Reviewerly/exHarmony huggingface dataset:
RandPro- A practical implementation of random projection-based feature extraction for high dimensional multivariate data analysis in R

<h2>Abstract</h2> The performance of the high dimensional multivariate data analysis is seriously affected by the curse of dimensionality. Feature extraction acts as an important pre-processing step in data analysis process to avoid the curse of dimensionality. Random projection method is the most underrated feature extraction technique that performs extremely well in the case of high dimensional data analysis. This technique is known for its characteristics like data independent projection, simpler computation and distance preserving property. The Johnson–Lindenstrauss lemma is the idea behind random projection method. It states that the small set of points in the high dimensional space can be embedded into smaller subspace and also approximately preserves the distance with higher probability. This article describes a practical implementation of random projection method in the popular statistical programming language R and it is compared with the other similar implementations. The software package for random projection method has been uploaded in Comprehensive R Archive Network(CRAN) repository as <i>RandPro</i> and the code has been distributed in github. The RandPro package is tested with different types of data including text, image and sensor data. The result shows that the RandPro package preserves the pairwise distance between the data points in the corresponding low dimensional space for further processing.
Context expansion approach for graph-based word sense disambiguation

Word sense disambiguation is a process to correctly identify the meanings of words in a given context. Being important in many natural language processing applications, this process is crucial in automatically understanding natural language expressions. Herein, we propose a variation of a well-known unsupervised graph-based word sense disambiguation method that utilizes all possible semantic information from a used lexical resource to increase graph-semantic connectivity for identifying the intended meanings of words in a given context. If the words have multiple potential meanings (senses) based on context, the proposed method builds an expanded graph representing most relevant semantic information of the words to be disambiguated. Nodes in the graph correspond to the context expansion set, which contains all associated information of each possible meaning of the word (word sense), and edges represent the semantic similarity between the expanded sets (nodes). Simultaneously, actual meaning is assigned to each target word using a locate graph centrality measure, which provides the degree of importance between graph nodes. Unlike most existing graph-based word sense disambiguation methods, wherein semantic relations (edges) between nodes are measured at the word level, the proposed method measures graph node semantic relations at the sentence level by expanding the words’ context, which contains all associated information for each possible word sense. Consequently, the proposed method can capture a higher degree of semantic information than existing approaches, thereby increasing semantic connectivity through a graph’s edges. Empirical results on benchmark datasets demonstrate that the proposed method outperforms all compared state-of-the-art graph-based word sense disambiguation approaches reported herein. We also report results obtained by applying the proposed method to a sentiment analysis task. These results demonstrate that the proposed method can determine the overall sentiment orientation of a given textual context.
A Green Power Traceability Technology Based on Timestamp in a Main-Side Chain System

With the low carbon emission target out of protecting environment, electric vehicle is a promising transportation tool. The number of electric vehicles is rising rapidly in recent years which creates lots of corresponding charging demands. With both users and charging piles rising, however, condition of charging pile and price of electricity in different district are not transparent for users. In order to promote the benign interaction of production, distribution and consumption of green power, merging blockchain technology is a promising strategy. Unchangeable and traceable property of blockchain can make green power traceable. In this paper, a green power traceable system based on blockchain technology is proposed for providing convenient green power charging services for users. This system consists of main chain, transaction side chain and data side chain. Through the three dimensions of quantity, price and time, the type of energy and the region can be traced. In this system, we proposed a fast tracing technology based on index by timestamp. Experiment contrast index technology with traversal and smart contact. It shows that index by timestamp technology is more effective than traversal and smart contract.
Towards Identification and Mitigation of Task-Based Challenges in Comparative Visualization Studies

The effectiveness of a visualization technique is dependent on how well it supports the tasks or goals of an end-user. To measure the effectiveness of a visualization technique, researchers often use a comparative study design. In a comparative study, two or more visualization techniques are compared over a set of tasks and commonly measure human performance in terms of task accuracy and completion time. Despite the critical role of tasks in comparative studies, the current lack of guidance in existing literature on best practices for task selection and communication of research results in evaluation studies is problematic. In this work, we systematically identify and curate the task-based challenges of comparative studies by reviewing existing visualization literature on the topic. Furthermore, for each of the presented challenges we discuss the potential threats to validity for a comparative study. The challenges discussed in this paper are further backed by evidence identified in a detailed survey of comparative tree visualization studies. Finally, we recommend best practices from personal experience and the surveyed tree visualization studies to provide guidelines for other researchers to mitigate the challenges. The survey data and a free copy of the paper is available at https://osf.io/g3btk/.
HANKE: Hierarchical Attention Networks for Knowledge Extraction in Political Science Domain

