Reviewerly/exHarmony huggingface dataset:
Modeling and Evaluation of Service Composition in Commercial Multiclouds Using Timed Colored Petri Nets

The increasing demand for Web services encourages commercial cloud service providers to publish their own services with various functional and nonfunctional capabilities in different cloud platforms. The aggregation of atomic services from multiple service repositories is the main idea of the service composition concept in multiclouds. The cloud Web service composition is a suitable way for satisfying users' complex requests by integrating services from different clouds in order to create a new value-added composite service. The time required to serve a composite service by a multicloud environment is an important parameter, which depends on different factors, ranging from the service composition and selection algorithm to the number of atomic services published in the clouds. In this paper, a model based on timed colored Petri nets (TCPNs) is proposed to evaluate the service composition in multicloud environments while minimizing the number of clouds involved in serving a composite service request. The proposed TCPN graphically models the process of request submission, composite service analysis, service selection, and service provisioning in a multicloud environment. It also assesses both mean response time of the environment and probability of dropping composite requests. The verification of the accuracy of the proposed model is done by comparing the results obtained from the TCPN model, in two different scenarios, with the results from the CloudSim framework. These results confirm that our proposed TCPN model can appropriately model the system and evaluate its performance more efficiently than the CloudSim.
Audience Design in Multiparty Conversation

How do speakers design what they say in order to communicate effectively with groups of addressees who vary in their background knowledge of the topic at hand? Prior findings indicate that when a speaker addresses a pair of listeners with discrepant knowledge, that speakers Aim Low, designing their utterances for the least knowledgeable of the two addressees. Here, we test the hypothesis that speakers will depart from an Aim Low approach in order to efficiently communicate with larger groups of interacting partners. Further, we ask whether the cognitive demands of tracking multiple conversational partners' perspectives places limitations on successful audience design. We find that speakers can successfully track information about what up to four of their partners do and do not know in conversation. When addressing groups of 3-4 addressees at once, speakers design language based on the combined knowledge of the group. These findings point to an audience design process that simultaneously represents the perspectives of multiple other individuals and combines these representations in order to design utterances that strike a balance between the different needs of the individuals within the group.
Spatio-temporal outlier detection algorithms based on computing behavioral outlierness factor

Abstract A major task in spatio-temporal outlier detection is to identify objects that exhibit abnormal behavior either spatially, and/or temporally. There have only been a few algorithms proposed for detecting spatial and/or temporal outliers. One example is the Local Density-Based Spatial Clustering of Applications with Noise (LDBSCAN). Density-Based Spatial Clustering of Applications with Noise (DBSCAN) is mainly for clustering; it just tells us whether an object belongs to a cluster or it is an outlier. A measure known as Local Outlier Factor (LOF) gives a quantitative measure of outlierness to each object, where a high LOF score means it is potentially an outlier. LDBSCAN algorithm, which combines the above notions, considers only the spatial context. Furthermore, the notion of a cluster is defeated (i.e. LDBSCAN may report clusters having less than the minimum required points in a cluster), and some of the outliers may not be detected because of the limitation of the existing conditions in the LDBSCAN algorithm. In this paper, we propose two algorithms, namely Spatio-Temporal Behavioral Density-based Clustering of Applications with Noise (ST-BDBCAN) and Approx-ST-BDBCAN. ST-BDBCAN algorithm adopts the proposed, new concept, called Spatio-Temporal Behavioral Outlier Factor (ST-BOF), which is a spatio-temporal extension to LOF. It also uses both spatial and temporal attributes simultaneously to define the context. By doing so, the relative importance of spatial continuity or temporal continuity appropriate to the application at hand can be established. The Approx-ST-BDBCAN algorithm achieves improved scalability, with minimal loss of detection accuracy by partitioning data points for parallel processing. Experimental results on synthetic, and buoy datasets suggest that our proposed algorithms are accurate and computationally efficient. Additionally, new Outlier Association with Hurricane Intensity Index (OAHII) measures are introduced for quantitative evaluation of the results from buoy dataset.
Perusal of readability with focus on web content understandability

The Web has become a popular and important medium of transmitting information from one place to another place. For making information accessible to all, we need to check their accessibility and readability score. Readability is a metric to measure the success of information being successfully conveyed to a large population when people are trying to access it. This survey paper analyzes the popular readability indices used to check the readability of websites and web pages. Most of the available readability metrics consider only textual features of the web while checking the readability of websites, and a lot of factors which affect readability are not taken into account. Based on survey findings, we provide some useful suggestions, which are, if considered while developing readability formulas, the results will be more effective.
Repairing 3D binary images using the BCC grid with a 4-valued combinatorial coordinate system

