Reviewerly/exHarmony huggingface dataset:
Semiparametric Clustering: A Robust Alternative to Parametric Clustering

Clustering aims at naturally grouping the data according to the underlying data distribution. The data distribution is often estimated using a parametric or nonparametric model, e.g., Gaussian mixture or kernel density estimation. Compared with nonparametric models, parametric models are statistically stable, i.e., a small perturbation of data points leads to a small change in the estimated density. However, parametric models are highly sensitive to outliers because the data distribution is far away from the parametric assumptions in the presence of outliers. Given a parametric clustering algorithm, this paper shows how to turn this algorithm into a robust one. The idea is to modify the original parametric density into a semiparametric one. The high-density data that form the core of each cluster are modeled with the original parametric density. The low-density data are often far away from the cluster cores and may have an arbitrary shape, thus are modeled using a nonparametric density. A combination of parametric and nonparametric clustering algorithms is used to group the data modeled as a semiparametric density. From the robust statistical point of view, the proposed method has good robustness properties. We test the proposed algorithm on several synthetic and 70 UCI data sets. The results indicate that the semiparametric method could significantly improve the clustering performance.
Digital Transformation from Leveraging Blockchain Technology, Artificial Intelligence, Machine Learning and Deep Learning

These are exciting times as new software development paradigms are fast emerging to cope up with the shift in focus from “mobile first” to “AI first” approach being adapted by Google, Facebook, Amazon and others. This can mainly be attributed to the stability of the cloud computing platform and the developments in search capabilities which have extended from traditional text and web pages to achieving voice and vision recognitions relating to images and videos. Continued research focus has brought the error rate in image recognition by machine to converge sharply with that of the human. Apart from developments in big data analytics, artificial intelligence, machine learning and deep learning, break throughs in peer to peer distributed ledgers with a blockchain technology platform, which incorporates multiple levels of strong encryptions, have created massive developmental interests. Most of the “popular apps” that we use today, are being built using AI algorithms. To achieve this, changes are being incorporated to computational architecture to make them compatible with “AI first” data centers equipped with AI driven features. Tensor Processing Unit (TPU), which powered Google’s developments in ML and AI, has now become part of cloud computing service. Anticipating cost related issues, new hardware developments are focusing on moving from the cloud to the edge with the new “Edge TPU”. Digital transformation is further augmented by the fact that block chain platforms, which are built on de-centralized tools and technology, are exhibiting greater maturity by the day. The paper highlights several blockchain applications to deliver on several of the promises. The paper also discusses the fundamentals of Neural network to demonstrate how well these concepts that are incorporated in deep learning have decreased error rates by tenfold compared to previous technologies.
Sentiment Analysis of Twitter Data Using Big Data Tools and Hadoop Ecosystem

Sentiment analysis and opinion mining help in the analysis of people’s views, opinions, attitudes, emotions and sentiments. In this twenty-first century, huge amount of opinionated data recorded in the digital form is available for analysis. The demand of sentiment analysis occupies the same space with the growth of social media such as Twitter, Facebook, Quora, blogs, microblogs, Instagram and other social networks. In this research work, the most popular microblogging site ‘twitter’ has been used for sentiment analysis. People’s views, opinions, attitudes, emotions and sentiments on an outdoor game ‘Lawn Tennis’ have been used for the analysis. This is done by analysing people’s positive, neutral and negative reviews posted on Twitter. Through this it has been analysed that how many people around the world really like this game and how popular this game is in different countries.
Accountable Tracing Signatures from Lattices

Group signatures allow users of a group to sign messages anonymously in the name of the group, while incorporating a tracing mechanism to revoke anonymity and identify the signer of any message. Since its introduction by Chaum and van Heyst (EUROCRYPT 1991), numerous proposals have been put forward, yielding various improvements on security, efficiency and functionality. However, a drawback of traditional group signatures is that the opening authority is given too much power, i.e., he can indiscriminately revoke anonymity and there is no mechanism to keep him accountable. To overcome this problem, Kohlweiss and Miers (PoPET 2015) introduced the notion of accountable tracing signatures ( $$\mathsf {ATS}$$ ) - an enhanced group signature variant in which the opening authority is kept accountable for his actions. Kohlweiss and Miers demonstrated a generic construction of $$\mathsf {ATS}$$ and put forward a concrete instantiation based on number-theoretic assumptions. To the best of our knowledge, no other $$\mathsf {ATS}$$ scheme has been known, and the problem of instantiating $$\mathsf {ATS}$$ under post-quantum assumptions, e.g., lattices, remains open to date. In this work, we provide the first lattice-based accountable tracing signature scheme. The scheme satisfies the security requirements suggested by Kohlweiss and Miers, assuming the hardness of the Ring Short Integer Solution ( $$\mathsf {RSIS}$$ ) and the Ring Learning With Errors ( $$\mathsf {RLWE}$$ ) problems. At the heart of our construction are a lattice-based key-oblivious encryption scheme and a zero-knowledge argument system allowing to prove that a given ciphertext is a valid $$\mathsf {RLWE}$$ encryption under some hidden yet certified key. These technical building blocks may be of independent interest, e.g., they can be useful for the design of other lattice-based privacy-preserving protocols.
A model-driven approach for behavior-driven GUI testing

