Reviewerly/exHarmony huggingface dataset:
Efficient Confidence-Based Hierarchical Stereo Disparity Upsampling for Noisy Inputs

Disparity upsampling techniques aim to restore high-resolution disparity maps from low-resolution disparity inputs. These inputs must be of high quality and are often obtained via complicated passive or active 3-D reconstruction methods. Each pixel in the input disparity maps guides the disparity assignment in the upsampling process. The quality of the upsampled results will decreases if the initial disparity inputs are noisy, as the upsampled results are closely related to the initial inputs. We herein propose a hierarchical confidence-based upsampling framework that can be used to obtain relatively high quality upsampled results even under the noisy inputs. Specifically designed confidence measuring schemes are employed in our upsampling process, allowing the disparity assignment of only high-confidence pixels. For an effective depth quality evaluation, we present a novel classification of the confidence according to depth-and texture-related information and develop a confidence examination method with improved precision by combining multiple depth confidence evaluation methods. Our hierarchical pipeline contains three steps: confidence-based upsampling, confidence-based fine-tuning, and confidence-based optimization. The upsampling combines multichannel information. Fine-tuning is carried out using the stereo texture information. Optimization is conducted utilizing the Markov random field method. All these proposed methods work together to suppress the low-confidence pixels and propagate the high-confidence pixels in the upsampling process. The cumulative error distribution is further analyzed, revealing the effectiveness of our confidence evaluation. Extensive comparison experiments are also performed using both the ground truth and stereo matching disparity maps as inputs to demonstrate the advantage of our framework over state-of-the-art upsampling methods.
Apollonius Representation and Complex Geometry of Entangled Qubit States

A representation of one qubit state by points in complex plane is proposed, such that the computational basis corresponds to two fixed points at a finite distance in the plane. These points represent common symmetric states for the set of quantum states on Apollonius circles. It is shown that, the Shannon entropy of one qubit state depends on ratio of probabilities and is a constant along Apollonius circles. For two qubit state and for three qubit state in Apollonius representation, the concurrence for entanglement and the Cayley hyperdeterminant for tritanglement correspondingly, are constant on the circles as well. Similar results are obtained also for n-tangle hyperdeterminant with even number of qubit states. It turns out that, for arbitrary multiple qubit state in Apollonius representation, fidelity between symmetric qubit states is also constant on Apollonius circles. According to these, the Apollonius circles are interpreted as integral curves for entanglement characteristics. The bipolar and the Cassini representations for qubit state are introduced, and their relations with qubit coherent states are established. We proposed the differential geometry for qubit states in Apollonius representation, defined by the metric on a surface in conformal coordinates, as square of the concurrence. The surfaces of the concurrence, as surfaces of revolution in Euclidean and Minkowski spaces are constructed. It is shown that, curves on these surfaces with constant Gaussian curvature becomes Cassini curves.
Effects of biased hypothesis generation on self-directed category learning.

Psychologists and educators have long pointed to myriad benefits of self-directed learning. Yet evidence of its efficacy in real-world domains is mixed and it remains unclear how it is constrained by basic perceptual and cognitive processes. Previous work suggests that, in particular, self-directed learning is affected by the way that people generate hypotheses as they learn. This study examines how biased hypothesis generation affects the learning of categorical rules, a basic building block of concept learning, through self-directed selection of training data. In both perceptual and abstract category learning tasks, participants' hypotheses regarding an unknown classification boundary were influenced by how features were represented. This bias had persistent effects on their ability to learn the underlying categorical relationship despite their opportunity to control the selection of training items. The results demonstrate that self-directed control can be beneficial for both perceptual and abstract category learning, but that the ability to discover rules of a particular form depends on how the learning environment guides the generation of new hypotheses. (PsycINFO Database Record (c) 2019 APA, all rights reserved).
RAMSES: Internet Forensic Platform for Tracking the Money Flow of Financially-Motivated Malware and Ransomware

This paper provides a discussion on the objectives, approach and findings of the EU H2020 funded RAMSES project. As the rise of the use of the Internet is followed by the rise of criminal activity on the Internet, new tools are needed for Law Enforcement Agencies to fight the crime and to collect forensic evidence. This project will provide LEAs with new tools and also covers the dark and deep web. Several tools and functionalities are described, alongside the importance of game theoretic models applied to the determination of the probability and efficacy of malware being used for profit.
Analytical evaluation of an innovative decision-making algorithm for VM live migration

