Reviewerly/exHarmony huggingface dataset:
Extracting Online Product Review Patterns and Causes: A New Aspect/Cause Based Heuristic for Designers

Identifying and analysing large amounts of online product reviews (OPR) have become a critical challenge for product design due to their valuable and insightful content. Despite the potential of the OPRs, designers cannot benefit from this opportunity because the analysis of the interpretations requires knowledge of different areas. This study proposes a novel framework for designers by utilizing online reviews for product design depending on the real-world OPRs of a sample product in terms of clarity, comparability, and validity. The framework contains five steps – retrieval of customer text, mining text polarity and product aspects by Document-Level Sentiment Analysis (DLSA) and Aspect-Based Sentiment Analysis (ABSA), summarizing and visualizing review candidate patterns by Correspondence Analysis (CA) and discovering and predicting possible causes of candidate patterns by correlation analysis.
An Experimental Study of Automatic Detection and Measurement of Counterfeit in Brand Search Results

Brand search results are poisoned by fake ecommerce websites that infringe on the trademark rights of legitimate holders. In this article, we study how to tackle and measure this problem automatically. We present a pipeline with two machine learning stages that can detect the ecommerce websites present in the list of brand search results and distinguish between legitimate and fake ecommerce websites. For each classification task, we identify and extract suitable learning features and study their relative importance. Through a prototype system termed RI.SI.CO., we show that this approach is feasible, fast, and more accurate than both existing systems for trustworthiness assessment and non-expert humans. We next introduce two complementary metrics for evaluating the counterfeit incidence in brand search results: namely, a chart-based and a single-value measure. They allow us to analyze and compare counterfeit at various levels, including single brands within a specific sector as well as whole sectors. Experimenting with two luxury goods sectors, we report a number of interesting findings about how the main search parameters (e.g., search engine, query type, number of search results seen) affect counterfeiting and how this activity changes with time. On the whole, our research offers new insights and some very practical and useful means of analyzing and measuring counterfeit in brand search results, thus increasing awareness of and knowledge about this phenomenon and enabling targeted anti-counterfeiting actions.
Integrating Chatbot Application with Qlik Sense Business Intelligence (BI) Tool Using Natural Language Processing (NLP)

So far, various approaches have been proposed for implementing chatbots with business intelligence tools, yet most of these approaches have limited adoption in practice. The objective of this paper is to offer an easy to understand approach by the business users that could be quickly adopted for integrating the chatbot with a business intelligence tool. This communication describes a process of applying artificial intelligence technologies; in particular, natural language processing (NLP) for conversation using chatbot. The chatbot implementation and integration with the BI tool has given encouraging results.
Evaluating Deep Learning for Image Classification in Adversarial Environment

Due to the superior performance, deep learning has been widely applied to various applications, including image classification, bioinformatics, and cybersecurity. Nevertheless, the research investigations on deep learning in the adversarial environment are still on their preliminary stage. The emerging adversarial learning methods, e.g., generative adversarial networks, have introduced two vital questions: to what degree the security of deep learning with the presence of adversarial examples is; how to evaluate the performance of deep learning models in adversarial environment, thus, to raise security advice such that the selected application system based on deep learning is resistant to adversarial examples. To see the answers, we leverage image classification as an example application scenario to propose a framework of Evaluating Deep Learning for Image Classification (EDLIC) to conduct comprehensively quantitative analysis. Moreover, we introduce a set of evaluating metrics to measure the performance of different attacking and defensive techniques. After that, we conduct extensive experiments towards the performance of deep learning for image classification under different adversarial environments to validate the scalability of EDLIC. Finally, we give some advice about the selection of deep learning models for image classification based on these comparative results.
Goal-based configuration analysis for networks of collaborative cyber-physical systems

Networks of collaborative cyber-physical systems can achieve goals individual systems are incapable of achieving on their own. However, which goals such a network can achieve depends, in part, on the networks current configuration, i.e. its composition of partaking individual systems. As networks of collaborative cyber-physical systems are of a dynamic nature, the composition of such a network can change during runtime, leading to a plethora of often similar albeit slightly different configurations. Due to the huge number of possible configurations and their various dependencies to the different goals of the network, it is infeasible to handle this amount of information manually. Hence, to provide support for reasoning about dependencies between different configurations and the goals they can achieve, this paper contributes an automated model-based reasoning approach using view generations. Our approach allows for exploring which goals can be fulfilled by which configurations and which goals cannot be fulfilled by these configurations. We evaluated the approach using an industrial case study which shows the applicability and benefits of the approach.
The potency of solar energy on Medan city of Indonesia: Comparison of clear sky, satellite and field measurements

