Reviewerly/exHarmony huggingface dataset:
Comparative analysis of properties of weakening buffer operators in time series prediction models

Reducing the negative influence of stochastic disturbances in sample data has always been a difficult problem in time series analysis. In this paper, three new fractional weakening buffer operators are proposed, and then some desirable properties of these proposed sequence operators are investigated. Their potential effect in smoothing unexpected disturbances while maintaining the normal trend in sample series is analyzed and compared with other widely used sequence operators in time series modeling. Results of theoretical and empirical research show that the proposed novel fractional weakening buffer operators are effective in improving the development pattern analysis of time series in disturbance scenarios, while also avoid too subjectively weighting experimental data from collected samples. The robust of the proposed operator-based prediction algorithm against noise effect is tested in five different types of noise scenarios. Result of empirical study demonstrates that the proposed method improves the series prediction performance and it also improves the robustness of corresponding forecasting algorithms. These unique properties of the proposed weakening buffer operators make them more attractive in time series analysis.
Usability Evaluation of a Novel Robotic Power Wheelchair for Indoor and Outdoor Navigation

To compare the Mobility Enhancement roBotic (MEBot) wheelchair's capabilities with commercial electric-powered wheelchairs (EPWs) by performing a systematic usability evaluation.Usability in effectiveness, efficacy, and satisfaction was evaluated using quantitative measures. A semistructured interview was employed to gather feedback about the users' interaction with MEBot.Laboratory testing of EPW driving performance with 2 devices in a controlled setting simulating common EPW driving tasks.A convenience sample of expert EPW users (N=12; 9 men, 3 women) with an average age of 54.7±10.9 years and 16.3± 8.1 years of EPW driving experience.Not applicable.Powered mobility clinical driving assessment (PMCDA), Satisfaction Questionnaire, National Aeronautics and Space Administration's Task Load Index.Participants were able to perform significantly higher number of tasks (P=.004), with significantly higher scores in both the adequacy-efficacy (P=.005) and the safety (P=.005) domains of the PMCDA while using MEBot over curbs and cross-slopes. However, participants reported significantly higher mental demand (P=.005) while using MEBot to navigate curbs and cross-slopes due to MEBot's complexity to perform its mobility applications which increased user's cognitive demands.Overall, this usability evaluation demonstrated that MEBot is a promising EPW device to use indoors and outdoors with architectural barriers such as curbs and cross-slopes. Current design limitations were highlighted with recommendations for further improvement.
Behavioral consequences of Pokémon GO: The exaggerated picture

Overlaying virtual species in real-world environments by Pokémon Go has sparked controversy over the security, privacy, social, ethical, and moral implications of mixed-reality technologies. While the GPS-enabled Pokémon Go in-game activities have contributed to improving players' relationships with their real-world surroundings and enriched players' mixed-reality experiences, the question of whether players' in-game experiences have driven them to unintentionally develop unwanted behaviors that could evolve to be part of their real-life habits remains unanswered. Inspired by previously reported real-world Pokémon Go-related stories, we formulate six hypothetical in-game scenarios that place respondents in vulnerable positions, and then ask 5739 crowd workers about the most likely behavioral decisions that they would take when being engaged in these mixed-reality scenarios. We then compare the decisions of 3492 active Pokémon Go players with the decisions obtained from non-players and present an extensive investigation of whether Pokémon Go has contributed to forming motives for trespassing into private properties, violating respected regulations, compromising personal physical safety, violating street-crossing rules and parking cars in illegal spots. Overall, the long-term negative impact that Pokémon Go players have exhibited in our studies is minimal, and could have been caused by other influential factors, such as players' contextual and personal attributes.
Data security against receiver corruptions: SOA security for receivers from simulatable DEMs

Data security and privacy protection issues are the primary network security threats. The notion of selective opening security (SOA) for receivers focuses on such a scenario of multi-user setting: there are one sender and multiple receivers. Upon receiving the multiple challenge ciphertexts, even if the adversary is allowed to corrupt some of the receivers (e.g. the Heartbleed attack) by obtaining the decryption keys corresponding to some of the challenge ciphertexts, the SOA security for receivers requires that the ciphertexts of the uncorrupted receivers remain secure. The setting of receiver corruptions is much less studied than that of sender corruptions, where the corrupted senders expose their messages and the random coins employed during the encryption. In this paper, we propose an approach to achieve simulation-based selective opening security for receivers under chosen-ciphertext attacks (RSIM-SO-CCA), with the help of the technique proposed by Heuer and Poettering in Asiacrypt 2016. Specifically, for a hybrid public-key encryption (PKE) scheme consisting of a blockcipher-based data encapsulation mechanism (DEM) and a key encapsulation mechanism (KEM), if the DEM and KEM meet some special properties, then the hybrid PKE scheme is RSIM-SO-CCA secure in the ideal cipher model.
Learning depth super-resolution by using multi-scale convolutional neural network

