Reviewerly/exHarmony huggingface dataset:
The Effect of Retinal Eccentricity on Visually Induced Motion Sickness and Postural Control

The present study investigated the effect of retinal eccentricity on visually induced motion sickness (VIMS) and postural control. Participants wore a head-mounted display masked for the central 10° (peripheral vision), the peripheral except for the central 10° (central vision), or unmasked (control) to watch a highly immersive 3D virtual reality (VR) ride along China’s Great Wall. The Simulator Sickness Questionnaire was administered to assess VIMS symptoms before and after the VR exposure. In addition, postural sway data were collected via sensors attached to each participant’s head, torso, and hip. Results demonstrated that peripheral vision triggered the most severe symptoms of motion sickness, whereas full vision most perturbed posture. The latter finding contradicts previous research findings demonstrating the peripheral advantage of postural control. Although the source of compromised postural control under peripheral stimulation is not clear, the provocative nature of visual stimulation depicting a roller-coaster ride along a rugged path likely contributed to the contradictory findings. In contrast, motion sickness symptoms were least severe, and posture was most stable, under central vision. These findings provide empirical support for the tactic assumed by VR engineers who reduce the size of the field of view to ameliorate the symptoms of motion sickness.
High performance person re-identification via a boosting ranking ensemble

Person re-identification is a very challenging task in computer vision due to severe appearance changes of a person across non-overlapping camera views. It is thus inadequate in most realistic re-identification scenarios to assess similarity using a single metric in a single feature space. Ensemble methods have been proven effective on improving the recognition rate. However, robust combination remains challenging due to the incompatibilities between different distance metrics. In this paper, we propose a novel framework for person re-identification called Boosting Ranking Ensemble (BRE), which adaptively assembles features and metrics from a ranking perspective. To further tackle the overfitting problem, we explore an effective rectifier loss in the proposed BRE framework to alleviate the influence of the misclassified samples. Extensive experimental analyses and evaluations on three commonly adopted benchmarks demonstrate the effectiveness of the proposed method, with superior performance over many state-of-the-art methods.
Acceleration of DNN Backward Propagation by Selective Computation of Gradients

The training process of a deep neural network commonly consists of three phases: forward propagation, backward propagation, and weight update. In this paper, we propose a hardware architecture to accelerate the backward propagation. Our approach applies to neural networks that use rectified linear unit. Considering that the backward propagation results in a zero activation gradient when the corresponding activation is zero, we can safely skip the gradient calculation. Based on this observation, we design an efficient hardware accelerator for training deep neural networks by selectively computing gradients. We show the effectiveness of our approach through experiments with various network models.
Finite-Difference Algorithm for Polynomial Phase Signal Parameter Estimation

A new algorithm, the finite-difference algorithm, is proposed for single-component polynomial phase signal parameter estimation. The proposed algorithm takes advantage of the framework of the quasi-maximum-likelihood (QML) estimator but estimates the finite difference of the polynomial instead of the derivative. The algorithm is computationally simpler and has comparable performance to the QML algorithm. A phase centering step is proposed to increase the detectable range of polynomial phase parameters as an alternative to phase unwrapping.
Configurable Anonymous Authentication Schemes For The Internet of Things (IoT)

The Internet of Things (IoT) has revolutionized the way of how pervasive computing devices communicate and disseminate information over the global network. A plethora of user data is collected and logged daily into cloud-based servers. Such data can be analyzed by the IoT infrastructure to capture users' behaviors (e.g. users' location, tagging of smart home occupancy). This brings a new set of security challenges, specifically user anonymity. Existing access control and authentication technologies failed to support user anonymity. They relied on the surrendering of the device/user authentication parameters to the trusted server, which hence could be utilized by the IoT infrastructure to track users' behavioral patterns. This paper, presents two novel configurable privacy-preserving authentication schemes. User anonymity capabilities were incorporated into our proposed authentication schemes through the implementation of two crypto-based approaches (i) Zero Knowledge Proof (ZKP) and (ii) Verifiable Common Secret Encoding (VCSE). We consider a user-oriented approach when determining user anonymity. The proposed authentication schemes are dynamically capable of supporting various levels of user privacy based on the user preferences. To validate the two schemes, they were fully implemented and deployed on an IoT testbed. We have tested the performance of each proposed schemes in terms of power consumption and computation time. Based on our performance evaluation results, the proposed ZKP-based approach provides better performance compared to the VCSE-based approach.
Near-Data Acceleration of Privacy-Preserving Biomarker Search with 3D-Stacked Memory

