Reviewerly/exHarmony huggingface dataset:
Are You Talking about Software Product Lines?

Community-question-answering systems, such as Stack Overflow, provide a platform for various communities to ask questions, discuss topics, and find knowledge. Especially software developers are heavily relying on such systems to identify solutions for their problems. While the content of community-question-answering systems may be less scientific, it usually represents practical knowledge from various perspectives and backgrounds. Thus, analyzing this content can be valuable for the scientific community to understand previous and current (i.e., open questions) needs of practitioners. In this paper, we report a systematic analysis of two websites that comprise communities with a focus on software development: Stack Exchange and Quora. We extract questions, answers, comments, and discussions on software product lines in general and feature modeling in particular. The results provide a historical perspective, an overview on commonly addressed scopes, and a classification of discussed topics and problems. Moreover, our findings are interesting to understand the practical impact of software-product-line techniques outside of well-analyzed case studies, to support lectures by identifying regularly asked questions, and to scope tool development based on reported technical problems.
Current directions in ecomusicology: music, culture, nature

Ecomusicology begins in wonder: What is it? Everyone agrees that the answer has something to do with nature and culture and music. Many seem to agree that the term probably cannot – perhaps should ...
Digital Plane Recognition with Fewer Probes

We present a new plane-probing algorithm, i.e., an algorithm that computes the normal vector of a digital plane from a starting point and a predicate “Is a point $$\varvec{x}$$ in the digital plane?”. This predicate is used to probe the digital plane as locally as possible and decide on-the-fly the next points to consider. We show that this algorithm returns the same vector as another plane-probing algorithm proposed in Lachaud et al. (J. Math. Imaging Vis., 59, 1, 23–39, 2017), but requires fewer probes. The theoretical upper bound is indeed lowered from $$O(\omega \log \omega )$$ to $$O(\omega )$$ calls to the predicate, where $$\omega $$ is the arithmetical thickness of the digital plane, and far fewer calls are experimentally observed on average. This reduction is made possible by a study that shows how to avoid computations that do not contribute to the final solution. In the context of digital surface analysis, this new algorithm is expected to be of great interest for normal estimation and shape reconstruction.
Ontology as a Tool for Building Life Cycle Modelling

In this on research-based article, models presented further are representing complex concepts and facilitating their understanding. The models describe the categorization of the recorded knowledge obtained from the analysis of the monitored processes. Some of these models are descriptive (for example, the Venn diagram representing the interconnection of BIM spaces), others are predictive (e.g. a model representing the effects of BIM on the life cycle of a project), and others will be instructive-clarifying the sequence of actions that follow on to achieve a predefined output, such as a workflow model, representing authorization, identification, classification, aggregation, and usage. We can look at BIM as a framework modelling set of minimal sets of principles needed to model the basic building characteristics, along with a clear categorization of knowledge in identified and recorded models. Taxonomy is an effective and efficient way to organize and consolidate knowledge. A well-structured taxonomy allows for "meaningful gathering of experience". As we develop specialized taxonomies for organizing BIM domain knowledge, we will try to adopt the guidelines introduced by research. That is, the taxonomy is expected to be complex and complete; including classes that covering all the phenomena of our interest; based on decision-making rules, which are simple and stingy to assign instances to classes; and classes should be mutually exclusive. Moreover, because taxonomy is here as a tool for better understanding, these classes should be comprehensible and look natural.
Quantum Photonic TRNG with Dual Extractor

An enhanced true random number generator (TRNG) architecture based on a photonic entropy source is presented. Photonic TRNGs are known to produce photon sequences at randomly distributed time intervals as well as random superimposed quantum states. We describe a TRNG architecture that takes advantage of both of these sources of entropy with a dual-source extractor function. We show that the amount of harvested entropy exceeds that compared to implementations comprised of only one of these sources. We also describe an implementation of a beam splitter used within the architecture that is suitable for implementation in a photonic integrated circuit that has been simulated and fabricated.
OPPCAT: Ontology population from tabular data

In order to present large amount of information on the Web to both users and machines, it is urgently needed to structure Web data. E-commerce is one of the areas where increasing data bottlenecks on the Web inhibit data access. Ontological display of the product information enables better product comparison and search applications using the semantics of the product specifications and their corresponding values. In this article, we present a framework called OPPCAT, which is used for semi-automatic ontology population from tabular data in e-commerce stores and product catalogues. As a result, OPPCAT allows tabular data to be used for mass production of ontology content. First, we present the common patterns in tabular data which obstruct semi-automatic production of ontologies. Then, we suggest solutions which automatically fix these errors. Finally, we define an algorithm to build ontology content semi-automatically.
Network and Application-Aware Cloud Service Selection in Peer-Assisted Environments

