Reviewerly/exHarmony huggingface dataset:
Test case generation for WS-BPEL from a static call graph

Web service is reusable, easy to maintain, widely used, and very popular. However, web service development is not adhered to any development languages in variety of platforms or frameworks. There must be a standard in controlling and coordinating a business process through a web service. This standard is called WS-BPEL. Web service testing is an important process in software development to meet the needs of business process. Recently some researches proposed concepts and tools used to generate test case for WS-BPEL, while they focus only a business processes within a single WS-BPEL file. This paper proposes an approach for generating a WS-BPEL test cases from a static call graph to support the case which has a calling subprocess between WS-BPEL files. The generated test cases are complied with test paths based on the branch coverage.
Privacy-aware cross-cloud service recommendations based on Boolean historical invocation records

In the age of big data, service recommendation has provided an effective manner to filter valuable information from massive data. Generally, by observing the past service invocation records (Boolean values) distributed across different cloud platforms, a recommender system can infer personalized preferences of a user and recommend him/her new services to gain more profits. However, the historical service invocation records are a kind of private information for users. Therefore, how to protect sensitive user data distributed across multiple cloud platforms is becoming a necessity for successful service recommendations. Additionally, the historical service invocation records often update with time, which call for an efficient and scalable service recommendation method. In view of these challenges, we introduce the multi-probe Simhash technique in information retrieval domain into the recommendation process and further put forward a privacy-preserving recommendation method based on historical service invocation records. At last, we design several experiments on the real-world service quality data in set WS-DREAM. Experimental results show the feasibility of the proposal in terms of producing accurate recommended results while protecting users’ private information contained in historical service invocation records.
Online Trajectory Planning and Collision Avoidance for a Group of Robots Using Distributed Model Predictive Control

In this paper the trajectory planning problem for unicycle robots is studied. This problem is particularly difficult to solve online due to its inherently nonlinear and nonconvex structure. The first aim of the paper is to overcome these difficulties using model predictive control (MPC) when a single robot is considered. Several requirements must be fulfilled by the optimal trajectory, such as respecting physical limitations as well as track constraints. The second aim of the paper is to extend the results to a group of robots using distributed MPC providing formation control and inter-robot collision avoidance.
A Historical Ontology of Semi-automatic Specification Extraction from Romanian Language

The increasing need for ontologies with practical results and the difficulties of manual construction guide us towards methods that propose automatic and semi-automated techniques for creating ontologies. The article presents a conceptual method for building ontology for historical documents aiming to support historians and researchers to organize metadata, search for relevant factors, extract information and derive new knowledge for localized metadata from documents.
Color Management for Cinematic Immersive Experiences

Immersive technologies offer new approaches for engaging audiences and providing visual information, whether for entertainment or more practical applications. To ensure that the content maintains the intended appearance across different devices and environments, one of the key challenges is accurate color management. In this paper, we discuss the challenges that have to be overcome to create color-accurate immersive experiences at a cinematic quality level. We propose workflows for efficiently characterizing different virtual and augmented reality devices and review their color reproduction capabilities against current display standards.
Auto-fit multiple movable projections into one display screen

This paper presents an analysis of auto fitting multiple projections to a single screen using a camera as feedback. The projector camera system is consisting of two (or more) mobile projectors, a stationary camera, and a planar screen. The method assumes the camera to be calibrated with the condition to capture the full planar screen site. For ideal system, the geometrically compensating images projected on the screen from both projectors need to coincide so that the displayed images will always be aligned and centered on the screen with a correct aspect ratio. The system automatically performs correction calculation on-line on the captured camera frames and recovers all transformations to recommend for movable/robot projector and/or to apply on each projector's frame. To realize these properties, we adopt software approach while presuming that hardware one is available on-demand. In the proposed software, the auto-correction is obtained by measuring the actual coordinates of the projectors and achieved by either finding the maximum intersection rectangle of both (or multiple) projections and auto-adjusting of each video frames in graphic card buffers, or by calculating the transformation matrix to mechanically correct positions of mobile/robot projectors. We adopt a fast method to find the maximum intersection rectangle and extend it to be able to deal with temporal changes of the system. We show experimental results obtained by a simulated testing system where the overall execution time is less than the frame's displaying frequency.
Information Technology for Entering Text Based on Tools of the Special Virtual Keyboard Mobile and Auxiliary Devices

