Reviewerly/exHarmony huggingface dataset:
A P- and S-Wave Picking Technique Based on the Probabilistic Density Function of Seismic-Waveform Amplitude

ABSTRACT Picking of P and S waves is a fundamental process in seismology, and various kinds of picking techniques have been developed. Seismic waveforms change dramatically depending on the magnitude, the mechanism of the earthquake, and the positional relationship between the hypocenter and the seismic station. The availability of various picking techniques is supposed to be helpful for appropriately dealing with a variety of seismic records. Hence, in addition to the revision of conventional techniques, the development of new picking techniques is worthwhile. In the present study, we developed a new stochastic technique to detect P and S waves based on the statistical amplitude distribution in the seismic record amplitude. In the proposed method, the probabilistic density function (PDF) of the amplitude is calculated for each segment of seismic records, and the similarity between the PDF of the amplitude and that of the Rayleigh or Gaussian distribution is evaluated by divergence. Because Rayleigh and Gaussian distributions are typically found in amplitude distributions of highly random waves, such as coda waves, the divergence indicates the randomness of the seismic records. P and S waves are found by tracing the temporal change of the divergence. We tested the proposed method using local seismic records for a series of seismic events that occurred before and after the 2016 Kumamoto earthquake. The mean absolute errors for picking P and S waves are 2.72×10−2 and 7.38×10−2 s, respectively. The proposed method is a simple and new statistical picking method that enables automatic detection of P- and S-wave arrivals.
The First Wikipedia Questions and Factoid Answers Corpus in the Thai Language

This article introduces a Thai questions-answers corpus for a question-answering task which was extracted from Thai Wikipedia which was downloaded on 17 December 2017. The answers comprise 5,000 annotated factoids. The corresponding questions are exact phrases/sentences that contain the answer, but are replaced by a question word, or synthetic questions acquired from phrases and/or sentences on the wiki page. A question must contain only one of a set of 7 specific question words and a complex question must be avoided. Fifteen annotators used an annotation system specifically designed for this task. Acceptance, rejection, and revision processes were monitored by a language specialist. The final set was divided into 4,000 pairs for a training set and 1,000 pairs for a validation set. A baseline evaluation was conducted and an F1 score of 27.25 was obtained from document readers and 71.24 from document retrievals.
Exploring How Augmented Reality and Virtual Reality Technologies Impact Business Model Innovation in Technology Companies in Germany

Newly-emerging, digitally-based technological innovations, such as augmented reality (AR) and virtual reality (VR), are new drivers for business model innovation. This study aims to develop a better understanding of the impact of AR/VR technologies on business model innovation in technology companies. The objective of the study is “to explore the impact of augmented reality and virtual reality technologies on business model innovation in technology companies in Germany”. This paper investigates the body of knowledge regarding contemporary business model innovation and presents a conceptual framework to guide the research. The philosophical underpinnings of the study are discussed, and the chosen research methodology is justified. A holistic multiple-case study design targets German business-to-business technology companies employing AR/VR technologies to innovate their business models. The paper concludes with a discussion of initial learnings garnered from the implementation of a pre-pilot case study test run, and a full pilot case study.
Towards a Taxonomy of Virtual Reality Usage in Education: A Systematic Review

Virtual reality market is undergoing a rapid extension and continually evolves with technological advancements. Today it is populated with solutions which are complex in terms of interaction and graphics. One such example is applications for educational purposes, a distinct class of VR solutions, development of which implies consideration of multiple disciplines. However, there is still no standard general-purposes classification of those solutions. Different interested parties develop separate and mosaic categorizations based on their field, audience and purposes. This paper reviews reported classification schemes of using virtual reality for learning and summarizes them towards a taxonomy. It tries to implement a multidisciplinary approach, without focusing on one particular aspect, but rather integrating prospects from such fields as human-computer interaction, pedagogy, psychology, and technology. The paper provides a brief overview of existing veins of VR research on education and training as well as proposes relevant research directions.
A High Efficiency Network Using DAG and Consensus in Blockchain

Decentralized autonomous system based on blockchain has replaced traditional trusted third parties to process electronic payments. Blockchain has the advantages of decentralization, transparency, security, etc. and thus has great potential for development. But the chain structure and mining in the blockchain increase transaction latency and reduce the number of transactions per second (TPS). Therefore, the SDVP model is proposed to solve the problem in this paper. The model uses a self-referencing directed acyclic graph (DAG) structure and a voting-based PBFT consensus algorithm. The new model not only has good reliability, but also significantly improves the speed of transaction generation and verification, and has high efficiency of transaction processing. These experimental results demonstrate that the TPS of SDVP model under different parameters is between 600 and 1400, which is better than PayPel and has highly efficient transaction processing capabilities. It can also be observed that the model exhibits good fault tolerance when attacked.
Semantic code clone detection for enterprise applications