Homomorphic encryption is a promising technology for enabling various privacy-preserving applications such as secure biomarker search. However, current implementations are not practical due to large performance overheads. A homomorphic encryption scheme has recently been proposed that allows bitwise comparison without the computationally-intensive multiplication and bootstrapping operations. Even so, this scheme still suffers from memory-bound performance bottleneck due to large ciphertext expansion. In this work, we propose HEGA, a near-data processing architecture that leverages this scheme with 3D-stacked memory to accelerate privacy-preserving biomarker search. We observe that homomorphic encryption-based search, like other emerging applications, can greatly benefit from the large throughput, capacity, and energy savings of 3D-stacked memory-based near-data processing architectures. Our near-data acceleration solution can speed up biomarker search by 6.3 × with 5.7× energy savings compared to an 8-core Intel Xeon processor.
Human Visual System Based Enhanced AMBTC for Color Image Compression Using Interpolation

This paper proposes a novel enhanced AMBTC image compression method for digital color images which uses the characteristics of human visual system. The proposed compression method first transforms the RGB image into YCbCr color space and then divides each component of the image into non-overlapping blocks. To improve the imperceptibility of caused distortion due to AMBTC compression, the proposed method computes four quantization levels and 32 bits bit-planes for luminance component (Y) blocks whereas the blocks of the remaining two components (i.e., Cb & Cr) are represented by two quantization levels and only 8 bits bit-planes. At the receiving end, the receiver can use interpolation technique to predict the remaining 8 bits of the bit-planes of Cb and Cr components. Thus, the proposed method provides better quality stego-image than the related existing methods, Further, the required bit-rate is also reduced for all the test images irrespective of their characteristics.
Saliency Driven Perceptual Quality Metric for Omnidirectional Visual Content

The problem of objectively measuring perceptual quality of omnidirectional visual content arises in many immersive imaging applications and particularly in compression. The interactive nature of this type of content limits the performance of earlier methods designed for static images or for video with a predefined dynamic. The non-deterministic impact must be addressed using statistical approach. One of the ways to describe, analyze and predict viewer interactions in omnidirectional imaging is through estimation of visual attention. We propose an objective metric to measure perceptual quality of omnidirectional visual content considering visual attention information.
Applying Semantic Web Technologies to Provide Feasibility Feedback in Early Design Phases

In the product development process, as it is currently practiced, production is still often neglected in the early design phases, leading to late and costly changes. Using the knowledge of product designers concerning production process design, this paper introduces an ontological framework that enables early feasibility analyses. In this way, the number of iterations between product and process design can almost certainly be reduced, which would accelerate the product development process. Additionally, the approach provides process engineers with possible production sequences that can be used for process planning. To provide feasibility feedback, the approach presented relies on semantic web technologies. An ontology was developed that supports designers to model the relations among products, processes, and resources in a way that allows the use of generic Sparql Protocol And RDF Query Language (SPARQL) queries. Future applicability of this approach is ensured by aligning it with the top-level ontology Descriptive Ontology for Linguistic and Cognitive Engineering (DOLCE). We also compare the ontology’s universals to fundamental classes of existing knowledge bases from the manufacturing and the batch processing domains. This comparison demonstrates the approach’s domain-independent applicability. Two proofs of concept are described, one in the manufacturing domain and one in the batch processing domain.
Vectorization Based Color Transfer for Portrait Images

This paper introduces a method for transferring colors between portrait images. Using a trained neural network to extract facial mask, we vectorize each image with a set of sparse diffusion curves to encode the low-frequency colors, and use the Laplacian of residual colors to represent the high-frequency details. Then we apply optimal mass transport to transfer the boundary colors between the diffusion curves of the source and reference images. Finally, the original or modified Laplacians of colors are added to the transferred diffusion curve image. Unlike the existing methods that either require 3D information or assume the source and reference images have similar poses and dense correspondence, our method is computationally efficient and flexible, which can work for portrait images with large pose and color differences.
DREDGE

Graph-based algorithms have gained significant interest in several application domains. Solutions addressing the computational efficiency of such algorithms have mostly relied on many-core architectures. Cleverly laying out input graphs in storage, by placing adjacent vertices in a same storage unit (memory bank or cache unit), enables fast access during graph traversal. Dynamic graphs, however, must be continuously repartitioned to leverage this benefit. Yet software repartitioning solutions rely on costly, cross-vault communication to query and optimize the graph layout between algorithm iterations.
Convolution, Approximation and Spatial Information Based Object and Color Signatures for Content Based Image Retrieval

