Reviewerly/exHarmony huggingface dataset:
Brain structure segmentation in the presence of multiple sclerosis lesions

Intensity-based multi-atlas segmentation strategies have shown to be particularly successful in segmenting brain images of healthy subjects. However, in the same way as most of the methods in the state of the art, their performance tends to be affected by the presence of MRI visible lesions, such as those found in multiple sclerosis (MS) patients. Here, we present an approach to minimize the effect of the abnormal lesion intensities on multi-atlas segmentation. We propose a new voxel/patch correspondence model for intensity-based multi-atlas label fusion strategies that leads to more accurate similarity measures, having a key role in the final brain segmentation. We present the theory of this model and integrate it into two well-known fusion strategies: Non-local Spatial STAPLE (NLSS) and Joint Label Fusion (JLF). The experiments performed show that our proposal improves the segmentation performance of the lesion areas. The results indicate a mean Dice Similarity Coefficient (DSC) improvement of 1.96% for NLSS (3.29% inside and 0.79% around the lesion masks) and, an improvement of 2.06% for JLF (2.31% inside and 1.42% around lesions). Furthermore, we show that, with the proposed strategy, the well-established preprocessing step of lesion filling can be disregarded, obtaining similar or even more accurate segmentation results.
Evolutionary hash functions for specific domains

Hash functions are a key component of many essential applications, ranging from compilers, databases or internet browsers to videogames or network devices. The same reduced set of functions are extensively used and have become “standard de facto” since they provide very efficient results in searches over unsorted sets. However, depending on the characteristics of the data being hashed, the overall performance of these non-cryptographic hash functions can vary dramatically, becoming a very common source of performance loss. Hash functions are difficult to design, they are extremely non-linear and counter-intuitive, and relationships among variables are often intricate and obscure. Surprisingly, very little scientific research is devoted to the design and experimental assessment of these widely used functions. In this work, in addition to performing an up-to-date comparison of state-of-the-art hash functions, we propose the use of evolutionary techniques for designing “ad hoc” non-cryptographic hash functions. Thus, genetic programming will be used to automatically design a tailor-made hash function that can be continuously evolved if needed, so that it is always adapted to real-world dynamic environments. To validate the proposed approach, we have compared several quality metrics for the generated functions and the most widely used non-cryptographic hash functions across eight different scenarios. The results of the evolved hash functions outperformed those of the non-cryptographic hash functions in most of the cases tested.
<b>ETAS</b>: An <i>R</i> Package for Fitting the Space-Time ETAS Model to Earthquake Data

The epidemic-type aftershock sequence (ETAS) model is the most widely used statistical model to describe earthquake catalogs.ETAS is an R package for fitting the space-time ETAS model to an earthquake catalog using the stochastic declustering approach introduced by Zhuang, Ogata, and Vere-Jones (2002).The package provides two classes and several functions to facilitate data preparation, model fitting and some simple diagnostic checks.The present paper is a description of the package and illustrates modeling earthquake data using the space-time ETAS model.
Speed-Oriented Architecture for Binary Field Point Multiplication on Elliptic Curves

This paper introduces a novel speed-oriented architecture of point multiplication in elliptic curve cryptography.A balanced full-precision multiplier is proposed to shorten latency, and a new modular inversion architecture is integrated to reduce the total number of clock cycles in point multiplication.A modified Montgomery Ladder algorithm that takes three clock cycles to calculate one input bit is proposed to best utilize hardware resources.A mixed-pipeline technique is used to balance the delay of different paths and increase frequency.The proposed architecture is implemented on GF(2 163 ) and GF(2 571 ), based on Xilinx Virtex-5 and Virtex-7 FPGA.For GF(2 163 ), the design reaches 211 MHz, with 29309 LUTs, and 547 clock cycles or 2.6 µs latency on Virtex-5; 320.5 MHz, with 28911 LUTs and 1.7 µs latency on Virtex-7.For GF(2 571 ), the design reaches 186 MHz, with 286400 LUTs, and 1813 clock cycles or 9.6 µs latency on Virtex-5; 267 MHz, 290001 LUTs and 6.79 µs latency on Virtex-7.The proposed design achieves the lowest latency among all existing works, and its performance is also among the top.Furthermore, it is demonstrated that the proposed architecture maintains a high speed for larger binary fields, making it more suitable to be implemented in large-bit-length platforms with a higher security level.Since the multiplier and its segments work in different bit-length and refer to different fields, the proposed architecture can also be upgraded to a reconfigurable design to support multiple-field point multiplication in the future.
Defending Our Public Biological Databases as a Global Critical Infrastructure