Information technology for the realization of human communication using residual human capabilities, obtained by organizing text entry using mobile and auxiliary devices is proposed.The components of the proposed technology are described in detail: the method for entering text information to realize the possibility of introducing a limited number of controls and the method of predicting words that are most often encountered after words al-ready entered in the sentence.A generalized representation of the process of entering text is described with the aid of an ambiguous virtual keyboard and the representation of control signals for the selection of control elements.The approaches to finding the optimal distribution of the set of alphabet characters for different numbers of control signals are given.The method of word prediction is generalized and improved, the "back-off" statistical language model is used, and the approach to the formation of the training corpus of the spoken Ukrainian language is proposed.
Associative Pattern Matching and Inference Using Associative Graph Data Structures

Inference on the still growing amount of data is a challenging problem that must be solved to operate efficiently and mine Big Data sources successfully and in a sensible time. This paper introduces a new inference method based on associative recalling of data and their relationships stored in associative graph data structures (AGDS) used for pattern matching for given criteria. These structures represent a richer set of relationships than popular tabular structures do because of their natural limitations. They recall relationships and related data faster than the time-consuming search algorithms based on many loops and conditions on tabular structures. It explains why brain processes trigger information so quickly, outperforming solution based on the Turing Machine and contemporary fast processors. The presented associative inference can work in constant time thanks to the specific data organization, access and direct representation of more relationships in AGDS structures than in the commonly used tabular structures.
Design and evaluation of graphical feedback on tangible interactions in a low-resolution edge display

We used the LED array around the edges of a custom tangible as a low-resolution display to provide users with real-time visual feedback on the current state of the system. We developed a guideline for mapping different types of edge feedback to different tangible interactions. We evaluated its effectiveness in two user studies. The first is an informal study, where experienced biologists worked with an existing tangible-tabletop biological system. The second is a formal study, where novice participants worked with a new tangible-tabletop biological system. Results of these studies suggest that edge feedback provides a better understanding of the system, increases user confidence, and can be useful in other interactive systems. Results also suggest that the proposed feedback mapping is intuitive and easy to remember.
The P2P–Grid–Agent Distributed Platform: A Distributed and Dynamic Platform for Developing and Executing Large-Scale Application Based on Deep Learning Techniques

Large-scale applications such as pattern recognition (PR) and especially based on deep learning techniques have been moving from centralized to decentralized or distributed platform to improve their scalability. Therefore, in the last few decades, researchers have put an enormous effort to implement different and robust deep learning techniques. Nevertheless, these techniques are focused for small and medium quantities of documents. In this paper, we propose the hybrid peer-to-peer (P2P), Grid computing and agent technology, that allows PR computations to scale out to multiple peers and grids and tolerate several types of faults. We performed an extensive experimental evaluation in the P2P–Grid–Agent Distributed Platform with a real large-scale dataset from the IFN/ENIT (Institute of Communications Technology (IFN)), Technical University Braunschweig, Germany, Ecole Nationale d’Ingénieurs de Tunis (ENIT), Tunisia. Results prove that our solution significantly reduces execution time when compared to traditional methods that achieve the same level of resilience and guarantee the performances of large-scale applications.
A theoretical parallel between cognitive and biological development with potential implications for measurement in psychology

This article identifies a formal parallel between quantitative analysis of cognitive and biological development based on work by Georg Rasch in psychometrics and Julian Huxley in biology. The analysis and synthesis results in an interpretation of the parameters of Rasch’s item response model whereby increases in latent cognitive development manifest as exponential increases in performance odds. This relationship directly parallels a relationship between biological development and its manifestation in weight, termed simple allometry. Attainment data analysed by Rasch were shown to exhibit a simple allometry. It is shown that the formal theoretical parallel may have applications for formulating and testing theory regarding cognitive development. Such tests may have implications for establishing a substantive basis for the measurement of cognitive abilities.
A metamaterial solves an integral equation

By iteratively processing an optical signal, the structure functions as an analog computer.
Efficient parallel semi-systolic array structure for multiplication and squaring in GF(2&lt;i&gt;&lt;sup&gt;m&lt;/sup&gt;&lt;/i&gt;)

In this paper, we develop an efficient parallel semi-systolic array structure to concurrently compute multiplication and squaring operations in the binary extension field, GF(2m), for efficient modular exponentiations. The proposed array is well suited to VLSI implementation that it has a regular structure as well as local communications between its processing elements. The obtained results show that the proposed array structure achieves a significant reduction in area-time (AT) complexity by at least 95.9% over the corresponding existing structures.
Distance Measures for Clustering of Documents in a Topic Space

