Miracle-dz/UniFAR-FTUs huggingface dataset:
Reusing process fragments for fast service composition: a clustering-based approach

ABSTRACT With the proliferation of Web services over the Internet and due to the increasing complexity of users’ needs, Web service composition has emerged as a powerful method of software reuse, allowing to deliver complete business processes as a set of interacting services. To guarantee a rapid and secure service composition, fragments of available business processes at different granularities may be considered as a composition unit and recombined to deliver effective compositions. Despite the benefits of this method, most of the existing works do not take into consideration the reuse of service process fragments (SPF). Reusing SPFs allows, not only to minimize the composition time, but also to improve the reliability of the composition process. In this paper, we propose a Web service composition approach that aims to combine service process fragments rather that atomic services. We adopt a powerful mathematical model called Formal Concept Analysis (FCA) to define the relationships between services and fragments. Moreover, we exploit the regrouping capabilities of FCA by proposing algorithms for the extraction of candidate fragments’ combinations. A scoring function is also defined to determine the quality level of each SPF and its ability to participate in a composition. The experimental studies proved the effectiveness of our FCA-based approach compared to existing state-of-the-art solutions.
A Graph-Based Collective Linking Approach with Group Co-existence Strength

This paper addresses a drawback of many existing graph-based collective entity-linking approaches by introducing the new concept of Group Co-existence Strength (GCS). Doing so, this work proposes an approach to the collective linking of text documents which extends an existing recent approach by taking into account GCS for all possible groups of candidate entities along with standard attributes. Preliminary experimental results indicate that the proposed approach leads to performance gains with selected real world data.
Quantitative Models for Visual Abstraction

Computer graphics is mostly associated with the creation of arti cial images that resemble real photographs as close as possible. However, realistic representations usually contain more information than necessary to transmit intended information. Abstract images can be used to convey information more e ectively. The eld of non-photorealistic rendering focuses on the automatic creation of these expressive illustrations, often inspired by the work of real artists. In this thesis three novel methods are presented within this eld, each working on a di erent representational drawing primitive: points, lines, and textured strokes. Inspired by the Linde– Buzo–Gray algorithm a novel stippling method is proposed that splits and merges Voronoi cells based on their size or other underlying image properties. The algorithm automatically adapts to various constraints and can handle variable point sizes. A novel line drawing technique is developed utilizing depth information to improve depth perception and object recognition in spatially complex scenes. The user can adapt the degree of abstraction and line drawing style with instant visual feedback. In painterly rendering, a new technique is introduced which decomposes input images into regions and layers, mimicking the process of real painters and reducing overpainting. Di erent styles can be applied to each area and layer to create painterly representations with more artistic freedom. The evaluation of abstract representations is challenging and often very subjective. Since it is di cult to nd objective functions that quantify the visual quality of such illustrations, we gather comparative data in a user study. From this relative data, we reconstruct absolute scores by employing a paired comparison model. Based on this we can accurately predict the perceived quality depending on properties of an input image. The proposed model is related to Weber– Fechner’s law from psychophysics and indicates a logarithmic relationship between the number of rendering primitives and perceived abstraction quality. We show the generalizability and applicability of this approach by comparing di erent stylization techniques.
Orientable ℤN-Distance Magic Graphs

Abstract Let G = (V, E) be a graph of order n. A distance magic labeling of G is a bijection ℓ: V → {1, 2, . , n} for which there exists a positive integer k such that ∑x∈N(v) ℓ(x) = k for all v ∈ V, where N(v) is the open neighborhood of v. Tuttes flow conjectures are a major source of inspiration in graph theory. In this paper we ask when we can assign n distinct labels from the set {1, 2, . , n} to the vertices of a graph G of order n such that the sum of the labels on heads minus the sum of the labels on tails is constant modulo n for each vertex of G. Therefore we generalize the notion of distance magic labeling for oriented graphs.
Probabilistic Load Flow Solution Considering Optimal Allocation of SVC in Radial Distribution System