In order to achieve the virtual machines live migration, the two pre-copy and post-copy strategies are presented. Each of these strategies, depending on the operating conditions of the machine, may perform better than the other. In this article, a new algorithm is presented that automatically decides how the virtual machine live migration takes place. In this approach, the virtual machine memory is considered as an informational object that has a revision number and it is constantly changing. We have determined precise criteria for evaluating the behavior of a virtual machine and automatically select the appropriate live migration strategy. Also in this article, different aspects of required simulations and implementations are considered. Analytical evaluation shows that using the proposed scheme and the presented algorithm, can significantly improve the virtual machines live migration process.
The Agnostic Structure of Data Science Methods

In this paper we argue that data science is a coherent and novel approach to empirical problems that, in its most general form, does not build understanding about phenomena. Within the new type of mathematization at work in data science, mathematical methods are not selected because of any relevance for a problem at hand; mathematical methods are applied to a specific problem only by `forcing’, i.e. on the basis of their ability to reorganize the data for further analysis and the intrinsic richness of their mathematical structure. In particular, we argue that deep learning neural networks are best understood within the context of forcing optimization methods. We finally explore the broader question of the appropriateness of data science methods in solving problems. We argue that this question should not be interpreted as a search for a correspondence between phenomena and specific solutions found by data science methods; rather, it is the internal structure of data science methods that is open to precise forms of understanding.
Detection of abnormal blood cells by segmentation and classification

Leukaemia is a cancer of the hematopoietic cells. The detection of abnormal blood cells before cancer degeneration is a medical problem. The aim of our work is to obtain maximum recognition rate of leukaemia. We propose the development of a system based on mathematical morphology and k-means methods capable of segmentation, classification, and detection of the cancerous blood cells. This allows the characterisation and the description the cancerous region, which is an important task in the interpretation and diagnosis of pathologies present in blood. The segmentation was carried out using an efficient and fast algorithmic processing. It turns out that the proposed system shows to better segmentation and classification for tested images. The obtained experimental results are very encouraging which help hematologists for identification of abnormal blood cells.
A small-scale experimental study of breast FNA consultation on the internet using Panoptiq

To test the potential for cytopathology consultation using Panoptiq (ViewsIQ, Richmond, BC, Canada; this is a new type of whole-slide image that is made manually and incorporates video content), we investigated its application in the cytopathological diagnosis of cases that were difficult to diagnose by breast fine-needle aspiration (FNA). Panoptiq files were created from liquid-based cytology slides prepared by the BD CytoRich Red (BD, Franklin Lakes, NJ) method. The slides were prepared from 23 consecutive samples of breast FNA that had been diagnosed as atypical or suspicious by the Hokkaido Cancer Center, Hokkaido, Japan. Nine volunteer reviewers, who were provided with the URL of the Panoptiq file, the original cytopathological diagnosis, and the clinical information, were asked to classify the cytopathological diagnosis of each case into 4 diagnostic categories (benign, atypical, suspicious, or malignant). We examined the consultation benefit (CB)—how much closer the reviewer’s cytopathology diagnosis came to the final histopathological diagnosis than the original cytodiagnosis. The CB scoring system was decided in advance. All 9 reviewers showed a positive total CB score and 2 reviewers showed a significantly higher CB score (Wilcoxon’s signed rank test). The representative diagnosis (ie, the most frequently rendered diagnosis in each case) also showed a significant CB. Our small-scale experimental study, in which Panoptiq was used in the diagnosis of cases that were difficult to diagnose definitively by breast FNA, revealed a positive CB score by every reviewer and the representative diagnosis showed a significant CB. The study suggests that Panoptiq could be used for cytopathology consultation.
Chebyshev wavelet collocation method for Ginzburg-Landau equation

The main aim of this paper is to investigate the efficient Chebyshev wavelet collocation method for Ginzburg-Landau equation. The basic idea of this method is to have the approximation of Chebyshev wavelet series of a non-linear PDE. We demonstrate how to use the method for the numerical solution of the Ginzburg-Landau equation with initial and boundary conditions. For this purpose, we have obtained operational matrix for Chebyshev wavelets. By applying this technique in Ginzburg-Landau equation, the PDE is converted into an algebraic system of non-linear equations and this system has been solved using MAPLE computer algebra system. We demonstrate the validity and applicability of this technique which has been clarified by using an example. Exact solution is compared with an approximate solution. Moreover, Chebyshev wavelet collocation method is found to be acceptable, efficient, accurate and computational for the non-linear or PDE.
Prediction of mobile image saliency and quality under cloud computing environment

