Reviewerly/exHarmony huggingface dataset:
Extractive Multi-document Summarization using K-means, Centroid-based Method, MMR, and Sentence Position

Multi-document summarization is more challenge than single-document summarization since it has to solve the problem of overlapping information among sentences from different documents. Also, since multi-document summarization dataset is rare, methods based on deep learning are difficult to be applied. In this paper, we propose an approach to multi-document summarization based on k-means clustering algorithm, combining with centroid-based method, maximal marginal relevance and sentence positions. This approach is efficient in finding salient sentences and preventing overlapping between sentences. Experiments using DUC 2007 dataset show that our system is more efficient than other researches in this field.
Toward End-to-end Prediction of Future Wellbeing using Deep Sensor Representation Learning

Wearable sensors can capture continuous, high resolution physiological and behavioral data that can be utilized to develop early health and wellbeing detection and lead to early warning, intervention, and recommendation systems to improve health and wellbeing. We have built and evaluated an end-to-end wellbeing prediction framework that pipelines raw wearable sensor data into an unsupervised autoencoder-based representation learning model and a supervised wellbeing regression model. We trained and evaluated the framework using the wearable sensor dataset and wellbeing labels collected from college students (total 6391 days from N=252). Wearable data include skin temperature, skin conductance, and acceleration; the wellbeing labels include self-reported alertness, happiness, energy, health, and calmness scored 0 – 100. We compared the performance of our framework with the performance of wellbeing regression models based on hand-crafted features. Our results showed that the proposed framework can automatically extract features from the current day's 24-hour multi-channel data and predict wellbeing scores for next day with mean absolute errors of 14–16. This result shows the possibility of predicting wellbeing accurately using an end-to-end framework, ultimately for developing real-time health and wellbeing monitoring and intervention systems.
Analysis and Detection of a Radical Extremist Discourse Using Stylometric Tools

The amount of radical extremist texts published online is constantly growing with researchers as well as agencies working hard to design tools to detect and remove this type of content. However, a great deal of this research effort is being focused on English-language materials. Meanwhile, in the last few years extremist groups including radical Islamic ones have started to produce content in languages other than English and, more specifically, in Russian which has been increasingly employed. Qualitative analysis of this content is important but not sufficient. We show the possibility of using stylometric tools allowing different types of texts analysis to be performed in order to analyze and detect radical extremist content. Using a meticulously designed dataset consisting of real-world extremist forum texts on two topics and texts by common Internet users on the same topics, we were able to show that the authors from extremist forum remain consistent in their style irrespective of the topic and their texts could thus be detected using a variety of methods. We conclude by the underscoring the importance of combining qualitative and quantitative analysis of a radical extremist discourse for better understanding of radical minds and development of counter-extremist tools.
The Analysis of Attribution Reduction of K-Nearest Neighbor (KNN) Algorithm by Using Chi-Square

Abstract Data reduction is one of the applicable techniques used to obtain the reduction representation from the data whose volume is much smaller, but still retains the original integrity of the data. Attribute reduction is a process to identify and eliminate an attribute with irrelevant or excessive values. In this study, attribute reduction was carried out using the Chi-Square Algorithm implemented in K-Nearest Neighbor (KNN) for classifying the objects based on the closest data to the objects.The test was carried out on the Pima Indians dataset with the total of 768 data. The Chi-Square method was used to reduce the dimensions of large datasets and to improve the accuracy of the prediction of the closest K-Nearest Neighbor results. K-Nearest Neighbor using Chi-Square as the choice of features proved to be accurate and effective in reducing data. The results of this study show that K-Nearest Neighbor using Chi-Square as the choice of features was accurate and effective in reducing data without reducing the integrity of the original data and reducing the quality of the information produced.
A Recurrent Neural Network-Based Approach to Automatic Language Identification from Speech

The task of automatically identifying the used language from speech signals is known as automatic language identification. It is very much important prior to speech recognition in multilingual scenarios where speakers use more than a single language in course of communication. In this paper, a recurrent neural network (RNN)-based system with long short-term memory (LSTM) along with handcrafted line spectral frequency-based features is proposed for language identification. Experiments were performed on as many as 21908 clips (more than 30 h of data) from the top three spoken languages of the world, namely, English, Chinese, and Spanish, and a highest average accuracy of 95.22% has been obtained.
Automatic Microseismic Events Detection Using Multiscale Morphological Characteristic Function