A 3D binary image I is called well-composed if the set of points in the topological boundary of the cubes in I is a 2-manifold. Repairing a 3D binary image is a process which produces a well composed image (or a polyhedral complex) from the non-well-composed image I. We propose here to repair 3D images by associating the Body-Centered Cubic grid (BCC grid) to the cubical grid. The obtained polyhedral complex is well composed, since two voxels in the BCC grid either share an entire face or are disjoint. We show that the obtained complex is homotopy equivalent to the cubical complex naturally associated with the image I. To efficiently encode and manipulate the BCC grid, we present an integer 4-valued combinatorial coordinate system that addresses cells of all dimensions (voxels, faces, edges and vertices), and allows capturing all the topological incidence and adjacency relations between cells by using only integer operations. We illustrate an application of this coordinate system on two tasks related with the repaired image: boundary reconstruction and computation of the Euler characteristic.
Constraint Handling Guided by Landscape Analysis in Combinatorial and Continuous Search Spaces

The notion and characterisation of fitness landscapes has helped us understand the performance of heuristic algorithms on complex optimisation problems. Many practical problems, however, are constrained, and when significant areas of the search space are infeasible, researchers have intuitively resorted to a variety of constraint-handling techniques intended to help the algorithm manoeuvre through infeasible areas and toward feasible regions of better fitness. It is clear that providing constraint-related feedback to the algorithm to influence its choice of solutions overlays the violation landscape with the fitness landscape in unpredictable ways whose effects on the algorithm cannot be directly measured. In this work, we apply metrics of violation landscapes to continuous and combinatorial problems to characterise them. We relate this information to the relative performance of six well-known constraint-handling techniques to demonstrate how some properties of constrained landscapes favour particular constraint-handling approaches. For the problems with sampled feasible solutions, a bi-objective approach was the best performing approach overall, but other techniques performed better on problems with the most disjoint feasible areas. For the problems with no measurable feasibility, a feasibility ranking approach was the best performing approach overall, but other techniques performed better when the correlation between fitness values and the level of constraint violation was high.
SeCo-LDA: Mining Service Co-Occurrence Topics for Composition Recommendation

Service composition remains an important topic where recommendation is widely recognized as a core mechanism. Existing works on service recommendation typically examine either association rules from mashup-service usage records, or latent topics from service descriptions. This paper moves one step further, by studying latent topic models over service collaboration history. A concept of service co-occurrence topic is coined, equipped with a mechanism developed to construct service co-occurrence documents. The key idea is to treat each service as a document and its co-occurring services as the bag of words in that document. Four gauges are constructed to measure self-co-occurrence of a specific service. A theoretical approach, Service Co-occurrence LDA (SeCo-LDA), is developed to extract latent service co-occurrence topics, including representative services and words, temporal strength, and services' impact on topics. Such derived knowledge of topics will help to reveal the trend of service composition, understand collaboration behaviors among services and lead to better service recommendation. To verify the effectiveness and efficiency of our approach, experiments on a real-world data set were conducted. Compared with methods of Apriori, content matching based on service description, and LDA using mashup-service usage records, our experiments show that SeCo-LDA can recommend service composition more effectively, i.e., 5% better in terms of Mean Average Precision than baselines.
Modular Product Programs

Many interesting program properties like determinism or information flow security are hyperproperties, that is, they relate multiple executions of the same program. Hyperproperties can be verified using relational logics, but these logics require dedicated tool support and are difficult to automate. Alternatively, constructions such as self-composition represent multiple executions of a program by one product program, thereby reducing hyperproperties of the original program to trace properties of the product. However, existing constructions do not fully support procedure specifications, for instance, to derive the determinism of a caller from the determinism of a callee, making verification non-modular. We present modular product programs, a novel kind of product program that permits hyperproperties in procedure specifications and, thus, can reason about calls modularly. We provide a general formalization of our product construction and prove it sound and complete. We demonstrate its expressiveness by applying it to information flow security with advanced features such as declassification and termination-sensitivity. Modular product programs can be verified using off-the-shelf verifiers; we have implemented our approach for both secure information flow and general hyperproperties using the Viper verification infrastructure. Our evaluation demonstrates that modular product programs can be used to prove hyperproperties for challenging examples in reasonable time.
Adapting topic map and social influence to the personalized hybrid recommender system

