Reviewerly/exHarmony huggingface dataset:
Characterizing User Decision based on Argumentative Reviews

Opinion mining from mobile app reviews has grown exponentially during the last decade. Most studies in this area, however, have focused on a sentiment analysis. In this study, we consider review mining from another perspective, that is, capturing user justifications behind whatever actions are explicitly stated in their app reviews, e.g., the reason behind user purchases. This study highlights how different app features can promote different user decisions, which in turn can be beneficial for software developers to gain valuable data-driven requirements for the planning and development of application updates. We collected, used, and shared our manually annotated 46k mobile app reviews from 12 different app categories in the Google Play Store. We designed three classification problems to filter reviews containing both arguments and decisions from non-argumentative reviews. We extracted three features, namely, structural, lexical, and contextual, from the body of review sentences. Four classifiers (naive Bayes, logistic regression, support vector machine, and random forest) and different feature combinations were trained on the dataset and evaluated to examine if such features can allow us to classify user arguments and decisions. The results show an improved performance over previous studies and show the efficacy of the proposed approach compared to human-assessments.
Graph Theory for Dimensionality Reduction: A Case Study to Prognosticate Parkinson's

In the present world, the commotion centering Big Data is somewhat obscuring the craft of mining information from smaller samples. Populations with limited examples but huge dimensionality are a common phenomenon, otherwise known as the curse of dimensionality-especially in the health sector-thanks to the recently-discovered potential of data mining and the enthusiasm for feature engineering. Correlated, noisy, redundant features are byproducts of this tendency, which makes learning algorithms converge with greater efforts. This paper proposes a novel feature-pruning technique relying on computational graph theory. Restoring the appeal of pre-AI conventional computing, the paper applies Disjoint Set Union (DSU) on unidirectional graphs prepared basis thresholded Spearman's rank correlation coefficient, r. Gradual withdrawal of leniency on Spearman's r caused a greater tendency in features to form clusters, causing the dimensionality to shrink. The results-extracting out finer, more representative roots as features-have been k-fold cross-validated on a case study examining subjects for Parkinson's. Qualitatively, the method overcomes Principal Component Analysis's (PCA) limitation of inexplicit merging of features and Linear Discriminant Analysis's (LDA) limitation of inextendibility to multiple classes. Statistical inference verified a significant rise in performance, establishing an example of conventional hard computing reinforcing modern soft computing.
Path‐based Monte Carlo Denoising Using a Three‐Scale Neural Network

Abstract Monte Carlo rendering is widely used in the movie industry. Since it is costly to produce noise‐free results directly, Monte Carlo denoising is often applied as a post‐process. Recently, deep learning methods have been successfully leveraged in Monte Carlo denoising. They are able to produce high quality denoised results, even with very low sample rate, e.g. 4 spp (sample per pixel). However, for difficult scene configurations, some details could be blurred in the denoised results. In this paper, we aim at preserving more details from inputs rendered with low spp. We propose a novel denoising pipeline that handles three‐scale features ‐ pixel, sample and path ‐ to preserve sharp details, uses an improved Res2Net feature extractor to reduce the network parameters and a smooth feature attention mechanism to remove low‐frequency splotches. As a result, our method achieves higher denoising quality and preserves better details than the previous methods.
An Assessment of the Usability of Machine Learning Based Tools for the Security Operations Center

Gartner, a large research and advisory company, anticipates that by 2024 80% of security operation centers (SOCs) will use machine learning (ML) based solutions to enhance their operations. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">11</sup> https://www.ciodive.com/news/how-data-science-tools-can-lighten-the-load-for-cybersecurity-teams/572209/ In light of such widespread adoption, it is vital for the research community to identify and address usability concerns. This work presents the results of the first in situ usability assessment of ML-based tools. With the support of the US Navy, we leveraged the national cyber range-a large, air-gapped cyber testbed equipped with state-of-the-art network and user emulation capabilities-to study six US Naval SOC analysts' usage of two tools. Our analysis identified several serious usability issues, including multiple violations of established usability heuristics for user interface design. We also discovered that analysts lacked a clear mental model of how these tools generate scores, resulting in mistrust a and/or misuse of the tools themselves. Surprisingly, we found no correlation between analysts' level of education or years of experience and their performance with either tool, suggesting that other factors such as prior background knowledge or personality play a significant role in ML-based tool usage. Our findings demonstrate that ML-based security tool vendors must put a renewed focus on working with analysts, both experienced and inexperienced, to ensure that their systems are usable and useful in real-world security operations settings.
A recognition method for time-frequency overlapped waveform-agile radar signals based on matrix transformation and multi-scale center point detection

