Reviewerly/exHarmony huggingface dataset:
iFeel 2.0: A Multilingual Benchmarking System for Sentence-Level Sentiment Analysis

Sentiment analysis became a hot topic, specially with the amount of opinions available in social media data. With the increasing interest in this theme, several methods have been proposed in the literature. Recent efforts have showed that there is no single method that always achieves the best prediction performance for different datasets. Additionally, novel methods have not being extensively compared with other methods and across different datasets, specially methods that are not designed to the English language. Consequently, researchers tend to accept any popular method as a valid methodology to measure sentiments, a practice that is usual in science. In this context, we propose iFeel 2.0, an online web system that implements 19 sentence-level sentiment analysis methods and allows users to easily label a dataset with all of them. iFeel aims at easing the comparison of new methods with baseline approaches and can also be helpful for those interested in using sentiment analysis, allowing them to choose an appropriate sentiment analysis method that works fine for a new dataset. We also incorporate a multiple language feature to allow methods designed for specific languages to be easily compared with a baseline approach that simply translates the input data to English and run these 19 methods. We hope this system can represent an important contribution to this field. Sentiment analysis became a hot topic, specially with the amount of opinions available in social media data.With the increasing interest in this theme, several methods have been proposed in the literature. Recent effortshave showed that there is no single method that always achieves the best prediction performance for different datasets. Additionally, novel methods have not being extensively compared with other methods and across different datasets, specially methods that are not designed to the English language.Consequently, researchers tend to accept any popular method as a valid methodology to measure sentiments, a practice that is usual in science.In this context, we propose iFeel 2.0, an online web system that implements 19 sentence-level sentiment analysis methods and allows users to easily label a dataset with all of them. iFeel aims at easing the comparison of new methods with baseline approaches and can also be helpful for those interested in using sentiment analysis, allowing them to choose an appropriate sentiment analysis method that works fine for a new dataset.We also incorporate a multiple language feature to allow methods designed for specific languages to be easily compared with a baseline approach that simply translates the input data to English and run these 19 methods. We hope this system can represent an important contribution to this field.
Feasibility Analysis of Blockchain for Donation-Based Crowdfunding of Ethical Projects

Donation is a necessary social tool that is plagued by many inherent shortcomings. A novel model in the form of a decentralized app was designed in the Ethereum blockchain to solve the challenges present and optimize the process of Zakaah donation. Load and stress tests on the prototype of the smart contract in the public testnet of Ethereum were analyzed to gauge the feasibility of mass usage. Similar tests were done in Hyperledger to conclude on the optimum blockchain platform for Zakaah. An anomaly was detected during the testing phase of the decentralized app in the public testnet of Ethereum and it was exploited to propose a novel strategy to enhance the throughput of Ethereum. The testing is a pioneer in evaluating the throughput and feasibility of a blockchain based financial product and provides a benchmark to validate the business and technical hypotheses of other similar financial products and services.
KernelHaven -- An Open Infrastructure for Product Line Analysis

KernelHaven is an open infrastructure for Software Product Line (SPL) analysis. It is intended both as a production-quality analysis tool set as well as a research support tool, e.g., to support researchers in systematically exploring research hypothesis. For flexibility and ease of experimentation KernelHaven components are plug-ins for extracting certain information from SPL artifacts and processing this information, e.g., to check the correctness and consistency of variability information or to apply metrics. A configuration-based setup along with automatic documentation functionality allows different experiments and supports their easy reproduction. Here, we describe KernelHaven as a product line analysis research tool and highlight its basic approach as well as its fundamental capabilities. In particular, we describe available information extraction and processing plug-ins and how to combine them. On this basis, researchers and interested professional users can rapidly conduct a first set of experiments. Further, we describe the concepts for extending KernelHaven by new plug-ins, which reduces development effort when realizing new experiments.
Efficiently approximating edit distance between pseudorandom strings