Extracting structured metadata from unstructured text in different domains is gaining strong attention from multiple research communities. In Political Science, these metadata play a significant role on studying intra and inter-state interactions between political entities. The process of extracting such metadata usually relies on domain specific ontologies and knowledge-based repositories. In particular, Political Scientists regularly use the well-defined ontology CAMEO, which is designed for capturing conflict and mediation relations. Since CAMEO repositories are currently human maintained, the high cost and extensive human effort associated with updating them makes it difficult to include new entries on a regular basis. This paper introduces HANKE: an innovative framework for automatically extracting knowledge representations from unstructured sources, in order to extend CAMEO ontology both in the same domain and towards other related domains in political science. HANKE combines Hierarchical Attention Networks as engine for identifying relevant structures in raw-text and the novel Frequency-Based Ranker approach to obtain a collection of candidate entries for CAMEO's repositories. To show the efficiency of the proposed framework, we evaluate its performance on capturing existing CAMEO representations in a soft-labelled dataset. We also empirically demonstrate the versatility and superiority of HANKE method by applying it to two case studies related to CAMEO extension on its actual domain and towards organized crime domain.
Comparison of Performances on Quantum Reading in Non-symmetric Loss Using Maximum and Non-maximum Quasi-Bell States

Quantum reading is the protocol that uses entanglement for reading a bit stored in digital memory. By using entanglement, it was shown by S. Pirandola that the data rate was improved. O. Hirota showed that in noiseless environments, error-free reading is possible by using a quasi-Bell state that has maximum entanglement. After that, some non-ideal cases were considered, such as symmetric loss and incomplete phase shift. In this study, we would like to clarify properties of the protocol in more realistic situations for experiment. In this paper, we compare and consider the effects using maximum and non-maximum quasi-Bell states on quantum reading with (1) only non-symmetric loss, (2) non-symmetric loss and incomplete phase shift, and (3) non-symmetric loss and phase noise. In the case (1), we derive the exact solutions of the error probabilities when using maximum and non-maximum quasi-Bell states. Then we clarify that the error probability using maximum quasi-Bell states is always lower than that using non-maximum quasi-Bell states. In the other two cases, we compute error probabilities numerically. In cases (2) and (3), the results are different from that of the case (1). That is, both maximum and non-maximum quasi-Bell states are not always supremer than the other.
Dual-Level Attention Based on Heterogeneous Graph Convolution Network for Aspect-Based Sentiment Classification

We introduce a flexible HIN (Heterogeneous Information Network) framework to model user-generated comments. It can integrate various types of additional information and capture the relationship between them to reduce the semantic sparsity of a small amount of labeled data. It can also take advantage of the hidden network structure information by spreading the information together with the graph. Then we propose to use a dual-level attention-based heterogeneous convolutional graph network to understand the importance of different adjacent nodes and of different types of nodes to the current node. By doing this, we can mitigate the shortcomings that most existing algorithms ignore, i.e. the network structure information between the words in the sentence and the sentence itself. The experimental results on the SemEval dataset prove the validity and reliability of our model.
Parameter Tuned Bi-Directional Long Short Term Memory Based Emotion With Intensity Sentiment Classification Model Using Twitter Data

With the advanced growth in the Internet and social networking technologies, a massive amount of comments are being generated on the Web at each and every second. In the age of big data, mining the sentiments and emotion classification using artificial intelligence (AI) techniques gained significant interest to properly understand the opinion of the public. This paper presents a new hyperparameter tuned bi-directional long short term memory (Bi-LSTM) using differential evolution (DE) model called DE-BiLSTM for the emotion with intensity based sentiment classification in Twitter data. The hyperparameters of Bi-LSTM namely batch size and number of hidden layers are determined by means of differential evolution algorithm (DE). The proposed method initially undergoes preprocessing is several stages like word to feature vector conversion and feature extraction process takes place. Finally, a softmax based classification process is carried out to identify the different intensities with diverse classes that exist in the tweets. The validation of the DE-BiLSTM model is carried out against SEMEVAL2018 Task-1Emotion Intensity Ordinal Classification dataset. The simulation results indicated that the DE-BiLSTM model has outperformed the other methods with an average precision of 93.83%, recall of 90.41%, F-measure of 91.76% and accuracy of 96.42%.
Covering dimension and ideal topological spaces

