Reviewerly/exHarmony huggingface dataset:
A Bayesian Model for Bivariate Causal Inference

We address the problem of two-variable causal inference without intervention. This task is to infer an existing causal relation between two random variables, i.e. $X \rightarrow Y$ or $Y \rightarrow X$ , from purely observational data. As the option to modify a potential cause is not given in many situations only structural properties of the data can be used to solve this ill-posed problem. We briefly review a number of state-of-the-art methods for this, including very recent ones. A novel inference method is introduced, Bayesian Causal Inference (BCI), which assumes a generative Bayesian hierarchical model to pursue the strategy of Bayesian model selection. In the adopted model the distribution of the cause variable is given by a Poisson lognormal distribution, which allows to explicitly regard the discrete nature of datasets, correlations in the parameter spaces, as well as the variance of probability densities on logarithmic scales. We assume Fourier diagonal Field covariance operators. The model itself is restricted to use cases where a direct causal relation $X \rightarrow Y$ has to be decided against a relation $Y \rightarrow X$ , therefore we compare it other methods for this exact problem setting. The generative model assumed provides synthetic causal data for benchmarking our model in comparison to existing State-of-the-art models, namely LiNGAM , ANM-HSIC , ANM-MML , IGCI and CGNN . We explore how well the above methods perform in case of high noise settings, strongly discretized data and very sparse data. BCI performs generally reliable with synthetic data as well as with the real world TCEP benchmark set, with an accuracy comparable to state-of-the-art algorithms. We discuss directions for the future development of BCI .
Prospects of Blockchain-Based Information Systems for the Protection of Intellectual Property

The article analyzes the possibilities of maintaining the register of intellectual property objects using blockchain technologies. Such registries have a number of advantages in comparison with traditional methods of registration and provide more opportunities for rightholders. However, they also have certain disadvantages, primarily due to the lack of full-fledged mechanisms for resolving intellectual property disputes. This problem requires not only technical, but also organizational and legal solutions. The authors conclude that the prospects for the development of blockchain platforms for registering and managing intellectual property are linked to the possibility of dividing such registries into two types. The first one is the registries of IP objects on the permissionless blockchain platform. Information about IP objects and their authors (owners) contained in them will not be official. The second type is the registries of IP objects on the basis of permission blockchain technologies, in which superuser rights will be granted to government bodies authorized in the field of IP protection. Entries in them will be official and will have validating rights value.
Random phase retrieval approach using Euclidean matrix norm of sum and difference map and fast least-squares algorithm

In order to meet the requirements of the in-situ optical measurement, a random phase retrieval approach using the Euclidean matrix norm of sum and difference map and fast least-squares algorithm (EMNSD&FLSA) is proposed. It can obtain the high accuracy as the iterative algorithm and cost less computational time as the non-iterative algorithm simultaneously. No pre-filtering, iteration and relatively accurate initial phase lead to the high accuracy, fast iterative method and only two phase shifted interferograms lead to the high speed. It can obtain absolutely accurate result when the background intensity and modulation amplitude are perfect. Moreover, it is suitable for different levels of noises. If the high accuracy is required, it would be best to suppress the noise before using EMNSD&FLSA, and the phase shift would be best to far away from 0 rad and π rad, furthermore, the numbers of patterns are best to be more than 2. Last but not least, it is robust for the circular, straight or complex fringes. The simulations and experiments verify the correctness and feasibility of EMNSD&FLSA.
Investigating Paradigms of Group Territory in Multiple Display Environments

Multiple-display environments (MDEs) have promise in helping co-located sensemaking tasks by supporting searching, organizing, and discussion tasks. Co-located sensemaking occurs when two or more sensemakers forage for useful information within a dataset, creating and leveraging knowledge structures individually and together. Group territories in MDEs support communicating and assembling findings, but questions remain regarding how to best represent individual sensemaking efforts in the group territory to support the sensemaking collaboration. This paper empirically examines exploration of a large Twitter dataset using three group territory paradigms: parallel, connected, and merged. Results reveal that merging group work increases task complexity while separating individuals' sections in the group territory supports monitoring, and more interactions are performed when individual work is not connected in the group territory.
Image Enhancement Using Successive Mean Quantization Transform and Homomorphic Filtering

