Reviewerly/exHarmony huggingface dataset:
Use of support vector machines with a parallel local search algorithm for data classification and feature selection

Over the last decade, the number of studies on machine learning has significantly increased. One of the most widely researched areas of machine learning is data classification. Most big data systems require a large amount of information storage for analytic purposes; however, this involves some disadvantages, such as the costs of processing and collecting data. Thus, many researchers and practitioners are working on effectively reducing the number of features used in classification. This paper proposes a method which jointly optimizes both feature selection and classification. A survey of the relevant literature shows that the vast majority of studies focus on either feature selection or classification. In this study, the proposed parallel local search algorithm both selects features and finds a classifier with high rates of accuracy. Moreover, the proposed method is capable of finding solutions for problems that have extremely high numbers of features within a reasonable computation time.
Digital Forensic Investigation Framework for Internet of Things (IoT): A Comprehensive Approach

Internet of Things (IoT) is receiving more and more attention in recent years. Even though a great number of researches have been going on focusing the field IoT, research on Digital Forensics Investigation (DFI) techniques are still little focused. Till now IoT platforms have not fully matured to adapt with the existing Digital Forensic(DF) tools, methods, and procedures. The prime reason is the nature of the cloud, network and IoT infrastructures (e.g. heterogeneity, distributed, jurisdiction, data duplication, multi-tenancy etc.). As a result, it is a very challenging task of locating, identifying, examining, analyzing, and presenting the potential IoT-based forensic evidence for digital forensic from the device, Network and, cloud environment. Consequently, there is no fully adapted international standard for an ideal framework to conduct DFIs. Because of this, we are proposing a comprehensive DFI process framework for the IoT environment that is able to enable more efficient and effective for DFI. The aim of this paper is to present a more comprehensible DFI framework for digital forensic professionals and experts. Also, it would be a proper guideline to guide the investigators and would also be useful in training of the fledgling i nvestigators. M oreover, i t r educes the dependence on the Cloud Service Provider (CSP) at the time investigation is going on.
High- and low-level chaos in the time and frequency market returns of leading cryptocurrencies and emerging assets

Inspired by the insurgence of the decade-old cryptocurrency market and its gradual acceptance into mainstream finance, this paper examines the evolving dynamic characteristics of the new currencies, in the midst of diverse emerging assets of the BRICS. We test for chaos in the time and scale return samples using LLE estimations. Our results accept (reject) chaotic structure for almost all the markets at the weekly (full and intraweek) data samples, and further found no copious disparity amongst the dynamic structure of the markets, contrary to the widely reported weak connectedness and somehow isolated cryptocurrencies from other financial assets. The findings hold implications for asset return forecasting, investment risk and regulatory policy.
Multi-Source Neural Machine Translation With Missing Data

Machine translation is rife with ambiguities in word ordering and word choice, and even with the advent of machine-learning methods that learn to resolve this ambiguity based on statistics from large corpora, mistakes are frequent. Multi-source translation is an approach that attempts to resolve these ambiguities by exploiting multiple inputs (e.g. sentences in three different languages) to increase translation accuracy. These methods are trained on multilingual corpora, which include the multiple source languages and the target language, and then at test time uses information from both source languages while generating the target. While there are many of these multilingual corpora, such as multilingual translations of TED talks or European parliament proceedings, in practice, many multilingual corpora are not complete due to the difficulty to provide translations in all of the relevant languages. Existing studies on multi-source translation did not explicitly handle such situations, and thus are only applicable to complete corpora that have all of the languages of interest, severely limiting their practical applicability. In this article, we examine approaches for multi-source neural machine translation (NMT) that can learn from and translate such incomplete corpora. Specifically, we propose methods to deal with incomplete corpora at both training time and test time. For training time, we examine two methods: (1) a simple method that simply replaces missing source translations with a special NULL symbol, and (2) a data augmentation approach that fills in incomplete parts with source translations created from multi-source NMT. For test-time, we examine methods that use multi-source translation even when only a single source is provided by first translating into an additional auxiliary language using standard NMT, then using multi-source translation on the original source and this generated auxiliary language sentence. Extensive experiments demonstrate that the proposed training-time and test-time methods both significantly improve translation performance.
One For All: A Mutual Enhancement Method for Object Detection and Semantic Segmentation

