Reviewerly/exHarmony huggingface dataset:
A case study on variability management in software product lines: identifying why real-life projects fail

Economies of scale can be seen as some kind of “holy grail” in state of the art literature on the development of sets of related software systems. Software product line methods are often mentioned in this context, due to the variability management aspects they propose, in order to deal with sets of related software systems. They realize the sought-after reusability. Both variability management and software product lines already have a strong presence in theoretical research, but in real-life software product line projects trying to obtain economies of scale still tend to fall short of target. The objective of this paper is to study this gap between theory and reality through a case study in order to see why such gap exists, and to find a way to bridge this gap. Through analysis of the causes of failure identified by the stakeholders in the case study, the underlying problem, which is found to be located in the requirements engineering phase, is crystallized. The identification of a framework describing the problems will provide practitioners with a better focus for future endeavors in the field of software product lines, so that economies of scale can be achieved.
History and Potential of Binary Segmentation for Exploratory Data Analysis

Exploratory data analysis has become more important as large rich data sets become available, with many explanatory variables representing competing theoretical constructs.The restrictive assumptions of linearity and additivity of effects as in regression are no longer necessary to save degrees of freedom.Where there is a clear criterion (dependent) variable or classification, sequential binary segmentation (tree) programs are being used.We explain why, using the current enhanced version (SEARCH) of the original Automatic Interaction Detector program as an illustration.Even the simple example uncovers an interaction that might well have been missed with the usual multivariate regression.We then suggest some promising uses and provide one simple example.
Wavelet Analysis of Tide-affected Low Streamflows Series

In certain rivers that drain very flat terrains in coastal areas, the streamflow series observed at a flow-gauging station may come under the direct influence of the backwater effects of tides.The phenomena may be negligible under conditions of high flows but can be critical under some extreme low-flow conditions.The errors in low flow estimation are large if a proper de-noising is not implemented to remove the effects of the tidal effects.Scrutinizing the hydrologic time series using a standard time-frequency domain based Fourier transform methodology cannot resolve conclusively the sources of the noise.However, a new perspective can be obtained by using a wavelet transformation to analyze the time series in the time-scale domain.By using this approach, a case study involving a streamflow series observed at Kapit, Sarawak, Malaysia yielded conclusive evidence of the influence of tides at the flow-gauging site during the low flow period.Upon confirmation that the noise is indeed of tidal origin, the observed water level series was subjected to an appropriate wavelet-based de-noising procedure to derive a smoothed series.Then, together with an established rating curve, a de-noised discharge series could also be approximated.Low-flow quantiles were subsequently derived by fitting a suitable frequency distribution to the annual minimum series abstracted from the de-noised discharge series.The methodology presented illustrates the potential of using wavelet analysis methods in solving other similar problems.
Making Sense of Contingency Tables in Archaeology: the Aid of Correspondence Analysis to Intra-Site Activity Areas Research

The use of contingency tables is widespread in archaeology.Crosstabulations are used in many different studies as a useful tool to synthetically report data, and are also useful when analyst wishes to seek for latent data structures.The latter case is when Correspondence Analysis (CA) comes into play.By graphically displaying the dependence between rows and columns, CA enables the analyst to explore the data in search of a meaningful inner structure.The article aims to show the utility of CA in archaeology in general and, in particular, for the identification of areas devoted to different activities within settlements.The application of CA to the data from a prehistoric village in north-eastern Sicily (P.Milazzese at Panarea, Aeolian Islands-Italy), taken as a case study, allows to show how CA succeeds in pinpointing different activity areas and in providing grounds to open new avenues of inquiry into other aspects of the archaeological documentation.
High-Level Representations for Game-Tree Search in RTS Games

From an AI point of view, Real-Time Strategy (RTS) games are hard because they have enormous state spaces, they are real-time and partially observable. In this paper, we explore an approach to deploy game-tree search in RTS games by using game state abstraction, and explore the effect of using different abstractions over the game state. Different abstractions capture different parts of the game state, and result in different branching factors when used for game-tree search algorithms. We evaluate the different representations using Monte Carlo Tree Search in the context of StarCraft.
Fast, Optimal Pathfinding with Compressed Path Databases