Low contrast images are not conducive to human perception and computer vision algorithms due to their low discernibility. In many application quality of acquired images are not suitable, so image has to be enhanced according to the need of a specific application. To solve the problems of detail loss, poor contrast and problems of noise, a new technique based on homomorphic filtering is proposed. This paper contributes to combine the filter based and Successive Mean Quantization Transform (SMQT) algorithm for better image quality of digital image by minimizing the properties such as gain and bias. First, we investigate the result for the standard images and experimental images. Second, we measure the results by using Peak Signal-to-Noise Ratio (PSNR), Structural Similarity (SSIM), blind/reference less image spatial quality evaluator (BRISQUE) and Blind Image Quality Indices (BIQI). Finally, model is compared with enhancement algorithms. Simulation and experimental results demonstrates that proposed model provides better results as compared to other state-of-art contrast enhancement algorithms. The implementation is done using MATLAB.
Characteristics that affect preference of decision models for asset selection: an industrial questionnaire survey

Abstract Modern software development relies on a combination of development and re-use of technical asset, e.g., software components, libraries, and APIs. In the past, re-use was mostly conducted with internal assets but today external; open source, customer off-the-shelf (COTS), and assets developed through outsourcing are also common. This access to more asset alternatives presents new challenges regarding what assets to optimally chose and how to make this decision. To support decision-makers, decision theory has been used to develop decision models for asset selection. However, very little industrial data has been presented in literature about the usefulness, or even perceived usefulness, of these models. Additionally, only limited information has been presented about what model characteristics determine practitioner preference toward one model over another. The objective of this work is to evaluate what characteristics of decision models for asset selection determine industrial practitioner preference of a model when given the choice of a decision model of high precision or a model with high speed. An industrial questionnaire survey is performed where a total of 33 practitioners, of varying roles, from 18 companies are tasked to compare two decision models for asset selection. Textual analysis and formal and descriptive statistics are then applied on the survey responses to answer the study’s research questions. The study shows that the practitioners had clear preference toward the decision model that emphasized speed over the one that emphasized decision precision. This conclusion was determined to be because one of the models was perceived faster, had lower complexity, was more flexible in use for different decisions, and was more agile on how it could be used in operation, its emphasis on people, its emphasis on “good enough” precision and ability to fail fast if a decision was a failure. Hence, we found seven characteristics that the practitioners considered important for their acceptance of the model. Industrial practitioner preference, which relates to acceptance, of decision models for asset selection is dependent on multiple characteristics that must be considered when developing a model for different types of decisions such as operational day-to-day decisions as well as more critical tactical or strategic decisions. The main contribution of this work are the seven identified characteristics that can serve as industrial requirements for future research on decision models for asset selection.
Light Field Salient Object Detection via Hybrid Priors

In this paper, we propose a salient object detection model on light field via hybird priors. The proposed model extracts four feature maps, including region contrast, background prior, depth prior and surface orientation prior maps. After that, the priors fusion stage is implemented to obtain and optimize the final salient object map. To verify the validity of the proposed model, comprehensive performance evaluation and comparative analysis are conducted on the public datasets LFSD and HFUT-Lytro. Experimental results show that the proposed method is superior to the existing light field saliency object detection model on the public two datasets.
SiamVGG-LLC: Visual Tracking Using LLC and Deeper Siamese Networks

Recently, visual tracking has been widely concerned in computer vision applications such as behavior analysis, autonomous driving with advanced trackers. Some trackers based on Discriminative Correlation Filters (DCF) have acquired great performance on benchmark, but it is not satisfied with the real-time visual tracking; Later, some people combined Deep Neural Networks (DNNs) and DCF to form some trackers, which has significantly improved the tracking accuracy and speed. Unfortunately, it cannot be applied to long-term visual tracking in many public places. To balance accuracy and speed of tracking, combining the siamese network and VGG is advanced tracker in long-term visual tracking. However, SiamVGG get an unsatisfactory result when the object is in background clutters and motion blur. In this paper, we propose a method called SiamVGG-LLC that combining the LLC (Locality-constrained Linear Coding) with SiamVGG to achieve visual tracking. Experimental results demonstrate that our method can obtain better accuracy on OTB-100 datasets when the object is in background clutters and motion blur.
Elemental Distribution: Overview

The elemental distribution of the ocean supports the role of the ocean in the Earth system and enables scientists to address questions regarding the pace of environmental change. In the past decades new sampling systems have enabled scientists to pursue systematic surveys of ocean chemistry at extremely low concentrations. In doing so, we are able to work towards a comprehensive categorization of the vertical profiles of distinct element classes.
The effect of using negative knowledge based intelligent tutoring system evaluator software to the academic success in English language education