Enterprise systems are widely adopted across industries as methods of solving complex problems. As software complexity increases, the software's codebase becomes harder to manage and maintenance costs raise significantly. One such source of cost-raising complexity and code bloat is that of code clones. We proposed an approach to identify semantic code clones in enterprise frameworks by using control flow graphs (CFGs) and applying various proprietary similarity functions to compare enterprise targeted metadata for each pair of CFGs. This approach enables us to detect semantic code clones with high accuracy within a time complexity of O(n2) where n is equal to the number of CFGs composed in the enterprise application (usually around hundreds). We demonstrated our solution on a blind study utilizing a production enterprise application.
Motion boundary emphasised optical flow method for human action recognition

This study proposes a three‐stream model using two different types of deep convolutional neural networks (CNNs): (i) a spatial stream with a CNN on images; (ii) a ResNet (residual network) on optical flows; and, (iii) a ResNet on the concatenation of motion features. This model is applied to four datasets: (i) UCF Sports; (ii) Youtube Sports; (iii) SBU action interaction; and (iv) a subset of the UCF‐1M Sports. Using two optical flow estimation methods: (i) a motion boundary emphasised Epicflow (Edge Preserving Interpolation Correspondences for Optical Flow) method, (MBEpicflow); and (ii) the Flownet 2 method, a learning optical flow estimation method. It was found that (i) the proposed MBEpicflow outperforms the Flownet 2 method on the SBU dataset, while the Flownet 2 performs equally well or better than the MBEpicflow method on the other three datasets, and these results are the best when compared with those obtained using other approaches on all datasets evaluated. These results showed the importance of accurate optical flow plays in human action recognition, an aspect which has been seldom addressed. Moreover, it showed that if some measure of the global behaviours of motion is incorporated, the generalisation performance is often improved by 1–2%.
Sentiment Analysis in Airline Tweets Using Mutual Information for Feature Selection

Social media is a media that many users need to be connected with other users in order to establish communication. One of the most widely used social media is Twitter. This Twitter contains opinions or short messages called tweets. The invited company also needs feedback from its customers to find out their view of the requested service. Therefore sentiment analysis is needed to collect sentiment classification of the company. This research uses a dataset from a collection of tweets about US Airlines. Because the dataset has been provided in Kaggle and already has several metadata so the experiment about feature selection can be done quickly. The features selection in this research uses the Mutual Information method. The method was chosen because it opposes the previous reference which the method is effective in correlation measurement from one attribute to another. The results obtained show that the training data created with features selected using complementary information has better. However, this mutual information when compared with the selection of other features such as Chi Square and Annova F to choose the old process and verify use for both methods.
Blockchain Based Framework For Driver Profiling in Smart Cities

Smart cities can deliver innovative solutions by leveraging technologies such as Internet of Things(IoT) and Blockchains. Driver profiling is of paramount importance in smart cities to monitor driving patterns, traffic violations, and resolve legal issues. However, preserving confidentiality of driver records while continuously monitoring their activities is a major challenge. Traditional mechanisms for driver profiling involve manual intervention and has a higher risk of exposure to being tampered. Thus it becomes necessary to equip smart cities with a tamper-resistant and a decentralized driver profiling system which is capable of storing driver records. In this work, we propose a Blockchain based framework for driver profiling in smart cities which enables secure collection and storage of driver and event records. The framework is composed of various services such as: 1) Driver registration and identity validation using Authentication service 2) Provide driver with a unique time-based token for recording event based data to ensure data authenticity. Finally, we evaluate the system by measuring the performance and power consumption and analyzing power-performance trade-offs. Our proposed framework is generic in that it can be utilized by online transportation networks for driver behavior tracking and profiling.
Hyperlink regression via Bregman divergence