Most existing pathfinding methods are based on runtime search. Despite an impressive progress achieved in recent years, search-based methods could explore, in bad cases, large portions of a map, leading to an increased running time. We take a significantly different approach from search-based methods. Given a map, our program precomputes all-pairs shortest paths (APSP) information. APSP data are compressed, reducing the size dramatically with no information loss. We call the resulting data a compressed path database (CPD). At runtime, optimal moves are retrieved one by one until a complete (or partial, if desired) optimal path is retrieved. CPDs lead to a fast path computation, eliminating the need for runtime search. The first move lag is very low, as each move is retrieved independently from subsequent moves. The price to pay includes a significant preprocessing time and memory to build and store a CPD.
Properties, Prediction, and Prevalence of Useful User-Generated Comments for Descriptive Annotation of Social Media Objects

User-generated comments in online social media have recently been gaining increasing attention as a viable source of general-purpose descriptive annotations for digital objects like photos or videos. Because users have different levels of expertise, however, the quality of their comments can vary from very useful to entirely useless. Our aim is to provide automated support for the curation of useful user-generated comments from public collections of digital objects. After constructing a crowd-sourced gold standard of useful and not useful comments, we use standard machine learning methods to develop a usefulness classifier, exploring the impact of surface-level, syntactic, semantic, and topic-based features in addition to extra-linguistic attributes of the author and his or her social media activity. We then adapt an existing model of prevalence detection that uses the learned classifier to investigate patterns in the commenting culture of two popular social media platforms. We find that the prevalence of useful comments is platform-specific and is further influenced by the entity type of the media object being commented on (person, place, event), its time period (e.g., year of an event), and the degree of polarization among commenters.
Tracking the Trackers: A Large-Scale Analysis of Embedded Web Trackers

We perform a large-scale analysis of third-party trackers on the World Wide Web. We extract third-party embeddings from more than 3.5 billion web pages of the CommonCrawl 2012 corpus, and aggregate those to a dataset representing more than 41 million domains. With that, we study global online tracking on two levels: (1) On a global level, we give a precise figure for the extent of tracking, and analyse which trackers (and subsequently, which companies) are used by how many websites. (2) On a country-specific level, we analyse which trackers are used by websites in different countries, and identify the countries in which websites choose significantly different trackers than in the rest of the world. We find that trackers are widespread (as expected), and that very few trackers dominate the web (Google, Facebook and Twitter), except for a few countries such as China and Russia.
StarCraft Winner Prediction

In game-playing, a challenging topic is to investigate an evaluation function that accurately predicts which player will be the winner of a two-player match. Our work investigates to what extent it is possible to predict the winner of a StarCraft match, regardless of the races that are involved. We developed models for individual match types, and also general models for predicting the winner of non-symmetric matches, symmetric matches, and general matches. The contribution of this paper is (1) a generic and relatively accurate model for winner prediction in StarCraft, and (2) a detailed analysis of which features are the principal component in accurately predicting the winner in this complex game. Specially, our results show that we can predict the winner of a match with an accuracy of more than 63% in average over all time slices, regardless of the time slice and the combination of the match types. A study of which features are most important for the prediction of the match results, shows that the economic aspects of StarCraft matches are the strongest predictors for winning, followed by the use micro commands.
Improvised Theatre Alongside Artificial Intelligences

This study presents the first report of Artificial Improvisation, or improvisational theatre performed live, on-stage, alongside an artificial intelligence-based improvisational performer. The Artificial Improvisor is a form of artificial conversational agent, or chatbot, focused on open domain dialogue and collaborative narrative generation. Using state-of-the-art machine learning techniques spanning from natural language processing and speech recognition to reinforcement and deep learning, these chatbots have become more lifelike and harder to discern from humans. Recent work in conversational agents has been focused on goal-directed dialogue focused on closed domains such as appointment setting, bank information requests, question-answering, and movie discussion. Natural human conversations are seldom limited in scope and jump from topic to topic, they are laced with metaphor and subtext and face-to-face communication is supplemented with non-verbal cues. Live improvised performance takes natural conversation one step further with multiple actors performing in front of an audience. In improvisation the topic of the conversation is often given by the audience several times during the performance. These suggestions inspire actors to perform novel, unique, and engaging scenes. During each scene, actors must make rapid fire decisions to collaboratively generate coherent narratives. We have embarked on a journey to perform live improvised comedy alongside artificial intelligence systems. We introduce Pyggy and A.L.Ex. (Artificial Language Experiment), the first two Artificial Improvisors, each with a unique composition and embodiment. This work highlights research and development, successes and failures along the way, celebrates collaborations enabling progress, and presents discussions for future work in the space of artificial improvisation.
Run-time environment for the SARL agent-programming language: the example of the Janus platform