Topic modeling is a method for discovery of topics (groups of words). In this paper we focus on clustering documents in a topic space obtained using the MALLET tool. We tested several different distance measures with two clustering algorithm (spectral clustering, agglomerative hierarchical clustering) and described those that served better (cosine distance, correlation distance, bhattacharyya distance) than the Euclidean metric for k-means algorithm. For evaluation purpose we used Adjusted Mutual Information (AMI) score. The need for such experiments comes from the difficulty of choosing appropriate grouping methods for the given data, which is specific in our case.
Application of Multivariate Statistical Methods for Assessment of Educational Competencies

Assessment of educational competencies of students in the Russian Federation higher education institutions is one of the most important objectives of the efficient training of competitive specialists in a smart environment, since the third-generation Russian educational standards prescribe the implementation of competence-oriented learning in higher education institutions. This aspect is important for the smart education system as well. The main features of a competence can be distinguished using Bloom’s taxonomy according to which each competence is assessed according to six characteristics: knowledge, understanding, application, analysis, synthesis, and assessment. The goal of this paper is to build a model of probabilistic assessment of the students’ competencies quality level on the basis of the multivariate normal distribution. That said, we see probabilistic assessment as the use of mathematical statistics’ ideas related to the statistical methods of experimental planning to minimize the differences in obtaining the final results in educational systems in response to uncontrolled factors and at the same time to increase maximally the possibility of obtaining the guaranteed result. The proposed model was tested in the smart education context during the learning process in the Toglyatti State University (Russian Federation, city of Toglyatti), on the basis of the distance technology of teaching the discipline “Econometrics (advanced level)”. Testing was carried out for 2.5 years (5 semesters). A total of 274 students participated in the experiment.
Further Tendency of Obtaining Potential Appropriate Respondents to Questions at Q&amp;A Sites through Additional Categories

In order to eliminate mismatches between the intentions of questioners and respondents of Question and Answer (Q&A) sites, nine factors of impressions for statements have experimentally been obtained. Factor scores are then estimated by using the feature values of statements. So far the possibility of searching respondents capable of giving appropriate answers to a newly posted question has been established for Auction, PC and Love. It has been shown that the distance and the number of appearance may help us select users who can give appropriate answers to a question. In the similar fashion, this paper tries to find the possibility of detecting respondents who can appropriately answer a newly posted question for other categories such as Internet, Politics, etc. As a result of analysis, several newly regarded categories shows the similar tendency as the previous analysis, while some categories related to Yahoo! JAPAN show less outstanding tendency.
Masking Fuzzy-Searchable Public Databases

We introduce and study the notion of keyless fuzzy search (KlFS) which allows to mask a publicly available database in such a way that any third party can retrieve content if and only if it possesses some data that is “close to” the encrypted data – no cryptographic keys are involved. We devise a formal security model that asks a scheme not to leak any information about the data and the queries except for some well-defined leakage function if attackers cannot guess the right query to make. In particular, our definition implies that recovering high entropy data protected with a KlFS scheme is costly. We propose two KlFS schemes: both use locality-sensitive hashes (LSH), cryptographic hashes and symmetric encryption as building blocks. The first scheme, is generic and works for abstract plaintext domains. The second scheme is specifically suited for databases of images. To demonstrate the feasibility of our KlFS for images, we implemented and evaluated a prototype system that supports image search by object similarity on masked database.
Investigating the use of duration‐based windows and estimation by analogy for COCOMO

Abstract In model‐based software estimation, using the right training data is a key contributor for making accurate predictions, which is crucial for the success of software projects. This study investigates the use of duration‐based windows and estimation by analogy to calibrate COCOMO and assess their estimation performance. We compare these approaches as well as the use of all available historical data using the COCOMO data set of 341 projects and NASA data set of 93 projects. The results show that timing information exists in the data sets affecting estimation accuracy. Given sufficient data for calibration, using recently completed projects within short durations generates more accurate estimates than retaining all historical data or using k ‐nearest neighbors based on estimation by analogy. More training data spanning a long period of time may not lead to improved estimation accuracy. This study offers evidence to support the use of projects completed within recent years for training estimation models.
Improved Particle Swarm Optimization for the Mutual Inductance of an Implantable Biomedical Application

