Reviewerly/exHarmony huggingface dataset:
A Classification Model For Class Imbalance Dataset Using Genetic Programming

Since the last few decades, a class imbalance has been one of the most challenging problems in various fields, such as data mining and machine learning.The particular state of an imbalanced dataset, where each class associated with a given dataset is distributed unevenly.This happens when the positive class is much smaller than the negative class.In this case, most standard classification algorithms do not identify examples related to the positive class.A positive class usually refers to the key interest of the classification task.In order to solve this problem, several solutions were proposed such as sampling-based over-sampling and under-sampling, changes at the classifier level or the combination of two or more classifiers.However the main problem is that most solutions are biased towards negative class, computationally expensive, have storage issues or taking long training time.An alternative approach to this problem is the genetic algorithm (GA), which has shown the promising results.The GA is an evolutionary learning algorithm that uses the principles of Darwinian evolution, it is a powerful global search algorithm.Moreover, the fitness function is a key parameter in GA.It determines how well a solution can solve the given problem.In this paper, we propose a solution which uses entropy and information gain as a fitness function in GA with an objective to improve the impurity and gives a more balanced result without changing the original dataset.The experiments conducted on different datasets demonstrate the effectiveness of the proposed solution in comparison with the several other state-of-the-art algorithms in term of Accuracy (Acc), geometric mean (GM), F-measure (FM), kappa, and Matthews correlation coefficient (MCC).
Multimodal adversarial network for cross-modal retrieval

Cross-modal retrieval aims to retrieve the pertinent samples across different modalities, which is important in numerous multimodal applications. It is challenging to correlate the multimodal data due to a large heterogeneous gap between distinct modalities. In this paper, we propose a Multimodal Adversarial Network (MAN) method to project the multimodal data into a common space wherein the similarities between different modalities can be directly computed by the same distance measurement. The proposed MAN consists of multiple modality-specific generators, a discriminator and a multimodal discriminant analysis (MDA) loss. With the adversarial learning, the generators are pitted against the discriminator to eliminate the cross-modal discrepancy. Furthermore, a novel MDA loss is proposed to preserve as much discrimination as possible into all available dimensions of the generated common representations. However, there are some problems in directly optimizing the MDA trace criterion. To be specific, the discriminant function will overemphasize 1) the large distances between already separated classes, 2) and the dominant eigenvalues. These problems may cause poor discrimination of the common representations. To solve these problems, we propose a between-class strategy and an eigenvalue strategy to weaken the largest between-class differences and the dominant eigenvalues, respectively. To the best of our knowledge, the proposed MAN could be one of the first works to specifically design for the multimodal representation learning (more than two modalities) with adversarial learning. To verify the effectiveness of the proposed method, extensive experiments are carried out on four widely-used multimodal databases comparing with 16 state-of-the-art approaches.
The impact of deep learning on document classification using semantically rich representations

This paper presents a semantically rich document representation model for automatically classifying financial documents into predefined categories utilizing deep learning. The model architecture consists of two main modules including document representation and document classification. In the first module, a document is enriched with semantics using background knowledge provided by an ontology and through the acquisition of its relevant terminology. Acquisition of terminology integrated to the ontology extends the capabilities of semantically rich document representations with an in depth-coverage of concepts, thereby capturing the whole conceptualization involved in documents. Semantically rich representations obtained from the first module will serve as input to the document classification module which aims at finding the most appropriate category for that document through deep learning. Three different deep learning networks each belonging to a different category of machine learning techniques for ontological document classification using a real-life ontology are used. Multiple simulations are carried out with various deep neural networks configurations, and our findings reveal that a three hidden layer feedforward network with 1024 neurons obtain the highest document classification performance on the INFUSE dataset. The performance in terms of F1 score is further increased by almost five percentage points to 78.10% for the same network configuration when the relevant terminology integrated to the ontology is applied to enrich document representation. Furthermore, we conducted a comparative performance evaluation using various state-of-the-art document representation approaches and classification techniques including shallow and conventional machine learning classifiers.
Facial Expression Classification Based on SVM, KNN and MLP Classifiers

