures are applied to the specific ontologies used (e.g., wave or particle) for the specific entity (e.g., photon or electron). We demonstrate the utility of the framework by coding individual responses from a representative sample of an introductory modern physics course to an array of written and multiple-choice questions on homework, exams, and pre- and postsurveys. We present and explore the patterns of use of ontologies across the three topical contexts and these various modalities. We demonstrate that students use a variety of ontologies and ontological structures across entities and topic areas, even when not explicitly prompted to do so. In addition to providing evidence of students' capacities for flexible use of ontologies, we find that the wording and framing of the question prompts impact students' use of ontologies. Expanding on the aggregate data, we engage in analysis of a few notable examples to demonstrate how the wording, framing, and content of prompts can intersect to collectively impact students' use of ontologies and ontological structures.7 MoreReceived 14 January 2019DOI:https://doi.org/10.1103/PhysRevPhysEducRes.15.010124Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasQuantum foundationsScientific reasoning & problem solvingStudent preparationProfessional TopicsLower undergraduate studentsPhysics Education Research