A collection of U(∈N) data vectors is called a U-tuple, and the association strength among the vectors of a tuple is termed as the hyperlink weight, that is assumed to be symmetric with respect to permutation of the entries in the index. We herein propose Bregman hyperlink regression (BHLR), which learns a user-specified symmetric similarity function such that it predicts the tuple's hyperlink weight from data vectors stored in the U-tuple. BHLR is a simple and general framework for hyper-relational learning, that minimizes Bregman-divergence (BD) between the hyperlink weights and estimated similarities defined for the corresponding tuples; BHLR encompasses various existing methods, such as logistic regression (U=1), Poisson regression (U=1), link prediction (U=2), and those for representation learning, such as graph embedding (U=2), matrix factorization (U=2), tensor factorization (U≥2), and their variants equipped with arbitrary BD. Nonlinear functions (e.g., neural networks), can be employed for the similarity functions. However, there are theoretical challenges such that some of different tuples of BHLR may share data vectors therein, unlike the i.i.d. setting of classical regression. We address these theoretical issues, and proved that BHLR equipped with arbitrary BD and U∈N is (P-1) statistically consistent, that is, it asymptotically recovers the underlying true conditional expectation of hyperlink weights given data vectors, and (P-2) computationally tractable, that is, it is efficiently computed by stochastic optimization algorithms using a novel generalized minibatch sampling procedure for hyper-relational data. Consequently, theoretical guarantees for BHLR including several existing methods, that have been examined experimentally, are provided in a unified manner.
A Sociometric Sensor Based on Proximity, Movement and Verbal Interaction Detection

This work proposes the development of a sociometric sensor capable of detecting proximity, movement and verbal interaction between people. The sensor will allow measuring the level of existing interrelations between the collaborators of an organization, in order to determine and to measure the organizational climate. The way to evaluate the current organizational climate is subjective, through surveys that are not accurate and that make the results are not adequate. The sensors developed until now focus on proximity or movement, but not on the 3 proposed variables: movement, proximity and speech. The proposed portable device consists of a proximity detector using RSSI (Received Signal Strength Indicator) based on the NodeMCU WIFI ESP 8266 module. It also has a motion detector that uses an accelerometer and finally a voice detection algorithm, acquired via microphone, aimed at determining verbal interaction between people. The validation of the equipment was done by measuring the 3 variables in a sample of 50 people. The results show a percentage of proximity detection of 90% up to a distance of 3 meters between individuals, a percentage of success of 92.5% in the detection of posture and physical activity and a percentage of success of 87.5% in the detection of verbal interaction between people.
An E-sports video highlight generator using win-loss probability model

Highlighting, to compile key scenes in a match, is an essential task in the E-sports industry. Because E-sports is less dependent on time and place in its nature, there are countless matches every day. However, creating a highlight is intensive and time-consuming labor. Even the quality of the outcome depends on the editor's ability and decision. We propose a new approach to an automatic highlight generator from E-sports match videos. Our highlight generator reduces the cost of production and allows stable quality control. We defined the smallest component of the highlight as a 'Point', the moment when the win-loss probability of each team changes drastically. A 'Clip' is the contextual scene around Points, and a set of Clips is a 'Highlight'. Our highlight generator can prioritize Points, using a model that detects changes in real-time win-loss probabilities. It can create various versions of a highlight by adjusting the number of Points. Also, because it operates in real-time, we can generate highlights instantly. Our generator recognizes and extracts real-time state information from a match video using OpenCV and CNN. It uses an MLP model and gets the win-loss probability of each moment to calculate the change rate between directly adjacent moments. This MLP is pre-trained with match results in the past. A threshold is set by partially implementing CART algorithm for Gini Impurity. If a moment's change rate satisfies the threshold, it is classified as a Point. Then, a Clip is specified by setting the interval before and after the Point using the average interval. Finally, merging the Clips becomes a highlight. We created various versions of highlight for 119 match videos of E-Sports, the league of legend 2018 World Championship. To calculate the win-loss probability, the MLP pretrained the 11,082 match results. As a result, the accuracy and f1 scores were 89.9% and 74.5%, respectively, for the version most similar to the official highlight. We also compare our highlights by human evaluation. About 65% of reviewers said our highlights are better than official ones. In the exclusive Clip evaluations, the Clips exclusively included in ours earn 4.02 Point, but the Clips only in the official ones earn 2.63, which says our highlights include much better Clips than the official highlights.
Adaptive colour‐guided non‐local means algorithm for compound noise reduction of depth maps

Depth maps are used to describe object positioning information in three-dimensional (3D) space, and they are crucial for RGB-D data representation, which is useful for numerous interactive visual applications. In practice, depth maps are often contaminated by compound noise, including intrinsic noise and missing regions owing to active illumination shadows. As existing noise models cannot describe the above-mentioned compound noise effectively, the subsequent filter design is a challenging task. In this study, an adaptive colour-guided non-local mean (NLM) filter is proposed to address such compound noise. First, the authors classify the depth map into hole and non-hole pixels. Then, the proposed filter is designed on the basis of the NLM framework, where the colour image is used as a guide prior for hole-artifact removal. Finally, the authors use a shock filter to effectively address the non-regularisation of the restored depth map edges and remove the remaining noise. Experiments show that the proposed filter qualitatively and quantitatively outperforms existing colour-guided and unguided filters. Moreover, the authors verify the superiority of the proposed filter through virtual view synthesis and 3D scene reconstruction applications.
Exploring Type Inference Techniques of Dynamically Typed Languages