Solar energy is expected to fill the renewable energy target of Indonesia. Thus, the potency of solar energy resource in Indonesia need to be known exactly. In this study, the potency of solar energy in Medan city of Indonesia is estimated theoretically using clear sky radiation model. The estimation is compared with satellite measurement and field measurement. The solar resource satellite data drawn from the Global Solar Atlas, belongs to the World Bank Group and provided by SOLARGIS. The measurement is carried out at a location in Medan city at latitude 3°35' North and longitude 98°39' East using HOBO micro station data logger. The results shows that the theoretical clear sky radiation model, as expected, shows a higher solar irradiation. The measurement values are closer to data of satellite.
Request for comments

Issue tracking systems play an important role in developing software systems. They provide a central place to store and maintain different development artifacts. Various studies are concerned with the contained bug reports, features, the relations among them and traces to the projects code base. However, an issue tracker can also be used as a communication channel between project contributors by attaching comments to issues. Less is known on how users actually utilize this functionality. In this paper, we study more than 270,000 comments from twelve open-source projects. We analyze to what extend comments are used and then study the structure occurring in threads of comments. Based on the order of comments and participating contributors, we identified three patterns of conversation: monolog, feedback, and collaboration. Our results show that most conversations are collaborations among two or more developers discussing the issue.
Autotuning based on frequency scaling toward energy efficiency of blockchain algorithms on graphics processing units

Abstract Energy-efficient computing is especially important in the field of high-performance computing (HPC) on supercomputers. Therefore, automated optimization of energy efficiency during the execution of a compute-intensive program is desirable. In this article, a framework for the automatic improvement of the energy efficiency on NVIDIA GPUs (graphics processing units) using dynamic voltage and frequency scaling is presented. As application, the mining of crypto-currencies is used, since in this area energy efficiency is of particular importance. The framework first determines the energy-optimal frequencies for each available currency on each GPU of a computer automatically. Then, the mining is started, and during a monitoring phase it is ensured that always the most profitable currency is mined on each GPU, using optimal frequencies. Tests with different GPUs show that the energy efficiency, depending on the GPU and the currency, can be increased by up to 84% compared to the usage of the default frequencies. This in turn almost doubles the mining profit.
Invested or Indebted

Online communities that curate knowledge critically depend on high-quality contributions from anonymous expert users. Understanding users’ motivation to contribute knowledge helps practitioners design such websites for optimal user contribution and user benefits. Researchers have studied reciprocity as a motivation for users to share knowledge online. In this study, we focus on two different types of reciprocity as drivers of online contribution: ex-post and ex-ante reciprocity. Ex-post reciprocity refers to users who received help from others in the past and pay back by helping others at present. Using a quasi-experiment performed via the instrumental variable method as the identification strategy, we test whether users who received more answers last week answer more questions in the current week on StackOverflow.com. We find a significant positive relationship between ex-post reciprocity and knowledge contribution, and such a reciprocal motivation diminishes with time. Ex-ante reciprocity refers to people helping others in expectation of future help from others. Using data from StackOverflow.com, we take advantage of a natural experiment with a difference-in-differences analysis and find evidence supporting the existence of ex-ante reciprocity. This study offers a new taxonomy for reciprocity and new insights on how reciprocity drives online knowledge sharing.
Towards a Blockchain-Based Zero-Knowledge Model for Secure Data Sharing and Access

Blockchain-based applications cover numerous fields from financial services to the Internet of Things (IoT). The smart contracts which are the programmable blocks that execute predefined command are executed on participating nodes. Such execution leads to privacy and confidentiality issues as nodes process data to execute the smart contracts. A zero-knowledge proof is used in cryptocurrencies for allowing spending digital money without revealing the identity of persons. In this paper, we aim to introduce a zero-knowledge proof model using blockchain to enable sharing data securely between services and devices without affecting the confidentiality or privacy of data. The security model fuses digital assets within a blockchain and serves as a trustless layer for protecting the data, which results in enhancing the security of data sharing. We apply the model to two recently introduced concepts in Bahrain: The National Healthcare Insurance Scheme and government-collected VAT. Applying the model for exchanging medical data and sharing medical records between the private sector and public sector will enable maintaining the privacy of patients and increasing efficiency of operations. For VAT, the model helps secure the overall process of VAT reporting and B2B transactional processes in general.
Particle swarm optimization: Stability analysis using <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si34.svg"><mml:mi>N</mml:mi></mml:math>-informers under arbitrary coefficient distributions