Surface-acquired microseismic events are commonly unpredictable and appear as weak signals with low signal-to-noise ratios (SNRs), which present a significant challenge in microseismic data analysis and events detection. We have developed the multiscale morphology method for automatically detecting microseismic events. The technique is based on the fact that the effective signal and noise have different features in the multiscale morphological section. According to these characteristics, the effective signal can be identified by waveform consistency analysis. The microseismic data are first decomposed into different scales by multiscale morphology decomposition. Then the multiscale morphology characteristic function is calculated by waveform cross correlation approach and the potential microseismic events are evaluated using the peak value of the multiscale morphology characteristic function. Applications of the proposed method on the synthesized data and field data have been explored in this article. Various examples demonstrate that the proposed method has the advantage of suppressing the effect of Gaussian noises adaptively. Furthermore, it is excellent in automatically detecting the low SNR signal so that more microseismic events can be identified. In addition, the method could be applied to other fields, such as in the identification of earthquake signals.
User Experience in Mobile Application Design: Utility Defined Context of Use

Abstract Mobile application has been offered tremendous benefit to the user in providing solution especially related to accessibility and availability. Product functions are important but with the better user experiences, it will have more advantages, which somehow increase its level adoption and retention within context. User experience (UX) is the outcome that this study want to achieve through implementing user centred design in the process by defining the set of methods and phases, which are loosely connected each other to solve the problem arisen. This study identified the lack of integration process within driving communities to have service in motorcycle driving practice, permission letter and rental.
Lotka–Volterra Competition Mechanism Embedded in a Decision-Making Method

Decision making is a fundamental capability of living organisms, and has recently been gaining increasing importance in many engineering applications. Here, we consider a simple decision-making principle to identify an optimal choice in multi-armed bandit (MAB) problems, which is fundamental in the context of reinforcement learning. We demonstrate that the identification mechanism of the method is well described by using a competitive ecosystem model, i.e., the competitive Lotka--Volterra (LV) model. Based on the "winner-take-all" mechanism in the competitive LV model, we demonstrate that non-best choices are eliminated and only the best choice survives; the failure of the non-best choices exponentially decreases while repeating the choice trials. Furthermore, we apply a mean-field approximation to the proposed decision-making method and show that the method has an excellent scalability of $O(\log N)$ with respect to the number of choices $N$. These results allow for a new perspective on optimal search capabilities in competitive systems.
Extracting Deep Personae Social Relations in Microblog Posts

Numerous studies have been conducted to extract relationships from different documents.However, extracting relationships from microblog posts is rarely studied.In this paper, we improve a novel kernel-based learning algorithm to mine the personae social relationships from microblog posts by combining the syntax and semantic meanings of the dependency trigram kernels (DTK).To deeply extract the personal social relationships of microblog posts, we define the relation feature words, provide seven rules for extracting these feature words, and propose a rule-based approach that mines these relation feature words from microblog posts.We construct relation feature word dictionaries for different relation types because of the lack of prominent relation features in microblog posts.We propose an algorithm to classify relation feature words by considering two features of the relation feature words, namely, syntax and semantic similarities between relation feature words in microblog posts and by using relation feature word dictionaries.Experimental results show that the average recall, precision, and F-measure of our proposed approach outperforms the original DTK in sentence selection, personae social relation extraction, and personae social relation classification.Finally, the relation graphs of five topics clarify that our proposed approach is effective for extracting personae social relations from microblog posts.
Expert system to diagnose child development growth disorders with forward chaining method

Abstract Growth and development in children determine quality throughout their lives. The lack of understanding of parents about the importance of child development makes parents often ignore a situation or disease that occurs in their children. For this reason, an expert system is needed using the forward chaining method in reasoning to the knowledge base, which can help parents to know the growth problems experienced by their children based on the symptoms found in children. In its development, the expert diagnostic system for child development disorders is implemented using PHP and MySQL programming as the database. After this expert system is implemented, the system can diagnose 5 disorders of child development (Autism, Asperger syndrome, attention deficit disorders and Hyperactivity, Down syndrome, and mental retardation) especially in children aged 6 - 12 years.
Region of interest–based segmented tiled adaptive streaming using head-mounted display tracking sensing data

To support 360 virtual reality video streaming services, high resolutions of over 8K and network streaming technology that guarantees consistent quality of service are required. To this end, we propose 360 virtual reality video player technology and a streaming protocol based on MPEG Dynamic Adaptive Streaming over HTTP Spatial Representation Description to support the player. The player renders the downsized video as the base layer, which has a quarter of the resolution of the original video, and high-quality video tiles consisting of tiles obtained from the tiled-encoded high-quality video (over 16K resolution) as the enhanced layer. Furthermore, we implemented the system and conducted experiments to measure the network bandwidth for 16K video streaming and switching latency arising from changes in the viewport. From the results, we confirmed that the player has a switching latency of less than 1000 ms and a maximum network download bandwidth requirement of 100 Mbps.
A Distributed Modular Scalable and Generic Framework for Parallelizing Population-Based Metaheuristics