SARL is a general-purpose agent-oriented programming language. This language aims at providing the fundamental abstractions for dealing with concurrency, distribution, interaction, decentralization, reactivity, autonomy and dynamic reconfiguration that are usually considered as essential for implementing agent-based applications. Every programming language specifies an execution model. For SARL, this run-time model is supported by a SARL run-time environment. The goals of this paper are to highlight the key principles for creating a SARL run-time environment, and its concrete implementation into the Janus agent platform.
Helstrom measurement: A nondestructive implementation

We discuss an implementation of the minimum error state discrimination measurement, originally introduced by Helstrom [Quantum Detection and Estimation Theory (Academic Press, New York, 1976)]. In this implementation, instead of performing the optimal projective measurement directly on the system, it is first entangled to an ancillary system and the measurement is performed on the ancilla. We show that, by an appropriate choice of the entanglement transformation, the Helstrom bound can be attained. The advantage of this approach is twofold. First, it provides an implementation when the optimal projective measurement cannot be directly performed. For example, in the case of continuous variable states (binary and $N$ phase-shifted coherent signals), the available detection methods, photon counting and homodyning, are insufficient to perform the required cat-state projection. In the case of symmetric states, the square-root measurement is optimal, but it is not easy to perform directly for more than two states. Our approach provides a feasible alternative in both cases. Second, the measurement is nondestructive from the point of view of the original system and one has a certain amount of freedom in designing the post-measurement state, which can then be processed further.
Tongue-able interfaces: Prototyping and evaluating camera based tongue gesture input system

Tongue-computer interaction techniques create a new pathway between human mind and computer, with particular utility for people with upper limb impairment. The high dexterity and resilience of the tongue make it a good candidate for interacting with computers. This paper introduces a new interaction technique, camera-based tongue computer interface (CBTCI), which employs tongue without any direct physical contact required. Through a two-phase study, the CBTCI was evaluated and its interaction problems were identified and discussed. In the first phase, the performance of the CBTCI prototype was evaluated through two user tests. The participants' behaviors were observed throughout the first phase and analyzed to scaffold the study of the design problems in gesture-based tongue computer interaction. The Phase 2 study investigated the usability problems of CBTCI which were reflected through the user behavior and participants' feedback; specifically the exploration of referential techniques to make users aware of their tongue position and adjust their gesture. Pros and cons of the referential strategies are discussed to foster future assistive tongue-computer interface design.
Inflation, Inflation Uncertainty, and Growth: Evidence from Ghana

Inflation and inflation uncertainty are critical factors influencing the functioning of markets and thus the efficient flow of economic activities. In this study, we investigated the effects of inflation and inflation uncertainty on growth in Ghana. Unlike the majority of the previous studies, we distinguished the short-run effects of inflation and inflation uncertainty on growth from the long-run effects. Also, unlike the previous studies, we examined whether increases in inflation uncertainty have the same effects on growth as decreases in it. By applying linear and nonlinear specifications to a data set covering the period 1963 to 2015, we found that inflation has both short and long-run negative effects on growth. Inflation uncertainty has a differential short-run effect and a negative long-run effect on growth. Increases in inflation uncertainty hurt growth, while decreases may reverse this pattern, albeit slowly. Both inflation and inflation uncertainty are critical determinants of growth in the country. To promote growth, policymakers should continue to pursue a low inflation target while ensuring minimal inflation uncertainty.
Graph-based keyword spotting in historical manuscripts using Hausdorff edit distance

Keyword spotting enables content-based retrieval of scanned historical manuscripts using search terms, which, in turn, facilitates the indexation in digital libraries. Recent approaches include graph-based representations that capture the complex structure of handwriting. However, the high representational power of graphs comes at the cost of high computational complexity for graph matching. In this article, we investigate the potential of Hausdorff edit distance (HED) for keyword spotting. It is an efficient quadratic-time approximation of the graph edit distance. In a comprehensive experimental evaluation with four types of handwriting graphs and four benchmark datasets (George Washington, Parzival, Botany, and Alvermann Konzilsprotokolle), we demonstrate a strong performance of the proposed HED-based method when compared with the state of the art, both, in terms of precision and speed.
Modelling the distribution of white matter hyperintensities due to ageing on MRI images using Bayesian inference