Behavior-driven development (BDD) brings requirement specifications and their test cases closer together by using a ubiquitous language to describe requirements that are automatically mapped to test methods. Although industry-proven tools support this automated requirement mapping, the test methods need to be implemented manually. The approach presented in this paper transforms BDD-like requirement specifications and low fidelity graphical user interface (GUI) descriptions in the format of wireframes into automatically executable graphical user interface test cases. The requirement descriptions are defined in a domain-specific language (DSL) that obeys the rules of the ubiquitous language enhanced by references to screens and widgets in a graphical user interface model. The paper reports on a controlled AB/BA crossover experiment comparing the efficiency of our approach to the industry-proven JBehave tooling. The results show that the introduced approach on average takes 63% less time to specify automated tests and thereby diminishes the problem of graphical user interface test cases being expensive to write. The experimental results were supported by experience made applying the approach in three differently sized components at a large Swiss bank.
Assessment of the 1.75 μm absorption feature for gypsum estimation using laboratory, air- and spaceborne hyperspectral sensors

High spectral resolution (hyperspectral) remote sensing has already demonstrated its capabilities for soil constituent mapping based on absorption feature parameters. This paper tests different parametrizations of the 1.75 μm gypsum feature for the determination of gypsum abundances, from the laboratory to remote sensing applications of recent as well as upcoming hyperspectral sensors. In particular, this study focuses on remote sensing imagery over the large body of the Omongwa pan located in the Namibian Kalahari. Four common absorption feature parameters are compared: band ratio through the introduction of the Normalized Differenced Gypsum Index (NDGI), the shape-based parameters Slope, and Half-Area, and the Continuum Removed Absorption Depth (CRAD). On laboratory soil samples from the pan, CRAD and NDGI approaches perform best to determine gypsum content tested in cross validated regression models with XRD mineralogical data (R² = 0.84 for NDGI and R² = 0.86 for CRAD). Subsequently the laboratory prediction functions are transferred to remote sensing imagery of spaceborne Hyperion, airborne HySpex and simulated spaceborne EnMAP sensor. Variable results were obtained depending on sensor characteristics, data quality, preprocessing and spectral parameters. Overall, the CRAD parameter in this wavelength region proved not to be robust for remote sensing applications, and the simple band ratio based parameter, the NDGI, proved robust and is recommended for future use for the determination of gypsum content in bare soils based on remote sensing hyperspectral imagery.
Perceptually learning multi-view sparse representation for scene categorization

Utilizing multi-channel visual features to characterize scenery images is standard for state-of-the-art scene recognition systems. However, how to encode human visual perception for scenery image modeling and how to optimally combine visual features from multiple views remains a tough challenge. In this paper, we propose a perceptual multi-view sparse learning (PMSL) framework to distinguish sceneries from different categories. Specifically, we first project regions from each scenery into the so-called perceptual space, which is established by combining human gaze behavior, color and texture. Afterward, a novel PMSL is developed which fuzes the above three visual cues into a sparse representation. PMSL can support absent channel visual features, which is frequently occurred in practical circumstances. Finally, the sparse representation from each scenery image is incorporated into an image kernel, which is further fed into a kernel SVM for scene categorization. Comprehensive experimental results on popular data sets have demonstrated the superiority of our method over well-known shallow/deep recognition models.
How do Practitioners Perceive the Relevance of Requirements Engineering Research?