A recommender system utilizes information filtering techniques to help users obtain accurate information effectively and efficiently. The existing recommender systems, however, recommend items based on the overall ratings or the click-through rate, and emotions expressed by users are neglected. Conversely, the cold-start problem and low model scalability are the two main problems with recommender systems. The cold-start problem is encountered when the system lacks initial rating. Low model scalability indicates that a model is incapable of coping with high-dimensional data. These two problems may mislead the recommender system, and thus, users will not be satisfied with the recommended items. A hybrid recommender system is proposed to mitigate the negative effects caused by these problems. Additionally, ontologies are applied to integrate the extracted features into topics to reduce dimensionality. Topics mentioned in the items are displayed in the form of a topic map, and users can refer to these similar items for further information.
Faster shift-reduce constituent parsing with a non-binary, bottom-up strategy

An increasingly wide range of artificial intelligence applications rely on syntactic information to process and extract meaning from natural language text or speech, with constituent trees being one of the most widely used syntactic formalisms. To produce these phrase-structure representations from sentences in natural language, shift-reduce constituent parsers have become one of the most efficient approaches. Increasing their accuracy and speed is still one of the main objectives pursued by the research community so that artificial intelligence applications that make use of parsing outputs, such as machine translation or voice assistant services, can improve their performance. With this goal in mind, we propose in this article a novel non-binary shift-reduce algorithm for constituent parsing. Our parser follows a classical bottom-up strategy but, unlike others, it straightforwardly creates non-binary branchings with just one Reduce transition, instead of requiring prior binarization or a sequence of binary transitions, allowing its direct application to any language without the need of further resources such as percolation tables. As a result, it uses fewer transitions per sentence than existing transition-based constituent parsers, becoming the fastest such system and, as a consequence, speeding up downstream applications. Using static oracle training and greedy search, the accuracy of this novel approach is on par with state-of-the-art transition-based constituent parsers and outperforms all top-down and bottom-up greedy shift-reduce systems on the Wall Street Journal section from the English Penn Treebank and the Penn Chinese Treebank. Additionally, we develop a dynamic oracle for training the proposed transition-based algorithm, achieving further improvements in both benchmarks and obtaining the best accuracy to date on the Penn Chinese Treebank among greedy shift-reduce parsers.
Secure sharing of mHealth data streams through cryptographically-enforced access control

Owners of mobile-health apps and devices often want to share their mHealth data with others, such as physicians, therapists, coaches, and caregivers. For privacy reasons, however, they typically want to share a limited subset of their information with each recipient according to their preferences. In this paper, we introduce ShareHealth, a scalable, usable, and practical system that allows mHealth-data owners to specify access-control policies and to cryptographically enforce those policies so that only parties with the proper corresponding permissions are able to decrypt data. The design and prototype implementation of this system make three contributions: (1) they apply cryptographically-enforced access-control measures to stream-based (specifically mHealth) data, (2) they recognize the temporal nature of mHealth data streams and support revocation of access to part or all of a data stream, and (3) they depart from the vendor- and device-specific silos of mHealth data by implementing a secure end-to-end system that can be applied to data collected from a variety of mHealth apps and devices.
Pengaruh Motivasi, Pengetahuan Akuntansi dan Relogiusitas terhadap Kualitas Lulusan Program Studi Akuntansi Syariah

Along with the development of economy and business, the competitiveness of it isincreasing. Increasing competitiveness requires educational institutions tocontinuously improve the quality of graduates.This study aims to determine the effectof motivation, knowledge of accounting, and religiosity on the quality of graduatesinSTEI SEBI Islamic Accounting courses. The sources of data in this study is theprimary data obtained directly from responses to questionnaires of IslamicAccounting graduates tudy program. Data collection techniques is performed by thesurvey. Data obtained using a questionnaire administration to graduates of IslamicAccounting courses. The sample selection is done by using random sampling method.The data is analysed by the test of descriptive statistics, test of validity and reliability,the assumptions of classical test (test of normality, multicollinearity, andheteroskedastisity), as well as multiple linear hypothesis test. The results of theresearch shows that the partial motivation test is not significantly by the quality ofgraduates, while knowledge of accounting is significantly influence on religiosity at95% confidence level. Simultaneously, motivation, knowledge of accounting, andreligiosity significantly influence the quality of graduates. R Square value in thisstudy is 0.373, which means that 37.3% changes in the level of quality of graduatescan be explained by the variables of motivation, knowledge of accounting, andreligiosity, while the rest is influenced by other variables that are not observed in thisstudy. The results of this study are expected to contribute towards improving thequality of graduates of Islamic Accounting courses as a consideration in determiningthe necessary policies.
Faktor-faktor yang berhubungan dengan kejadian unintended pregnancy pada remaja