The new digital programmable radar systems allow the development of waveform agility technique that can be reconfigured to work with different kinds of waveforms without the introduction of any hardware modification, and it also extends to the combination of the carrier frequency agility, intra-pulse chirp, and pulse repetition frequency jittering. The challenging recognition problem of waveform-agile radar signals results in the difficult implementation of the prior electronic countermeasures and interferences. Meanwhile, the common problem under wideband reception is that multiple overlaps easily occur in the time and frequency domain among sources. In this paper, we take a fresh look at this problem, a method based on matrix transformation is proposed from the perspective of the source independence, to extract the waveform-agile radar sources in the underdetermined receiving scenario. Next, a multi-scale center point detection method is designed to locate and identify the internal waveforms to recognize the whole signal, and an additional box selecting algorithm is proposed to reduce the computing burden. Simulations demonstrate that the developed technique effectively solves the source attribution problem, and also outperforms the traditional methods in recognition performance under low signal-to-noise ratio (SNR).
Non-Contact In-Home Activity Recognition System Utilizing Doppler Sensors

In recent years, various approaches for smart home technology have been developed, such as home appliances control, services for energy saving and support of daily life. In order to realize such services, we need a system which is able to accurately recognize various human activities using low-cost devices. To realize such a system, we need to address several problems: the required sensors are too expensive (P1); it is difficult to precisely recognize place-independent activities like reading (P2), and putting on a device causes a burden to people (P3) the information such as images infringe on the privacy of the occupants (P4). In this paper, we propose a method for activity recognition by utilizing a doppler sensor as a motion detection sensor and a machine learning technique to solve the problems above (P1-P4). Specifically, frequency characteristic is obtained from the signals of the doppler sensor and we construct a machine learning model using effective features, which is presented by Anguita, and speed of target calculated from the doppler frequency. In order to examine the usefulness of the proposed method and find out critical issues of realizing activity recognition, we have collected sensor data of 6 kinds of activities(stationary, smartphone operation, PC operation, reading, writing, and eating) performed by 10 participants. For leave-one-session-out cross-validation, the maximum average recognition accuracy was 95.7%, and the average for 10 participants was 81.0%. For leave-one-person-out cross validation, the average recognition accuracy of logistic regression shows maximum accuracy of 42.1%.
Registration of the quantum state of a single photon to create a satellite quantum network

Abstract Artificial satellites employed as trusted nodes can increase the distance between two parties to establish quantum key distribution (QKD), unlike fiber based communication lines that are limited up to a few hundred kilometers. This report summarises our progress on a ground receiver for satellite QKD and its tracking receiving system (TRS). The authors demonstrate the operation of the TRS in practice and achieve standard tracking error of 1.4/Ltrad observing four satellites over 80% of the time they were available. The optical signal, which was the sunlight reflected of low Earth orbit satellites, was attenuated to a single photon level and was detected effectively using avalanche single photon detectors. The authors have thus proved that the developed system is capable of stable reception of quantum signal from a satellite.
A Robust Model Using SIFT and Gamma Mixture Model for Texture Images Classification: Perspectives for Medical Applications

The texture analysis of medical images is a powerful calculation tool for the discrimination between pathological and healthy tissue in different organs in medical images. Our paper proposes a novel approach named, GGD-GMM, based on statistical modeling in wavelet domain to describe texture images. Firstly, we propose a robust algorithm based on the combination of the wavelet transform and Scale Invariant Feature Transform (SIFT). Secondly, we implement the aforementioned algorithm and fit the result by using the finite Gamma Mixture Model (GMM). The results, obtained for two benchmark datasets, show that our proposed algorithm has a good relevance as it provides higher classification accuracy compared to some other well known models. Moreover, it displays others advantages relied to Noise-resistant and rotation invariant. Our algorithm could be useful for the analysis of several medical issues.
A Survey on Blockchain: Architecture, Applications, Challenges, and Future Trends

Blockchain is a decentralized network ledger. Based on the core characteristics of blockchain such as decentralization, tamper-proof, and smart contract, it has been applied in many fields including finance, healthcare, food traceability and so on. However, blockchain technology still faces many challenges, such as scalability, interoperability, privacy leakage, and legal regulation, to be solved. This paper conducts a comprehensive survey of blockchain technology. Specifically, we firstly introduce the architecture of blockchain. Secondly, several typical application scenarios are described. Then, we list some technical and legal challenges of blockchain. Finally, the development of future trends of blockchain is analyzed.
HumTouch: Localization of Touch on a Cylindrical Object

The following topics are dealt with: learning (artificial intelligence); feature extraction; image classification; video signal processing; neural nets; computer aided instruction; cameras; natural language processing.
Fast Permutation Architecture on Encrypted Data for Secure Neural Network Inference

