Reviewerly/exHarmony huggingface dataset:
Bayesian inference of causal effects from observational data in Gaussian graphical models

We assume that multivariate observational data are generated from a distribution whose conditional independencies are encoded in a Directed Acyclic Graph (DAG). For any given DAG, the causal effect of a variable onto another one can be evaluated through intervention calculus. A DAG is typically not identifiable from observational data alone. However, its Markov equivalence class (a collection of DAGs) can be estimated from the data. As a consequence, for the same intervention a set of causal effects, one for each DAG in the equivalence class, can be evaluated. In this paper, we propose a fully Bayesian methodology to make inference on the causal effects of any intervention in the system. Main features of our method are: (a) both uncertainty on the equivalence class and the causal effects are jointly modeled; (b) priors on the parameters of the modified Cholesky decomposition of the precision matrices across all DAG models are constructively assigned starting from a unique prior on the complete (unrestricted) DAG; (c) an efficient algorithm to sample from the posterior distribution on graph space is adopted; (d) an objective Bayes approach, requiring virtually no user specification, is used throughout. We demonstrate the merits of our methodology in simulation studies, wherein comparisons with current state-of-the-art procedures turn out to be highly satisfactory. Finally we examine a real data set of gene expressions for Arabidopsis thaliana.
Data challenges for public libraries: African perspectives and the social context of knowledge

This article sheds light on the collection and use of data by libraries in sixteen countries across Africa. It highlights the challenges that librarians and library organizations face in gathering, analyzing, and presenting data of various types for self-advocacy. In this study, qualitative data from a meeting of library representatives was analyzed to identify main challenges including: data integrity in terms of completeness, accuracy, credibility, and relevancy; infrastructure; capacity; local investment in libraries; time; and participation of data collectors and respondents. Implications for those collecting data on African libraries as well as those supporting the use of data in these contexts are discussed. The purpose of this paper is not to feed into representations of African libraries as chronically under-resourced and lacking in capacity, but rather, to constructively engage with first-hand accounts of how librarians are experiencing and navigating barriers in order to offer potential avenues forward for the field.
Single Backlit Image Enhancement

Backlit images are from an excessive reflection of light being opposite to a capturing device. The existing image enhancement methods cannot be directly applied to the backlit images because they are not designed to enhance both broad light and dark regions simultaneously. Moreover, the techniques have several limitations about over-saturation or losing contrast. This paper presents a single backlit image enhancement method based on the novel full-piecewise non-linear automatic stretching, without input parameter provided by a user, e.g., gamma and so on. The computer simulation results confirm that the proposed approach can (i) undoubtedly reveal hidden details in the dark region; (ii) preserve features and color of common and light (over-brightness) regions, and (iii) increase a local contrast of dark areas. The proposed approach tested on Li's backlit image database and several backlit images from commercial devices. The simulation results demonstrate the efficiency of the proposed approach and its advantages over the cutting-edge backlit image enhancement methods in perceptual quality.
All You Need is a Second Look: Towards Tighter Arbitrary Shape Text Detection

Deep learning-based scene text detection methods have progressed substantially over the past years. However, there remain several problems to be solved. Generally, long curve text instances tend to be fragmented because of the limited receptive field size of CNN. Besides, simple representations using rectangle or quadrangle bounding boxes fall short when dealing with more challenging arbitrary-shaped texts. In addition, the scale of text instances varies greatly which leads to the difficulty of accurate prediction through a single segmentation network. To address these problems, we innovatively propose a two-stage segmentation based arbitrary text detector named NASK (Need A Second looK). Specifically, NASK consists of a Text Instance Segmentation network namely TIS (1 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">st</sup> stage), a Text RoI Pooling module and a Fiducial pOint eXpression module termed as FOX (2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">nd</sup> stage). Firstly, TIS conducts instance segmentation to obtain rectangle text proposals with a proposed Group Spatial and Channel Attention module (GSCA) to augment the feature expression. Then, Text RoI Pooling transforms these rectangles to the fixed size. Finally, FOX is introduced to reconstruct text instances with a more tighter representation using the predicted geometrical attributes including text center line, text line orientation, character scale and character orientation. Experimental results on two public benchmarks including Total-Text and SCUT-CTW1500 have demonstrated that the proposed NASK achieves state-of-the-art results.
Aspect and Opinion Terms Extraction Using Double Embeddings and Attention Mechanism for Indonesian Hotel Reviews