Generally, most approaches using methods such as cropping, rotating, and flipping achieve more data to train models for improving the accuracy of detection and segmentation. However, due to the difficulties of labeling such data especially semantic segmentation data, those traditional data augmentation methodologies cannot help a lot when the training set is really limited. In this paper, a model named OFA-Net (One For All Network) is proposed to combine object detection and semantic segmentation tasks. Meanwhile, using a strategy called “1-N Alternation” to train the OFA-Net model, which can make a fusion of features from detection and segmentation data. The results show that object detection data can be recruited to better the segmentation accuracy performance, and furthermore, segmentation data assist a lot to enhance the confidence of predictions for object detection. Finally, the OFA-Net model is trained without traditional data augmentation methodologies and tested on the KITTI test server. The model works well on the KITTI Road Segmentation challenge and can do a good job on the object detection task.
SD-GAN: Saliency-Discriminated GAN for Remote Sensing Image Superresolution

Recently, convolutional neural networks have shown superior performance in single-image superresolution. Although existing mean-square-error-based methods achieve high peak signal-to-noise ratio (PSNR), they tend to generate oversmooth results. Generative adversarial network (GAN)-based methods can provide high-resolution (HR) images with higher perceptual quality, but produce pseudotextures in images, which generally leads to lower PSNR. Besides, different regions in remote sensing images (RSIs) reflect discrepant surface topography and visual characteristics. This means a uniform reconstruction strategy may not be suitable for all targets in RSIs. To solve these problems, we propose a novel saliency-discriminated GAN for RSI superresolution. First, hierarchical weakly supervised saliency analysis is introduced to compute a saliency map, which is subsequently employed to distinguish the diverse demands of regions in the following generator and discriminator part. Different from previous GANs, the proposed residual dense saliency generator takes saliency maps as a supplementary condition in the generator. Simultaneously, combining the characteristic of RSIs, we design a new paired discriminator to enhance the perceptual quality, which measures the distance between generated images and HR images in salient areas and nonsalient areas, respectively. Comprehensive evaluations validate the superiority of the proposed model.
Segmentation of Coronary Arteries from CTA axial slices using Deep Learning techniques

Coronary Artery disease (CAD) is a type of Cardio Vascular disease caused due to disorders in blood vessels of the heart. Stenosis is sudden narrowing or blockage in coronary arteries and this happens because of cholesterol formation, fatty deposition, and damages in blood cells. In order to detect stenosis in Computed Tomography Angiography (CTA) images, the segmentation of the coronary artery is essential. In this paper, we propose a deep learning based model to segment the coronary arteries from 2D slices of CTA heart images and reconstruct them into 3D coronary artery. The coronary arteries may not present in all the 2D slices, so a combination of Convolutional Neural Network and Recurrent Neural Network (CNN-RNN) model is used to identify the presence of coronary arteries in the 2D slices. The identified coronary arteries are then segmented using the U-Net model. The segmented coronary arteries are then reconstructed into 3D images using the Maximum Intensity Projection (MIP) reconstruction algorithm in order to analyze the presence of stenosis. The proposed system was evaluated for segmentation using IOU (Intersection Over Union) and for reconstruction using Structure Similarity Index metric (SSIM) for the CTA images obtained from Bilroth hospitals, Chennai.
An Adaptive Gaussian Sum Kalman Filter Based on a Partial Variational Bayesian Method

In this article, we address the online state estimation problem of linear discrete-time systems in the presence of inaccurate and slowly time-varying non-Gaussian measurement noise (NGMN). Recently, the variational Bayesian (VB) method has been successfully used to jointly estimate the system state along with the statistics of the unknown Gaussian measurement noise. However, we prove that the original VB method for the non-Gaussian state-space models, modeled by the Gaussian mixture distributions, is analytically intractable. To overcome this problem, we propose a partial VB-based adaptive Gaussian sum Kalman filter, which uses a feedback-based filtering framework to independently calculate the posterior distribution of the state and posterior distribution of the NGMN. Experimental results demonstrate the effectiveness of the proposed filter.
Uncovering Data Landscapes through Data Reconnaissance and Task Wrangling

Domain experts are inundated with new and heterogeneous types of data and require better and more specific types of data visualization systems to help them. In this paper, we consider the data landscape that domain experts seek to understand, namely the set of datasets that are either currently available or could be obtained. Experts need to understand this landscape to triage which data analysis projects might be viable, out of the many possible research questions that they could pursue. We identify data reconnaissance and task wrangling as processes that experts undertake to discover and identify sources of data that could be valuable for some specific analysis goal. These processes have thus far not been formally named or defined by the research community. We provide formal definitions of data reconnaissance and task wrangling and describe how they relate to the data landscape that domain experts must uncover. We propose a conceptual framework with a four-phase cycle of acquire, view, assess, and pursue that occurs within three distinct chronological stages, which we call fog and friction, informed data ideation, and demarcation of final data. Collectively, these four phases embedded within three temporal stages delineate an expert's progressively evolving understanding of the data landscape. We describe and provide concrete examples of these processes within the visualization community through an initial systematic analysis of previous design studies, identifying situations where there is evidence that they were at play. We also comment on the response of domain experts to this framework, and suggest design implications stemming from these processes to motivate future research directions. As technological changes will only keep adding unknown terrain to the data landscape, data reconnaissance and task wrangling are important processes that need to be more widely understood and supported by the data visualization tools. By articulating a concrete understanding of this challenge and its implications, our work impacts the design and evaluation of data visualization systems.
The Links between Business Environment, Economic Growth and Social Equity: A Study of African Countries