There are a vast number of cloud service providers, which offer virtual machines (VMs) with different configurations. From the companies perspective, an appropriate selection of VMs is an important issue, as the proper service selection leads to improved productivity, higher efficiency, and lower cost. An effective service selection cannot be done without a systematic approach due to the modularity of requests, the conflicts between requirements, and the impact of network parameters. In this paper, we introduce an innovative framework, called PCA, to solve service selection problem in the hybrid environment of peer-assisted, public, and private clouds. PCA detects the conflicts between the requests and enterprises policies, finds proper services based on the requirements, and reduces VMs rent and end-to-end network costs. PCA selects the services from multiple clouds to utilize resources and reduce the total cost. Our proposed framework utilizes set theory, B+ tree, and greedy algorithms to meet its goals. The simulation results show that PCA can reduce up to 30 percent of cloud-related costs and can achieve answers at least seven times faster in comparison to recent studies.
Background Modeling Method Based On Quad Tree Decomposition and Contrast Measure

Motion detection is a complex task that builds upon a range of underlying components. In order to assure accurate object detection, it is quite requisite to implement these components meticulously, especially in the presence of complex objects in the scene. This paper proposes a new block-based method of background modeling in dynamic scenes using quad tree decomposition and contrast measure. Experimental results show that our approach can effectively generate the background even under challenging circumstances.
Secure Big Data using QKD protocols

In this paper, we study the Big Data security based on quantum information theory. In fact, we discuss the opportunities offered by quantum Key distribution (QKD) protocols and quantum algorithms to increase the security level of Big data. In this respect, we used the BB84 protocol to transfer the sent qubits via a quantum channel and Grover’s algorithm to search in NoSql databases in quantum environment. In order to analyze the effectiveness of quantum information theory on big data, we compare the obtained results with those of classical approaches
Time-Multiplexed Methods for Optical Quantum Information Processing

Quantum information processing with photons can be greatly enhanced by incorporating time-multiplexing methods. Not only can time-bin encoding be very useful in its own right, multiplexing techniques can lead to more efficient single- and multi-photon sources, improved detectors, and high-bandwidth quantum memories, as well as enhanced applications such as quantum random walks and entanglement swapping. Here we present an overview of some of the methods used and the results achievable when explicitly using the time degree of freedom of photons.
Using Blockchain to Control Access to Cloud Data

As cloud storage becomes more common, data security is an increasing concern. In this paper, we propose a new approach to control access to the user’s data stored in the cloud with the state-of-the-arts decentralized blockchain technology. In general, an access control solution for cloud data involves three components: authentication, authorization and auditing. It is expensive for the cloud server to ensure authentication, authorization and auditing for access control of the user’s data in cloud computing environment. In addition, it is hard to prevent the malicious cloud server from access to the user’s data and disclose the user’s privacy. Our approach distributes the access control tasks for authentication, authorization and auditing to a network of nodes like bitcoin. In particular, we keep the auditing records in the transparent blockchain. In addition, we employ the Shamir secret sharing scheme to manage the encryption key for cloud users.
Evaluation of Automatic Image Color Theme Extraction Methods

Color themes are quite important in several fields from visual and graphic art design to image analysis and manipulation. Color themes can be extracted from an image manually by humans or automatically by a software. Plenty of automatic color theme extraction methods, either supervised or unsupervised, have been presented in the state of the art in the last years. Evaluation of a color theme goodness with respect to a reference one is based on visual and subjective comparisons, that may be affected by cultural and social aspects, they are time consuming and not costless. In this paper we experiment several supervised and unsupervised state-of-the-art methods for color theme extraction. To overcome the burden of a subjective evaluation, we experiment the use of a computational metric based on the Earth Mover's distance for goodness evaluation instead of a subjective one. Results show the best color theme is extracted by using a supervised method based on a regression model trained on user-defined color themes and that the computational metric adopted is comparable to a subjective one.
Changing Roles for Research and Information Skills Development: Librarians as Teachers, Researchers as Learners

University libraries have been dealing with the training needs of their users for a long time. Today, this need continues to be pressing with day to day immersion in print and digital information and the transformations of the tools for its access. Librarians play a fundamental pedagogical role for teachers and researchers who are urged by the Open Science movement to acquire new information skills. They develop information literacy training adapted to these needs. The paper presents a case study that explains three pedagogical paths: tutorial support for teachers and researchers in their daily lives; the provision of electronic resources and training for its proper use; and the development and dissemination of an online publication that seeks to improve their knowledge and practice skills related to Open Science. The actions that librarians can develop are vital because they are the basis for the creation and application of cross-curricular skills in lifelong learning, so necessary for teachers and researchers who need to quickly adapt to new information contexts.
Complexity and Nesting Evolution in Open Source Software Systems: Experimental Study