Progress in modern biology is being driven, in part, by the large amounts of freely available data in public resources such as the International Nucleotide Sequence Database Collaboration (INSDC), the world's primary database of biological sequence (and related) information. INSDC and similar databases have dramatically increased the pace of fundamental biological discovery and enabled a host of innovative therapeutic, diagnostic, and forensic applications. However, as high-value, openly shared resources with a high degree of assumed trust, these repositories share compelling similarities to the early days of the Internet. Consequently, as public biological databases continue to increase in size and importance, we expect that they will face the same threats as undefended cyberspace. There is a unique opportunity, before a significant breach and loss of trust occurs, to ensure they evolve with quality and security as a design philosophy rather than costly "retrofitted" mitigations. This Perspective surveys some potential quality assurance and security weaknesses in existing open genomic and proteomic repositories, describes methods to mitigate the likelihood of both intentional and unintentional errors, and offers recommendations for risk mitigation based on lessons learned from cybersecurity.
Of Smarthomes, IoT Plants, and Implicit Interaction Design

There seems to be a danger to carelessly replace routine tasks in homes through automation with IoT-technology. But since routines, such as watering houseplants also have positive influences on inhabitants' wellbeing, they should be transformed through carefully performed designs. To this end, an attempt to use technology for augmenting a set of houseplants' non-verbal communication capabilities is presented. First, we describe in detail how implicit interactions have been designed to support inhabitants in watering their plants through meaningful interactions. Then, we report on a field study with 24 participants, comparing two alternative design implementations based on contrasting embodied interaction technologies (i.e., augmented reality and embedded computing technology). The study results highlight shortcomings of today's smartphone mediated augmented reality compared to physical interface alternatives, considering measurements of perceived attractiveness and expected effects on determinants of wellbeing, and discusses potentials of combining both modalities for future solutions.
Scalable High-Order Gaussian Process Regression

While most Gaussian processes (GP) work focus on learning single-output functions, many applications, such as physical simulations and gene expressions prediction, require estimations of functions with many outputs. The number of outputs can be much larger than or comparable to the size of training samples. Existing multi-output GP models either are limited to low-dimensional outputs and restricted kernel choices, or assume oversimplified low-rank structures within the outputs. To address these issues, we propose HOGPR, a High-Order Gaussian Process Regression model, which can flexibly capture complex correlations among the outputs and scale up to a large number of outputs. Specifically, we tensorize the high-dimensional outputs, introducing latent coordinate features to index each tensor element (i.e., output) and to capture their correlations. We then generalize a multilinear model to a hybrid of a GP and latent GP model. The model is endowed with a Kronecker product structure over the inputs and the latent features. Using the Kronecker product properties and tensor algebra, we are able to perform exact inference over millions of outputs. We show the advantage of the proposed model on several real-world applications.
Improved Related-Cipher Attack on Salsa20 Stream Cipher

The Salsa20 stream cipher was designed by Bernstein in 2005 as a candidate for eSTREAM and Salsa20/12 was accepted in the eSTREAM software portfolio in 2008. In this paper, we present an improved related-cipher attack on Salsa20. If a secret key is used in Salsa20/12 and Salsa20/8 with 2 different IVs, we can recover the 256-bit secret key with time complexity of about 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">193.58</sup> , which improves the existing attack by a factor of 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">30.42</sup> . To the best of our knowledge, this is the best related-cipher attack on Salsa20 so far. Furthermore, we build a binary integer optimization model to search for the best related-cipher attack on Salsa20. The results show that our attack is the best related-cipher attack on Salsa20 in this model.
Image Steganalysis in Frequency Domain Using Co-Occurrence Matrix and Bpnn

In the last two decades, steganalysis has become a fertile research area to minimize the security risks left behind by Misuse of data concealment in digital computer files. As the propagation of hidden writing increased, the need for the steganalysis emerged and grew to a large extent necessary to deter illicit secret communications. This paper introduces a steganalysis system to detect hidden information in images through using co-occurrence matrix, frequency domain transform, the first three moments, and back propagation neural network (BPNN). Four varieties of the system implemented. Firstly, the co-occurrence matrix calculated for the input image, which suspected to be a carrier of hidden secret information. Second, three levels of discrete wavelet transform (DWT) are applied resulting in 12 subbands. Then, those subbands along with the original image are transformed by discrete Fourier transform (DFT) or discrete cosine transform (DCT) to produce 13 subbands. After that, the first three moments are calculated resulting feature vector with 39 features. Finally, BPNN is used as a classifier to determine whether the image is containing hidden information or not. The system is tested with and without co-occurrence matrix, each of them once using DFT and another time using DCT. The results have shown that using co-occurrence matrix with DFT has the highest performance, which was 81.82% on the Hiding Ratio of 0.5 bit per pixel. This work demonstrates a good effect comparing to previous works.
Optimal Orientation and Tilt Angle for Maximizing in-Plane Solar Irradiation for PV Applications in Japan