Content Based Image Retrieval (CBIR) is a challenging ground to retrieve the images based on feature description and extraction. Salient image features are the potential candidates to search and index the images based on primitive features. This paper presents a novel way to extract and describe the image features based on their object and color signatures. Gaussian of variance is used along with image convolution and the resultant features are approximated using matrix hashing. The high range frequency feature information is then separated to carry out the spatial indexes. High resolution images from versatile image semantic groups are resulted in massive image signatures which are computation effective. Principal component analysis is performed on these feature vectors to obtained reduced statistical information. These grey level information carriers are then combined with RGB channel based spatially arranged features to possess to color and object information. Bags-of-Word (BoW) architecture is applied to these information carriers to index and retrieve the images. Competitive results show that the proposed method outperforms in many categories of benchmarks Caltech-101 and Corel-1000 datasets. The proposed method shows outstanding results in complex and occluded image categories.
A Framework for User Agency during Development of Interactive Risk Visualization Tools

Participating in user-centered design provides potential users of interactive risk visualization tools agency in influencing tool development. This article identifies and characterizes pathways for agency that users may experience as they participate in design of interactive tools for visualizing environmental risks. We present an empirically based conceptual framework for better understanding user agency during visualization tool development based on findings from interviews with professional visualization tool developers and discuss practical implications and future research recommendations.
RAGra: Leveraging Monolithic 3D ReRAM for Massively-Parallel Graph Processing

With the maturity of monolithic 3D integration, 3D ReRAM provides impressive storage-density and computational-parallelism with great opportunities for parallel-graph processing acceleration. In this paper, we present RAGra, a 3D ReRAM-based graph processing accelerator, which has two significant technical highlights. First, monolithic 3D ReRAM usually has the complexly-intertwined feature with shared input wordlines and output bitlines for different layers. We propose novel mapping schemes, which can guide to apply different graph algorithms into 3D ReRAM seamlessly and correctly for exposing the inherently-irregular parallelism of 3D ReRAM. Second, consider the sparsity of real-world graphs, we further propose a row- and column-mixed execution model, which can filter invalid subgraphs for exploiting the massive parallelism of 3D ReRAM. Our evaluation on 8-layer stacked ReRAM shows that RAGra outperforms state-of-the-art planar (2D) ReRAM based graph accelerator GraphR by 6.18× performance improvement and 2.21 ×energy saving, on average. In particular, RAGra significantly outperforms Grid-Graph (a typical CPU-based graph system) by up to 293.12×.
An Integrated Two-Pose Calibration Method for Estimating Head-Eye Parameters of a Robotic Bionic Eye

Active vision systems with independent eye motion are required to update extrinsic parameters continuously to perform stereo reconstruction. The stereo parameters could be calculated through forward kinematics model plus head-eye parameters of each eye. The head-eye parameters could be calibrated through the traditional head-eye calibration method separately. However, the head-eye calibration method could not reduce the impact of the kinematics model errors, which may cause reconstruction result unacceptable. Instead, we propose an integrated two-pose calibration (ITPC) method to calculate head-eye parameters of both eyes simultaneously to reduce the impact of model error by deriving two new equations from two poses of the robotic bionic eye. The implemented algorithm has been validated on GAZEBO and our robotic bionic eye. The results demonstrate that the head-eye parameters obtained by our method are more accurate and more robust to kinematics model errors. We have also analyzed the reason why the proposed method is better.
Towards the selection of Optimum Requirements Prioritization Technique: A Comparative Analysis

Requirement Prioritization (RP) bears a sheer significance in requirements engineering. It is deployed to rank the requirements for subsequent release in their respective order based on predefined criteria. Software systems primarily get more complex accompanying size of requirements. In most cases, it is not possible to consider all requirements due to limited time and resources. Thus, requirements need to be prioritized correctly according to their importance. Quality of software to be developed greatly depends upon the order of requirements. Many studies reveal, no single RP technique can be labeled as best for all applications. Hence there is a need to analyze the available RP techniques to identify most favorable one in a given context. In this paper, a comparative analysis of ten leading RP techniques have been performed, based on some key factors like RP scales, complexity, scalability, customizability, accuracy, suitable dataset and handling risk factor. Strengths and weaknesses of these techniques have been analyzed and discussed. A case study is also incorporated as an example of selection of most suitable RP technique. This study contributes in selection of most appropriate RP technique in a particular scenario.
Learning to rank faulty source files for dependent bug reports

With the rise of autonomous systems, the automation of faults detection and localization becomes critical to their reliability. An automated strategy that can provide a ranked list of faulty modules or files with respect to how likely they contain the root cause of the problem would help in the automation bug localization. Learning from the history if previously located bugs in general, and extracting the dependencies between these bugs in particular, helps in building models to accurately localize any potentially detected bugs. In this study, we propose a novel fault localization solution based on a learning-to-rank strategy, using the history of previously localized bugs and their dependencies as features, to rank files in terms of their likelihood of being a root cause of a bug. The evaluation of our approach has shown its efficiency in localizing dependent bugs.
Continuous Non-Malleable Codes in the 8-Split-State Model