Abstract: This research aims to study factors related to the eventunintended pregnancy adolescent. The analysis used is the chi squaretest. Results of research on the variable level of knowledge about theunintended pregnancy in the case group and 46.7% less category 66.7%of control group both categories. In variable teenage promiscuity themajority of the cases 86.7% less category, 40% of the control groupboth categories. In variable parenting parents, the majority of the cases66.7% less category, 73.3% of the control group both categories. Inthe variable of mass media, the majority of the cases 14% less diversecategories, 33.3% of control group is very diverse categories. There isa relationship between the level of knowledge, relationships teens,parenting parents, and the media with a value of Ã± value (0.000) &lt;Ã¡(0.05).
Finite Element Modelling

The work described in this paper represents an effort to demonstrate the validity of the hybrid approach to the analysis of structural deformation. Mathematical modelling was incorporated into the process of reducing data from a digital correlation analysis of a speckled surface. The experimental data was collected both by directly imaging the surface onto a digitizing vidicon system and by transmitting the image through a coherent optical fiber bundle. Considerable savings of time and resources can be realized through the hybrid approach and the strengths of the theoretical and experimental procedures complement each other beautifully. The final results agreed within a few percent of each other and with values obtained by another independent method (high frequency moire), demonstrating the accuracy of the procedure. Hybrid approach The idea of a hybrid approach to structural analysis is not new(1,2,3,4). Although a long history of development can be traced for engineering studies based on mathematical models (finite-difference equations, boundary value integrals, finite element models) and on experimentally obtained data (gaging, optical or acoustical metrology), each method has well known limitations. Mathematical studies depend on the correspondence between some abstract model undergoing specified affects and a real structure subjected to a complex of interacting forces. A detailed model quickly becomes very large and mathematically complex, placing large burdens on the computational and financial resources of the designer. Also, the validity of the results depends on how well boundary conditions have been incorporated into the model. On the other hand, direct experimental methods of analysis often depend on only a few data values, measured at isolated points on the structure. The obtaining of this data for certain critical regions may be difficult or dangerous (complex structures, inaccessible locations, hazardous environments). In such cases, the surface can be illuminated by an incoherent fiber optic bundle and the image transmitted back through a coherent fiber optic bundle to a remote analyzing system. The hybrid method is an attempt to take advantage of the strengths of these two approaches, while minimizing their weaknesses. Basically, the idea is to drastically reduce the finite element mesh required and to incorporate real measured values instead of generalized boundary conditions. Speckle metrology
Contributions Of Cognitive Engineering Methods To Engineering Education

socio-technical system for design and evaluation.It is the
Managing and Visualizing Unstructured Big Data

One of the most common terms that is used in a significant amount of popular and scholarly discussion is big data. As pointed out earlier, the term has a dubious history and different people claim ownership. What remains true of the notion of big data is that it exists. With the increasing rate at which institutions and individuals are digitizing many different kinds of information the amount of data can only go up in volume. Therefore, big data has become an object of analysis for a variety of groups, from academics to marketers, all of whom are interested in understanding how big data could provide highly granular information about people.
Probabilistic and More General Uncertainty-Based (e.g., Fuzzy) Approaches to Crisp Clustering Explain the Empirical Success of the K-Sets Algorithm

Recently, a new empirically successful algorithm was proposed for crisp clustering: the K-sets algorithm. In this paper, we show that a natural uncertainty-based formalization of what is clustering automatically leads to the mathematical ideas and definitions behind this algorithm. Thus, we provide an explanation for this algorithm’s empirical success.
Simulated Despondency for Robots in Distress

It is widely accepted that increased human interaction with natural systems is responsible for complex environmental issues, with most current thinking centered on the provision of advanced technological solutions. One response emerging from current bioinspired robotics research proposes artificial neural networks (ANNs) enhanced with artificial hormones for increased performance and efficiency. Here the authors discuss their artistic project concept, developed in collaboration with a bioinspired artificial life lab, considering the affordance of emotional robotics to develop despondency in the field.