Recent years have witnessed the explosive growth of multimedia applications over networks and increasingly high requirements of consumers for multimedia signals in terms of quality of experience (QoE). Effective and efficient yet energy-saving saliency detection model and quality prediction method are eagerly desired, since they play critical roles in raising users' QoE and promoting the progress of green multimedia communication. Current studies of saliency detection and quality evaluation are far from ideal yet. In this paper we investigate the influence of complexity on visual saliency and quality. Complexity is an essential concept in human perception to visual stimulus, but it is substantially abstract and hard to be endowed with a clear definition. We suppose that brain systematically combines global and local features during the whole process of human perception. Global features lead a dominant position in seeking salient areas under the condition that image complexity is high, namely without obviously isolated foreground objects, whereas local features play a key role in an opposite situation. With this consideration, this paper establishes a novel framework for detecting visual saliency based on image complexity estimation before complexity-adaptive merging of global and local features. Furthermore, the concept of complexity is deployed for blind photographic image quality assessment (IQA) by means of saliency-based weighting. Features which refer to contrast, artifacts, brightness and natural scene statistics (NSS) are modified and integrated to derive a blind IQA model and predict the quality of photos. Based on the above two technologies, this paper introduces smart phones as mobile terminals, cloud platforms for speed-up and energy-saving, and wireless networks for transmission, and provides a practical mobile multimedia application. Comparative experiments validate that, within this application system, our proposed saliency detection model and blind photographic IQA method implement better than existing relevant competitors in terms of effectiveness and efficiency comparison.
DISCOver: DIStributional approach based on syntactic dependencies for discovering COnstructions

Abstract One of the goals in Cognitive Linguistics is the automatic identification and analysis of constructions, since they are fundamental linguistic units for understanding language. This article presents DISCOver, an unsupervised methodology for the automatic discovery of lexico-syntactic patterns that can be considered as candidates for constructions. This methodology follows a distributional semantic approach. Concretely, it is based on our proposed pattern-construction hypothesis: those contexts that are relevant to the definition of a cluster of semantically related words tend to be (part of) lexico-syntactic constructions. Our proposal uses Distributional Semantic Models for modelling the context taking into account syntactic dependencies. After a clustering process, we linked all those clusters with strong relationships and we use them as a source of information for deriving lexico-syntactic patterns, obtaining a total number of 220,732 candidates from a 100 million token corpus of Spanish. We evaluated the patterns obtained intrinsically, applying statistical association measures and they were also evaluated qualitatively by experts. Our results were superior to the baseline in both quality and quantity in all cases. While our experiments have been carried out using a Spanish corpus, this methodology is language independent and only requires a large corpus annotated with the parts of speech and dependencies to be applied.
Uncovering Hidden Structure in Sequence Data via Threading Recurrent Models

Long Short-Term Memory (LSTM) is one of the most powerful sequence models for user browsing history \citetan2016improved,korpusik2016recurrent or natural language text \citemikolov2010recurrent.Despite the strong performance, it has not gained popularity for user-facing applications, mainly owing to a large number of parameters and lack of interpretability. Recently \citetzaheer2017latent introduced latent LSTM Allocation (LLA) to address these problems by incorporating topic models with LSTM, where the topic model maps observed words in each sequence to topics that evolve using an LSTM model. In our experiments, we found the resulting model, although powerful and interpretable, to show shortcomings when applied to sequence data that exhibit multi-modes of behaviors with abrupt dynamic changes. To address this problem we introduce thLLA: a threading LLA model. thLLA has the ability to break each sequence into a set of segments and then model the dynamic in each segment using an LSTM mixture. In that way, thLLA can model abrupt changes in sequence dynamics and provides a better fit for sequence data while still being interpretable and requiring fewer parameters. In addition, thLLA uncovers hidden themes in the data via its dynamic mixture components. However, such generalization and interpretability come at a cost of complex dependence structure, for which inference would be extremely non-trivial. To remedy this, we present an efficient sampler based on particle MCMC method for inference that can draw from the joint posterior directly. Experimental results confirm the superiority of thLLA and the stability of the new inference algorithm on a variety of domains.
Discussion: To Bot or Not? Challenging the Top Social Media Influencers in #PlasticSurgery

Boston, Mass. From Boston University School of Medicine. RReceived for publication June 18, 2018; accepted July 25, 2018. Disclosure:The author has no financial interest to declare in relation to the content of this Discussion or of the associated article. Smita R. Ramanadham, M.D., SR Plastic Surgery PC, East Brunswick, N.J. 08816, [email protected], Instagram: dr.smita.ramanadham, Twitter: SRamanadhamMD, Facebook: Smita Ramanadham, MD
Can permissionless blockchains be regulated and resolve some of the problems of copyright law?