In the present paper, microservices, container virtualization and the publish/subscribe messaging paradigm are exploited to develop a distributed, modular, scalable and generic framework for parallelizing population-based metaheuristics. The proposed approach paves the way for an easy deployment of existing metaheuristic algorithms such as Evolutionary Algorithms (EAs) on a scalable runtime environment with full runtime automation. Furthermore, it introduces simple mechanisms to work efficiently with other components like forecasting frameworks and simulators. In order to analyze the feasibility of the design, the EA GLEAM (General Learning Evolutionary Algorithm and Method) is integrated and deployed on a cluster with 4 nodes and 128 cores for benchmarking. The overhead of the framework is measured and the obtained results show not only low values but also a small increase with growing number of computing nodes.
Semi-separation axioms of the infinite Khalimsky topological sphere

In this paper we denote by ((Z2)⁎,(κ2)⁎) the Alexandroff one point compactification of the Khalimsky (K-, brevity) plane. We often call this space the infinite K-topological sphere or the infinite K-sphere for brevity in the present paper. After studying various properties of the infinite K-topological sphere such as a non-Alexandroff structure, we study low and semi-separation axioms of it. Finally, let (X,T) be a topological space which is semi-T2 and each point x(∈X) has an open neighborhood V(∋x) such that the closure of V (or ClX(V)) is compact, and (X⁎,T⁎) an Alexandroff one point compactification of (X,T). Then, we prove that (X⁎,T⁎) is a semi-T2-space. Thus it turns out that the infinite K-sphere is a semi-T2-space.
Geological Remote Sensing

The field of remote sensing has recently witnessed major innovations that have been translated to Earth science applications. Before they can be used, remote sensing data must be corrected for effects originating from the sensors, the platforms on which they are deployed, atmospheric characteristics, and geometrical constraints. When the data are calibrated and geolocated, they can be used either as physical quantities, such as reflectance and temperatures, or as images. The recent development of new sensors has permitted the remote measurement of a large area of the Earth's surface, with direct geological applications. Additionally, advances in machine vision, machine learning and artificial intelligence, combined with an unprecedented increase in computer processing power, have lead to innovative remote sensing data processing techniques that simplify the handling of large amounts of complex data. As a consequence, it is now possible to characterize the geological settings of large areas with precision and even their changes through time. Remote sensing data are now directly integrated into modeling algorithms that describe surface and subsurface processes at different scales. Geological remote sensing currently encompasses multi-temporal, multi-source and multi-scale approaches. The retrieval of big data in disseminated archives, as well as (near) real-time processing are the challenges that remain to be solved. These new applications in geology ensure cost-efficient, safe and rapid surveys and monitoring that not only benefit the research community but society at large.
Online maximum a posteriori tracking of multiple objects using sequential trajectory prior

In this paper, we address the problem of online multi-object tracking based on the Maximum a Posteriori (MAP) framework. Given the observations up to the current frame, we estimate the optimal object trajectories via two MAP estimation stages: object detection and data association. By introducing the sequential trajectory prior, i.e., the prior information from previous frames about "good" trajectories, into the two MAP stages, the inference of optimal detections is refined and the association correctness between trajectories and detections is enhanced. Furthermore, the sequential trajectory prior allows the two MAP stages to interact with each other in a sequential manner, which jointly optimizes the detections and trajectories to facilitate online multi-object tracking. Compared with existing methods, our approach is able to alleviate the association ambiguity caused by noisy detections and frequent inter-object interactions without using sophisticated association likelihood models. The experiments on publicly available challenging datasets demonstrate that our approach provides superior tracking performance over state-of-the-art algorithms in various complex scenes.
How to prove decidability of equational theories with second-order computation analyser SOL

Abstract We present a general methodology of proving the decidability of equational theory of programming language concepts in the framework of second-order algebraic theories. We propose a Haskell-based analysis tool, i.e. Second-Order Laboratory, which assists the proofs of confluence and strong normalisation of computation rules derived from second-order algebraic theories. To cover various examples in programming language theory, we combine and extend both syntactical and semantical results of the second-order computation in a non-trivial manner. We demonstrate how to prove decidability of various algebraic theories in the literature. It includes the equational theories of monad and λ-calculi, Plotkin and Power’s theory of states and bits, and Stark’s theory of π-calculus. We also demonstrate how this methodology can solve the coherence of monoidal categories.
Analysing deep reinforcement learning agents trained with domain randomisation