We present an algorithm for approximating the edit distance ed(x, y) between two strings x and y in time parameterized by the degree to which one of the strings x satisfies a natural pseudorandomness property. The pseudorandomness model is asymmetric in that no requirements are placed on the second string y, which may be constructed by an adversary with full knowledge of x.We say that x is (p, B)-pseudorandom if all pairs a and b of disjoint B-letter substrings of x satisfy ed(a,b) ≥ pB. Given parameters p and B, our algorithm computes the edit distance between a (p, B)-pseudorandom string x and an arbitrary string y within a factor of O(1/p) in time O(nB), with high probability. If x is generated at random, then with high probability it will be (Ω(1),O(logn))-pseudorandom, allowing us to compute ed(x, y) within a constant factor in near linear time. For strings x of varying degrees of pseudorandomness, our algorithm offers a continuum of runtimes.Our algorithm is robust in the sense that it can handle a small portion of x being adversarial (i.e., not satisfying the pseudorandomness property). In this case, the algorithm incurs an additive approximation error proportional to the fraction of x which behaves maliciously.The asymmetry of our pseudorandomness model has particular appeal for the case where x is a source string, meaning that ed(x, y) will be computed for many strings y. Suppose that one wishes to achieve an O(α)-approximation for each ed(x, y) computation, and that B is the smallest block-size for which the string x is (1/α, B)-pseudorandom. We show that without knowing B beforehand, x may be preprocessed in time [MATH HERE], so that all future computations of the form ed(x,y) may be O(α)-approximated in time O(nB). Furthermore, for the special case where only a single ed( x, y) computation will be performed, we show how to achieve an O(α)-approximation in time O(n4/3B2/3).
Learning to Align Question and Answer Utterances in Customer Service Conversation with Recurrent Pointer Networks

Customers ask questions, and customer service staffs answer those questions. It is the basic service manner of customer service (CS). The progress of CS is a typical multi-round conversation. However, there are no explicit corresponding relations among conversational utterances. This paper focuses on obtaining explicit alignments of question and answer utterances in CS. It not only is an important task of dialogue analysis, but also able to obtain lots of valuable train data for learning dialogue systems. In this work, we propose end-to-end models for aligning question (Q) and answer (A) utterances in CS conversation with recurrent pointer networks (RPN). On the one hand, RPN-based alignment models are able to model the conversational contexts and the mutual influence of different Q-A alignments. On the other hand, they are able to address the issue of empty and multiple alignments for some utterances in a unified manner. We construct a dataset from an in-house online CS. The experimental results demonstrate that the proposed models are effective to learn the alignments of question and answer utterances.
Exploratory evaluation of solar radiation and ambient temperature in twenty locations distributed in United Kingdom

Solar radiation and ambient temperature is a foundation resource programs of large-scale deployment of solar energy technologies. This paper summarizes the analysis of a weather station network in United Kingdom. Whistle 3 years (January 2015 – December 2017) of data from twenty weather stations distributed across the country. The data comprises of Global Horizontal Irradiance (GHI), Diffuse Horizontal Irradiance (DHI), Direct Normal Irradiance (DNI), ambient temperature, wind speed, and the humidity. Network design, implementation, and data quality assurance are described, in order to document the network extent and quality. From all observed datasets, it was found that Plymouth (located in southwest England) has the dominant GHI, and ambient temperature among all other 19 locations. The least GHI is observed for Aberdeen (located in northeast Scotland) estimated at 77.3 kWh/m2. However, the least average ambient temperature is equal to 9.1 °C, this data was detected from the weather station located in the capital of Scotland (Edinburgh). Although continued measurements are needed to understand the interannual resource variability, the current study provides significant guidance for preliminary technology selection, power plant modeling, and resource forecasting.
Single Remote Sensing Multispectral Image Dehazing Based on a Learning Framework

Given that a single remote sensing image dehazing is an ill-posed problem, this is still a challenging task. In order to improve the visibility of a single hazy remote sensing multispectral image, we developed a novel and effective algorithm based on a learning framework. A linear regression model with the relevant features of haze was established. And the gradient descent method is applied to the learning model. Then a hazy image accurate transmission map is obtained by learning the coefficients of the linear model. In addition, we proposed a more effective method to estimate the atmospheric light, which can restrain the influence of highlight areas on the atmospheric light acquisition. Compared with the traditional haze removal methods, the experimental results demonstrate that the proposed algorithm can achieve better visual effect and color fidelity. Both subjective evaluation and objective assessments indicate that the proposed method achieves a better performance than the state-of-the-art methods.
Road Anomaly Detection Using Smartphone: A Brief Analysis

Identification of road anomaly not only helps drivers to reduce the risk, but also support for road maintenance. Arguably, with the popularity of smartphones including multiple sensors, many road anomaly detection systems using mobile phones have been proposed. This paper aims at analyzing a number of typical road anomaly detection methods in terms of resource requirements, energy consumption, fitness conditions. From these measurements, we suggest some improvement directions to build road anomaly detection algorithms appropriate for smartphones.
Quantum-inspired microwave signal processing for implementing unitary transforms

