Reviewerly/exHarmony huggingface dataset:
Optimal road accident case retrieval algorithm based on <i>k</i>-nearest neighbor

An optimal algorithm which can help traffic managers to make more accurate decisions from previous road accident case knowledge has been proposed. The algorithm based on k-nearest neighbor determines weight value of each accident case feature based on information entropy index, establishes a road accident case retrieval base using two-step cluster algorithm, and proposes a global similarity model of road accident cases. Then, a new comprehensive evaluation index called matching degree is presented. And then, a prototype system is developed to conduct case retrieval experiments to verify the performance of the proposed algorithm for road accident case retrieval. The result of the experiments clearly demonstrates the effectiveness of this case retrieval algorithm for road accident management in real time.
Traitor Tracing After Visible Watermark Removal

Watermarks are often used to protect copyright-protected videos from illegal re-distribution. More specifically, a unique watermark that represents the receiver’s identifier is embedded into the video. In this way, malicious users can be identified when they leak their received version of the video. However, when the watermark is embedded as visible text in the video, it is easy for digital pirates to delete it such that they can no longer be identified. Therefore, copyright owners will benefit from a technique that allows the detection of a visible watermark when it is removed. This paper demonstrates how a visible watermark indirectly generates imperceptible variations over the entire video. As such, these variations in the non-watermarked area can be used as an alternative watermark representation, and thus enable watermark detection even after watermark removal. The experimental results prove that the watermark can be detected as long as the quality of the watermarked video is not significantly reduced, especially if the originally-distributed watermarked video has a high quality. Moreover, the watermark should be embedded into a video with sufficient motion. In conclusion, the proposed technique enables copyright-owners to identify pirates when they illegally distribute visibly-watermarked videos, even when the watermarked area is removed.
Quantum technology for optical wireless communication in data-center security and hacking

In the last decade data-centers have become a crucial element in our society. As a result, in the coming years there will be increased threat from hackers, who could cause outage of the data-center, extract millions of account details, or steal financial and sensitive information. One way to reduce these threats is to divide the data-center into sub data-centers, and connect them by secure links which prevent access by unauthorized users. A possible technology to connect between the sub data-centers is optical wireless communication (OWC) or free space optics (FSO). An OWC link includes an optical receiver and laser transmitter. The transmitter transmits a light beam that carries information which propagates to the receiver. The light beam carrying the information propagates through the air. In order to increase the security and make the penetration and intrusion difficult quantum key distribution (QKD) is added to the link. By doing so the information exchange between the sub data-centers is encrypted. In this paper we review up to date literature in the field, propose an implementation scheme of an OWC network in sub data-centers, and discuss ways to hack the system.
Do Journal Administrators solve the reviewer assignment problem as well as editors? Consideration of reviewer rigour and timeliness

The study aimed to find whether journal editorial office administrators were as effective as editors at assigning rigorous reviewers. We analysed four journals using blended systems of editor‐selected reviewers (ESR) and Journal Administrator‐selected reviewers (JASR) from four disciplines: medicine, sociology, education, and business/applied psychology. In blended journals, both editors and Journal Administrators select authors to review using web‐based expert‐finding tools. All reviewers selected were both authors and reviewers. We primarily wanted to assess the quality of reviews from both ESR and JASR reviewer selection methodologies. Reviewer rigour was defined as differences between editor decisions and reviewer recommendations. Timeliness data were also recorded and analysed separately as an indicator of efficiency. Reviewer rigour, the quality of reviewers’ evaluations, was estimated from the level of agreement between editors and reviewers. Timeliness was not considered a direct measure of rigour. For two journals, no statistically significant differences were observed; for two, in a small proportion of cases, ESR reviews were more negative. One journal showed some statistically significant major differences but only in 2% of reviews. Timeliness data indicated some statistically significant trends that JASR return reviews more promptly. Therefore, where editors rely on at least two reviewers’ recommendations, JASR is equally rigorous as ESR.
Stochastic cognitive mapping to build common ground for selecting cases in research projects

Creating common ground among research groups is a prerequisite for scientifically sound case study research, especially in multi-national and multi-disciplinary research projects. Therefore, this paper proposes a new procedure for case study selection: stochastic cognitive mapping (sCM). sCM complements the previously illustrated conceptual content cognitive mapping (3CM) with email enquiry on concepts and their interconnections, simple multi-attribute rating and stochastic estimation of priorities. The procedure was applied to select case studies in a study on the role of community-based initiatives (CBIs) in societal change towards sustainability. The procedure performed well, based on project members’ evaluations, and enabled them to consistently identify a map and ranked list of criteria for selecting case initiatives. Researchers of the project had two to some extent exclusive orientations towards case selection: sampling and searching strategies, i.e. emphasis on the representativeness of a case set or on the features that interesting and useful study cases should possess. Strategies were integrated and CBIs selected through the sequence of snowball, random, and purposive samplings. Moreover, the method and the criteria list are adaptable to support comparable studies across diverse contexts.
M‐WRSF model for medical image segmentation