Recently, the secure neural network inference, an organic combination of the homomorphic encryption (HE) and the deep neural network (DNN), has attracted much attention. Nevertheless, the large number computations, brought by the HE scheme, form the bottleneck for real-time applications. A significant portion of the network is the permutation (Perm), which is mainly made up of the number theoretic transform (NTT). In this paper, for the first time, we propose an efficient architecture for the Perm by incorporating algorithmic transformations and architectural level optimizations. First, the core butterfly unit (BU) of NTT is optimized, which reduces the multiplication operations by about 30% compared with the original BU. Then, based on the optimization, a highly parallelized architecture is devised for the Perm. The operations in different modules are well managed by a merging strategy to balance the data path and reduce the memory access. The proposed architecture is synthesized under the TSMC 28-nm CMOS technology. The experimental results show that for the ciphertext size of 2048×60 bits, the proposed design achieves a 7.54x speedup compared to the implementation on an Intel(R) Core(TM) i7-6850K 3.60Hz CPU. Moreover, we apply eight Perm engines to the 1D convolution, which shows a 17.25x speedup over the software implementation.
A Comparison of the State-of-the-Art Deep Learning Platforms: An Experimental Study

Deep learning, a subfield of machine learning, has proved its efficacy on a wide range of applications including but not limited to computer vision, text analysis and natural language processing, algorithm enhancement, computational biology, physical sciences, and medical diagnostics by producing results superior to the state-of-the-art approaches. When it comes to the implementation of deep neural networks, there exist various state-of-the-art platforms. Starting from this point of view, a qualitative and quantitative comparison of the state-of-the-art deep learning platforms is proposed in this study in order to shed light on which platform should be utilized for the implementations of deep neural networks. Two state-of-the-art deep learning platforms, namely, (i) Keras, and (ii) PyTorch were included in the comparison within this study. The deep learning platforms were quantitatively examined through the models based on three most popular deep neural networks, namely, (i) Feedforward Neural Network (FNN), (ii) Convolutional Neural Network (CNN), and (iii) Recurrent Neural Network (RNN). The models were evaluated on three evaluation metrics, namely, (i) training time, (ii) testing time, and (iii) prediction accuracy. According to the experimental results, while Keras provided the best performance for both FNNs and CNNs, PyTorch provided the best performance for RNNs expect for one evaluation metric, which was the testing time. This experimental study should help deep learning engineers and researchers to choose the most suitable platform for the implementations of their deep neural networks.
Unsupervised aspect-based Sentiment Analysis in the Persian language: Extracting and clustering aspects

Sentiment analysis is the field of natural language processing to analyze user feedback, preferences, and evaluations from the text. Different organizations in most social scopes use this context as an appropriate tool to find their strengths and weaknesses. In this field of research, the objective is to determine the positive or negative orientations of users towards the features of a product or commodity. Solving this problem consists of two main steps: aspect extraction and identifying the positive or negative tendencies of users towards those aspects. Two of the most critical issues of sentiment analysis in the Persian language are the lack of comprehensive labeled data and the significant difference between colloquial and formal sentences in Persian. One of the most important methods to confront the first problem is to apply unsupervised methods. In this research, a system for aspect-based sentiment analysis in the Persian language is proposed using unsupervised methods. The Sentiment words extraction step is done in another article [1]. The aspect extraction and clustering steps are done using topic modeling methods and neural networks and taking benefit of rule-based methods. This system has been evaluated using precision and recall criteria. The F1 criterion for extracting aspect words in the proposed system was 0.766.
What causes merge conflicts?

During the software development process, several developers commonly change artifacts in parallel. A merge process can combine parallel changes. In the case of changes that cannot be automatically combined, the developer responsible for the merge must reconcile decisions and resolve conflicts. Some studies are concerned with investigating ways to deal with merge conflicts and measuring the effort that this activity may require. However, the investigation of factors that may reduce the occurrence of conflicts needs more and deeper attention. This paper aims at identifying and analyzing attributes of past merges with and without conflicts to understand what may induce physical conflicts. We analyzed 182,273 merge scenarios from 80 projects written in eight different programming languages to find characteristics that increase the chances of a merge to have a conflict. We found that attributes such as the number of committers, the number of commits, and the number of changed files seem to have the biggest influence in the occurrence of merge conflicts. Moreover, attributes in the branch that is being integrated seem to be more influential than the same attributes in the other branch. Additionally, we discovered positive correlations between the occurrence of conflicts and both the duration of the branch and the intersection of developers.
A Data-Driven Approach of Takagi-Sugeno Fuzzy Control of Unknown Nonlinear Systems