Inspired by photonic one-way quantum computation, we describe a microwave signal processing method for implementing unitary transforms based on measuring the cebits encoded in the "classical microwave graph state (CMGS)." Here the terms "cebit" and "CMGS" defined in our system are classical analogies of a qubit and certain target quantum graph states in quantum physics respectively, which can exhibit some similar behaviors and resultants. The constructions of 4- and 16-cebit CMGSs as examples are discussed in detail and specific tomography methods are introduced to characterize their qualities. By performing operations on these CMGSs, we implement some basic 2 × 2, 4 × 4, and specific generalized unitary transforms, and obtain output results with high fidelities. Furthermore, we also demonstrate that a simulation of an efficient Grover's search algorithm, which has been executed in one-way quantum computing schemes, can be directly realized via a certain 4-cebit CMGS. Due to the excellent parallel efficiency and credible outcomes in the proposal, this quantum-inspired method may provide benefits for exploring new ways to microwave information processing, or in turn as an alternative tool for simulating particular quantum systems to some extent.
Wavelet denoising and cubic spline interpolation for observation data in groundwater pollution source identification problems

Abstract As the identified results of groundwater pollution source identification (GPSI) can influence the cost for the polluter in paying for remediating groundwater resources, it is important that the accuracy of the estimated result should be as high as possible. However, many factors can influence the result, such as noisy concentration data and incomplete concentration data. Thus, this paper is aimed at studying the difference between using the observation data before and after denoising and interpolating for solving GPSI problems. Four kinds of noise level and 20 groups of missing data were designed to test the performance of wavelet denoising and cubic spline interpolation, respectively. The results show that the denoising process can improve the estimated result for the GPSI problem, and the higher the noise level, the stronger this effect. In terms of interpolation, more accurate results can be made after interpolating if the missing data belong to the period after the source releases the pollutant. If the missing data are from when the pollution source is active, interpolation cannot help increase the estimated performance.
A Big Data Recommendation Engine Framework Based on Local Pattern Analytics Strategy for Mining Multi-Sourced Big Data

Organisations that perform business operations in a multi-sourced big data environment are in imperative need to discover meaningful patterns of interest from their diversified data sources. With the advent of big data technologies such as Hadoop and Spark, commodity hardwares play vital role in the task of data analytics and process the multi-sourced and multi-formatted big data in a reasonable cost and time. Though various data analytic techniques exist in the context of big data, recommendation system is more popular in web-based business applications to suggest suitable products, services, and items to potential customers. In this paper, we put forth a big data recommendation engine framework based on local pattern analytics strategy to explore user preferences and taste for both branch level and central level decisions. The framework encourages the practice of moving computing environment towards the data source location and avoids forceful integration of data. Further it assists decision makers to reap hidden preferences and taste of users from branch data sources for an effective customer campaign. The novelty of the framework has been evaluated in the benchmark dataset, MovieLens100k and results clearly confirm the advantages of the proposal.
Practical Distributed Learning: Secure Machine Learning with Communication-Efficient Local Updates.

We consider distributed on-device learning with limited communication and security requirements. We propose a new robust distributed optimization algorithm with efficient communication and attack tolerance. The proposed algorithm has provable convergence and robustness under non-IID settings. Empirical results show that the proposed algorithm stabilizes the convergence and tolerates data poisoning on a small number of workers.
CaseNote: Mobile phone call data obfuscation &amp; techniques for call correlation

The use of call data records (CDRs) to establish links between suspects is well known and understood. In a number of major enquiries in the UK, however, it was found that CDRs contained apparently erroneous or nonsensical data which prevented the use of well-established techniques based on caller IDs contained within CDRs. Further analysis suggested that some form of number “spoofing” was being used and techniques were developed to associate calls made using one of the two mechanisms which appeared to be in operation. A working hypothesis about how the calls are being handled in order to implement obfuscation has been developed, based on additional data leaked by some of the mobile network providers.
OMNIREP: originating meaning by coevolving encodings and representations

A major effort in the practice of evolutionary computation goes into deciding how to represent individuals in the evolving population. This task is actually composed of two subtasks: defining a data structure that is the representation and defining the encoding that enables to interpret the representation. In this paper we employ a coevolutionary algorithm—dubbed OMNIREP—to discover both a representation and an encoding that solve a particular problem of interest. We describe four experiments that provide a proof-of-concept of OMNIREP’s essential merit. We think that the proposed methodology holds potential as a problem solver and also as an exploratory medium when scouting for good representations.
CRESA: A Deep Learning Approach to Competing Risks, Recurrent Event Survival Analysis