Facial Expression Recognition (FER) has been an active topic of papers that were researched during 1990s till now, according to its importance, FER has achieved an extremely role in image processing area. FER typically performed in three stages include, face detection, feature extraction and classification. This paper presents an automatic system of face expression recognition which is able to recognize all eight basic facial expressions which are (normal, happy, angry, contempt, surprise, sad, fear and disgust) while many FER systems were proposed for recognizing only some of face expressions. For validating the method, the Extended Cohn-Kanade (CK+) dataset is used. The presented method uses Viola-Jones algorithm for face detection. Histogram of Oriented Gradients (HOG) is used as a descriptor for feature extraction from the images of expressive faces. Principal Component Analysis (PCA) applied to reduce dimensionality of the Features, to obtaining the most significant features. Finally, the presented method used three different classifiers which are Support Vector Machine (SVM), K-Nearest Neighbor (KNN) and Multilayer Perceptron Neural Network (MLPNN) for classifying the facial expressions and the results of them are compared. The experimental results show that the presented method provides the recognition rate with 93.53% when using SVM classifier, 82.97% when using MLP classifier and 79.97% when using KNN classifier which refers that the presented method provides better results while using SVM as a classifier.
Cooperative Aerial–Ground Vehicle Route Planning With Fuel Constraints for Coverage Applications

Low-cost unmanned aerial vehicles (UAVs) need multiple refuels to accomplish large area coverage. We propose the use of a mobile ground vehicle (GV), constrained to travel on a given road network, as a refueling station for the UAV. Determining optimal routes for a UAV and GV, and selecting rendezvous locations for refueling to minimize coverage time is NP-hard. We develop a two-stage strategy for coupled route planning for UAV and GV to perform a coverage mission. The first-stage computes refueling sites that ensure reachability of all points of interest by the UAV and feasible routes for both the UAV and GV. In the second stage, mixed-integer linear programming (MILP) based exact methods are developed to plan optimal routes for the UAV and GV. As the problem is NP-Hard, we also develop computationally efficient heuristics that can find good feasible solutions within a given time limit. Extensive simulations are conducted to corroborate the effectiveness of the developed approaches. Field experiments are also performed to verify the performance of the UAV-GV solution.
Simple Model for Predicting Time Series Soiling of Photovoltaic Panels

Results from a model to predict soiling losses of photovoltaic (PV) arrays are presented. The model uses ambient airborne particulate matter concentrations, both PM <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">10</sub> and PM <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2.5</sub> , the tilt of the PV array including if the array is tracking, and rain data to estimate soiling losses over time. The model uses relationships between average airborne PM concentrations and dust accumulation, rain removal of accumulated dust, and the accumulated dust and transmission loss that have been developed in the literature to estimate soiling losses in a time series. Multiple model runs were performed and were compared with measured soiling data at seven locations. The model was run with both typical Meteorological Year rain data and with recorded rain data from Oregon State University's PRISM database. Each model was run with variable deposition velocities, static deposition velocities, and static settling velocities. When the model was run with recorded rain data and static settling velocities the results matched the general slopes, frequencies, and magnitudes of soiling losses when compared with measured soiling data. Overall, the simple model demonstrates the ability at accurately simulating soiling.
Dependency Versioning in the Wild

Many modern software systems are built on top of existing packages (modules, components, libraries). The increasing number and complexity of dependencies has given rise to automated dependency management where package managers resolve symbolic dependencies against a central repository. When declaring dependencies, developers face various choices, such as whether or not to declare a fixed version or a range of versions. The former results in runtime behaviour that is easier to predict, whilst the latter enables flexibility in resolution that can, for example, prevent different versions of the same package being included and facilitates the automated deployment of bug fixes. We study the choices developers make across 17 different package managers, investigating over 70 million dependencies. This is complemented by a survey of 170 developers. We find that many package managers support - and the respective community adapts - flexible versioning practices. This does not always work: developers struggle to find the sweet spot between the predictability of fixed version dependencies, and the agility of flexible ones, and depending on their experience, adjust practices. We see some uptake of semantic versioning in some package managers, supported by tools. However, there is no evidence that projects switch to semantic versioning on a large scale. The results of this study can guide further research into better practices for automated dependency management, and aid the adaptation of semantic versioning.
DeFusionNET: Defocus Blur Detection via Recurrently Fusing and Refining Multi-Scale Deep Features