This paper proposes a solution procedure for probabilistic load flow problem considering the optimal allocation of Static Var Compensator (SVC) in radial distribution systems. Pareto Envelope-based Selection Algorithm II (PESA-II) with fuzzy logic decision maker is developed to determine the optimal location and size of SVC based on the minimum total power losses and Voltage Deviation (VD). Combined cumulants and gram-chalier expansion are used for solving the probabilistic load flow problem. The proposed algorithm is tested on 33- bus and 69-bus distribution systems. The developed algorithm gives an acceptable solution with low number of iterations and less computation cost compared with the Monte Carlo method.
Efficient Query Processing for Scalable Web Search

Search engines are exceptionally important tools for accessing information in today’s world. In satisfying the information needs of millions of users, the effectiveness (the quality of the search results) and the efficiency (the speed at which the results are returned to the users) of a search engine are two goals that form a natural trade-off, as techniques that improve the effectiveness of the search engine can also make it less efficient. Meanwhile, search engines continue to rapidly evolve, with larger indexes, more complex retrieval strategies and growing query volumes. Hence, there is a need for the development of efficient query processing infrastructures that make appropriate sacrifices in effectiveness in order to make gains in efficiency. This survey comprehensively reviews the foundations of search engines, from index layouts to basic term-at-a-time (TAAT) and document-at-a-time (DAAT) query processing strategies, while also providing the latest trends in the literature in efficient query processing, including the coherent and systematic reviews of techniques such as dynamic pruning and impact-sorted posting lists as well as their variants and optimisations. Our explanations of query processing strategies, for instance the WAND and BMW dynamic pruning algorithms, are presented with illustrative figures showing how the processing state changes as the algorithms progress. Moreover, acknowledging the recent trends in applying a cascading infrastructure within search systems, this survey describes techniques for efficiently integrating effective learned models, such as those obtained from learning-torank techniques. The survey also covers the selective application of query processing techniques, often achieved by predicting the response times of the search engine (known as query efficiency prediction), and making per-query tradeoffs between efficiency and effectiveness to ensure that the required retrieval speed targets can be met. Finally, the survey concludes with a summary of open directions in efficient search infrastructures, namely the use of signatures, real-time, energy-efficient and modern hardware & software architectures.
Deep Learning for Detection of Routing Attacks in the Internet of Things

Cyber threats are a showstopper for Internet of Things (IoT) has recently been used at an industrial scale. Network layer attacks on IoT can cause significant disruptions and loss of information. Among such attacks, routing attacks are especially hard to defend against because of the ad-hoc nature of IoT systems and resource constraints of IoT devices. Hence, an efficient approach for detecting and predicting IoT attacks is needed. Systems confidentiality, integrity and availability depends on continuous security and robustness against routing attacks. We propose a deep-learning based machine learning method for detection of routing attacks for IoT. In our study, the Cooja IoT simulator has been utilized for generation of high-fidelity attack data, within IoT networks ranging from 10 to 1000 nodes. We propose a highly scalable, deep-learning based attack detection methodology for detection of IoT routing attacks which are decreased rank, hello-flood and version number modification attacks, with high accuracy and precision. Application of deep learning for cyber-security in IoT requires the availability of substantial IoT attack data and we believe that the IoT attack dataset produced in this work can be utilized for further research.
The regulation of CTP:Phosphocholine cytidylyltransferase in fetal type 2 cells

textabstractRespiratory distress syndrome (RDS), also known as hyaline membrane disease, is an important cause of neonatal and infant mortality. Together with congenital malformations, it is the leading cause of death in preterm infants and is responsible for serious morbidity in survivors, associated with high costs to society. Since 1959 it is known that RDS is caused by lung immaturity with concomitant surfactant deficiency. Numerous small studies and large multicenter trials have demonstrated decreased death rates and complications from RDS as a result of treatment with surfactant. Despite its success however, surfactant therapy is not a panacea. Recent meta-analyses from the available data do not show a consistent decrease in long term pulmonary complications such as bronchopulmonary dysplasia, nor in the major nonpulmonary complications such as intraventricular haemorrhage.
Semantic Knowledge Management and Ontology Development for Online Library

Nowadays people with computer-related background and programming knowledge are focusing more and more towards the Semantic Web. The reason behind this trend is because many years ago, as the World Wide Web began to develop with the continued vision of Tim Berners-Lee; we saw how the computers could interact with humans by providing information on all kind of different topics people were interested in. Since its creation the Internet has evolved through time into the amazing World Wide Web that we see today. A negative aspect of it is that all we presently have is information that only humans can understand. We want this problem to be solved by a technology which would enable this information to be understandable by computers too. A technology that processes the data not only in a machine-readable way but also understands most of that information’s meaning. The focus of this paper is to see how Semantic Web aims to do this by using ontology and most fundamentally, the Resource Description Framework (RDF).
Designing Professional Development in Literacy: A Framework for Effective Instruction

