Reviewerly/exHarmony huggingface dataset:
Online Variance Reduction with Mixtures

Adaptive importance sampling for stochastic optimization is a promising approach that offers improved convergence through variance reduction. In this work, we propose a new framework for variance reduction that enables the use of mixtures over predefined sampling distributions, which can naturally encode prior knowledge about the data. While these sampling distributions are fixed, the mixture weights are adapted during the optimization process. We propose VRM, a novel and efficient adaptive scheme that asymptotically recovers the best mixture weights in hindsight and can also accommodate sampling distributions over sets of points. We empirically demonstrate the versatility of VRM in a range of applications.
PAM-lite: fast and accurate k-medoids clustering for massive datasets

The Partitioning Around Medoids (PAM) clustering algorithm is well-known for its robustness and accuracy, but it is computationally expensive. This paper proposes a fast and accurate version, named PAM-lite. Like CLARA which also addresses PAM's inefficiency, PAM-lite applies PAM to random samples. However, unlike CLARA, it does not choose one of the obtained medoid sets (which would involve evaluating each set), but simply applies PAM again to the combination of all the obtained medoids. This simple change yields accuracy and speed improvement. We discuss the rationale behind PAM-lite's approach and evaluate the algorithm on benchmark datasets. In all cases tested, PAM-lite achieves better speed-up and clustering quality than CLARA; the speed-up margin increasing with problem size. PAM-lite competes so closely with the clustering quality produced by the full PAM algorithm, that in one high cluster variance case, it beats PAM's clustering quality slightly.
Text line segmentation and binarization of handwritten historical documents using the fast and adaptive bidimensional empirical mode decomposition

Text line segmentation is a basic step that can be used to serve several processes in the recognition stage. Image segmentation is thus inevitable. A necessary step for text line segmentation is the image binarization. However, it is challenging due to complex background in such images. In this paper, we present a new text line segmentation and banarization method based on fast and adaptive bidimensional empirical mode decomposition (FABEMD). To achieve this, we use the FABEMD method to decompose each image into two components. The binarization threshold is calculated taking into account the characteristics of each component. The inspiration for the new binarization method was drawn from the standard Niblack's and Sauvola's Algorithms. Then the text line segmentation is performed after eliminating the BIMF1 (bidimensional intrinsic mode functions) which could disrupt the segmentation process. The results show that the proposed approach is less sensitive to noise and provides improved text line segmentation and binarization compared to iterative methods.
Structured graph regularized shape prior and cross-entropy induced active contour model for myocardium segmentation in CTA images

Accurate segmentation of myocardium tissue from computed tomography angiography (CTA) images is a crucial step in development of cardiac clinical applications. Automatic methods are highly desirable to release the burden of manual delineation, however, it is challenging due to the presence of images with intensity non-uniformity and coarse, weak and even pseudo boundary. To this end, a new geometric active contour (GAC) model that integrates high-level shape prior and low-level local intensity statistics is proposed. First, the cardiac-type specific shape prior is learned by structured graph-regularized principal component analysis, so that allows to be faithful to the shape of the desired myocardium. Second, local intensity distribution with inhomogeneity is modeled by cross-entropy energy functional and segmentation-oriented image decomposition. Third, the proposed model is solved as variational level set formulation and a distance regularized energy functional is also incorporated for the stability of numerical computation. The model was evaluated on a set of cardiac CTA images with comparison to related shape prior and local region-based methods and multi-atlas joint label fusion methods, and experimental results show it achieves competitive accuracies of segmenting myocardial epicardium and endocardium parts. It also presents advantage of computational burden in comparison to some popular methods such as multi-atlas joint label fusion. Since the framework of proposed method is general and adaptive, it could be potentially extended to segment similar objects in CT images.
The psychological interaction of spam email features

This study explored distinct perceptual and decisional contributions to spam email mental construal. Participants classified spam emails according to pairings of three stimulus features - presence or absence of awkward prose, abnormal message structure, and implausible premise. We examined dimensional interactions within general recognition theory (GRT; a multidimensional extension of signal detection theory). Classification accuracy was highest for categories containing either two non-normal dimension levels (e.g. awkward prose and implausible premise) or two normal dimension levels (e.g. normal prose and plausible premise). Modelling indicated both perceptual and decisional contributions to classification responding. In most cases, perceptual discriminability was higher along one dimension when stimuli contained a non-normal level of the paired dimension (e.g. prose discriminability was higher with abnormal structure). Similarly, decision criteria along one dimension were biased in favour of the non-normal response when stimuli contained a non-normal level of the paired dimension. Potential applications for training are discussed. Practitioner summary: We applied general recognition theory (i.e. multivariate signal detection theory) to spam email classification at low or high levels of three stimulus dimensions: premise plausibility, prose quality, and email structure. Relevant to training, this approach helped identify perceptual and decisional biases that could be leveraged to individualise training.
A logical study of group-size based social network creation