After a brief introduction on general regulatory issues in the blockchain, this chapter explores the impact of the blockchain on copyright. They argue that the more the blockchain becomes widespread, the more lawmakers develop an interest in regulating it. Most existing regulations, policies, and case law take a top-down approach and focus on Bitcoin and, therefore, on fraud and money laundering. A more participatory and holistic approach would be more suitable. Indeed, it is important to involve all the stakeholders and keep in mind all the potential socio-legal issues if one wants to ensure that the blockchain unleashes its full potential and benefits all the players involved.
Licensing Electronic Resources in Academic Libraries: A Practical Handbook

The proliferation of electronic resources (e-resources) arguably brings several benefits to libraries, including instant, from-anywhere access for patrons and automated workflows for technical services that are potentially more efficient. However, it also brings new challenges. One of these challenges is licensing the many e-resources offered through the contemporary library. Halaychik and Reagan address this challenge in Licensing Electronic Resources in Academic Libraries: A Practical Handbook. This resource primarily covers three license-related topics in five chapters. Chapters 1 and 3 discuss the basics of licensing and licensing law. Chapters 2 and 4 address the licensing process through the full life cycle of licenses. The final chapter discusses negotiating licenses.
Towards Combining Multitask and Multilingual Learning

Machine learning is an increasingly important approach to Natural Language Processing. Most languages however do not possess enough data to fully utilize it. When dealing with such languages it is important to use as much auxiliary data as possible. In this work we propose a combination of multitask and multilingual learning. When learning a new task we use data from other tasks and other languages at the same time. We evaluate our approach with a neural network based model that can solve two tasks – part-of-speech tagging and named entity recognition – with four different languages at the same time. Parameters of this model are partially shared across all data and partially they are specific for individual tasks and/or languages. Preliminary experiments show that this approach has its merits as we were able to beat baseline solutions that do not combine data from all the available sources.
Programmable Education Infrastructure: Cloud resources as HPC Education Environments

Cloud computing is growing area for educating students and performing meaningful scientific research.The challenge for many educators and researchers is knowing how to use some of the unique aspects of computing in the cloud.One key feature is true elastic computing -resources on demand.The elasticity and programmability of cloud resources make them an excellent tool for educators who require access to a wide range of computing environments.In the field of HPC education, such environments are an absolute necessity, and getting access to them can create a large burden on the educators above and beyond designing content.While cloud resources won't replace traditional HPC environments for large research projects, they are an excellent option for providing both user and administrator education on HPC environments.The highly configurable nature of cloud environments allows educators to tailor the educational resource to the needs of their attendees, and provide a wide range of hands-on experiences.In this demo, we'll show how the Jetstream cloud environment can be used to provide training for both new HPC administrators and users, by showing a ground-up build of a simple HPC system.While this approach uses the Jetstream cloud, it is generalizable across any cloud provider.We will show how this allows an educator to tackle everything from basic command-line concepts and scheduler use to advanced cluster-management concepts such as elasticity and management of scientific software.
A Service-Oriented Platform for Approximate Bayesian Computation in Population Genetics

Approximate Bayesian computation (ABC) is a useful technique developed for solving Bayesian inference without explicitly requiring a likelihood function. In population genetics, it is widely used to extract part of the information about the evolutionary history of genetic data. The ABC compares the summary statistics computed on simulated and observed data sets. Typically, a forward-in-time approach is used to simulate the genetic material of a population starting from an initial ancestral population and following the evolution of the individuals by advancing generation by generation under various demographic and genetic forces. This approach is computationally expensive and requires a large number of computations making the use of high-performance computing crucial for decreasing the overall response times. In this work, we propose a fully distributed web service-oriented platform for ABC that is based on forward-in-time simulations. Our proposal is based on a client-server approach. The client enables users to define simulation scenarios. The server enables efficient and scalable population simulations and can be deployed on a distributed cluster of processors or even in the cloud. It is composed of four services: a workload generator, a simulation controller, a simulation results analyzer, and a result builder. The server performs multithread simulations by executing a simulation kernel encapsulated in a proposed libgdrift library. We present and evaluate three different libgdrift library approaches whose algorithms aim to reduce execution times and memory consumption.
A Large-Scale Wired Network Energy Model for Flow-Level Simulations

The use of simulators to predict network energy consumption is a good way for scientists to improve and develop new algorithms and also to assess them. However, the average size of a network platforms is continuously increasing with the emergence of new technologies like the Internet Of Things and Fog Computing. Packet-level simulators start to reach their limits in terms of performance and this calls for newer solutions in the domain of large-scale platform energy models. In this paper, we propose two energy models for wired networks adapted to flow level simulators in order to estimate the energy consumption of large platforms. An evaluation of these models is proposed and it demonstrates their applicability in practice and their accuracy. Indeed, we obtain simulation results with a relative error lower than 4% compared to an ns-3-based solution, and our flow-based simulation is 120 times faster.