In this Letter, multichannel weighted region-scalable fitting segmentation model (M-WRSF) is proposed for medical image segmentation. The authors have utilised a new edge detection function to improve the performance of image segmentation approaches that already exists on weak edges and the grey-scale inhomogeneity of some medical images. The M-WRSF model introduces a novel punishment item to improve numerical stability and augments time interval to boost iterative efficiency. The Gaussian kernel function is added on the basis of original model to enhance robustness. On the other side, the original medical images need to make de-noising operation before the multichannel active contour model segment them, and also level-set initial curve has no effect on the segmentation results. The benefits of the new M-WRSF model, specifically, the increased efficiency and usability, are verified in the simulation experiment.
Vulnerabilities of the McEliece Variants Based on Polar Codes

Several variants of the McEliece public key encryption scheme present interesting properties for post-quantum cryptography. In this article we pursue a study of one potential variation, namely the McEliece scheme based on polar codes, and, more generally, based on any weakly decreasing monomial code. Recently, both polar as well as Reed-Muller codes were redefined using a polynomial formalism using different partial orders on the set of monomials over the ring of polynomials of m variables with coefficients in $$\mathbb {F}_2$$ . We use this approach to study the star product of two weakly decreasing monomial codes and determine its dimension. With these results at hand, we will identify particular types of weakly decreasing monomial codes for which the star product allows for an efficient distinguisher. These results support our quest for efficient key recovery attacks against these variants of the McEliece scheme.
A Study on Image Segmentation in Curvelet Domain Using Snakes and Fractals for Cancer Detection in Mammograms

Breast cancer is one of the common diseases that affect the quality of life in women. To improve the mortality rate, screening procedures are recommended by medical practitioners. The procedure involves detecting and diagnosing the suspected regions. Detecting the suspected regions in the digital mammograms is a challenging task. In this work, multiresolution analysis is done to resolve the curve discontinuities and procedures like fractals and snakules are applied for segmenting the suspected region. The algorithms have been tested on mini-MIAS and observed that snakes have identified the regions better than fractals. Snakes are able to converge within few iterations determining the circumference of the suspected region and also able to differentiate tumorous and non-tumorous regions.
Study on eliminating the effect of parasitic reflection on deflectometry measurement of planar optical element surface figure

Phase measuring deflectometry (PMD) with structured light projection and phase-shifting technique is a highly accurate optical surface measuring method. For surface shape measurement of transparent planar elements, PMD suffers from parasitic reflection. To avoid the unwanted effect of parasitic reflection, a method based on fringe frequency tuning and Fourier-transform is introduced in this paper. Numerical simulations and experiments are both conducted to evaluate the performance of the proposed method. An optical planar element with a thickness of 24.5mm is measured, and measurement error is within 200nm PV.
PaperMiner—a real-time spatiotemporal visualization for newspaper articles

In 2005, the National Library of Australia (NLA) began a pilot project to selectively digitize back issues of major Australian newspapers to provide free public access to over 60 million digitized newspaper articles, dating from the first years of Australian colonization to the early 1960s. Trove, a faceted search engine maintained by NLA, provides access to this very large collection. Unfortunately, Trove lacked any means to filter by location, which raised the tantalizing possibility of using advanced computational techniques to identify long-term patterns and trends in newspaper reportage of people, events, concepts, and many other historical entities. PaperMiner, which utilizes text mining techniques for extracting metadata information, was developed that enabled the inclusion of geolocations of the places cited in the newspaper articles and supported the searching of articles by location and visualizing the results of searches using both location and time using a map of Australia. Using PaperMiner, researchers could see when and where the anti-Chinese leagues movement started in Australia and how it spread, to better focus their subsequent research. PaperMiner can be used as a digital humanities tool to assist in research by replacing the tedium of a shallow scan through thousands of Trove search results with a more efficient method that draws the researchers’ attention to more significant times and places where their time can be better spent in deeper analysis. In this article, we describe the techniques utilized in creating PaperMiner and discuss its usability testing with a group of leading researchers in Australian history.
Energy-Aware Coflow and Antenna Scheduling for Hybrid Server-Centric Data Center Networks