<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Context</i> : The relevance of Requirements Engineering (RE) research to practitioners is vital for a long-term dissemination of research results to everyday practice. Some authors have speculated about a mismatch between research and practice in the RE discipline. However, there is not much evidence to support or refute this perception. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Objective</i> : This article presents the results of a study aimed at gathering evidence from practitioners about their perception of the relevance of RE research and at understanding the factors that influence that perception. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Method</i> : We conducted a questionnaire-based survey of industry practitioners with expertise in RE. The participants rated the perceived relevance of 435 scientific papers presented at five top RE-related conferences. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Results</i> : The 153 participants provided a total of 2,164 ratings. The practitioners rated RE research as essential or worthwhile in a majority of cases. However, the percentage of non-positive ratings is still higher than we would like. Among the factors that affect the perception of relevance are the research's links to industry, the research method used, and respondents’ roles. The reasons for positive perceptions were primarily related to the relevance of the problem and the soundness of the solution, while the causes for negative perceptions were more varied. The respondents also provided suggestions for future research, including topics researchers have studied for decades, like elicitation or requirement quality criteria. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Conclusions</i> : The study is valuable for both researchers and practitioners. Researchers can use the reasons respondents gave for positive and negative perceptions and the suggested research topics to help make their research more appealing to practitioners and thus more prone to industry adoption. Practitioners can benefit from the overall view of contemporary RE research by learning about research topics that they may not be familiar with, and compare their perception with those of their colleagues to self-assess their positioning towards more academic research.
On-line weighted pattern matching

A weighted sequence is a sequence of probability distributions over an alphabet of size σ. Weighted sequences arise naturally in many applications. We study the problem of weighted pattern matching in which we are given a string pattern P of length m, a weight threshold 1z, and a weighted text X arriving on-line. We say that P occurs in X at position i if the product of probabilities of the letters of P at positions i−m+1,…,i in X is at least 1z. We first discuss how to apply a known general scheme that transforms off-line pattern matching algorithms to on-line algorithms to obtain an on-line algorithm that requires O((σ+log⁡z)log⁡m) or O(σlog2⁡m) time per arriving position; with the space requirement however being O(mmin⁡(σ,z)). Our main result is a new algorithm that processes each arriving position of X in O(z+σ) time using O(m+z) extra space.
Augmented Reality in Order Picking—Boon and Bane of Information (Over-) Availability

Nowadays, 75% of warehouses are operated manually with more than half of the costs caused by the process of order picking. To enhance efficiency various technical support systems are used. Recently, augmented reality applications gain great attention in operational processes in logistics. While most of them focus on the different process steps, there is a lack of research on the question how to employ augmented reality under consideration of cognitive ergonomics and related design principles. Making use of literature as well as empirical data gained in qualitative interviews with eleven order pickers, this research proposes designs for the user interfaces for three primal levels of order picking support to make the amount of available information controllable. Furthermore, three calibration options used to control the visualization of information are evaluated regarding usability and necessity. The results indicate that individual calibration of the interface is seen as crucial. Additionally, requirements for the primal support levels were directed towards a clear line-of-sight and presentation of additional order-related information.
Learning Personalized Topical Compositions with Item Response Theory

A user-generated review document is a product between the item's intrinsic properties and the user's perceived composition of those properties. Without properly modeling and decoupling these two factors, one can hardly obtain any accurate user understanding nor item profiling from such user-generated data. In this paper, we study a new text mining problem that aims at differentiating a user's subjective composition of topical content in his/her review document from the entity's intrinsic properties. Motivated by the Item Response Theory (IRT), we model each review document as a user's detailed response to an item, and assume the response is jointly determined by the individuality of the user and the property of the item. We model the text-based response with a generative topic model, in which we characterize the items' properties and users' manifestations of them in a low-dimensional topic space. Via posterior inference, we separate and study these two components over a collection of review documents. Extensive experiments on two large collections of Amazon and Yelp review data verified the effectiveness of the proposed solution: it outperforms the state-of-art topic models with better predictive power in unseen documents, which is directly translated into improved performance in item recommendation and item summarization tasks.
Tomographic reconstruction of 2-convex polyominoes using dual Horn clauses

Among the very many research interests of Maurice Nivat, a special role, according to the produced literature, was played by the study of the algorithmic and combinatorial aspects of connected finite discrete sets of points called polyominoes. In particular, he addressed the problem of a faithful reconstruction of some subclasses of them by imposing convexity constraints. The present study fits in this research line, and relies on a well known algorithm that Maurice Nivat and co-authors defined 1996 for the reconstruction of hv-convex polyominoes by orthogonal projections in polynomial time. Here, we consider a hierarchy on this class of polyominoes, and we continue a longstanding research on the reconstruction of its first levels by specializing the above mentioned result. The algorithm we propose bases on the possibility of characterizing the elements of the second level of the hierarchy, by a logic formula belonging to Dual-Horn and so polynomially solvable. Some related open problems are also presented.
Multi-vision tracking and collaboration based on spatial particle filter