Aspect and opinion terms extraction from review texts is one of the key tasks in aspect-based sentiment analysis. In order to extract aspect and opinion terms for Indonesian hotel reviews, we adapt double embeddings feature and attention mechanism that outperform the best system at SemEval 2015 and 2016. We conduct experiments using 4000 reviews to find the best configuration and show the influences of double embeddings and attention mechanism toward model performance. Using 1000 reviews for evaluation., we achieved Fl-measure of 0.914 and 0.90 for aspect and opinion terms extraction in token and entity (term) level respectively.
Learning Path Recommendation Based on Knowledge Tracing Model and Reinforcement Learning

In recent years, studies on personalized learning path recommendation have drawn much attentions in E-learning area. Most of the existing methods generate the learning path based on learning costs that are formulated manually by education experts. However, this kind of learning costs cannot record the knowledge level change during the learning process and therefore does not accurately reflect the learning situation of the learner. To tackle this problem, we propose a knowledge tracing method which models learners' knowledge level over time, so that the learners' learning situation can be accurately predicted. Then, we propose a learning path recommendation algorithm based on the knowledge tracing model and Reinforcement Learning. A series of experiments have been carried out against learning resource datasets. Experiments results demonstrate that our proposed method can make sound recommendations on appropriate learning paths in terms of accuracy and efficiency.
Analysis of chemical compounds in beverages – Guidance for establishing a compositional analysis

In recent years, much analysis has been carried out regarding the chemical compositions of nonalcoholic and alcoholic beverages. However, no study has ever considered the compositional pitfalls inherent in such studies, which may lead to arbitrary results. Two approaches were compared in this paper, compositional data analysis (CoDa) and classical statistical analyses, to demonstrate (1) how aging affects beer and (2) how the results vary depending on the applied approach. Exemplarily, chemical compounds of 43 beer samples were analyzed. Our work has led us to obtain different results when the correlation analysis and principal component analysis were conducted on the unmodified-, log-transformed-, or closed-data in contrary to the analyses of log-ratio coordinates. The outcome of the latter provided better separation between aged and fresh beer samples and also easier interpretability. These results lead us to advise the usage of CoDa methods for the analyses of chemical compositions of beverages.
Topic-aware Web Service Representation Learning

The advent of Service-Oriented Architecture (SOA) has brought a fundamental shift in the way in which distributed applications are implemented. An overwhelming number of Web-based services (e.g., APIs and Mashups) have leveraged this shift and furthered development. Applications designed with SOA principles are typically characterized by frequent dependencies with one another in the form of heterogeneous networks, i.e., annotation relations between tags and services, and composition relations between Mashups and APIs. Although prior work has shown the utility gained by exploring these networks, their analysis is still in its infancy. This article develops an approach to learning representations of the Web service network, which seeks to embed Web services in low-dimensional continuous vectors with preserved information of the network structure, functional tags, and service descriptions, such that services with similar functional properties and network structures are mapped together in the learned latent space. We first propose a topic generative model for constructing two topic distribution networks (Mashup-Topic and API-Topic) from the service content. Then, we present an efficient optimization process to derive low-dimensional vector representations of Web services from a tri-layer bipartite network with the Mashup-Topic and API-Topic networks on two ends and the Mashup-API composition network in the middle. Experiments on real-word datasets have verified that our approach is effective to learn robust low-rank service representations, i.e., 25% F1-measure gain over the state-of-the-art in Web service recommendation task.
WaveFFJORD: FFJORD-Based Vocoder for Statistical Parametric Speech Synthesis

Free-form Jacobian of Reversible Dynamics(FFJORD) is a flowbased invertible generative model defined by ordinary differential equations (ODE). Inspired by WaveGlow, in this paper, we propose WaveFFJORD, a neural vocoder that can synthesize speech waveforms from acoustic features, by combining FFJORD and WaveNet. WaveFFJORD can generate speech waveforms directly by the blackbox ODE solvers, without the need for autoregressive structures. Our experimental results show that WaveFFJORD can achieve a smaller model size, lower memory cost, and better speech quality than WaveGlow. Besides, the ODE framework allows users to control the generation speed and quality by setting the error tolerance of the ODE solvers.
High-Precision 5DoF Tracking and Visualization of Catheter Placement in EVD of the Brain Using AR