Background: Software complexity affects its quality; a complex software is not only difficult to read, maintain and less efficient, but it also can be less secure with many vulnerabilities. Complexity metrics, e.g. cyclomatic complexity and nesting levels, are commonly used to predict and benchmark software cost and efficiency. Complexity metrics are also used to decide if code refactoring is needed. Objective: Software systems with high complexity need more time to develop and test and may lead to bad understandability and more errors. Nesting level in the target structure may result in developing more complex software in what is so-called the nesting problem. Nesting problem should be shortened by rewriting the code or breaking into several functional procedures. Method: In this paper, the relationship between the nesting levels, the cyclomatic complexity, and lines of code (LOC) metrics are measured through several software releases. In order to address how strong a relationship between these factors with the nesting level, correlation coefficients are calculated. Moreover, to examine to what extent the developers are aware of and tackle the nesting problem, the evolution of nesting levels for ten releases of five open sources systems is studied to see if it is improving over successive versions or not. Results: The result shows that the nesting level has variant effects on the cyclomatic complexity and SLOC for the five studied systems. Conclusion: nesting level has the tendency to have a positive correlation with other factors (cyclomatic complexity and LOC).
Generalized Entanglement Monogamy and Polygamy Relations for N-Qubit Systems

We investigate the generalized monogamy and polygamy relations $N$-qubit systems. We give a general upper bound of the $\alpha$th ($0\leq\alpha\leq2$) power of concurrence for $N$-qubit states. The monogamy relations satisfied by the $\alpha$th ($0\leq\alpha\leq2$) power of concurrence are presented for $N$-qubit pure states under the partition $AB$ and $C_1 . . . C_{N-2}$, as well as under the partition $ABC_1$ and $C_2\cdots C_{N-2}$. These inequalities give rise to the restrictions on entanglement distribution and the trade off of entanglement among the subsystems. Similar results are also derived for negativity.
Distance-Preserving Vector Space Embedding for Consensus Learning

Learning a prototype from a set of given objects is a core problem in machine learning and pattern recognition. A commonly used approach for consensus learning is to formulate it as an optimization problem in terms of generalized median computation. Recently, a prototype-embedding approach has been proposed to transform the objects into a vector space, compute the geometric median, and then inversely transform back into the original space. This vector space embedding approach has been successfully applied in several domains, where the generalized median problem has inherent high-computational complexity (typically NP-hard) and thus approximate solutions are required. Generally, it can be expected that the embedding should be done in a distance-preserving manner. However, the previous work based on the prototype-embedding approach did not take this embedding aspect into account. In this paper, we discuss the drawbacks of the current prototype-embedding approach and present an extensive empirical study that provides strong evidence of significantly improved quality of generalized median computation using distance-preserving embedding (DPE) methods. We also give concrete suggestions about suitable DPE methods. Moreover, we show that this framework can be used to effectively compute other consensus objects like the closest string. Finally, a MATLAB toolbox resulting from this paper is made publically available in order to encourage other researchers to explore the embedding-based consensus computation.
On the Evolution of Feature Dependencies

Feature dependencies are the subject of several studies and have been approached with different goals, such as software quality. However, we still It is not known whether feature dependencies are prone to change during the lifetime of configurable systems. To minimize this lack, we perform an empirical study to assess the evolution of feature dependencies. We focus in feature dependencies at the source-code level written with preprocessor directives, for this, we analyze the history of feature dependency changes in 15 preprocessor based systems written in C. In order to identify the most common types of changes on feature dependencies, we propose a catalog of possible changes in these dependencies. We found out that about 9% of the feature dependencies between two subsequent versions are added, and less of 4% are excluded. Whereas, about 87% of the dependencies between two subsequent versions are preserved, and 14% has some kind of structural modification. Besides, the results show that functions are 3x more prone to change than variables in feature dependencies. In summary, our results show that: (i) feature dependencies prone to be preserved during the evolution; (ii) about 1/5 of the dependencies preserved between two versions have structural changes; (iii) functions changes more than variables; (iv) developers may have to deal with 1/4 of the dependencies on each evolution of the configurable system.
Extraction of human face features from color images