This paper is part of an on-going research programme on the logical aspects of social network creation. In our proposed setting we make use of a notion of distance between agents, defined as the number of features distinguishing them. Following the ideas of the cognitive science literature, we build the social network by adopting a group-size approach: each agent's new social space will contain the λ agents that are closer to her. The paper studies the properties of the resulting networks, providing a sound and complete axiom system for a logical language which describes the changes that the social network creation operation brings about. This is done not only for the basic case in which the distance is relative to all basic features and the more general one in which the distance is relative to a given subset of them, but also for the more interesting scenario in which the distance is relative to a set of more complex social features.
CORAL: A Corpus of Ontological Requirements Annotated with Lexico-Syntactic Patterns

Ontological requirements play a key role in ontology development as they determine the knowledge that needs to be modelled. In addition, the analysis of such requirements can be used (a) to improve ontology testing by easing the automation of requirements into tests; (b) to improve the requirements specification activity; or (c) to ease ontology reuse by facilitating the identification of patterns. However, there is a lack of openly available ontological requirements published together with their associated ontologies, which hinders such analysis. Therefore, in this work we present CORAL (Corpus of Ontological Requirements Annotated with Lexico-syntactic patterns), an openly available corpus of 834 ontological requirements annotated and 29 lexico-syntactic patterns, from which 12 are proposed in this work. CORAL is openly available in three different open formats, namely, HTML, CSV and RDF under “Creative Commons Attribution 4.0 International” license.
Automatic Detection of Ambiguous Software Requirements: An Insight

Requirements Engineering is one of the most important phases of the software development lifecycle. The success of the whole software project depends upon the quality of the requirements. But as we know that mostly the software requirements are stated and documented in the natural language. The requirements written in natural language can be ambiguous and inconsistent. These ambiguities and inconsistencies can lead to misinterpretations and wrong implementations in design and development phase. To address these issues a number of approaches, tools and techniques have been proposed for the automatic detection of natural language ambiguities form software requirement documents. However, to the best of our knowledge, there is very little work done to compare and analyze the differences between these tools and techniques. In this paper, we presented a state of art survey of the currently available tools and techniques for the automatic detection of natural language ambiguities from software requirements. We also focused on figuring out the popularity of different tools and techniques on the basis of citations. This research <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{will}$</tex> help the practitioners and researchers to get the latest insights in the above-mentioned context.
Does FLOSS in Software Engineering Education Narrow the Theory-Practice Gap? A Study Grounded on Students’ Perception

Software engineering education is challenged by the need to convey practical experience in the context of a rich and large body of theoretical knowledge. This study investigates whether the use of open source projects can reduce the gap between theory and practice in undergraduate software engineering courses. Two qualitative case studies were conducted with students performing activities in an open source project, each one in a different course: software testing and software requirements. Results point out that the use of open source projects provides a concrete experience similar to industry experience, allows high cognitive engagement when performing tasks, favors understanding and content retention, and leads to the recognition of the usefulness of software engineering principles, techniques and methods.
Long short-term memory fuzzy finite state machine for human activity modelling

A challenging key aspect of recognising and modelling human activity is to design a model that can deal with the uncertainty in human behaviour. Several machine learning and deep learning techniques are employed to model the Activity of Daily Living (ADL). This paper provides a new method based on Fuzzy Finite State Machine (FFSM) and Long Short-Term Memory (LSTM) neural network for modelling and recognising human activities. The learning capability in the LSTM allows the system to learn the relations in the temporal data to identify the parameters of the rule-based system through time steps in the learning mode. The learned parameters are then used for generating the fuzzy rules that govern the transitions between the system's states. Experimental results are presented to demonstrate the effectiveness of the proposed approach.
Depth-based color stereo images retrieval using joint multivariate statistical models