Data center networks (DCNs) continuously evolve to support emerging applications. For instance, server-centric architectures are becoming popular because they improve the modularity and resilience of data centers. In a server-centric DCN, switching functionalities are embedded in each server instead of in dedicated switches. Recently, wireless technologies have been introduced into the design of server-centric DCNs. Wireless interconnects provide great flexibility by creating direct flyways between servers. These wireless links help to reduce the length of paths assigned to flows so that fewer links must be powered-on to transfer data. In this paper, we investigate energy-aware scheduling of coflows and antenna directions in a server-centric DCN. Coflows are groups of parallel and high-volume flows generated by big data applications. We assume that the DCN is hybrid, i.e., that it includes both wired and wireless links. We formulate this scheduling problem as an optimization problem named energy-aware coflow and antenna scheduling (ECAS) and solve it using a mathematical programming solver. We further prove the NP-hardness of the formulated ECAS problem. We also introduce a heuristic algorithm to reduce the computational complexity. Our simulation results show that our optimal solution saves up to 48% of energy compared with representative competitors. Our heuristic algorithm achieves lower energy savings but still generates the shortest paths and lowest transferring time of flows. Further simulations on the network topology with a different size also confirm the efficiency of our proposed model and algorithm.
Methods for Adaptive Video Streaming and Picture Quality Assessment to Improve QoS/QoE Performances

This paper introduces recent trends in video streaming and four methods proposed by the authors for video streaming.Video traffic dominates the Internet as seen in current trends, and new visual contents such as UHD and 360-degree movies are being delivered.MPEG-DASH has become popular for adaptive video streaming, and machine learning techniques are being introduced in several parts of video streaming.Along with these research trends, the authors also tried four methods: route navigation, throughput prediction, image quality assessment, and perceptual video streaming.These methods contribute to improving QoS/QoE performance and reducing power consumption and storage size.
Active Learning with Label Proportions

Active Learning (AL) refers to the setting where the learner has the ability to perform queries to an oracle to acquire the true label of an instance or, sometimes, a set of instances. Even though Active Learning has been studied extensively, the setting is usually restricted to assume that the oracle is trustworthy and will provide the actual label. We argue that, while common, this approach can be made more flexible to account for different forms of supervision. In this paper, we propose a new framework that allows the algorithm to request the label for a bag of samples at a time. Although this label will come in the form of proportions of class labels in the bags and therefore encode less information, we demonstrate that we can still learn effectively.
Bayesian Kantorovich Deconvolution in Finite Mixture Models

This chapter addresses the problem of recovering the mixing distribution in finite kernel mixture models, when the number of components is unknown, yet bounded above by a fixed number. Taking a step back to the historical development of the analysis of this problem within the Bayesian paradigm and making use of the current methodology for the study of the posterior concentration phenomenon, we show that, for general prior laws supported over the space of mixing distributions with at most a fixed number of components, under replicated observations from the mixed density, the mixing distribution is estimable in the Kantorovich or $$L^1$$ -Wasserstein metric at the optimal pointwise rate $$n^{-1/4}$$ (up to a logarithmic factor), n being the sample size.
Photorealistic Face Completion with Semantic Parsing and Face Identity-Preserving Features

Tremendous progress on deep learning has shown exciting potential for a variety of face completion tasks. However, most learning-based methods are limited to handle general or structure specified face images (e.g., well-aligned faces). In this article, we propose a novel face completion algorithm, called Learning and Preserving Face Completion Network (LP-FCN), which simultaneously parses face images and extracts face identity-preserving (FIP) features. By tackling these two tasks in a mutually boosting way, the LP-FCN can guide an identity preserving inference and ensure pixel faithfulness of completed faces. In addition, we adopt a global discriminator and a local discriminator to distinguish real images from synthesized ones. By training with a combined identity preserving, semantic parsing and adversarial loss, the LP-FCN encourages the completion results to be semantically valid and visually consistent for more complicated image completion tasks. Experiments show that our approach obtains similar visual quality, but achieves better performance on unaligned faces completion and fine detailed synthesis against the state-of-the-art methods.
Blur kernel estimation using sparse representation and cross-scale self-similarity