This research was conducted to determine the effects of the using negative knowledge-based Intelligent Teaching System (ITS) evaluator software in the assessment and evaluation processes of English Teaching as a Foreign language. Experimental design with the pretest-posttest control group was used in this study. The study group consists of 67 students who are studying at the Vocational College of Technical Sciences located in Canakkale/Turkey. In this study, educational evaluator software based on correcting misconceptions developed by the researcher was used within the education process only in the experimental group. At the end of the fifth week following the 4-week experimental procedure, the academic achievement of the students was determined by a posttest. As a result, it was found to be a significant difference [t(33) = −7.13, p < .05] between the pretest-posttest academic achievement of the experimental group. In addition, there was no significant difference [t(65) = −1.15, p > .05] between the pretest scores of the experimental and control groups. There was a significant difference [t(65) = −2.25, p < .05] in posttest scores’ of control and experimental group students. As a result, using knowledge-based ITS evaluator software increased academic achievement much more than traditional education.
Mechanism and a new algorithm for nonconvex clustering

Nonconvex clustering is based on over-parameterization and uses a shrinkage term to achieve sparsity in the differences among individual centroids. In this paper, we elaborate the mechanism of nonconvex clustering from a new perspective by reformulating the basic alternating direction method of multipliers (ADMM) algorithm, and explain for the first time how nonconvex clustering can identify outliers that are far from the nearest cluster centre [Knorr EM, Ng RT. Algorithms for mining distance-based outliers in large datasets. Proceedings of the 24th VLDB Conference; New York, USA: 1998. p. 392–403]. To overcome the computational burden owing to complexity, inspired by the mechanism of nonconvex clustering, we develop another novel and accelerating algorithm called aBlock_ADMM, which combines adaptive block clustering and the ADMM algorithm. We evaluate the clustering performance of the new algorithm on both simulated and real data examples.
Deep Reinforcement Learning approach for Small Bodies Shape Reconstruction Enhancement

This paper tackles Autonomous Small Bodies Exploration (ASBE), i.e. the problem of mapping an unknown comet or asteroid while autonomously navigating in its proximity. A Deep Reinforcement Learning (DRL) based planning policy is designed to increase mapping efficiency with a smart autonomous selection of the images acquisition times. Such policy is verified with an Image Processing algorithm that generates a point cloud of the body shape model, based on the acquired images, via feature detection and matching.
Multiscale Texture Analysis and Color Coherence Vector Based Feature Descriptor for Multispectral Image Retrieval

Content Based Image Retrieval (CBIR) for remote sensing image data is a tedious process due to high resolution and complexity of image interpretation. Development of feature extraction technique is a major portion to represent the image content in an optimal way.In this paper, we propose a feature descriptor which combines the color coherent pixel information and GLCM texture features in multi scale domain.Curvelet transform is used to decompose the image into coarse and detail coefficients.Then Gabor magnitude is computed for each coefficient to improve the directional information.GLCM texture features are extracted from the Gabor magnitude response.The novel feature set by combining the CCV and GLCM using curvelet and Gabor filter is developed.Mahalanobis distance measure is used to find the similarity between query and feature database.Average Normalized Modified Retrieval Rate (ANMRR) is computed to evaluate the performance with the state of art methods.
A semantic approach for topic-based polarity detection: a case study in the Spanish language

In recent years, surprising amounts of news, messages, and reviews of products and services are generated in the online social media. Several efforts are being dedicated to detecting topics, as well as mining opinions in these unstructured texts. There are several approaches that compute opinion polarity, and some of them consider topics for their textual analysis. Nevertheless, discovering topics in opinions continues being challenging; due to opinions are generally short and informally written. Besides, opinions do not have a defined structure in several paragraphs; they are presented most of the time as a composition in a paragraph. In this paper, we propose a method to detect polarity in opinions by topics. Our proposal contributes to the fuzzy polarity calculation of detected topics in Spanish opinions. This method is comprised of three main steps: (1) preprocessing, (2) topic detection and (3) fuzzy polarity detection. It is important to notice the added values of this paper are: (1) the topic detection proposal based on the semantic processing when applying the clustering algorithm on opinion sentences, and (2) the evaluation of different aggregation operators for determining the opinion polarity from a fuzzy logic perspective. Aiming at assessing the quality of the resultant polarity detection by topics, we have conducted two main experiments over the Spanish corpus of opinions about Andalusian Hotels from the TripAdvisor site. The results have shown that our method is able to detect the topics correctly, as well as calculating their opinion polarities.
Rain Streak Removal from Video Sequence using Spatiotemporal Appearance