Imaging techniques are the most rapidly growing area of computer vision, and the resolution has reached a new level. Super-resolution is a technique that enhances the resolution of images from the low-resolution input and help to accurately analyze and derive the data. Recently convolutional neural network are becoming mainstream in computer vision. Most existing CNN models based super-resolution either directly reconstruct the low-resolution input and then improve the resolution at the last layer, or another way is, to firstly enlarge the low-resolution input to high resolution (HR), then reconstruct the HR to obtain the desired output. These models encounter some major flows; large computational resources and losing information. In this paper, we adopt gradual process for training the CNN, to propose an efficient super-resolution model. The gradual strategy helps network to progressively magnify and reconstruct the LR image in each step, and thereby possibly avoid of losing information (second problem). In addition, we optimize the number of layers, add the residual network and skip connection to the proposed network to ease the difficulty of training (first problem). The proposed model not only achieves a compatible performance with the existing prominent methods but also, efficiently reduce the computational expenses.
Face Alignment With Expression- and Pose-Based Adaptive Initialization

Face alignment is a critical task in many multimedia and vision applications that use face-based algorithms. Recent research has focused on achieving efficient initialization to improve performance; however, the use of facial attributes and the extent of their correlation with initialization have not been fully exploited. This paper presents a lightweight method called expression- and pose-based adaptive initialization (EXPAI), in which facial attributes, that is, expression and pose information, are used as priors. This approach can significantly improve the face alignment performance. In addition, reliable expression and head pose information can be derived simultaneously in the same framework. First, an expression- and pose-based template dictionary is formed by augmenting the mean shape across three degrees of freedom, thereby substantially improving the robustness of the initial shape with respect to large head pose variations. Second, each the template corresponds to an image of interest, which is jointly determined using a shape-constrained multiclass classifier and binary classifiers, and is assigned a pretrained confidence coefficient. The initial shape that is thus generated for subsequent cascaded regression is more adaptive and enables higher accuracy. Furthermore, EXPAI enables initialization with significantly increased computational efficiency because of its independence from the original dataset. The experimental results obtained on the widely used 300-W dataset show that our method achieves very competitive performance compared with that of state-of-the-art methods. In particular, for the challenging subset of 300-W, EXPAI reduces errors by more than 14% compared with coarse-to-fine shape searching (CFSS), which currently exhibits the best performance among regression-based approaches. Furthermore, a speed increase of more than 10 times compared with CFSS is achieved.
Building an Efficient Lattice Gadget Toolkit: Subgaussian Sampling and More

Many advanced lattice cryptography applications require efficient algorithms for inverting the so-called "gadget" matrices, which are used to formally describe a digit decomposition problem that produces an output with specific (statistical) properties. The common gadget inversion problems are the classical (often binary) digit decomposition, subgaussian decomposition, Learning with Errors (LWE) decoding, and discrete Gaussian sampling. In this work, we build and implement an efficient lattice gadget toolkit that provides a general treatment of gadget matrices and algorithms for their inversion/sampling. The main contribution of our work is a set of new gadget matrices and algorithms for efficient subgaussian sampling that have a number of major theoretical and practical advantages over previously known algorithms. Another contribution deals with efficient algorithms for LWE decoding and discrete Gaussian sampling in the Residue Number System (RNS) representation. We implement the gadget toolkit in PALISADE and evaluate the performance of our algorithms both in terms of runtime and noise growth. We illustrate the improvements due to our algorithms by implementing a concrete complex application, key-policy attribute-based encryption (KP-ABE), which was previously considered impractical for CPU systems (except for a very small number of attributes). Our runtime improvements for the main bottleneck operation based on subgaussian sampling range from 18x (for 2 attributes) to 289x (for 16 attributes; the maximum number supported by a previous implementation). Our results are applicable to a wide range of other advanced applications in lattice cryptography, such as GSW-based homomorphic encryption schemes, leveled fully homomorphic signatures, other forms of ABE, some program obfuscation constructions, and more.
Exploiting Parallelism for CNN Applications on 3D Stacked Processing-In-Memory Architecture