The growing interest in using the three dimensional information in various application fields has led to the generation of huge color stereo image databases. As a result, it becomes necessary to design efficient content-based image retrieval systems well adapted to the indexing of such large databases. To this end, we propose in this paper different statistical-based retrieval approaches where the associated estimated model parameters are considered as a feature vector in the indexing process. More precisely, the Gaussian copula based multivariate Generalized Gaussian model will be used to capture the different correlations existing in color stereo images. While the first strategy aims at exploiting the cross-view as well as the cross-color channel redundancies, the second one resorts to a more general joint statistical model exploiting the correlation between the texture and depth information. Experimental results, performed on various datasets, confirm the benefits that can be drawn from the proposed approaches.
Detecting Anomalous Behavior Towards Predictive Maintenance

A key Industry 4.0 element is predictive maintenance, which leverages machine learning, IoT and big data applications to ensure that the required equipment is fully functional at all times. In this work, we present a case study of smart maintenance in a real-world setting. The rationale is to depart from model-based and simple rule-based techniques and adopt an approach, which detects anomalous events in an unsupervised manner. Further, we explore how incorporation of domain knowledge can assist the unsupervised anomaly detection process and we discuss practical issues.
Everybody’s a Target: Scalability in Public-Key Encryption

For $$1\le m \le n$$, we consider a natural m-out-of-n multi-instance scenario for a public-key encryption (PKE) scheme. An adversary, given n independent instances of PKE, wins if he breaks at least m out of the n instances. In this work, we are interested in the scaling factor of PKE schemes, $$\mathrm {SF}$$, which measures how well the difficulty of breaking m out of the n instances scales in m. That is, a scaling factor $$\mathrm {SF}=\ell $$ indicates that breaking m out of n instances is at least $$\ell $$ times more difficult than breaking one single instance. A PKE scheme with small scaling factor hence provides an ideal target for mass surveillance. In fact, the Logjam attack (CCS 2015) implicitly exploited, among other things, an almost constant scaling factor of ElGamal over finite fields (with shared group parameters). For Hashed ElGamal over elliptic curves, we use the generic group model to describe how the scaling factor depends on the scheme’s granularity. In low granularity, meaning each public key contains its independent group parameter, the scheme has optimal scaling factor $$\mathrm {SF}=m$$; In medium and high granularity, meaning all public keys share the same group parameter, the scheme still has a reasonable scaling factor $$\mathrm {SF}=\sqrt{m}$$. Our findings underline that instantiating ElGamal over elliptic curves should be preferred to finite fields in a multi-instance scenario. As our main technical contribution, we derive new generic-group lower bounds of $$\varOmega (\sqrt{m p})$$ on the complexity of both the m-out-of-n Gap Discrete Logarithm and the m-out-of-n Gap Computational Diffie-Hellman problem over groups of prime order p, extending a recent result by Yun (EUROCRYPT 2015). We establish the lower bound by studying the hardness of a related computational problem which we call the search-by-hypersurface problem.
CloudCity: A Live Environment for the Management of Cloud Infrastructures

Cloud computing has emerged as the de facto approach for providing services over the Internet.Although having increased popularity, challenges arise in the management of such environments, especially when the cloud service providers are constantly evolving their services and technology stack in order to maintain position in a demanding market.This usually leads to a combination of different services, each one managed individually, not providing a big picture of the architecture.In essence, the end state will be too many resources under management in an overwhelming heterogeneous environment.An infrastructure that has considerable growth will not be able to avoid its increasing complexity.Thus, this papers introduces liveness as an attempt to increase the feedback-loop to the developer in the management of cloud architectures.This aims to ease the process of developing and integrating cloud-based systems, by giving the possibility to understand the system and manage it in an interactive and immersive experience, thus perceiving how the infrastructure reacts to change.This approach allows the real-time visualization of a cloud infrastructure composed of a set of Amazon Web Services resources, using visual city metaphors.
Product reputation mining: bring informative review summaries to producers and consumers

Product reputation mining systems can help customers make their buying decision about a product of interest. In addition, it will be helpful to investigate the preferences of recently released products made by enterprises. Unlike the conventional manual survey, it will give us quick survey results on a low cost budget. In this article, we propose a novel product reputation mining approach based on three dimensional points of view that are word, sentence, and aspect?levels. Given a target product, the aspect?level method assigns the sentences of a review document to the desired aspects. The sentence?level method is a graph-based model for quantifying the importance of sentences. The word?level method computes both importance and sentiment orientation of words. Aggregating these scores, the proposed approach measures the reputation tendency and preferred intensity and selects top-k informative review documents about the product. To validate the proposed method, we experimented with review documents relevant with K5 in Kia motors. Our experimental results show that our method is more helpful than the existing lexicon?based approach in the empirical and statistical studies.
Fuzzy Systems with Sigmoid-Based Membership Functions as Interpretable Neural Networks