In this paper we study dimensions of the type dim for the class of ideal topological spaces. We shall start from the classical definition of the dimension functions dim and dim*, continue with the dimension -dim and finally, give two different kinds of functions of covering dimensional type.
A 1.5-<i>μ</i>J/Task Path-Planning Processor for 2-D/3-D Autonomous Navigation of Microrobots

Autonomous microrobots have been utilized in a wide range of applications. Energy-efficient, real-time path planning for navigation is essential. This work presents a path-planning processor for 2-D/3-D autonomous navigation. Energy and latency are minimized through algorithm-architecture optimization. The processor utilizes the rapidly exploring random tree (RRT) algorithm to ensure efficient planning on maps that have higher dimensions and a higher resolution. Dual-tree planning, branch extension, and parallel expansion are adopted in order to reduce both computational complexity and memory requirements. A prune-and-reuse strategy is also adopted so as to quickly respond to dynamic scenarios. An array of processing engines (PEs) is deployed in order to enable parallel expansion. The number of PEs is minimized through latency analysis. Low-complexity implementation for the PE is proposed while maintaining a high performance. Fabricated in a 40-nm CMOS technology, the chip integrates 2M logic gates in an area of 3.65 mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> . The processor supports path-planning tasks for both 2-D and 3-D maps, with latencies of less than 1 and 10 ms, respectively. For a 2-D map that has 100 × 100 grids, the proposed processor dissipates 1.5 μ/task at a clock frequency of 200 MHz from a 0.9-V supply. Compared with the state-of-the-art designs, the proposed path-planning processor achieves a 1467× shorter processing latency based on an energy dissipation that is 2133× lower, despite the capability for larger maps.
Deceptive Opinion Spam based On Deep Learning

The revolution of web technologies and e-commerce platforms such as Amazon and eBay have promoted the businesses and have become essential in our daily life. Despite that these tools have helped the ease of purchases, there is a lot of scams in these kinds of technologies. Unfortunately, several companies use fake opinions to influence the customers about buying a product or to demote the competitors' one. The detection of deceptive opinion spam is a hard task because of the way it is written. The majority of existing deceptive opinion detection models focus on machine learning with hand-engineered feature extraction. Unfortunately, these architectures do not provide the semantic information of the reviews, which is the key to the detection of deceptive opinions. In this paper, we address the comparison between the different neural network architectures and their effectiveness in the detection of deceptive opinion spam. The results show that Convolutional Neural Networks perform better compared to other models.
Development of an integrated path planning algorithm for autonomous driving of unmanned surface vessel

This paper proposes a path planning and obstacle avoidance algorithm for USV(Unmanned Surface Vessel). Path planning for autonomous driving is largely classified into global path generation and regional path generation. First, the A* algorithm is used when creating the global path. At this time, when applying the simple A* algorithm, since the rotation radius and dynamic characteristics of USV are not taken into account, a path generated from the A* algorithm is modified by applying a cardinal spline. Next, the P-DWA (Predictive-Dynamic Window Approach) algorithm was applied to avoid the fixed/moving obstacles of the local path and follow the waypoints generated from the global path.
Geometrical Fusion Based on Chain Code Representation

In this paper, a new geometrical image fusion method introduced by using the representation of chain code for the image objects that been matched among the images to be fused. The input images are separated into number of objects. A matching method is applied to extract the matching objects from the overall objects that included in the origin images. Then the chain code string is calculated for each one. The run length encoding is applied on that code strings to reduce the space that required. The image resulted from the geometrical fusion is more informative which contain the more important features those included in the origin images.
An Exploratory Study on How Software Reuse is Discussed in Stack Overflow