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Investigating Searchers’ Mental Models to Inform Search Explanations

Modern web search engines use many signals to select and rank results in response to queries. However, searchers’ mental models of search are relatively unsophisticated, hindering their ability to use search engines efficiently and effectively. Annotating results with more in-depth explanations could help, but search engine providers need to know what to explain. To this end, we report on a study of searchers’ mental models of web selection and ranking, with more than 400 respondents to an online survey and 11 face-to-face interviews. Participants volunteered a range of factors and showed good understanding of important concepts such as popularity, wording, and personalization. However, they showed little understanding of recency or diversity and incorrect ideas of payment for ranking. Where there are already explanatory annotations on the results page—such as “ad” markers and keyword highlighting—participants were familiar with ranking concepts. This suggests that further explanatory annotations may be useful.
The impact of extraneous features on the performance of recurrent neural network models in clinical tasks

Electronic Medical Records (EMR) are a rich source of patient information, including measurements reflecting physiologic signs and administered therapies. Identifying which variables or features are useful in predicting clinical outcomes can be challenging. Advanced algorithms, such as deep neural networks, were designed to process high-dimensional inputs containing variables in their measured form, thus bypass separate feature selection or engineering steps. We investigated the effect of extraneous input features on the predictive performance of Recurrent Neural Networks (RNN) by including in the input vector extraneous features that were randomly drawn from theoretical and empirical distributions. RNN models using different input vectors (EMR features only; EMR and extraneous features; extraneous features only) were trained to predict three clinical outcomes: in-ICU mortality, 72-h ICU re-admission, and 30-day ICU-free days. The measured degradations of the RNN’s predictive performance with the inclusion of extraneous features to EMR variables were negligible.
Quantum Attacks Against Type-1 Generalized Feistel Ciphers and Applications to CAST-256

Generalized Feistel Schemes (GFSs) are important components of symmetric ciphers, which have been extensively studied in the classical setting. However, detailed security evaluations of GFS in the quantum setting still remain to be explored. In this paper, we give improved polynomial-time quantum distinguishers on Type-1 GFS in quantum chosen-plaintext attack (qCPA) setting and quantum chosen-ciphertext attack (qCCA) setting. In qCPA setting, we give a new quantum polynomial-time distinguisher on $$(3d-3)$$ -round Type-1 GFS with branches $$d\ge 3$$ , which gains $$(d-2)$$ more rounds than the previous distinguishers. This leads us to obtain a better key-recovery attack with reduced time complexities by a factor of $$2^{\frac{(d-2)n}{2}}$$ , where n is the bit length of the branch. We also show a quantum distinguishing attack against $$(d^2-d+1)$$ -round version in qCCA setting, and this gives a key-recovery attack with much lower time complexity. In addition, based on a 14-round quantum distinguisher, we give quantum key-recovery attacks on round-reduced CAST-256 block cipher. For the 256-bit key version, we could attack up to 20-round CAST-256 in time $$2^{111}$$ , which is faster than the quantum brute-force attack by a factor of $$2^{17}$$ . For the 128-bit key version, we could attack 17 rounds in time $$2^{55.5}$$ , while the best previous classical or quantum attacks are no more than 16 rounds.
Analyzing Wildland Fire Smoke Emissions Data Using Compositional Data Techniques