Particle Swarm Optimization (PSO) and Differential Evolution (DE) are two dominant metaheuristic optimization techniques with different special features. These specialties are combined together in an improved particle swarm optimization (DPSO) algorithm is estimated. The proposed idea consists of incorporating the concept of the DE algorithm when updating velocity equation of the PSO algorithm in sort to guarantee the global optimal. Also, the objective is to bias the solution, the difference between the selected randomly and the target particle, with an adjust parameter. The improved algorithm is simulated on several test functions and compared between three references metaheuristics and one variant most used in the literature. Then, DPSO has been used to optimize the mutual inductance of an implantable biomedical problem.
3‐3: The effect of larger field‐of‐view on visual search times for world‐locked augmented reality objects

Augmented reality systems supplement human vision, allowing virtual objects to be placed within the real world; however, a small field‐of‐view can limit this experience. This work documents how field‐of‐view affects visual search times for augmented reality objects, showing improving search performance for larger field‐of‐view up to a plateau.
Multi-Distribution Characteristics Based Chinese Entity Synonym Extraction from The Web

Entity synonyms play an important role in natural language processing applications, such as query expansion and question answering. There are three main distribution characteristics in web texts:1) appearing in parallel structures; 2) occurring with specific patterns in sentences; and 3) distributed in similar contexts. The first and second characteristics rely on reliable prior knowledge and are susceptive to data sparseness, bringing high accuracy and low recall to synonym extraction. The third one may lead to high recall but low accuracy, since it identifies a somewhat loose semantic similarity. Existing methods, such as context-based and pattern-based methods, only consider one characteristic for synonym extraction and rarely take their complementarity into account. For increasing recall, this article proposes a novel extraction framework that can combine the three characteristics for extracting synonyms from the web, where an Entity Synonym Network (ESN) is built to incorporate synonymous knowledge. To improve accuracy, the article treats synonym detection as a ranking problem and uses the Spreading Activation model as a ranking means to detect the hard noise in ESN. Experimental results show the proposed method achieves better accuracy and recall than the state-of-the-art methods.
Toward human-like summaries generated from heterogeneous software artefacts

Automatic text summarisation has drawn considerable interest in the field of software engineering. It can improve the efficiency of software developers, enhance the quality of products, and ensure timely delivery. In this paper, we present our initial work towards automatically generating human-like multi-document summaries from heterogeneous software artefacts. Our analysis of the text properties of 545 human-written summaries from 15 software engineering projects will ultimately guide heuristics searches in the automatic generation of human-like summaries.
An Improved D-S Evidence Theory Based on Gray Relational Analysis

Dempster-Shafer (D-S) evidence theory is an uncertain reasoning method which has great application value in data fusion and other areas. However, it can usually lead to counter-intuitive results when fusing the highly conflictive evidences. In this paper, an improved D-S evidence theory based on gray relational analysis is proposed. The proposed method considers the basic probability assignment (BPA) of every evidence as a data sequence, and assigns a weight to each sequence to modify the original evidence model, and then fuses the evidences by D-S combination rule. The new evidence model is more reliable so that the fusion result is more accurate. A numerical example and an experiment on Iris dataset are given to illustrate effectiveness by comparing with other D-S evidence based methods.
Tablets and Geography. Initial Findings from a Study in Primary School Settings

The study examines whether the use of tablets can improve the performance of sixth-grade primary school students in the course of Geography. Four teaching interventions were planned and carried out in three groups of students (twenty-two in each), in primary schools in Athens, Greece. While students in all groups worked in pairs, the first used the textbooks, the second used computers and the third was taught exclusively through the use of tablets and micro-applications developed by the class’s teacher. The learning outcomes of the third group were equally good compared to the ones of the second, while both groups outperformed the first one. No notable differences regarding students’ misconceptions were observed. The results can be attributed to a number of factors such as students’ increased motivation and autonomy, but also highlight the need for finding more efficient methods for integrating tablets into the teaching process.
A Method for Image Co-segmentation with Seeded Foreground

Image segmentation is a key technology from image processing to analysis. Without proper segmentation, it is impossible to recognize correctly. In this paper, we propose a method for image co-segmentation based on the biased normalized cuts using a semi-supervised way to deal with foreground regions. In order to take advantage of biased normalized cuts to solve problem, we use 2D adaptive Wiener filter to smooth the seeded parts of images, then divide images into a set of super-pixels, after that take super-pixels as vertices to form a weighted undirected graph. Thus, the co-segmentation can be seen as an issue of graph partition that solved by biased normalized cuts. The experiments on image data sets show the superior performance of our method.