To maximize the direct insolation received by flat-plate photovoltaic (PV) modules, the tilt angle is usually the site’s latitude and the modules are oriented towards the equator. However, this may not be the optimal placement, as the local climatic conditions will influence the optimal orientation and tilt angle. Transposition models can be used to simulate the insolation on planes with various tilts and azimuths, using a single set of (horizontal) global and diffuse irradiance measurements. Following this method, five maps including optimal orientations, tilt angles, maximum annual tilted irradiations, percentage improvements of the optimally-tilted PV installation versus the conventional latitude-tilted PV installation, and annual diffuse fraction were plotted over the geographical area of Japan. Spatial patterns in these maps were observed and analyzed. The key contribution of this work is to establish a database of optimal PV installations in Japan. Compared to the conventional rule of thumb of tilting the module at latitude facing south, it is shown that the optimally tilted surface receives up to 2% additional annual solar irradiation.
When a computer speaks institutional talk: Exploring challenges and potentials of virtual assistants in face-to-face advisory services

Advisory services are a highly sensitive form of collaboration: they rely on a clear distribution of roles between human participants who act according to an implicit set of practices and scripts.As such, they do not offer a specific role to a virtual assistant.At the same time, the technological improvements make the promise that institutional settings may be soon complemented with technology that allows for asking questions using natural speech, understands the context, and provides answers based on online processing of data.This article explores challenges and potentials of virtual assistants in advisory services while analyzing data from interviews and a workshop with clients and advisors from financial advisory services.It links the insights from the field with the institutional talk perspective.The findings unveil, that the concerns and hopes of potential users relate to their position and an implicit understanding of what an advisory service is about.This calls for careful and attentive design approach towards virtual assistants in advisory services.
Development of a Consumer Health Vocabulary by Mining Health Forum Texts Based on Word Embedding: Semiautomatic Approach

Background: The vocabulary gap between consumers and professionals in the medical domain hinders information seeking and communication. Consumer health vocabularies have been developed to aid such informatics applications. This purpose is best served if the vocabulary evolves with consumers’ language.
Fast kernel extreme learning machine for ordinal regression

Ordinal regression is a special kind of machine learning problem, which aims to label patterns with an ordinal scale. Due to the ubiquitous existence of the ordering information in many practical cases, ordinal regression has received much attention and can be found in a great variety of applications. Meanwhile, Kernel Extreme Learning Machine (KELM), as the extension of Extreme Learning Machine in the framework of kernel learning, has shown its strength in many machine learning tasks. Nevertheless, existing KELM methods have paid little attention to ordinal regression especially in large-scale situations. In this paper, we consider the kernel technique and ordinal scales in labels at the same time, and propose a new KELM model for ordinal regression by exploiting a quadratic cost-sensitive encoding scheme. In order to make the training process more efficient in the big data scenario, a fast algorithm is designed based on the low rank approximation. The incomplete Cholesky factorization and the Sherman–Morrison–Woodbury formula are utilized together to avoid computing the inverse of the kernel matrix. The time complexity of the new algorithm is thus linear with the number of the training instances, which makes it suitable to the large-scale occasions. Numerical experiments on multiple public datasets validate the effectiveness and efficiency of the proposed methods.
Enhanced Vulnerable Pedestrian Detection using Deep Learning

Road forms an integral part of quotidian commute and yet remains equally unsafe for commuters especially for pedestrians. Among the various categories that exists for object detection, detecting pedestrians remain a daunting task owing to a large changeability in the background as well as severe overlapping or occlusion. Also achieving high accuracy and real time speed pose a serious challenge since pedestrian detection should be both accurate as well as fast enough to be deployed in real time. This paper addresses these requirements through the usage of depth wise separable convolution and Single Shot Detector framework employing different activation maps using OpenCV to achieve a reliable, robust and competent deep learning based pedestrian detection for real time operations.
Exploiting Noisy Data in Distant Supervision Relation Classification

Kaijia Yang, Liang He, Xin-yu Dai, Shujian Huang, Jiajun Chen. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). 2019.
Extracting Dialogical Explanations for Review Aggregations with Argumentative Dialogical Agents

The aggregation of online reviews is fast becoming the chosen method of quality control for users in various domains, from retail to entertainment. Consequently, fair, thorough and explainable aggregation of reviews is increasingly sought-after. We consider the movie review domain, and in particular Rotten Tomatoes' ubiquitous (and arguably over-simplified) aggregation method, the Tomatometer Score (TS). For a movie, this amounts to the percentage of critics giving the movie a positive review. We define a novel form of argumentative dialogical agent (ADA) for explaining the reasoning within the reviews. ADA integrates: 1.) NLP with reviews to extract a Quantitative Bipolar Argumentation Framework (QBAF) for any chosen movie to provide the underlying structure of explanations, and 2.) gradual semantics for QBAFs for deriving a dialectical strength measure for movies, as an alternative to the TS, satisfying desirable properties for obtaining explanations. We evaluate ADA using some prominent NLP methods and gradual semantics for QBAFs. We show that they provide a dialectical strength which is comparable with the TS, while at the same time being able to provide dialogical explanations of why a movie obtained its strength via interactions between the user and ADA.
Experimentation of STAD and Jigsaw Learning Models on Learning Achievements in terms of Learning Motivation