Deep convolutional neural networks (CNNs) are widely adopted in intelligent systems with unprecedented accuracy but at the cost of a substantial amount of data movement. Although the emerging processing-in-memory (PIM) architecture seeks to minimize data movement by placing memory near processing elements, memory is still the major bottleneck in the entire system. The selection of hyper-parameters in the training of CNN applications requires over hundreds of kilobytes cache capacity for concurrent processing of convolutions. How to jointly explore the computation capability of the PIM architecture and the highly parallel property of neural networks remains a critical issue. This paper presents Para-Net, that exploits Parallelism for deterministic convolutional neural Networks on the PIM architecture. Para- Net achieves data-level parallelism for convolutions by fully utilizing the on-chip processing engine (PE) in PIM. The objective is to capture the characteristics of neural networks and present a hardware-independent design to jointly optimize the scheduling of both intermediate results and computation tasks. We formulate this data allocation problem as a dynamic programming model and obtain an optimal solution. To demonstrate the viability of the proposed Para-Net, we conduct a set of experiments using a variety of realistic CNN applications. The graph abstractions are obtained from deep learning framework Caffe. Experimental results show that Para-Net can significantly reduce processing time and improve cache efficiency compared to representative schemes.
Vibration measurement method based on point tracking for irregular structures

A novel vibration measurement method through binocular photogrammetry is proposed to solve the problem of vibration sensing for 3D structures with irregular surfaces. Radial and tangential distortions in the image modeling process are considered for camera calibration through nonlinear optimization with Levenberg-Marquardt algorithm. Precise positioning, tracking and matching techniques are analyzed and developed in image processing. As an example, a wind turbine blade’s vibration is photographed and calculated, and the measured vibration displacement is consistent with that of the hammer method. The result shows that the relative errors of the first and second order natural vibration frequencies are below 4%. The proposed photogrammetry method is simple, efficient, feasible and reliable, and can be applied to the field of dynamic testing of large irregular structures.
Level sets-based image segmentation approach using statistical shape priors

A robust 3-D segmentation technique incorporated with the level sets concept and based on both shape and intensity constraints is introduced. A partial differential equation (PDE) is derived to describe the evolution of the level set contours. This PDE does not contain weighting parameters that need to be tuned, which overcomes the drawbacks of other PDE approaches. The shape information is collected from a set of co-aligned manually segmented contours of the training data. A promising statistical approach is used to get the distribution of the intensity gray values. The introduced statistical approach is built by modeling the empirical PDF (normalized histogram of occurrences) for the intensity level distribution with a linear combination of Gaussians (LCG) incorporating both negative and positive components. An Expectation-Maximization (EM) algorithm is modified to deal with the LCGs, and we also proposed an EM-based sequential technique to acquire a close initial LCG approximation for the modified EM algorithm to start with. The PDF of the intensity levels is incorporated in the speed function of the moving level set to specify the evolution direction. Experimental results show how accurately the approach is in segmenting various types of 2-D and 3-D datasets comprising medical images.
Spatial-domain fitness landscape analysis for combinatorial optimization

Fitness landscape analysis has been effectively used to analyze the characteristics of combinatorial optimization problems (COPs) while investigating the behavior of applied algorithms. However, most COPs are high dimensional and an intuitive understanding of traditional fitness landscape analysis is challenging. To address this issue, the present study proposes a spatial-domain fitness landscape analysis framework. This framework aims to visualize the fitness landscapes regarding of a target COP and evaluate the properties of the COP. To construct the landscapes, a mapping strategy inspired by lexicographic order is designed to establish the connection between the high-dimensional and low-dimensional spaces. Accordingly, a spatial-domain parameter, namely, the slope of the digital elevation models (DEMs), is utilized to assess the ruggedness of landscapes. As an auxiliary value, the neutrality ratio is introduced to reflect the degree of neutrality. In addition, a parameter control mechanism based on these landscape metrics is advanced to enhance the adaptability of the algorithm. Finally, the experimental results reveal that the spatial-domain fitness landscape can help extract the characteristics of COPs and study the behavior of algorithms effectively. In addition, the proposed adaptive parameter control strategy significantly improves the performance of the algorithm for solving COPs.
Adapting Semantic Segmentation Models for Changes in Illumination and Camera Perspective

Semantic segmentation using deep neural networks has been widely explored to generate high-level contextual information for autonomous vehicles. To acquire a complete $180^\circ$ semantic understanding of the forward surroundings, we propose to stitch semantic images from multiple cameras with varying orientations. However, previously trained semantic segmentation models showed unacceptable performance after significant changes to the camera orientations and the lighting conditions. To avoid time-consuming hand labeling, we explore and evaluate the use of data augmentation techniques, specifically skew and gamma correction, from a practical real-world standpoint to extend the existing model and provide more robust performance. The presented experimental results have shown significant improvements with varying illumination and camera perspective changes.
AUEB at BioASQ 7: Document and Snippet Retrieval