External ventricular drainage (EVD) is a high-risk medical procedure that involves inserting a catheter inside a patient’s skull, through the brain and into a ventricle, to drain cerebrospinal fluid and thus relieve elevated intracranial pressure. Once the catheter has entered the skull, its tip cannot be seen. The neurosurgeon has to imagine its location inside the cranium and direct it toward the ventricle using only anatomic landmarks. The EVD catheter is thin and thus hard to track using infra-red depth sensors. Traditional optical tracking using fiducial or other markers inevitably changes the shape or weight of the medical instrument. We present an augmented reality system that depicts the catheter for EVD and a new technique to precisely track the catheter inside the skull. Our technique uses a new linear marker detection method that requires minimal changes to the catheter and is well suited for tracking other thin medical devices that require high-precision tracking.
MedLinker: Medical Entity Linking with Neural Representations and Dictionary Matching

Progress in the field of Natural Language Processing (NLP) has been closely followed by applications in the medical domain. Recent advancements in Neural Language Models (NLMs) have transformed the field and are currently motivating numerous works exploring their application in different domains. In this paper, we explore how NLMs can be used for Medical Entity Linking with the recently introduced MedMentions dataset, which presents two major challenges: (1) a large target ontology of over 2M concepts, and (2) low overlap between concepts in train, validation and test sets. We introduce a solution, MedLinker, that addresses these issues by leveraging specialized NLMs with Approximate Dictionary Matching, and show that it performs competitively on semantic type linking, while improving the state-of-the-art on the more fine-grained task of concept linking (+4 F1 on MedMentions main task).
Camera Configuration Design in Cooperative Active Visual 3d Reconstruction: A Statistical Approach

Visual 3D reconstruction is an essential technique in computer vision which restores the 3D model of the scene from multi-view images. In this paper, we propose a statistical framework for the active visual 3D reconstruction. We first derive a closed-form expression to characterize the dependence of the reconstruction performance on 3D point's position and camera setting parameters under the binocular camera setting. Then we derive several closed-form solutions and propose an efficient optimization algorithm to find the optimal camera configurations with the best reconstruction quality under various settings. Numerical results validate the performance of our methods in terms of both reconstruction accuracy and computational efficiency.
Wearing Safety Helmet Detection in Substation

Wearing safety helmet is very essential for the safety of staff of power substations. In this paper, it proposes a detection framework of safety helmet based on the theory of machine learning and image processing technology. TensorFlow model enables to detect pedestrians, while the head position will be detected by the analysis of human Head-to-Body Ratio. Then the color space transformation and the color feature discrimination of head position are used to detect whether the staff wearing safety helmets or not. The experimental results of a large number of substation monitoring video sequences have shown that the recognition rate of wearing safety helmet detection system reach 89.0%, which proves the effectiveness of the proposed framework.
A multidistance constraint method for three-dimensional reconstruction with coaxial fringe projection measurement system

Fringe projection profilometry (FPP) is a popular technique for measuring three-dimensional (3D) objects and is widely used in industrial production, quality detection, and visual guidance, among other applications. However, with traditional FPP systems based on triangulation, significant changes in the surface height of the measured object can lead to occlusion and shadowing in measured scenes. To deal with this problem, a multidistance constraint (MDC) approach based on a coaxial fringe projection system is developed here. This proposed method changes the position of the projector multiple times to increase the number of geometric constraints, and every pixel on the imaging plane of the camera can then obtain more phase information from different-frequency patterns. By constructing a model of the geometric relationship between the phase and the 3D coordinates of object, depth information can be obtained by a least-squares algorithm. Experiments confirm that the MDC method can effectively recover 3D shapes of objects with stepped heights and deep holes while avoiding shadowing.
Fake Review Detection on Yelp Dataset Using Classification Techniques in Machine Learning

This paper provides a summary of our research, which aims to build a machine learning model that can detect whether the reviews on Yelp's dataset are true or fake. In particular, we applied and compared different classification techniques in machine learning to find out which one would give the best result. Brief descriptions for each of the classification techniques are provided to aid understanding of why some methods are better than others in some cases. The best result was achieved by using the XGBoost classification technique, with F-1 score reaching 0.99 in prediction.
Multi-View Wasserstein Discriminant Analysis with Entropic Regularized Wasserstein Distance