The purpose of this study was to compare the value of the pretest and posttest in the experimentation of the STAD and Jigsaw learning models on learning achievement in terms of learning motivation. This research is a quantitative research with an experimental method. The population of this study was all X grade students of IPS of SMA Negeri 3 Boyolali in the Academic Year of 2017/2018with a total of 108 students, the study sample was determined by a cluster sampling technique of 2 classes with a total sample of 72 students. The learning model in the experimental class used the STAD method and the control class used the Jigsaw method. Data collection techniques in this study were the method of documentation, tests, and questionnaires. The research hypothesis test used two-way analysis of variance with a 2x2 factorial design and a significant level of 5%. The analysis prerequisite test that is carried out is the normality test and homogeneity test using SPSS 23. From the results of the analysis it can be concluded that (1) differences in the effect of economic learning achievement between students who learn using STAD and Jigsaw cooperative learning models; (2) differences in the effect oflearning motivation of students using STAD and Jigsaw cooperative models on economic learning achievement; (3) whether there is an influence of the interaction between the learning model and the learning motivation of students on economic learning achievement.
A Predictive Guidance Obstacle Avoidance Algorithm for AUV in Unknown Environments

A predictive guidance obstacle avoidance algorithm (PGOA) in unknown environments is proposed for autonomous underwater vehicle (AUV) that must adapt to multiple complex obstacle environments. Using the environmental information collected by the Forward-looking Sonar (FLS), the obstacle boundary is simplified by the convex algorithm and Bessel interpolation. Combining the predictive control secondary optimization function and the obstacle avoidance weight function, the predicting obstacle avoidance trajectory parameters are obtained. According to different types of obstacle environments, the corresponding obstacle avoidance rules are formulated. Lastly, combining with the obstacle avoidance parameters and rules, the AUV’s predicting obstacle avoidance trajectory point is obtained. Then AUV can successfully achieve obstacle avoidance using the guidance algorithm. The simulation results show that the PGOA algorithm can better predict the trajectory point of the obstacle avoidance path of AUV, and the secondary optimization function can successfully achieve collision avoidance for different complex obstacle environments. Lastly, comparing the execution efficiency and cost of different algorithms, which deal with various complex obstacle environments, simulation experiment results indicate the high efficiency and great adaptability of the proposed algorithm.
Crowdsourced Traffic Event Detection and Source Reputation Assessment Using Smart Contracts

Real-time data about various traffic events and conditions—offences, accidents, dangerous driving, or dangerous road conditions—is crucial for safe and efficient transportation. Unlike roadside infrastructure data which are often limited in scope and quantity, crowdsensing approaches promise much broader and comprehensive coverage of traffic events. However, to ensure safe and efficient traffic operation, assessing trustworthiness of crowdsourced data is of crucial importance; this also includes detection of intentional or unintentional manipulation, deception, and spamming. In this paper, we design and demonstrate a road traffic event detection and source reputation assessment system for unreliable data sources. Special care is taken to adapt the system for operation in decentralized mode, using smart contracts on a Turing-complete blockchain platform, eliminating single authority over such systems and increasing resilience to institutional data manipulation. The proposed solution was evaluated using both a synthetic traffic event dataset and a dataset gathered from real users, using a traffic event reporting mobile application in a professional driving simulator used for driver training. The results show the proposed system can accurately detect a range of manipulative and misreporting behaviors, and quickly converges to the final trust score even in a resource-constrained environment of a blockchain platform virtual machine.
Dual Asymmetric Deep Hashing Learning

Due to the impressive learning power, deep learning has achieved a remarkable performance in supervised hash function learning. In this paper, we propose a novel asymmetric supervised deep hashing method to preserve the semantic structure among different categories and generate the binary codes simultaneously. Specifically, two asymmetric deep networks are constructed to reveal the similarity between each pair of images according to their semantic labels. Furthermore, since the binary codes in the Hamming space also should keep the semantic affinity existing in the original space, another asymmetric pairwise loss is introduced to capture the similarity between the binary codes and real-value features. This asymmetric loss not only improves the retrieval performance, but also contributes to a quick convergence at the training phase. By taking advantage of the two-stream deep structures and two types of asymmetric pairwise functions, an alternative algorithm is designed to efficiently optimize the deep features and high-quality binary codes. Experimental results on three real-world datasets substantiate the effectiveness and superiority of our approach as compared with the state-of-the-art.