Defocus blur detection aims to detect out-of-focus regions from an image. Although attracting more and more attention due to its widespread applications, defocus blur detection still confronts several challenges such as the interference of background clutter, sensitivity to scales and missing boundary details of defocus blur regions. To deal with these issues, we propose a deep neural network which recurrently fuses and refines multi-scale deep features (DeFusionNet) for defocus blur detection. We firstly utilize a fully convolutional network to extract multi-scale deep features. The features from bottom layers are able to capture rich low-level features for details preservation, while the features from top layers can characterize the semantic information to locate blur regions. These features from different layers are fused as shallow features and semantic features, respectively. After that, the fused shallow features are propagated to top layers for refining the fine details of detected defocus blur regions, and the fused semantic features are propagated to bottom layers to assist in better locating the defocus regions. The feature fusing and refining are carried out in a recurrent manner. Also, we finally fuse the output of each layer at the last recurrent step to obtain the final defocus blur map by considering the sensitivity to scales of the defocus degree. Experiments on two commonly used defocus blur detection benchmark datasets are conducted to demonstrate the superority of DeFusionNet when compared with other 10 competitors. Code and more results can be found at: http://tangchang.net.
Pattern-Based Mining of Opinions in Q&amp;A Websites

Informal documentation contained in resources such as Q&A websites (e.g., Stack Overflow) is a precious resource for developers, who can find there examples on how to use certain APIs, as well as opinions about pros and cons of such APIs. Automatically identifying and classifying such opinions can alleviate developers' burden in performing manual searches, and can be used to recommend APIs that are good from some points of view (e.g., performance), or highlight those less ideal from other perspectives (e.g., compatibility). We propose POME (Pattern-based Opinion MinEr), an approach that leverages natural language parsing and pattern-matching to classify Stack Overflow sentences referring to APIs according to seven aspects (e.g., performance, usability), and to determine their polarity (positive vs negative). The patterns have been inferred by manually analyzing 4,346 sentences from Stack Overflow linked to a total of 30 APIs. We evaluated POME by (i) comparing the pattern-matching approach with machine learners leveraging the patterns themselves as well as n-grams extracted from Stack Overflow posts; (ii) assessing the ability of POME to detect the polarity of sentences, as compared to sentiment-analysis tools; (iii) comparing POME with the state-of-the-art Stack Overflow opinion mining approach, Opiner, through a study involving 24 human evaluators. Our study shows that POME exhibits a higher precision than a state-of-the-art technique (Opiner), in terms of both opinion aspect identification and polarity assessment.
Path Planning for Multi-UAV Formation Rendezvous Based on Distributed Cooperative Particle Swarm Optimization

This paper studies the problem of generating cooperative feasible paths for formation rendezvous of unmanned aerial vehicles (UAVs). Cooperative path-planning for multi-UAV formation rendezvous is mostly a complicated multi-objective optimization problem with many coupled constraints. In order to satisfy the kinematic constraints, i.e., the maximum curvature constraint and the requirement of continuous curvature of the UAV path, the Pythagorean hodograph (PH) curve is adopted as the parameterized path because of its curvature continuity and rational intrinsic properties. Inspired by the co-evolutionary theory, a distributed cooperative particle swarm optimization (DCPSO) algorithm with an elite keeping strategy is proposed to generate a flyable and safe path for each UAV. This proposed algorithm can meet the kinematic constraints of UAVs and the cooperation requirements among UAVs. Meanwhile, the optimal or sub-optimal paths can be obtained. Finally, numerical simulations in 2-D and 3-D environments are conducted to demonstrate the feasibility and stability of the proposed algorithm. Simulation results show that the paths generated by the proposed DCPSO can not only meet the kinematic constraints of UAVs and safety requirements, but also achieve the simultaneous arrival and collision avoidance between UAVs for formation rendezvous. Compared with the cooperative co-evolutionary genetic algorithm (CCGA), the proposed DCPSO has better stability and a higher searching success rate.
Pose-Guided Representation Learning for Person Re-Identification

The large pose variations and misalignment errors exhibited by person images significantly increase the difficulty of person Re-Identification (ReID). Existing works commonly apply extra operations like pose estimation, part segmentation, etc., to alleviate those issues and improve the robustness of pedestrian representations. While boosting the ReID accuracy, those operations introduce considerable computational overheads and make the deep models complex and hard to tune. To chase a more efficient solution, we propose a Part-Guided Representation (PGR) composed of Pose Invariant Feature (PIF) and Local Descriptive Feature (LDF), respectively. We call PGR "Part-Guided" because it is trained and supervised by local part cues. Specifically, PIF approximates a pose invariant representation inferred by pose estimation and pose normalization. LDF focuses on discriminative body parts by approximating a representation learned with body region segmentation. In this way, extra pose extraction is only introduced during the training stage to supervise the learning of PGR, but is not required during the testing stage for feature extraction. Extensive comparisons with recent works on five widely used datasets demonstrate the competitive accuracy and efficiency of PGR.
Blockchain technology for decentralized autonomous organizations