"Gallimore, "Foreword. Introduction. Tenets. Foundations. Organization. Implementation. Professional Development into Practice. Coaching to Improve Practice. Accountability and Other Practical Matters. Research on the Model. Appendix A. Going Live with Martin: Inside Session 9, Teaching Oral Language. Appendix B. Going Live with Stephanie: Inside Session 5, Acknowledging Curriculum Expectations.
Predicting Secondary School Students' Performance Utilizing a Semi-supervised Learning Approach

Educational data mining constitutes a recent research field which gained popularity over the last decade because of its ability to monitor students' academic performance and predict future progression. Numerous machine learning techniques and especially supervised learning algorithms have been applied to develop accurate models to predict student's characteristics which induce their behavior and performance. In this work, we examine and evaluate the effectiveness of two wrapper methods for semisupervised learning algorithms for predicting the students' performance in the final examinations. Our preliminary numerical experiments indicate that the advantage of semisupervised methods is that the classification accuracy can be significantly improved by utilizing a few labeled and many unlabeled data for developing reliable prediction models.
A technique for defining the architecture and weights of a neural image classifier

An approach to setting the architecture and the initial weights of an artificial neural network for solving classification problems is presented. A nonneural phase finds an approximate solution to the classification problems by constraining the shape of classification regions. After an appropriate mapping into a neural net, neural training is applied to refine the solution. Results on an image recognition application are presented. <<ETX>>
Path-dependent volatility: an application to the South African market

Industry and academia have thus far focussed on three classes of volatility models, namely, constant volatility, local volatility and stochastic volatility. Path-dependent volatility models are a lesser known class of models which possess the key characteristic of completeness together with the ability to generate a wide range of volatility dynamics with respect to the underlying asset (Guyon, 2014). This dissertation highlights the usefulness and practicality of these models for application in the South African market, while drawing comparisons with other widely used models. The tests cover both pricing and hedging of vanilla European options on the FTSE JSE Top 40. The Black-Scholes, Heston and CEV models are used as comparative benchmarks for each of the other classes of models.
Experience in design and learning approaches: enhancing the framework for experience

In design and learning studies, an increasing amount of attention has been paid to experience. Many design approaches relate experience to embodiment and phenomenology. The growth in the number of applications that use the Internet of Things (IoT) has shifted human interactions from mobile devices and computers to tangible, material things. In education, the pressure to learn and update skills and knowledge, especially in work environments, has underlined the challenge of understanding how workers learn from reflection while working. These directions have been fuelled by research findings in the neurosciences, embodied cognition, the extended phenomenological–cognitive system and the role of emotions in decision-making and meaning making. The perspective on experience in different disciplines varies, and the aim is often to categorise experience. These approaches provide a worthwhile view of the importance of experience in learning and design, such as the recent emphasis on conceptual and epistemological knowledge creation. In pragmatism, experience plays a considerable role in research, art, communication and reflection. Therefore, I rely on Peirce’s communicative theory of signs and Dewey’s philosophy of experience to examine how experience is connected to reflection and therefore how it is necessarily tangible.
On Training Automated Agents

Learning to solve problem-solving tasks is a hallmark of intelligence. Intelligent agents learn not only from their own experiences but also from the experiences of others. One would also like a computerized agent to do this: to exploit both its own experiences and those of other agents when learning to solve problem-solving tasks. To this end, we introduce a model of learner/trainer interaction that describes how a learning agent and training agent work together to help the learning agent learn. This proposed model presents the learner as an agent that must make decisions about how it is going to learn. For example, when should the learning agent ask the trainer for help? The training agent must also make decisions about how it interacts with the learning agent. For example, when the learning agent requests help, should the trainer provide it or ignore the request? These decisions drive the interactions, which are the mechanisms by which the training agent provides knowledge to the learning agent. We propose to examine the issues that arise in implementing particular aspects of the learner/trainer model. We will present a restricted learner/trainer model, Interactive Training, and discuss the requirements on the training agent and the learning agent. In our proposed research we will explore the trainer's ability to interact with the learner as well as the learner's ability to beneet from that interaction. Our goal is to develop a systematic method by which a training agent|human or automated|can train a learning agent eeectively, allowing automated agents to be built more quickly. Furthermore, such a training method will allow agents to be built for problems that are currently deemed too diicult for automated learning agents to tackle.
Single-Objective Linear Goal Programming Problem with Neutrosophic Numbers