In this paper new interpretable neural network architectures are proposed. A Neural Network with sigmoid activation function is converted into a sigmoid-based approximation operator, which, at its turn, can be approximated by a fuzzy system of Takagi-Sugeno type. Altogether this process shows that a neural network with sigmoid activation functions can be approximated by a Takagi-Sugeno fuzzy system. As the TS fuzzy system provides interpretability, while Neural Networks provide approximation capability, we obtain a novel interpretable Neural Network Architecture. Interpretability of the TS fuzzy system can provide insights into data in a new way, enhancing decision making.
Image saliency detection based on geodesic‐like and boundary contrast maps

Image saliency detection is the basis of perceptual image processing, which is significant to subsequent image processing methods. Most saliency detection methods can detect only a single object with a high-contrast background, but they have no effect on the extraction of a salient object from images with complex low-contrast backgrounds. With the prior knowledge, this paper proposes a method for detecting salient objects by combining the boundary contrast map and the geodesics-like maps. This method can highlight the foreground uniformly and extract the salient objects efficiently in images with low-contrast backgrounds. The classical receiver operating characteristics (ROC) curve, which compares the salient map with the ground truth map, does not reflect the human perception. An ROC curve with distance (distance receiver operating characteristic, DROC) is proposed in this paper, which takes the ROC curve closer to the human subjective perception. Experiments on three benchmark datasets and three low-contrast image datasets, with four evaluation methods including DROC, show that on comparing the eight state-of-the-art approaches, the proposed approach performs well.
Usability evaluation of the top 10 Universities of Pakistan through Guideline Scoring

In this digital era, the website of a university is a doorway to its information and services. Users expect that a university website must be designed professionally with user-friendly interface, which help them to search, navigate and collect information effectively. In this paper, the usability evaluation of the websites of the universities of Pakistan is performed through guideline scoring. In doing so, literature has been reviewed and a set of 77 guidelines divided into 11 categories are developed. Further, these guidelines are then used to evaluate top 10 universities of Pakistan. Based on the results of this study, we provided the scores to the website designs. Through the scores, we identified the top 3 university website designs, and the top designs in the particular categories of guidelines as well. We also identified the common usability issues in the designs of the university websites of Pakistan. The results of this research will help academia, practitioners, website developers and usability specialists to understand the problems of existing websites, which in overall may help them to design more usable websites.
The Activist Syllabus as Technical Communication and the Technical Communicator as Curator of Public Intellectualism

Recently, educators have created crowdsourced syllabi using social media. Activist syllabi are digitally circulated public collections of knowledge and knowledge-making about events and social movements. As technical communicators, we can function as curators of public intellectualism by providing accessibility and usability guidance for these activist syllabi in collaboration with activist syllabi creators. In turn, technical communicators can work with syllabi creators as a coalitional social justice strategy to enhance the circulation of these activist syllabi.
An experimental evaluation of bow-tie analysis for security

Purpose Within critical-infrastructure industries, bow-tie analysis is an established way of eliciting requirements for safety and reliability concerns. Because of the ever-increasing digitalisation and coupling between the cyber and physical world, security has become an additional concern in these industries. The purpose of this paper is to evaluate how well bow-tie analysis performs in the context of security, and the study’s hypothesis is that the bow-tie notation has a suitable expressiveness for security and safety. Design/methodology/approach This study uses a formal, controlled quasi-experiment on two sample populations – security experts and security graduate students – working on the same case. As a basis for comparison, the authors used a similar experiment with misuse case analysis, a well-known technique for graphical security modelling. Findings The results show that the collective group of graduate students, inexperienced in security modelling, perform similarly as security experts in a well-defined scope and familiar target system/situation. The students showed great creativity, covering most of the same threats and consequences as the experts identified and discovering additional ones. One notable difference was that these naïve professionals tend to focus on preventive barriers, leading to requirements for risk mitigation or avoidance, while experienced professionals seem to balance this more with reactive barriers and requirements for incident management. Originality/value Our results are useful in areas where we need to evaluate safety and security concerns together, especially for domains that have experience in health, safety and environmental hazards, but now need to expand this with cybersecurity as well.