This paper derives, under minimal modelling assumptions, a simple to use theorem for obtaining both order-1 and order-2 stability criteria for a common class of particle swarm optimization (PSO) variants. Specifically, PSO variants that can be rewritten as a finite sum of stochastically weighted difference vectors between a particle’s position and swarm informers are covered by the theorem. Additionally, the use of the derived theorem allows a PSO practitioner to obtain stability criteria that contains no artificial restriction on the relationship between control coefficients. The majority of previous stability results for PSO variants provided stability criteria under the restriction that certain control coefficients are equal; such restrictions are not present when using the derived theorem. Using the derived theorem, as demonstration of its ease of use, stability criteria are derived without the imposed restriction on the relation between the control coefficients for four popular PSO variants.
Computer Vision and Machine Learning Approach for Malaria Diagnosis in Thin Blood Smears from Microscopic Blood Images

Malaria is still a serious health problem globally, which is caused by a parasite of genus Plasmodium through Anopheles mosquitoes. An automated and accurate diagnosis is needed for an appropriate intervention to reduce mortality and to prevent anti-malaria resistance. In general, pathologists investigate blood-stained slides through conventional microscopy for diagnosis. However, these approaches are done by a clinical expert which biased, error-prone and moderate. This research addresses the development of robust computer-assisted malaria diagnosis in light microscopic blood images. In addition, microscopic images were obtained through stained slides which consist of illuminations and noise levels. To overcome this situation used computer vision approach i.e., improved k-SVD denoising method, fuzzy type II-based segmentation, feature extraction through local and global features, feature selection with Extra Trees Classifier and classify the different stages of malaria have been explored. The proposed classification strategy can be achieved by unifying Extremely Randomized Trees (ERT). The obtained experimental results showed Extremely Randomized Trees classifier accuracy of 98.02% that have been achieved in the process of malaria diagnosis.
Clone Notifier: Developing and Improving the System to Notify Changes of Code Clones

A code clone is a code fragment that is identical or similar to it in the source code. It has been identified as one of the main problems in software maintenance. When a developer fixes a defect, they need to find the code clones corresponding to the code fragments. In this paper, we present Clone Notifier, a system that alerts on creations and changes of code clones to software developers. First, Clone Notifier identifies creations and changes of code clones. Subsequently, it groups them into four categories (new, deleted, changed, stable) and assigns labels (e.g., consistent, inconsistent) to them. Finally, it notifies on creations and changes of code clones along with the corresponding categories and labels. Clone Notifier and its video are available at: https://github.com/s-tokui/CloneNotifier.
An Image Dehazing Algorithm Based on Single-Scale Retinex and Homomorphic Filtering

The traditional single-scale retinex (SSR) algorithm has good color fidelity advantage when the surrounding scale is large enough, but the image with uneven illumination is prone to local halo. To solve this problem, a new algorithm for smog images is proposed in this paper. Firstly, the smog image is processed by SSR to ensure good color fidelity. It is then filtered by a modified Gaussian homomorphic filter to eliminate halo artifacts. Finally, the CLAHE algorithm is used to enhance the edge details to achieve the final defogging effect. It can be concluded from the simulation results that the proposed algorithm can restore the image color and the image details while reducing the local halo compared to the traditional dehazing algorithm.
QIM-based Audio Watermarking using Polar-based Singular Value in DCT Domain

This paper presents an audio watermarking scheme that ensures the efficient of robustness-distortion ratio and a pleasing amount of capacity. Quantization Index Modulation (QIM) method synergism along with Discrete Wavelet Transform (DWT), Discrete Cosine Transform (DCT), Singular Value Decomposition (SVD), and Cartesian to Polar Transformation (CPT) to emphasize copyright protection for harmonical work of art. DWT synergism along with DCT, SVD, and CPT to emphasize copyright protection for harmonical work of art. The system establishes with DWT that divides the signal within a specific range of frequency, alter it to the frequency domain by DCT, partite with mathematical processing SVD, and transform the peculiar value of matrix-S to polar value using CPT, furthermore, QIM embed the watermark into the angular value from the CPT. The system performance is astonishing, tested using 4 different audio host signal and evaluated considers to desirable Bit Error Rate (BER) <; 0.025, Signal-to-Noise Ratio (SNR)>21dB, a great amount of data payload that is 525 bit per second, and the noise caused by watermark is inaudible. All of the experiments, trials, and results show that the designed audio watermarking system is robust and imperceptible.
We Are Family: Analyzing Communication in GitHub Software Repositories and Their Forks