This paper deals with single-objective linear goal programming problem with neutrosophic numbers. The coefficients of objective function and the constraints are considered as neutrosophic numbers of the form (p +qI), where p, q are real numbers and I denotes indeterminacy. In the solution process, the neutrosophic numbers are transformed into interval numbers.
Blob tracking technique to range image segmentation

A robust segmentation scheme is one of the primary requirements for three-dimensional object recognition. The task of partitioning a given image into homogeneous regions has been the centerpiece of investigations of several major researchers all of these years. In this paper we propose a simplistic range image segmentation scheme for un-occluded cluttered three- dimensional objects in a scene. An assumption relating to the three-dimensional objects being quadric in nature, would further enable us to demonstrate an object recognition scheme which has been proposed in an earlier publication. The proposed method involves the extraction of jump edges which we refer to as global edges. After having median filtered the resultant image, a thinning algorithm is implemented which is subsequently followed by the blob determination algorithm thereby completing the segmentation process. Experiments have been conducted on range images of scenes consisting of several quadric surfaces with promising results. Application of the object recognition scheme subsequently successfully classified most of the objects in the scenes as either spherical, cylindrical, or planar in nature.
A qualitative study on the perception of intellectual capital among a group of senior managers of Italian social enterprises

ABSTRACT The aim of this explorative research is to understand how senior management of Italian social enterprises perceive the role played by the inteletual capital within their organisations in generating knowledge and organisational growth. Semi-structured,in-depth interviews were individually conducted with 81 senior managers of Italian SEs. Interview data were analysed using different qualitative techniques: discourse analysis (performed by two independent judges) as well as content analysis, run through the T-Lab software (analysis of word occurrence and co-word mapping). Findings show that senior managers have a limited understanding of the IC concept of their own organisations as a generative intangible resource. The contents of their perception of IC is more related to the management of human resources and the activities performed by their organisations. Implications for scholars and pratictioners are discussed.
Secure File Sharing Mechanism and Key Management for Mobile Cloud Computing Environment

Objectives: The use of the mobile devices in cloud computing environment is susceptible to various kinds of attacks like, unauthorized access, account/service hijacking, data breach and malicious insider. These vulnerabilities make the cloud environment unsafe to share and store data for mobile users. Methods/Analysis: In this paper, we propose a secure file storing and retrieving mechanism to avoid the limitations in existing systems like, file encryption, access rights and key management. Asymmetric key cryptography is utilized to protect the data and retrieval of the data with minimal access rights. Findings: Privacy of the mobile users are protected from the malicious insiders along with the preservation of confidentiality and integrity of the files being accessed. The comparative analysis of different public key infrastructure algorithms utilizing the proposed methodology for key computation, encryption, decryption and resource utilization shows the performance of each algorithm for different file sizes. Application/Improvement: The proposed system provides user access management, key management, encryption and decryption of files through trusted third party to make the data secured in mobile cloud environment.
Oppmerksomhetstrening (OT) og stressmestring

Nasjonalt kunnskapssenter for helsetjenesten har utgitt et hefte om oppmerksomhetstrening og stressmestring. Heftet har som malsetting a styrke evnen til naervaer og muligheten til a mestre det stresset vi alle utsettes for.
Handbook Of Affect And Social Cognition

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The Research of Electronics Equipment Online Failure Prediction Based on Gray Theory and Expert System

A combinational failure prediction method based on improved gray theory and expert system for electronic equipment′s gradual onset failure is proposed.This prediction algorithm can not only fulfill single measurement point and multiple measurement point prediction for complex electronic equipment,but also meet the demands of online prediction.The original gray theory model is improved and a bitwise and matching algorithm is used to enhance the speed and accuracy of failure prediction by applying the proposed prediction algorithm.And the experiment results verify the validity and correctness of the proposed method.