Abstract By conservation of mass, the mass of wildland fuel that is pyrolyzed and combusted must equal the mass of smoke emissions, residual char, and ash. For a given set of conditions, these amounts are fixed. This places a constraint on smoke emissions data that violates key assumptions for many of the statistical methods ordinarily used to analyze these data such as linear regression, analysis of variance, and t tests. These data are inherently multivariate, relative, and nonnegative parts of a whole and are then characterized as so‐called compositional data. This paper introduces the field of compositional data analysis to the biomass burning emissions community and provides examples of statistical treatment of emissions data. Measures and tests of proportionality, unlike ordinary correlation, allow one to coherently investigate associations between parts of the smoke composition. An alternative method based on compositional linear trends was applied to estimate trace gas composition over a range of combustion efficiency that reduced prediction error by 4% while avoiding use of modified combustion efficiency as if it were an independent variable. Use of log‐ratio balances to create meaningful contrasts between compositional parts definitively stressed differences in smoke emissions from fuel types originating in the southeastern and southwestern United States. Application of compositional statistical methods as an appropriate approach to account for the relative nature of data about the composition of smoke emissions and the atmosphere is recommended.
Feature Consistency Training With JPEG Compressed Images

Deep neural networks (DNNs) are recently found to be vulnerable to JPEG compression artifacts, which distort the feature representations of DNNs leading to serious accuracy degradation. Most existing training methods which aim to address this problem add compressed images to the training data to enhance the robustness of DNNs. However, their improvements are limited since these methods usually regard the compressed images as new training samples instead of distorted samples. The feature distortions between the raw images and the compressed images are not investigated. In this work, we propose a new training method, called Feature Consistency Training, that is designed to minimize the feature distortions caused by JPEG artifacts. At each training iteration, we simultaneously input a raw image and its compressed version with a randomly sampled quality into a DNN model and extract the features from the internal layers. By adding feature consistency constraint to the objective function, the feature distortions in the representation space are minimized in order to learn robust filters. Besides, we present a residual mapping block which takes the quality factor of the compressed image as an additional information to further reduce the feature distortion. Extensive experiments demonstrate that our method outperforms several existed training methods on JPEG compressed images. Furthermore, DNN models trained by our method are found to be more robust to unseen distortions.
SUN-Spot: An RGB-D Dataset With Spatial Referring Expressions

We introduce a new dataset, SUN-Spot, for localizing objects using spatial referring expressions (REs). SUN-Spot is the only RE dataset which uses RGB-D images. It also contains a greater average number of spatial prepositions and more cluttered scenes than previous RE datasets. Using a simple baseline, we show that including a depth channel in RE models can improve performance on both generation and comprehension.
A high-precision calibration approach for Camera-IMU pose parameters with adaptive constraints of multiple error equations

Abstract In the calibration of the pose parameters of a camera and inertial measurement unit (Camera-IMU), the camera depth information is unreliable due to the uneven spatial distribution of calibration points, because the calibration points have random errors due to the IMU drift and the inadequate robustness of stereovision and because the Camera-IMU pose parameters lack self-adaptation. This paper proposes a high-precision calibration approach for Camera-IMU pose parameters with adaptive constraints of multiple error equations (adaptive constraint calibration approach, ACCA). The approach calibrates pose parameters of the Camera-IMU jointly via error equations, such as lens distortion correction, camera parallax correction and error compensation of the inertial sensor. The experimental results show that the calibration approach for Camera-IMU pose parameters with adaptive constraints of multiple error equations improves the measurement accuracy by 84.0% and can effectively suppress IMU drift with good robustness.
Detecting Hot Topics From Academic Big Data

Detecting hot topics from massive academic data is a very challenging task.Because various types of academic information are overgrowing, e.g., papers, news, and blogs, which has gone far beyond the limits that researchers can accept.Therefore, how to efficiently and accurately detect hot topics from big academic data is the main problem that researchers are facing.In view of this, we design a general framework for Academic Hot Topic Detection (AHTD).Specifically, in this framework, a DeepWalk-based keyword extraction algorithm for a single paper (S-DWKE) is proposed to detect popular topics in diverse academic fields dynamically.Moreover, we propose a keyword extraction algorithm to extract keywords from multiple articles (M-GCKE), which enables us to detect new topics in emerging academic areas.Then, hot topics can be generated from keywords extracted by the S-DWKE and M-GCKE.A large number of experiments demonstrates the proposed framework effectively improves the performance of hot topic detection in the academic field and performs better than the comparison algorithms.We have applied the above work to the ''Academic Headline'' application to provide the hot topics for researchers.
Ego-Semantic Labeling of Scene from Depth Image for Visually Impaired and Blind People

This work is devoted to scene understanding and motion ability improvement for visually impaired and blind people. We investigate how to exploit egocentric vision to provide semantic labeling of scene from head-mounted depth camera. More specifically, we propose a new method for locating ground from depth image whatever the camera's pose. The rest of planes of the scene are located using RANSAC method, semantically coded by their attributes and mapped as cylinders into a generated 3D scene which will serve as a feedback to users. Experiments are conducted and the obtained results are discussed.