Survival analysis refers to a gamut of statistical techniques developed to infer the survival time from time-to-event data. In particular, we are interested in recurrent event survival analysis in the presence of one or more competing risks in each recurrent time-step, in order to obtain the probabilistic relationship between the input covariates and the distribution of event times. Since traditional survival analysis techniques suffer from drawbacks due to strong parametric model constraints and constant hazard based assumptions, we propose a modern deep learning based flexible probabilistic framework for cause-specific recurrent survival analysis. In single-risk scenarios, we propose an LSTM-based model where the time-steps represent the recurrent events for each participant whose covariates may be static or time-varying. To cater to multi-risk scenarios, we build on the single-risk LSTM model and introduce a cumulative incidence curve approach to handle the multiple competing risks using a joint distribution over the event times and each of the competing risks over multiple time-steps and term the proposed novel architecture as CRESA. We use the concordance index per risk and the maximum absolute error in every time-step as the metrics of performance. We demonstrate a superior predictive performance of the proposed approach (single and multiple risk scenarios) as compared to traditional model-based approaches, and deep learning based approaches for synthetic and state-of-the-art public datasets.
Higher-order type-level programming in Haskell

Type family applications in Haskell must be fully saturated. This means that all type-level functions have to be first-order, leading to code that is both messy and longwinded. In this paper we detail an extension to GHC that removes this restriction. We augment Haskell’s existing type arrow, |-&gt;|, with an unmatchable arrow, | &gt;|, that supports partial application of type families without compromising soundness. A soundness proof is provided. We show how the techniques described can lead to substantial code-size reduction (circa 80%) in the type-level logic of commonly-used type-level libraries whilst simultaneously improving code quality and readability.
Reinforcement Emotion-Cognition System: A Teaching Words Task

The goal of this paper is to suggest a system for intelligent learning environments with robots modeling of emotion regulation and cognition based on quantitative motivation. A detailed interactive situation for teaching words is proposed. In this study, we introduce one bottom-up collaboration method for emotion-cognition interplay and behaviour decision-making. Integration with gross emotion regulation theory lets the proposed system adapt to natural interactions between students and the robot in emotional interaction. Four key ideas are advocated, and they jointly set up a reinforcement emotion-cognition system (RECS). First, the quantitative motivation is grounded on external interactive sensory detection, which is affected by memory and preference. Second, the emotion generation triggered by an initial motivation such as external stimulus is also influenced by the state in the previous time. Third, the competitive and cooperative relationship between emotion and motivation intervenes to make the decision of emotional expression and teaching actions. Finally, cognitive reappraisal, the emotion regulation strategy, is introduced for the establishment of emotion transition combined with personalized cognition. We display that this RECS increases the robot emotional interactive performance and makes corresponding teaching decision through behavioural and statistical analysis.
Comparations of Supervised Machine Learning Techniques in Predicting the Classification of the Household’s Welfare Status

Poverty has been a major problem for most countries around the world, including Indonesia. One approach to eradicate poverty is through equitable distribution of social assistance for target households based on Integrated Database of social assistance. This study has compared several well-known supervised machine learning techniques, namely: Naïve Bayes Classifier, Support Vector Machines, K-Nearest Neighbor Classification, C4.5 Algorithm, and Random Forest Algorithm to predict household welfare status classification by using an Integrated Database as a study case. The main objective of this study was to choose the best-supervised machine learning approach in predicting the classification of household’s welfare status based on attributes in the Integrated Database. The results showed that the Random Forest Algorithm was the best.
Sparse and heuristic support vector machine for binary classifier and regressor fusion

Single-hidden layer feedforward networks (SLFNs) are always viewed as classical methods for binary classification and regression. There are several variant types of SLFNs, such as support vector machines (SVM), extreme learning machines (ELM), etc. It is an open problem for SLFNs to obtain a powerful feature mapper with a simple network structure. In this paper, we propose a framework called sparse and heuristic SVM (SH-SVM) to fuse different SLFNs from the aspect of feature mapping to obtain powerful feature mapping capability and improve the generalization performance. By fusing different SLFNs, the SH-SVM benefits from the learning capabilities of each model. As an example, the fusion of SVM and ELM is studied in detail. Then with the sparse representation method, a compact SLFN is obtained and the most powerful hidden nodes are selected. Furthermore, an efficient method for solving the sparse representation problem in SH-SVM is proposed. Experiments on 25 data sets with eight methods show that SH-SVM has satisfactory results with a compact network structure.