Blind image deconvolution, i.e., estimating both the latent image and the blur kernel from the only observed blurry image, is a severely ill-posed inverse problem. In this paper, we propose a blur kernel estimation method for blind motion deblurring using sparse representation and cross-scale self-similarity of image patches as priors to recover the latent sharp image from a single blurry image. Sparse representation indicates that image patches can always be represented well as a sparse linear combination of atoms in an appropriate dictionary. Cross-scale self-similarity results in that any image patch can in some way be well approximated by a number of other similar patches across different image scales. Our method is based on the observations that almost any image patch in a natural image has multiple similar patches in down-sampled versions of the image, and down-sampling produces image patches that are sharper than those in the blurry image itself. In our method, the dictionary for sparse representation is trained adaptively from sharper patches sampled from the down-sampled latent image estimate to make the similar patches of the latent sharp image well represented sparsely, and meanwhile, all patches from the latent image estimate are optimized to be as close to the sharper similar patches searched from the down-sampled version to enforce the sharp recovery of the latent image by constructing a non-local regularization. Experimental results on both simulated and real blurry images demonstrate that our method outperforms state-of-the-art blind deblurring methods.
A Hybrid Approach for Document Analysis in Digital Forensic Domain

Our daily life is characterized by the continuous interaction with many devices and technologies, which build a digital ecosystem around each person. In this context, if, for example, it is necessary to investigate the commission of a crime, the adoption of digital forensic sciences is of fundamental importance. These disciplines are in fact concerned with gathering any information that may be of some interest to the legal system, in full respect of its nature and without its characteristics being altered in any way; in this way the probative value of the information collected and its admissibility in the trial are guaranteed. The complexity of the scenario in which the forensic operators are to operate derives from the widespread diffusion of devices with a large variety of characteristics and, mostly, from the heterogeneous and unstructured representation of relevant information. In this work we present a semantic approach for textual document analysis to enrich the correlation process of a forensics investigation.
The Elephant in the Room: Towards the Application of Genetic Programming to Automatic Programming

We outline a research journey which set out to tackle automatic programming and got distracted by success in a series of real-World applications including Software-defined Cellular Communications Networks, Design, Engineering, Business Analytics and Finance and Search-based Software Engineering. In particular the domain of software-defined communications networks represents a significant opportunity for the application of automatic programming and will be the focus of this chapter. When one takes a problem, as opposed to method, perspective what becomes important is finding the best method or combination of methods to extract the best performance in the problem domain of interest. We propose that the field of Genetic Programming needs to take a broader perspective, to return to its roots in the problem domain of Automatic Programming, redefine itself to this end, and embrace a wider set of methods in order to achieve success in this holy grail problem.
Improving the Embedding Strategy for Batch Adaptive Steganography

Recent works have demonstrated that images with more texture regions should be selected as the sub-batch of covers to carry the total message when applying batch steganography to adaptive steganography and the core challenge of which is how to evaluate the texture complexity of image accurately according to the need of steganography security. In this paper, we first propose three methods for measuring the texture complexity of image to select images with highly textured content, then put forward our universal embedding strategy for batch adaptive steganography in both spatial and JPEG domain. To assess the security of embedding strategies for batch adaptive steganography, we use a pooling steganalysis method based majority decision for the omniscient Warden, who informed by the average payload, embedding algorithm and cover source. Given a batch of images, our proposed embedding strategy is to select images with largest residual values to carry the total message, which is named max-residual-greedy (MRG) strategy. Experimental results show that the proposed embedding strategy outperforms the previous ones for batch adaptive steganography.
Three Dimensional Reconstruction Models for Medical Modalities: A Comprehensive Investigation and Analysis

Background: Image reconstruction is the mathematical process which converts the signals obtained from the scanning machine into an image. The reconstructed image plays a fundamental role in the planning of surgery and research in the medical field. Discussion: This paper introduces the first comprehensive survey of the literature about medical image reconstruction related to diseases, presenting a categorical study about the techniques and analyzing advantages and disadvantages of each technique. The images obtained by various imaging modalities like MRI, CT, CTA, Stereo radiography and Light field microscopy are included. A comparison on the basis of the reconstruction technique, Imaging Modality and Visualization, Disease, Metrics for 3D reconstruction accuracy, Dataset and Execution time, Evaluation of the technique is also performed. Conclusion: The survey makes an assessment of the suitable reconstruction technique for an organ, draws general conclusions and discusses the future directions.
Orthogonality is superiority in piecewise-polynomial signal segmentation and denoising

Segmentation and denoising of signals often rely on the polynomial model which assumes that every segment is a polynomial of a certain degree and that the segments are modeled independently of each other. Segment borders (breakpoints) correspond to positions in the signal where the model changes its polynomial representation. Several signal denoising methods successfully combine the polynomial assumption with sparsity. In this work, we follow on this and show that using orthogonal polynomials instead of other systems in the model is beneficial when segmenting signals corrupted by noise. The switch to orthogonal bases brings better resolving of the breakpoints, removes the need for including additional parameters and their tuning, and brings numerical stability. Last but not the least, it comes for free!