Developers often prefer dynamically typed programming languages, such as JavaScript, because such languages do not require explicit type declarations. However, such a feature hinders software engineering tasks, such as code completion, type related bug fixes and so on. Deep learning-based techniques are proposed in the literature to infer the types of code elements in JavaScript snippets. These techniques are computationally expensive. While several type inference techniques have been developed to detect types in code snippets written in statically typed languages, it is not clear how effective those techniques are for inferring types in dynamically typed languages, such as JavaScript. In this paper, we investigate the type inference techniques of JavaScript to understand the above two issues further. While doing that we propose a new technique that considers the locally specific code tokens as the context to infer the types of code elements. The evaluation result shows that the proposed technique is 20-47% more accurate than the statically typed language-based techniques and 5–14 times faster than the deep learning techniques without sacrificing accuracy. Our analysis of sensitivity, overlapping of predicted types and the number of training examples justify the importance of our technique.
Mining Version Control Systems and Issue Trackers with LibVCS4j

Mining version control systems, such as Git, Mercurial, and Subversion, is a good way to analyze different aspects of software evolution. Several previous studies, for example, analyzed how cloning in software evolves by tracking findings through the change history of source code files. By linking in issues (reported and discussed in issue tracking systems), results can be further refined—for instance, identifying commits that removed long-lived clones and fixed known issues. However, due to the absence of suitable tools, most of these studies implemented their own data extraction approaches. In this paper we present LibVCS4j, a Java programming library which can be integrated into existing analysis tools—for example, tools that detect code smells in source code files—to i) iterate through the history of software repositories, ii) run the desired analysis on a selected set of revisions, and iii) fetch additional information (commit messages, file differences, and so on) while processing a repository. The library unites different version control systems and issue tracking systems under a common interface and, thus, is well suited to decouple data analysis from data extraction.
Adaptive Temporal Frame Interpolation Algorithm for Frame Rate Up-Conversion

This article presents a novel adaptive temporal frame interpolation (FI) algorithm for frame rate up-conversion with reduced complexity. According to the adaptability criteria in the proposed algorithm, frames with a low temporal activity are interpolated using frame averaging, which significantly decreases the algorithm complexity. In the case of a high temporal activity, motion compensated FI is used and the high quality of interpolated frames is achieved. Compared to well-known frame rate up-conversion algorithms, the proposed approach is of significantly lower computational complexity, while maintaining the high quality of the interpolated frames.
Characterization of Interestingness Measures Using Correlation Analysis and Association Rule Mining

Objective interestingness measures play a vital role in association rule mining of a large-scaled database because they are used for extracting, filtering, and ranking the patterns. In the past, several measures have been proposed but their similarities or relations are not sufficiently explored. This work investigates sixty-one objective interestingness measures on the pattern of A → B, to analyze their similarity and dissimilarity as well as their relationship. Three-probability patterns, P(A), P(B), and P(AB), are enumerated in both linear and exponential scales and each measure's values of those conditions are calculated, forming synthesis data for investigation. The behavior of each measure is explored by pairwise comparison based on these three-probability patterns. The relationship among the sixty-one interestingness measures has been characterized with correlation analysis and association rule mining. In the experiment, relationships are summarized using heat-map and association rule mined. As the result, selection of an appropriate interestingness measure can be realized using the generated heat-map and association rules.
Combining instance and feature neighbours for extreme multi-label classification

Extreme multi-label classification problems occur in different applications such as prediction of tags or advertisements. We propose a new algorithm that predicts labels using a linear ensemble of labels from instance- and feature-based nearest neighbours. In the feature-based nearest neighbours method, we precompute a matrix containing the similarities between each feature and label. For the instance-based nearest neighbourhood, we create an algorithm that uses an inverted index to compute cosine similarity on sparse datasets efficiently. We extend this baseline with a new top-k query algorithm that combines term-at-a-time and document-at-a-time traversal with tighter pruning based on a partition of the dataset. On ten real-world datasets, we find that our method outperforms state-of-the-art methods such as multi-label k-nearest neighbours, instance-based logistic regression, binary relevance with support vector machines and FastXml on different evaluation metrics. We also find that our algorithm is orders of magnitude faster than these baseline algorithms on sparse datasets and requires less than 20 ms per instance to predict labels for extreme datasets without the need for expensive hardware.