With continuously changing operational and business needs of the organizations, Decentralized Autonomous Organizations (DAO) is the current need of the organizations. Centralized Autonomous Organization (CAO) lack transparency and are managed by few efficient managers whereas Decentralized autonomous Organization's (DAO) is novel scalable, self-organizing coordination on the blockchain, controlled by smart contracts and its essential operations are automated agreeing to rules and principles assigned in code without human involvement. In this chapter we discuss the needs for Decentralized Autonomous Organizations (DAO) and key efforts in this field. We then introduce a prospective solution employing blockchain Ethereum, which incorporates a Turing complete programming language with smart contract computing functionality. A solution is elaborated that permits the formation of organizations where participants preserve straight real-time check of contributed collects and governance policies are formalized, automatized and imposed using software. Basic code for smart contract is composed to make a Decentralized Autonomous Organization (DAO) on the Ethereum blockchain. We also explain the working of DAOs code, centering on fundamental establishment and governance characteristics, which includes organization, formation and voting rights. DAOs are considered to agree to the expectation of the business work in the future. But there is still lack of operational base for DAOs in the blockchain community.
Optimal Margin Distribution Machine

Support Vector Machine (SVM) has always been one of the most successful learning algorithms, with the central idea of maximizing the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">minimum margin</i> , i.e., the smallest distance from the instances to the classification boundary. However, recent theoretical results disclosed that maximizing the minimum margin does not necessarily lead to better generalization performance, and instead, the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">margin distribution</i> has been proven to be more crucial. Based on this idea, we propose the Optimal margin Distribution Machine (ODM), which can achieve a better generalization performance by optimizing the margin distribution explicitly. We characterize the margin distribution by the first- and second-order statistics, i.e., the margin mean and variance. The proposed method is a general learning approach which can be applied in any place where SVMs are used, and its superiority is verified both theoretically and empirically in this paper.
Taming the blockchain beast? Regulatory implications for the cryptocurrency Market

This paper uses a unique dataset of 120 regulatory events from five classes to test the relevance of the regulatory framework for cryptocurrency value. Time-series market-wide estimates and panel estimates for 300 individual coins and tokens show statistically and economically significant impact of anti-money laundering and issuance regulation. Tighter regulation and more active role of government decrease cryptocurrency prices, evidencing that potentially lower risks and wider adoption commonly attributed to the establishment of the regulatory framework do not compensate for respective efficiency and consumer utility losses. The market is generally efficient in reflecting regulatory information in cryptocurrency prices.
Feature Fusion Encoder Decoder Network for Automatic Liver Lesion Segmentation

Liver lesion segmentation is a difficult yet critical task for medical image analysis. Recently, deep learning based image segmentation methods have achieved promising performance, which can be divided into three categories: 2D, 2.5D and 3D, based on the dimensionality of the models. However, 2.5D and 3D methods can have very high complexity and 2D methods may not perform satisfactorily. To obtain competitive performance with low complexity, in this paper, we propose a Feature-fusion Encoder-Decoder Network (FED-Net) based 2D segmentation model to tackle the challenging problem of liver lesion segmentation from CT images. Our feature fusion method is based on the attention mechanism, which fuses high-level features carrying semantic information with low-level features having image details. Additionally, to compensate for the information loss during the upsampling process, a dense upsampling convolution and a residual convolutional structure are proposed. We tested our method on the dataset of MICCAI 2017 Liver Tumor Segmentation (LiTS) Challenge and achieved competitive results compared with other state-of-the-art methods.
Digital data archives as knowledge infrastructures: Mediating data sharing and reuse

Digital archives are the preferred means for open access to research data. They play essential roles in knowledge infrastructures—robust networks of people, artifacts, and institutions—but little is known about how they mediate information exchange between stakeholders. We open the “black box” of data archives by studying DANS, the Data Archiving and Networked Services institute of The Netherlands, which manages 50+ years of data from the social sciences, humanities, and other domains. Our interviews, weblogs, ethnography, and document analyses reveal that a few large contributors provide a steady flow of content, but most are academic researchers who submit data sets infrequently and often restrict access to their files. Consumers are a diverse group that overlaps minimally with contributors. Archivists devote about half their time to aiding contributors with curation processes and half to assisting consumers. Given the diversity and infrequency of usage, human assistance in curation and search remains essential. DANS' knowledge infrastructure encompasses public and private stakeholders who contribute, consume, harvest, and serve their data—many of whom did not exist at the time the DANS collections originated—reinforcing the need for continuous investment in digital data archives as their communities, technologies, and services evolve.