GitHub facilitates software development practices that encourage collaboration and communication. Part of GitHub's model includes forking, which enables users to make changes on a copy of the base repository. The process of forking opens avenues of communication between the users from the base repository and the users from the forked repositories. Since forking on GitHub is a common mechanism for initiating repositories, we are interested in how communication between a repository and its forks (forming a software family) relates to stars. In this paper, we study communications within 385 software families comprised of 13,431 software repositories. We find that the fork depth, the number of users who have contributed to multiple repositories in the same family, the number of followers from outside the family, familial pull requests, and reported issues share a statistically significant relationship with repository stars. Due to the importance of issues and pull requests, we identify and compare common topics in issues and pull requests from inside the repository (via branching) and within the family (via forking). Our results offer insights into the importance of communication within a software family, and how this leads to higher individual repository star counts.
Optimization Of Parameter Support Vector Machine (SVM) using Genetic Algorithm to Review Go-Jek’s Services

SVM method can be utilized to classify opinion data based on assessment attributes to discriminate whether an opinion is classified as positive or negative sentiment. The advantages of SVM implementation such as generalization, its stability, classification, and ability to process both linear and non-linear data have made SVM being considered as a reliable classification method. According to these considerations, the authors carried out a sentiment analysis using SVM algorithm to evaluate Go-Jek service reviews. This sentiment analysis is expected to provide benefits for the stakeholders, particularly for Go-Jek. Support Vector Machine (SVM) algorithm is capable of overcoming high dimensional data set, classification problems, and both kernel linear and non-linear regression, which make SVM reliable for an algorithm for classification and regression. However, support vector machine has a drawback in selecting relevant parameters to enhance its optimization. A genetic algorithm method is necessary to resolve the problem of selecting relevant parameters in support machine vector method.
SOHUPDS

High utility pattern mining has emerged to overcome the limitation of frequent pattern mining where only frequency is taken as importance without considering the actual importance of items. Existing algorithms for mining high utility patterns over a data stream are two-phase algorithms that are not scalable due to the large number of candidates generation in the first phase, particularly when the minimum utility threshold is low. Moreover, in the second phase, the algorithm needs to scan the database again to find out actual utility for candidates. In this paper, we propose a novel algorithm SOHUPDS to mine high utility patterns over a data stream with the sliding window technique using the projected database approach. In addition, we propose a data structure IUDataListSW, which stores utility and upper-bound values of the items in the current sliding window. Moreover, IUDataListSW stores position of items in the transaction to get the initial projected database of items efficiently. Furthermore, we propose an update strategy to utilize mined high utility patterns from the previous sliding window to update high utility patterns in the current sliding window. Therefore, SOHUPDS is able to mine high utility patterns over a data stream in a single pass and one phase. Experimental results illustrate that SOHUPDS is more efficient than the state-of-the-art algorithms in terms of execution time as well as memory usage.
Robust One-Class Kernel Spectral Regression

The kernel null-space technique is known to be an effective one-class classification (OCC) technique. Nevertheless, the applicability of this method is limited due to its susceptibility to possible training data corruption and the inability to rank training observations according to their conformity with the model. This article addresses these shortcomings by regularizing the solution of the null-space kernel Fisher methodology in the context of its regression-based formulation. In this respect, first, the effect of the Tikhonov regularization in the Hilbert space is analyzed, where the one-class learning problem in the presence of contamination in the training set is posed as a sensitivity analysis problem. Next, the effect of the sparsity of the solution is studied. For both alternative regularization schemes, iterative algorithms are proposed which recursively update label confidences. Through extensive experiments, the proposed methodology is found to enhance robustness against contamination in the training set compared with the baseline kernel null-space method, as well as other existing approaches in the OCC paradigm, while providing the functionality to rank training samples effectively.
BlockFedML: Blockchained Federated Machine Learning Systems

With the emphasis on security and privacy, Federated Machine Learning (FML) systems have become a research hotspot due to it can perform machine learning models without compromising security and privacy. However, there are two crucial challenges. One is a gradient information leak, and the other is vulnerable to integrity attacks. In this paper, we proposed a Blockchained Federated Machine Learning System, which called "BlockFedML." In BlockFedML, we develop Security Parameter Aggregation Mechanisms, Checkpoint based-Smart Contracts, Incentive Mechanisms, and Transfer Learning. Finally, we outlined the BlockFedML system and its applications and explained our future work.