White matter hyperintensities (WMH), also known as white matter lesions, are localised white matter areas that appear hyperintense on MRI scans. WMH commonly occur in the ageing population, and are often associated with several factors such as cognitive disorders, cardiovascular risk factors, cerebrovascular and neurodegenerative diseases. Despite the fact that some links between lesion location and parametric factors such as age have already been established, the relationship between voxel-wise spatial distribution of lesions and these factors is not yet well understood. Hence, it would be of clinical importance to model the distribution of lesions at the population-level and quantitatively analyse the effect of various factors on the lesion distribution model. In this work we compare various methods, including our proposed method, to generate voxel-wise distributions of WMH within a population with respect to various factors. Our proposed Bayesian spline method models the spatio-temporal distribution of WMH with respect to a parametric factor of interest, in this case age, within a population. Our probabilistic model takes as input the lesion segmentation binary maps of subjects belonging to various age groups and provides a population-level parametric lesion probability map as output. We used a spline representation to ensure a degree of smoothness in space and the dimension associated with the parameter, and formulated our model using a Bayesian framework. We tested our algorithm output on simulated data and compared our results with those obtained using various existing methods with different levels of algorithmic and computational complexity. We then compared the better performing methods on a real dataset, consisting of 1000 subjects of the UK Biobank, divided in two groups based on hypertension diagnosis. Finally, we applied our method on a clinical dataset of patients with vascular disease. On simulated dataset, the results from our algorithm showed a mean square error (MSE) value of 7.27×10−5, which was lower than the MSE value reported in the literature, with the advantage of being robust and computationally efficient. In the UK Biobank data, we found that the lesion probabilities are higher for the hypertension group compared to the non-hypertension group and further verified this finding using a statistical t-test. Finally, when applying our method on patients with vascular disease, we observed that the overall probability of lesions is significantly higher in later age groups, which is in line with the current literature.
Phonon-induced dephasing in quantum-dot–cavity QED

We present a semi-analytic and asymptotically exact solution to the problem of phonon-induced decoherence in a quantum dot-microcavity system. Particular emphasis is placed on the linear polarization and optical absorption, but the approach presented herein may be straightforwardly adapted to address any elements of the exciton-cavity density matrix. At its core, the approach combines Trotter's decomposition theorem with the linked cluster expansion. The effects of the exciton-cavity and exciton-phonon couplings are taken into account on equal footing, thereby providing access to regimes of comparable polaron and polariton timescales. We show that the optical decoherence is realized by real phonon-assisted transitions between different polariton states of the quantum dot-cavity system, and that the polariton line broadening is well-described by Fermi's golden rule in the polariton frame. We also provide purely analytic approximations which accurately describe the system dynamics in the limit of longer polariton timescales.
Induction of Non-Monotonic Logic Programs to Explain Boosted Tree Models Using LIME

We present a heuristic based algorithm to induce nonmonotonic logic programs that will explain the behavior of XGBoost trained classifiers. We use the technique based on the LIME approach to locally select the most important features contributing to the classification decision. Then, in order to explain the model’s global behavior, we propose the LIME-FOLD algorithm —a heuristic-based inductive logic programming (ILP) algorithm capable of learning nonmonotonic logic programs—that we apply to a transformed dataset produced by LIME. Our proposed approach is agnostic to the choice of the ILP algorithm. Our experiments with UCI standard benchmarks suggest a significant improvement in terms of classification evaluation metrics. Meanwhile, the number of induced rules dramatically decreases compared to ALEPH, a state-of-the-art ILP system.
Original intensity preserved inhomogeneity correction and segmentation for liver magnetic resonance imaging

Abstract Intensity inhomogeneity (IIH), also named as bias field, is an undesired phenomenon of liver magnetic resonance imaging (MRI) which severely affects the quantitative analysis of medical image and decreases the performance of subsequent computer aided diagnosis (CAD) of liver cirrhosis. Many algorithms have been proposed to reduce or eliminate IIH of MRI, and some notable achievements for brain MRI have been obtained. However, IIH correction of abdominal MRI receives less attention and is challenging because of the irregular structure and the wide intensity range of different tissues. In this paper, an automatic method based on the global intensity, the local intensity and the spatial continuity information is presented for reducing IIH of liver MRI. What should be noted is that the gray level should be preserved after correction since it is important for subsequent quantitative image analysis. Therefore, a constraint term is introduced based on the information of bias field intensity for appropriate IIH correction. In addition, the objective function introduces a fuzzy membership mask to remove the background noise and avoid misclassification. Our method is successfully applied to the clinical liver MRI and acquires desirable results. Compared with other approaches, our method obtains the best segmentation with Jaccard similarity (JS) = 0.88  ±  0.06, Dice index (DI) = 0.94  ±  0.03, and accuracy (ACC) = 0.99  ±  0.01.