Analysis of multi-view data has recently garnered growing attention because multi-view data frequently appear in real-world applications, which are collected or taken from many sources or captured using various sensors. A simple and popular promising approach is to learn a latent subspace shared by multi-view data. Nevertheless, because one sample lies in heterogeneous structure types, many existing multi-view data analyses show that discrepancies in within-class data across multiple views have a larger value than discrepancies within the same view from different views. To evaluate this discrepancy, this paper presents a proposal of a multi-view Wasserstein discriminant analysis, designated as MvWDA, which exploits a recently developed optimal transport theory. Numerical evaluations using three real-world datasets reveal the effectiveness of the proposed MvWDA.
Quantum-inspired algorithm with fitness landscape approximation in reduced dimensional spaces for numerical function optimization

In realistic numerical optimization problems, the efficiency of the algorithm is a crucial consideration. Fitness landscape approximation is beneficial for improving the search efficiency in evolutionary algorithms, but this method increases the computational cost significantly with an increase in the dimension. This paper proposes a quantum-inspired algorithm with dimension decomposition and fitness landscape approximation. The high-dimensional problem space is transformed into a low-dimensional projection space for fitness landscape approximation through mapping transformation. Linear approximation for the fitness landscape is performed in the transformed multiple 1D projected spaces. In accordance with two different diversity-retaining strategies, reference sampling points with different characteristics are selected to perform two-point linear approximation. Candidate points in the projection space are estimated and then synthesized as candidate solutions in the problem space. Then, candidate solutions are used in an appropriate population reconstruction mechanism to improve the performance of the algorithm. The proposed algorithms are implemented on benchmark functions abstracted from real-world optimization problems and compared with several algorithms. Experimental results on errors, success rates, and their rankings show the superiority of the proposed approach with the solution diversity priority with regard to effectiveness, adaptability, and stability.
Correction Codes in the System of Residual Classes

The report considers correction codes with mutually non-prime bases (CMNPB). To date, almost no one has been engaged in a deep study of the corrective properties of systems of residual classes (SRC), the bases of which are not mutually prime numbers. Such a system has certain corrective properties, which makes it necessary to assess the possibility and feasibility of using CMNPB to increase the reliability of computing in computer systems (CS). For the purpose of further research and development of CMNPB, we will consider a number of scientific statements, the use of the results of which will allow us to more fully study the corrective properties of codes with non-prime bases. Algorithms for monitoring and correcting errors in the SRC with mutually non-prime in pairs bases have been developed. Using these algorithms makes it relatively easy to implement a procedure for detecting and correcting one-time errors. Note that by the simplicity of the design of the decoder circuits, CMNPB have no analogues in the positional number systems. This is achieved by limiting the class of possible correctable errors, by introducing additional hardware redundancy in the representation of code words.
A Group Genetic Algorithm for Resource Allocation in Container-Based Clouds

Containers have gain popularity because they support fast development and deployment of cloud-native software such as micro-services and server-less applications. Additionally, containers have low overhead, hence they save resources in cloud data centers. However, the difficulty of the Resource Allocation in Container-based clouds (RAC) is far beyond Virtual Machine (VM)-based clouds. The allocation task selects heterogeneous VMs to host containers and consolidate VMs to Physical Machines (PMs) simultaneously. Due to the high complexity, existing approaches use simple rule-based heuristics and meta-heuristics to solve the RAC problem. They either prone to stuck at local optima or have inherent defects in their indirect representations. To address these issues, we propose a novel group genetic algorithm (GGA) with a direct representation and problem-specific operators. This design has shown significantly better performance than the state-of-the-art algorithms in a wide range of test datasets.
Efficient Predictive Model for Utilization of Computing Resources using Machine Learning Techniques

Data mining is a viable innovation to break down and extract patterns from crude information, which can change the original data into up-to-date information. Predictive analytics includes an assortment of factual systems that analyze present and historical facts to make forecasts about future or generally obscure occasions. Machine learning incorporates statistical methods for regression and classification. The objective of machine learning is to create a predictive model that is unclear from the correct model. The assessed relative execution qualities were evaluated by Ein-Dor and feldermesser utilizing a linear regression method considering the properties machine cycle time, minimum main memory, maximum main memory, cache memory, minimum channels, and maximum channels. This relationship is communicated as a mathematical statement that predicts the reaction variable published relative performance as a linear function of the parameters. The proposed technique utilizes machine learning work to re-phrase prediction as an optimization problem. Confidence prediction and polynomial regression include imaginative application utilization and promising research. The experimental evaluation platform contains detailed performance analysis of the preferred methods. It is expected that this machine learning approach gives a quick and straightforward approach to fabricate applications.