Software reuse is an important and crucial quality attribute in modern software engineering, where almost all software projects, open source or commercial, no matter small or ultra-large, source code reuse in one way or another. Although software reuse has experienced an increased adoption throughout the years with the exponentially growing number of available third-party libraries, frameworks and APIs, little knowledge exists to investigate what aspects of code reuse developers discuss. In this study, we look into bridging this gap by examining Stack Overflow to understand the challenges developers encounter when trying to reuse code. Using the Stack Overflow tags “code-reuse” and “reusability”, we extracted and analyzed 1,409 posts, composed of questions and answers. Our findings indicate that despite being popular, reuse questions take relatively longer than typical other questions to receive an accepted answer. From these posts, we identified 9 categories that group the different ways developers discuss software reuse. We found Java and ASP.NET MVC to be the most discussed programming language and framework, respectively. Based on the programming languages and frameworks mentioned in the posts, we noted that Web software development is the most frequently targeted environment. This study can be utilized to further analyze aspects about software reuse and develop guidelines to be practiced in industry and taught when forming new developers.
Close Weighted Shortest Paths on 3D Terrain Surfaces

This paper proposes an efficient method for the weighted region problem (WRP) on the surface of three-dimensional terrains. WRP is a classical path planning problem, asking for the minimum cost path between two given points crossing different regions in which each region is assigned a traversal cost per unit distance. Although WRP has been studied for decades, the exact solution for WRP, even in a two-dimensional environment, is unknown. Thus, the existing solutions for WRP are all approximations with decomposition-based and heuristic methods being the most widely-used in practice. However, when a very-close to optimal path is required, especially on real terrains with many regions, these approaches are not guaranteed or cannot return a satisfactory result in reasonable time. In this paper, we first present a new algorithm of finding a very-close optimal path, based on a user-defined parameter δ, between two points, crossing the surface of a sequence of regions in 3D, using Snell's law of physical refraction. We then show how to combine this algorithm with one existing decomposition-based method to compute a close optimal path over the whole terrain. In addition to a theoretical analysis, with an extensive set of test cases, the practicality and feasibility of our method are confirmed by that, our method always runs faster and returns closer to optimal paths in comparison with the existing ones.
Creating a Traceable Product Story in Manufacturing Supply Chains Using IPFS

Evolving traceability requirements increasingly challenge manufacturing supply chain actors to collect tamperproof and auditable evidence about what inputs they process, in what way these inputs are used, and what the resulting process outputs are. Traceability solutions based on blockchain technology have shown ways to satisfy the requirements of creating a tamper-proof and auditable trail of traceability data. However, the existing solutions struggle to meet the increasing storage requirements necessary to create an evidence trail using manufacturing data. In this paper, we show a way to create a tamper-proof and auditable evolving product story that uses a decentralized file system called the InterPlanetary File System (IPFS). We also show how using linked data can help auditors derive a traceable product story from such an accumulating evidence trail. The solution proposed herein can supplement existing blockchain-based traceability solutions and enable traceability in global manufacturing supply chains where forming a consortium incurs prohibitive costs and where storage requirements are high.
Detecting Protected Health Information with an Incremental Learning Ensemble: A Case Study on New Zealand Clinical Text

Clinical narratives host vast accumulations of patient data pivotal for research and development of health related products. In order for this data to be utilized, the underlying protected health information needs to be de-identified to ensure medical confidentiality. Given the voluminous size of clinical texts, manual de-identification of such large datasets is both expensive and impractical. Therefore, the concept of automated de-identification is a highly appealing prospect. Machine learning or model based sequential labeling algorithms, such as the named entity recognition algorithms and rule-based algorithms are among the most effective approaches to automated de-identification. A natural question to ask is how we can combine them to have the best of both worlds. In this paper, we present an analytical and easy to interpret framework to dynamically combine a sequential labeling model and a soft-rule-based model in an incremental learning setup. This framework is applied to a case study, which is part of a project prototyping automated de-identification system for New Zealand clinical free text data. Evaluations show that our approach can accommodate changes in the incoming data through dynamic updating. The simplicity of the framework also allowed us to gain insights on behaviour e.g. change of importance between the machine learning and rule models.
Soccer Field Lines Determination and 3D Reconstruction

Analysis of soccer videos is an area of recent interest, particularly the 3D reconstruction of soccer matches from broadcasted videos. Three algorithms are proposed that, when combined, will support this 3D reconstruction automatically. Initially, the region of the game is determined, which corresponds to the region of the image where the ball, the judge and the players are located. Subsequently, the field lines are determined using the Hough transform, while geometric and color filters are used to remove false positives. The intersections between the lines determine invariant points that are mapped to points in the field model. The correct mapping allows the correction of the perspective distortion. To determine the correct mapping, correspondence combinations are made between the invariant points and points in the field model. The mapping of the goal plane is also determined.