We present the submissions of aueb to the bioasq 7 document and snippet retrieval tasks (parts of Task 7b, Phase A). Our systems build upon the methods we used in bioasq 6. This year we also experimented with models that jointly learn to retrieve documents and snippets, as opposed to using separate pipelined models for document and snippet retrieval. We also experimented with models based on bert [5]. Our systems obtained the best document and snippet retrieval results for all batches of the challenge that we participated in.
Knowledge Acquisition Using Group Support Systems

This paper reports on a project in which a group support system (GSS) equipped with a causal mapping facility was used to acquire knowledge from experts in seven European cities in order to understand the systemicity of risks which cities may face. The practical constraints demanded that participants’ experience and wisdom about the city risk environment was collected in a short period of time: three 1-day workshops. The acquisition of knowledge posed a number of important epistemological challenges which are explored in our discussion. The GSS was faced with the need to (1) facilitate sharing of knowledge with others, (2) manage the complexity of expert knowledge, (3) acknowledge the time demands on experts, (4) manage and merge multiple perspectives, and (5) acknowledge the subjectivity of knowledge in this domain. By discussing how the GSS process attended directly to these epistemological issues and to methodological considerations that linked to these issues, the paper contributes to a better understanding of the application of GSS for knowledge acquisition, particularly in comparison with other possible methods.
FunkR-pDAE: Personalized Project Recommendation Using Deep Learning

In open source communities, developers always need to spend plenty of time and energy on discovering specific projects from massive open source projects. Consequently, the study of personalized project recommendation for developers has important theoretical and practical significance. However, existing recommendation approaches have clear limitations, such as ignoring developers' operating behavior, social relationships and practical skills, and are very inefficient for large amounts of data. To address these limitations, this paper proposes FunkR-pDAE (Funk singular value decomposition Recommendation using pearson correlation coefficient and Deep Auto-Encoders), a novel personalized project recommendation approach using a deep learning model. FunkR-pDAE first extracts data related to developers and open source projects from open source communities, which build a developer-open source project relevance matrix and a developer-developer relevance matrix. Meanwhile, Pearson Correlation Coefficient is utilized to calculate developer similarity using the developer-developer relevance matrix. Second, deep auto-encoders are used to learn the factor vectors that represent developers and open source projects. Finally, a sorting method is defined to provide personalized project recommendations. Experimental results on real-world GitHub data sets show that FunkR-pDAE has a precision rate of 75.46 percent and a recall rate of 40.32 percent, which provides more effective recommendation compared with state-of-the-art approaches.
Optical asymmetric watermarking using 4D hyperchaotic system and modified equal modulus decomposition in hybrid multi resolution wavelet domain

In this paper, an asymmetric optical watermarking scheme using 4D hyperchaotic system with coherent superposition and modified equal modulus decomposition in hybrid multi-resolution wavelet domain is proposed. The 4D hyperchaotic system is used for generating permutation keystream for a pixel-swapping mechanism. The hybrid multi-resolution is generated using both the fractional Fourier transform of multiple orders and Walsh transform. The 4D hyperchaotic system's parameters and initial conditions along with the multiple fractional orders of the fractional Fourier transform increase the key-space and hence provide additional strength to the watermarking mechanism. The designed watermarking scheme is non-linear in nature and resists brute-force attack due to its large key-space. Computer-based simulations have been performed on grayscale images to verify the validity and performance of the scheme against various attacks including special attack. Results show that the proposed watermarking scheme not only offers high resistance to occlusion and noise attacks but is also invulnerable to the special attack.
Robust Optical Flow Estimation in Cardiac Ultrasound Images Using a Sparse Representation

This paper introduces a robust 2-D cardiac motion estimation method. The problem is formulated as an energy minimization with an optical flow-based data fidelity term and two regularization terms imposing spatial smoothness and the sparsity of the motion field in an appropriate cardiac motion dictionary. Robustness to outliers, such as imaging artefacts and anatomical motion boundaries, is introduced using robust weighting functions for the data fidelity term as well as for the spatial and sparse regularizations. The motion fields and the weights are computed jointly using an iteratively re-weighted minimization strategy. The proposed robust approach is evaluated on synthetic data and realistic simulation sequences with available ground-truth by comparing the performance with state-of-the-art algorithms. Finally, the proposed method is validated using two sequences of in vivo images. The obtained results show the interest of the proposed approach for 2-D cardiac ultrasound imaging.