In existing multi-vision tracking methods, a distributed collaborative tracking mode based on homography constraints is often adopted, yet there are significant shortcomings to this approach. For example, visual information complementation is not used to improve the robustness of tracking, and collaborative tracking is limited by homography constraints. In this study, a three-dimensional spatial particle filter tracking method was proposed, and multi-vision joint tracking and collaboration were effectively achieved. This method was based on the existing particle filter framework. A two-dimensional plane particle was taken as the projection of a three-dimensional spatial particle on the imaging plane, and the formula for calculating a spatial particle’s weight was derived based on Bayesian posterior probability recursion. In addition, an approximation method to determine spatial particle weight was given. The resampling of spatial particles was performed by using an epipolar line resampling method, and a collaborative tracking mechanism was established based on the concept of resolution. The results showed that the proposed method had higher tracking precision and anti-occlusion performance than other existing methods. In this method, the robustness of tracking was effectively improved, and unlimited optimization cooperation between visual sensing was achieved.
Gated CNN for visual quality assessment based on color perception

Visual quality assessment aims to build a computational model which can evaluate the quality of image with respect to human perception. As one of the important aspects to represent images, color provides rich information of images and deeply influences the visual perception. Thus, color harmony, which is defined as “two or more colors are sensed together as a single, pleasing, collective impression” (Holtzschue, 2011), is one of the important features to determine the visual/aesthetic quality of images. Despite the recent progress of color harmony models in respect to aesthetic quality assessment, most conventional approaches represent the color harmony only considering the distribution of co-occurrence colors but ignoring the spatial relationships between those neighbored colors. To overcome this limitation, we propose to take advantage of the intrinsic structural properties from conditional random field (CRF) to model the color harmony of images. In the CRF framework, we present a novel method that uses gated convolutional neural networks (CNNs) to calculate the probabilities of being high aesthetic quality for small patches and compute the harmony compatibilities between them, which can be considered as the associated and interactive potentials of CRF. Semantic tag of each image is also employed in our work to improve the proposed harmony model’s discriminant capability, which shows promising improvements for aesthetic quality assessment compared with existing color harmony models.
Smooth manifolds and types to sets for linear algebra in Isabelle/HOL

We formalize the definition and basic properties of smooth manifolds in Isabelle/HOL. Concepts covered include partition of unity, tangent and cotangent spaces, and the fundamental theorem for line integrals. We also construct some concrete manifolds such as spheres and projective spaces. The formalization makes extensive use of the existing libraries for topology and analysis. The existing library for linear algebra is not flexible enough for our needs. We therefore set up the first systematic and large scale application of ``types to sets''. It allows us to automatically transform the existing (type based) library of linear algebra to one with explicit carrier sets.
Learning Lexical-Semantic Relations Using Intuitive Cognitive Links

Identifying the specific semantic relations between words is crucial for IR and NLP systems. Our goal in this paper is twofold. First, we want to understand whether learning a classifier for one semantic relation (e.g. hypernymy) can gain from concurrently learning another classifier for a cognitively-linked semantic relation (e.g. co-hyponymy). Second, we evaluate how these systems perform where only few labeled examples exist. To answer the first question, we rely on a multi-task neural network architecture, while for the second we use self-learning to evaluate whether semi-supervision improves performance. Our results on two popular datasets as well as a novel dataset proposed in this paper show that concurrent learning of semantic relations consistently benefits performance. On the other hand, we find that semi-supervised learning can be useful depending on the semantic relation. The code and the datasets are available at https://bit.ly/2Qitasd .
A Reference Architecture to support the development of mobile applications based on self-adaptive services

Nowadays, most human daily tasks can be performed by means of Mobile Applications (MobApps). Devices running such applications have some limitations (e.g., processing and storage) compared to personal computers. Therefore, integration of MobApps into service-based systems has been a feasible alternative to overcome these limitations. Moreover, these applications must be prepared to deal with the changes at runtime (e.g., user’s new needs or modifications in their execution environment). In parallel, Reference Architectures (RA) have been used as an important way to support the development, standardization, and evolution of software systems. Although relevant, RA for the domain of MobApps based on services that require adaptation at runtime is still an issue to be explored in depth. This paper presents a RA for Self-MobApps (named RA4Self-MobApps), which aims to support the development of such applications. To show the applicability of our RA, a case study was conducted. As result, we observe our RA has good perspective to efficiently contribute to the Self-MobApps domain.