Face detection has been widely studied by researchers. However, detection and extraction of human face features is very important as it plays a vital role in variety of applications involving automated face processing. This article focuses on extraction of face parts such as eyes, nose, lips, musta che, and beard on Indian people, for which we have prepared our own face dataset containing variety in faces, from both urban and rural areas. This study focuses on how a detected face part becomes useful in detecting other face parts. We implement our approaches of detecting face parts and evaluate them on our dataset. We exploit YCbCr color model, Viola Jones technique, landmark detection, and level set evolution technique in our approaches of face part detection and extraction. We found that our approaches are effective on extracting face boundary, eyes, nose, and lips and provide comparable results.
Some Thoughts About Appealing Directions for the Future of Fuzzy Theory and Technologies Along the Path Traced by Lotfi Zadeh

The quoted text is an interesting instance of a fuzzy object: it is currently known in slightly diversified forms, each rather different from the quoted one, which corresponds to the first known appearance in English of this adage.
An assured deletion scheme for encrypted data in Internet of Things

With the development of Internet of Things, heterogeneous data from all kinds of sensors are processed and stored in the cloud server provider. Cloud can be regarded as one of the important layers in the Internet of Things architecture and Internet of Things is the biggest customer of the cloud. As to the security in the Internet of Things, encryption is one of the important mechanisms to achieve confidentiality of the data. However, data encryption introduces new challenges for its assured deletion in cloud, which becomes crucial for big data storage and processing in cloud for Internet of Things. Most existing solutions of cloud data assured deletion only delete the key while the ciphertext is still intact. On the other hand, the decryption time of the user increases with the size of data in the solutions. In this article, we propose a novel assured deletion scheme over encrypted cloud data based on the idea of uploading by sampling slice and outsourcing the decryption of ciphertext policy attribute–based encryption. The analysis and simulation results show that our scheme is secure and efficient, especially on reducing the decryption time of the user. We also designed a ciphertext deduplication scheme based on this scheme and explained its flow.
A Multi-client DSSE Scheme Supporting Range Queries

We consider the need for security while providing services that are comparable to that of traditional applications to fully exploit cloud services to its fullest potential. While Dynamic Searchable Symmetric Encryption (DSSE) supports such needs, we want to be able to protect against file-injection attacks. Hence, we require forward privacy and a scheme which allows for a wide range of searching capabilities. We propose an extension, based on the RSA problem, to a DSSE scheme that supports range queries allowing the scheme to also support multiple clients. Furthermore, we describe how we can further manage clients using Attribute-Based Encryption (ABE) such that clients cannot decrypt ciphertexts that fall outside of their access rights.
Internet of Things With Object Detection

In recent years, everyday objects and locating of people become an active area in IoT-based visual surveillance system. Internet of things (IoT) is basically transferring data with numerous other things. In visual surveillance systems, conventional methods are very easily susceptible to the environmental changes (i.e., illumination changing, slow motion in the background due to waving tree leaves, rippling of water, and variation in lightening condition). This chapter describes the current challenging issues present in literature along with major application areas, resources and dataset, tools and advantages of IoT-based visual surveillance systems.
Introducing Practical SHA-1 Collisions to the Classroom

There is a lack of hands-on exercises in cryptanalysis offered at the university level for a variety of reasons. One reason is the high amount of computations needed to complete cryptanalysis attacks. However, enabling students to perform fixed prefix SHA-1 collision attacks on PDFs does not require any advanced computation power. This paper presents a hands-on exercise for university students and professionals to generate SHA-1 collisions. The exercise shows how SHA-1 is vulnerable to fixed-prefix collisions, and how students are able to leverage existing tools to create their own SHA-1 collisions between arbitrary PDF files. In our two classroom runs of the exercise, over 90% of the students who performed the exercise found it to be a useful tool to reinforce their knowledge of SHA-1 collisions. The exercise increases awareness about hash collisions and urges caution when dealing with digitally signed PDFs that utilize SHA-1 as their hashing mechanism. To our knowledge, this is the first exercise designed to introduce practical SHA-1 collisions for students in the classroom.
Group Method of Data Handling as a Tool to Determine Vertical Displacements

Surveying measurements carried out to determine displacements and deformations of existing civil structures and their surroundings provide information making it possible to represent their geometry in space and any changes it undergoes over time. Data acquired through geodetic monitoring can be modelled using artificial neural networks, capable of learning (adaptability) and quick operation and providing the possibility of visualisation by means of computer simulation. Neural networks, however, require specification of an optimal architecture by the user, as a result of which any resulting solutions are flawed by a difficult to identify error of method. Therefore, this article proposes an alternative approach in the form of the Group Method of Data Handling (GMDH) based on evolutionary algorithms. The article presents the fundamental assumptions for the GMDH and the principles of development and training of static neural networks with multiple inputs and one output. The GMDH network was used to develop a geometric model of vertical displacements determined on the basis of periodic measurements taken on civil structures.