Capturing images in the challenging atmosphere for example during rain and snow critically degrades the quality of the images. These external phenomena create low contrast, blur and thereby reduce the visibility of the images. Many computer vision applications like visual traffic surveillance, intelligent vehicles, and entertainments are affected by reduced visibility. Rain streaks removal in a video (RSRV) has significant importance in the outdoor vision systems like surveillance video and has recently been studied comprehensively. In the last few years, many approaches have been made towards solving this problem including matrix decomposition, convolutional neural network, streak orientation modelling, Gaussian mixture modelling (GMM), etc. In this paper, we propose a concise model for RSRV based on the spatiotemporal appearance (STA) of rain streak in a video to exploit the rain streak appearance property. Here we apply Gaussian mixture modelling (GMM) to separate the background and foreground. Then, we use STA to separate the rain streak from moving foreground. The experimental results establish that the proposed method outperforms the state-of-the-art methods significantly for both real and synthetic rains.
A Novel Method Based on Optical Flow Combining with Wide Residual Network for Fall Detection

Fall is an abnormal activity event in daily life, which is becoming a major cause of accidental death for the elderly. The purpose of fall detection is to minimize serious consequences and negative impacts after falling. Recently, most conventional vision-based fall detection methods mainly rely on hand-crafted features, which is inclined to be influenced by noises, etc. In this paper, a novel method is proposed to detect fall event during a video sequence by combining optical flow and wide residual network. The wide residual network contains fewer layers and achieves the same performance as residual network, which can make the neural network train faster. In addition, to model the video motion, we adopt optical flow images as input to the wide residual network. And finally, softmax classifier is utilized to distinguish fall event. The experimental results also confirm the fact that the proposed algorithm can achieve a reliable accuracy.
A Survey of Discourse Representations for Chinese Discourse Annotation

A key element in computational discourse analysis is the design of a formal representation for the discourse structure of a text. With machine learning being the dominant method, it is important to identify a discourse representation that can be used to perform large-scale annotation. This survey provides a systematic analysis of existing discourse representation theories to evaluate whether they are suitable for annotation of Chinese text. Specifically, the two properties, expressiveness and practicality, are introduced to compare the representations of theories based on rhetorical relations and the representations of theories based on entity relations. The comparison systematically reveals linguistic and computational characteristics of the theories. After that, we conclude that none of the existing theories are quite suitable for scalable Chinese discourse annotation because they are not both expressive and practical. Therefore, a new discourse representation needs to be proposed, which should balance the expressiveness and practicality, and cover rhetorical relations and entity relations. Inspired by the conclusions, this survey discusses some preliminary proposals on how to represent the discourse structure that are worth pursuing.
ProReveal: Progressive Visual Analytics With Safeguards

We present a new visual exploration concept - Progressive Visual Analytics with Safeguards - that helps people manage the uncertainty arising from progressive data exploration. Despite its potential benefits, intermediate knowledge from progressive analytics can be incorrect due to various machine and human factors, such as a sampling bias or misinterpretation of uncertainty. To alleviate this problem, we introduce PVA-Guards, safeguards people can leave on uncertain intermediate knowledge that needs to be verified, and derive seven PVA-Guards based on previous visualization task taxonomies. PVA-Guards provide a means of ensuring the correctness of the conclusion and understanding the reason when intermediate knowledge becomes invalid. We also present ProReveal, a proof-of-concept system designed and developed to integrate the seven safeguards into progressive data exploration. Finally, we report a user study with 14 participants, which shows people voluntarily employed PVA-Guards to safeguard their findings and ProReveal's PVA-Guard view provides an overview of uncertain intermediate knowledge. We believe our new concept can also offer better consistency in progressive data exploration, alleviating people's heterogeneous interpretation of uncertainty.
Cybersecurity Governance on Social Engineering Awareness

Raising awareness on social engineering is becoming more essential. When we are connected to the internet, we are at the risk of becoming victims of numerous attacks. It is very difficult to protect ourselves from all of these threats, but the least we could do is not jeopardize our own safety. In order to achieve this, the need to have substantial knowledge on social engineering techniques and how to safeguard one from being victimized are mandatory. This research presents the overview of social engineering attacks. This plays a crucial role in supporting victims of cyber assaults.
Dynamic Temporal Mix-Zone Placement Approach for Location-Based Services Privacy

This paper proposes the Dynamic Temporal Mix-Zone Placement (DTMZP), a novel heuristic approach for the optimal mix-zone placement problem in Location-Based Services privacy. Mix-zone is a privacy model that changes the pseudonym of n mobile entities (e.g., vehicles) that cross a specific geographic region at a given time interval. We present a formal definition of DTMZP and evaluate it through simulations using the Cab-spotting dataset. We evaluate DTMZP considering the mix-zones coverage and pseudonymisation rates. The obtained results reveal a coverage rate of 88.56% and a pseudonymisation rate of 77.86% in the best scenario.