Non-malleable codes (NMCs), introduced by Dziembowski, Pietrzak and Wichs [20], provide a useful message integrity guarantee in situations where traditional error-correction (and even error-detection) is impossible; for example, when the attacker can completely overwrite the encoded message. NMCs have emerged as a fundamental object at the intersection of coding theory and cryptography. In particular, progress in the study of non-malleable codes and the related notion of non-malleable extractors has led to new insights and progress on even more fundamental problems like the construction of multi-source randomness extractors. A large body of the recent work has focused on various constructions of non-malleable codes in the split-state model. Many variants of NMCs have been introduced in the literature, e.g., strong NMCs, super strong NMCs and continuous NMCs. The most general, and hence also the most useful notion among these is that of continuous non-malleable codes, that allows for continuous tampering by the adversary. We present the first efficient information-theoretically secure continuously non-malleable code in the constant split-state model. We believe that our main technical result could be of independent interest and some of the ideas could in future be used to make progress on other related questions.
LEAC: An efficient library for clustering with evolutionary algorithms

This paper introduces LEAC, a new C++ partitioning clustering library based on evolutionary computation. LEAC provides plenty of elements (individual encoding schemes, genetic operators, evaluation metrics, among others) which allow an easy and fast development of new clustering algorithms. Furthermore, it includes 23 algorithms which represent the state-of-the-art in Evolutionary Algorithms for partial clustering. The paper describes through examples the main features and the design principles of the software, as well as how to use LEAC to carry out a comparison between different proposals and how to extend it by including new algorithms.
Assessment of Complex Performances in Digital Environments

Digital technologies hold the potential to transform educational assessment. Recent advances reveal that digital environments will support the development of learning and assessment activities in ways that will both increase the inferential fidelity of assessments and change the form of assessments altogether. Digital technologies can also automate data collection and the production of assessment inferences on a massive scale. Here, we discuss the wide variation in digital learning experiences and explain how they are transforming traditional language for discussing assessment. We argue that the predigital constraints on assessment have skewed our thinking about assessment and give examples of new and novel approaches. Second, we discuss how digital environments can allow us to capture and make inferences from simple or complex learning activities in new ways. Third, we point to advances in machine learning and AI that have the potential to change current and future assessment practices. Finally, we argue for balancing enthusiasm for digital environments against the challenges of making appropriate assessment inferences.
Lossless Compression of Light Fields Using Multi-reference Minimum Rate Predictors

This paper presents a method to improve the lossless compression efficiency of light field encoding based on Minimum Rate Predictors (MRP). The proposed method relies on the use of multiple references, either micro-images or sub-aperture images, to provide a richer set of correlated pixels for prediction. The results show better compression ratios than conventional versions of MRP, for both representation formats (micro-image and sub-aperture image arrays), achieving gains ranging from 16.9% to 21.2%. Furthermore, it is also shown that the proposed method consistently outperforms the state-of-the-art lossless encoders HEVC and JPEG-LS.
Evaluation of auto-generated distractors in multiple choice questions from a semantic network

Despite their drawback, multiple-choice questions are an enduring feature in instruction because they can be answered more rapidly than open response questions and they are easily scored. However, it can be difficult to generate good incorrect choices (called “distractors”). We designed an algorithm to generate distractors from a semantic network for four types of multiple choice questions in biology. By recruiting 200 participants from Amazon Mechanical Turk, the machine-generated distractors were compared to human-generated distractors in terms of question difficulty, question discrimination and distractor usefulness. The machine-generated and human-generated distractors performed very closely on all the three measures, suggesting that generating distractors from a semantic network for simple multiple choice questions is a viable method.
A MapReduce‐based modified Grey Wolf optimizer for QoS‐aware big service composition

Summary Big services are the collection of interrelated web services across virtual and physical domains, integrating service oriented computing and big data. The rapid growth of Big services that offer similar functionality with varying QoS attributes makes the process of selection and composition of these big services as highly challenging and complex. In this paper, we develop an efficient QoS‐aware Big service composition approach by applying a MapReduce based Modified Grey Wolf Optimizer (MR‐MGWO) that explores more search space, especially in a multidimensional environment. Our approach ensures an optimal balance of exploration and exploitation that enhances the convergence rate and minimizes the computational time. The empirical analysis illustrates that the performance of MR‐MGWO is superior to other similar approaches for solving Big service composition.