A novel approach to build a Takagi-Sugeno (T-S) fuzzy model of an unknown nonlinear system from experimental data is presented in the paper. The neuro-fuzzy models or, more specifically, fuzzy basis function networks (FBFNs) are trained from input–output data to approximate the nonlinear systems for which analytical mathematical models are not available. Then, the T-S fuzzy models are derived from the direct linearization of the neuro-fuzzy models. The operating points for linearization are chosen using the evolutionary strategy to minimize the global approximation error so that the T-S fuzzy models can closely approximate the original unknown nonlinear system with a reduced number of linearizations. Based on T-S fuzzy models, optimal controllers are designed and implemented for a nonlinear two-link flexible joint robot, which demonstrates the possibility of implementing the well-established model-based optimal control method onto unknown nonlinear dynamic systems.
Analysis of automated estimation models using machine learning

Plenty of practice based on software estimation has been developed in software industry. Algorithmic models represent the most formal approach that have provided the most reliable results. However, the use of informal practice is still prevalent just like the expert judgment which will not allow Software Engineering grow up.An important activity in big and small companies is to generate reliable estimation models. The development of these models is usually based on information obtained from past projects and requires a deep and precise analysis.This paper presents the application of the automated estimation-model generator system that uses machine learning techniques whit the objective of analysing the accuracy of these models comparing them to the traditional estimation methods using an international database and the internal database of a company.
Blockchain Application in Remote Condition Monitoring

Through advanced sensor technologies, satellite-based authentication, and high bandwidth data networks, Remote Condition Monitoring (RCM) systems are now an essential `Internet of Things' (IoT) resource for efficient operation of railway infrastructure. However, the full potential of this big data has yet to be realized. Data is currently collected and used in siloes, with limited visibility of all possible datasets for exploitation. The RSSB on behalf of the UK Rail Industry established a cross-industry research program, T1010, to build stronger cooperation between stakeholders in sharing RCM data. This research builds upon T1010, to explore the use of blockchain and smart contracts to automate, in an auditable and tamper-proof way, the commercial agreements and payment processes for data trading. By removing the limitations of paper-based agreements, our goal is to enable innovation in shared business processes and an IoT data marketplace. Building on existing smart contract-based schemes for trading and sharing IoT data over blockchain networks, this research identifies novel ways to enforce agreements and ensure fair cost attribution between parties, without a Trusted Third Party. The initial design of a blockchain-based framework is presented, oriented around the data provider, consumer, and smart contracts. Blockchain-hosted data access agreement and accounting models are specified in detail. The processors in the efficient permissioned blockchain platforms Hyperledger Fabric, Sawtooth, and Iroha have been analyzed for their suitability for implementation. We then outline our future work to evaluate and validate two industrial use cases: monitoring systems for unattended overhead line equipment and axle bearings.
Self-consistent quantum tomography with regularization

A reliable method for characterizing quantum operations that is suitable for improving and validating their accuracies is indispensable for realizing a practical quantum computer. Known methods are still not sufficient because they lack reliability or are not suitable for use in the improvement and validation steps. Here we propose a reliable characterization method that is suitable for the accuracy validation step. First, we introduce a new self-consistent estimator with regularization and physicality constraints that are designed for improvement and validation. Second, we mathematically prove that the method provides estimation results that are stringently physical and converge to the gauge-equivalence class of the quantum operations of interest at the limit of data size going to infinity. The asymptotic convergence guarantees the reliability of the method, and the physical and regularized results ensure the suitability to the validation task. We also derive the asymptotic convergence rate, which would be optimal. Finally, we show numerical results on 1-qubit system, which confirm the theoretical results and prove that the method proposed is practical.
Consortium Blockchain-based V2G Energy Trading System Using Tokens

Recently, under the influence of the Fourth Industrial Revolution, the government is attempting a new field of revitalization of the power energy trading market in the energy industry. With the advent of prosumers that generate and consume electricity by themselves, the need to promote transparent transactions by establishing a safe and reasonable transaction platform for energy transactions has increased. With the development of blockchain technology, the research has been actively conducted, and interest in services incorporating blockchain is increasing in various fields, and various studies incorporating blockchain into the energy transaction field have been actively conducted. Along with the need for a transparent energy trading platform, the need for electric vehicle charging infrastructure and power load management, one of the core technologies of smart grid, is also increasing. Therefore, this study aims to build a V2G energy transaction system based on the consortium blockchain and develop an application for transaction activation. In addition, it intends to incorporate the blockchain system into real life and lay the foundation for commercialization of the blockchain system.