Deep reinforcement learning has the potential to train robots to perform complex tasks in the real world without requiring accurate models of the robot or its environment. A practical approach is to train agents in simulation, and then transfer them to the real world. One popular method for achieving transferability is to use domain randomisation, which involves randomly perturbing various aspects of a simulated environment in order to make trained agents robust to the reality gap. However, less work has gone into understanding such agents - which are deployed in the real world - beyond task performance. In this work we examine such agents, through qualitative and quantitative comparisons between agents trained with and without visual domain randomisation. We train agents for Fetch and Jaco robots on a visuomotor control task and evaluate how well they generalise using different testing conditions. Finally, we investigate the internals of the trained agents by using a suite of interpretability techniques. Our results show that the primary outcome of domain randomisation is more robust, entangled representations, accompanied with larger weights with greater spatial structure; moreover, the types of changes are heavily influenced by the task setup and presence of additional proprioceptive inputs. Additionally, we demonstrate that our domain randomised agents require higher sample complexity, can overfit and more heavily rely on recurrent processing. Furthermore, even with an improved saliency method introduced in this work, we show that qualitative studies may not always correspond with quantitative measures, necessitating the combination of inspection tools in order to provide sufficient insights into the behaviour of trained agents.
Implementation and comparison analysis of apriori and fp-growth algorithm performance to determine market basket analysis in Breiliant shop

Abstract The creativity of an online store owner who develops a business in a retail business in determining a marketing strategy will affect his competitive ability with other online stores. However, services that are in accordance with costumers attitude is a stuff that should be awarded by shop owners in order to improve not only customer satisfaction but also store income. The method used in this study is Market Basket Analysis to know customers attitude by analysing data from sales transactions to show customers attitude toward one sold item and the others. Research aimed that it is necessary to have a system that can help to form a combination among products using the association rule method, the algorithm used to analyse this method is apriori and fp-growth algorithm. Result from data analysis test using monthly sales transaction data of cosmetics in Breiliant Store during November 2018 with 34 number of data sales transaction. It could be concluded that a combination of products which have strong support and confidence were Original Liquid Bleaching Seeds, Harva Peeling Gel and Castor oil. It had support value of 8.8% and 30% confidence value, with a filtering time of 0.036 seconds.
Feature selection algorithm based on PDF/PMF area difference

The research devoted to feature selection has been studied extensively in the last years and as a result, important results have been obtained. However, a feature selection technique remains a complex problem because of the necessity for its outcomes to be reliable and accurate within different limitations. Nowadays, there is no feature selection algorithm that outperforms any others. Our research is focused on the development of a feature selection algorithm and its comparison with other well-known feature selection techniques. The proposed algorithm is used for searching for the most reliable features. Despite the fact that we used the proposed algorithm in a specific case in our study (catheter detection task), the algorithm can also be used for general purposes as well. Therefore, it can be easily scaled to find the relevant features of the region of interest in other tasks. The proposed algorithm is based on the difference in probability density functions (PDFs) or probability mass functions (PMFs). We introduced a score value to assess how stable a certain feature is. The score value determines the degree of intersection of areas under the density/mass distribution functions. In our case where a catheter is detected, we described the region of interest by dividing a 20-feature set into several groups: morphometric, statistical, intensity-based, textural and geometric. To evaluate the results, we compared the results of the proposed algorithm with state-of-the-art feature selection techniques. The sets of features selected by different algorithms were used to train and test a linear SVM classifier. As an additional estimator, we used a neural network with the cascade-forward backpropagation (CFB). The proposed Area Difference Feature Selection (ADFS) algorithm obtained the following accuracy on the intracardiac catheter dataset: 78.7 ± 3.2 % for a 3-feature subset, 82.3 ± 2.8 % for a 6-feature subset and 86.7 ± 1.5 % for a 12-feature subset. According to the obtained accuracy, ADFS was on the list of the three best algorithms. Additionally, we tested the ADFS algorithm on the breast cancer dataset from the UCI machine learning repository and observed that it outperformed other compared algorithms on the 3-feature and 12-feature subsets. Regarding the 6-feature subset, ADFS is inferior in accuracy to only MRMR and MCFS by approximately 1.8 % and 0.3 %, respectively. According to the processing time assessment, the most time-consuming algorithms were FSCM, MRMR, UDFS, and RFFS. In turn, the fastest algorithms were INFS, CFS, and ADFS with the following processing times: 2 ± 4, 2 ± 5 and 6 ± 16 ms. By testing and comparing the different feature selection algorithms, the proposed feature selection algorithm is shown to be accurate and effective for various tasks, including medical imaging and visualization.