Miracle-dz/UniFAR-FTUs huggingface dataset:
How do cultural activities influence happiness? Investigating the relationship between self-reported well-being and leisure

Leisure is a human condition related to the discretionary use of time in a rewarding way that contributes to individual well-being. Individuals decide to engage on cultural activities to enjoy pleasant experiences. Cultural participation can be explained in different dimension, considering how the individual access those cultural experiences: by live attendance, by home consumption, by community practice. We find that the magnitude of the positive effect of cultural activities on the top happiness goes in this descending order: cinema going, participat- ing in cultural events,listening to music,reading books and watching TV.
Slacking Off in Comfort: A Dual-Pathway Model for Psychological Safety Climate

Research on psychological safety climate has primarily focused on its salutary effects on group risk-taking behaviors. We developed a group-level dual-pathway model in which psychological safety climate also exerts a simultaneous negative effect on risk-taking behaviors by diminishing group average work motivation. In a field survey, we found that psychological safety climate was positively related to group learning behavior and voice through a reduction in group average fear of failure but negatively related to them through a reduction in group average work motivation. This dual-pathway model and its mechanisms were conceptually replicated in a laboratory experiment with group creativity as a different risk-taking behavior. In this experiment, we examined the moderating effects of group individualism/collectivism and found that psychological safety climate increased the originality and flexibility dimensions of group creativity through a reduction in group average fear of failure only in groups with a collectivistic orientation and reduced the fluency dimension of and time spent on creativity through a reduction in group average work motivation only in individualistic groups.
Review of spectral imaging technology in biomedical engineering: achievements and challenges

Abstract. Spectral imaging is a technology that integrates conventional imaging and spectroscopy to get both spatial and spectral information from an object. Although this technology was originally developed for remote sensing, it has been extended to the biomedical engineering field as a powerful analytical tool for biological and biomedical research. This review introduces the basics of spectral imaging, imaging methods, current equipment, and recent advances in biomedical applications. The performance and analytical capabilities of spectral imaging systems for biological and biomedical imaging are discussed. In particular, the current achievements and limitations of this technology in biomedical engineering are presented. The benefits and development trends of biomedical spectral imaging are highlighted to provide the reader with an insight into the current technological advances and its potential for biomedical research.
Stochastic pronunciation modelling for spoken term detection

A major challenge faced by a spoken term detection (STD) system is the detection of out-of-vocabulary (OOV) terms. Although a subword-based STD system is able to detect OOV terms, performance reduction is always observed compared to in-vocabulary terms. Current approaches to STD do not acknowledge the particular properties of OOV terms, such as pronunciation uncertainty. In this paper, we use a stochastic pronunciation model to deal with the uncertain pronunciations of OOV terms. By considering all possible term pronunciations, predicted by a joint-multigram model, we observe a significant performance improvement.
Integration of Uncertainty in the Analysis of Dyadic Human Activities

Action analysis from video data has been attract-ing more and more attention in computer vision over the pastdecade. The main focus has been classifying videos into oneof k action classes from fully observed videos. However, allof the time, intelligent systems are enforced to make decisionsunder uncertainty and based on incomplete information. Thisneed motivates us to introduce the problem of analyzing theuncertainty associated with dyadic human activities and moveto a new level of generality in the action analysis problem. Analyzing the uncertainty, here, refers to categorizing thelikelihood of activities in trimmed video clips called time-slices which are extracted from the full video. To this intent, we exploit the state-of-the-art methods to extract interestpoints in time-slices and represent them. We also present anaccumulative uncertainty to depict the uncertainty associatedwith partially observed videos for the task of early activityrecognition. The experiments demonstrate the effectiveness ofour framework in analyzing dyadic activities under uncertaintyand in evaluating the performance of early activity recognition methods.
Design activism as a new method for inquiring into mixed emotions in uncomfortable social interaction

political cause, notably the globally growing dissatisfaction with neo-liberalism’s greed for growth and consumption, the resulting financial and economical crises and the negative impact on our environment (Fuad-Luke, 2009; Julier, 2013; Thorpe, 2012). However, at the micro-political level, we rarely meet conflicts that are determined by an overtly political disagreement. As a matter of fact, it can be fairly difficult in the first place to see that there even is a conflict. Politics, here, is a matter of how power is subtly interwoven into the way in which individuals, in their everyday life, are constrained by systems of authority and control. Therefore, the central questions are: How is people’s ability to act, feel and communicate regulated by these systems of power? How do these systems configure roles of identity, and determine who has the power to speak, move and work. Systems of power can be found in the form of public institutions and services (healthcare, education, work) or they can be materialized into physical form as when public furniture is designed to prevent certain societal groups to use them (e.g. homeless and alcoholics). To fathom how designers can help vulnerable groups to escape the limitations and Abstract Over the last couple of years, design activism has attracted increasing attention among designers and design researchers, but the approach seems not to have found it’s way yet to the Design & Emotion research community. In this paper, we introduce design activism as a new approach valuable for probing into the role of mixed emotions in interpersonal interaction. To set the scene, we critically examine recent sources on design activism in order provide a general characterisation of what constitute the approach. Next, we argue for how social game can be seen as an instantiation of design activism used, in a current research project, to inquiry into how teenagers feel about visiting their incarcerated fathers in top secure prisons. Finally, we relate our findings to discourse on ambiguous emotions and use this to visualize the eliciting pattern of ambiguous emotions.
UvA-DARE ( Digital Academic Repository ) What can we learn from Plausible Values ?

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Stable complex formation of CENP-B with the CENP-A nucleosome

CENP-A and CENP-B are major components of centromeric chromatin. CENP-A is the histone H3 variant, which forms the centromere-specific nucleosome. CENP-B specifically binds to the CENP-B box DNA sequence on the centromere-specific repetitive DNA. In the present study, we found that the CENP-A nucleosome more stably retains human CENP-B than the H3.1 nucleosome in vitro. Specifically, CENP-B forms a stable complex with the CENP-A nucleosome, when the CENP-B box sequence is located at the proximal edge of the nucleosome. Surprisingly, the CENP-B binding was weaker when the CENP-B box sequence was located in the distal linker region of the nucleosome. This difference in CENP-B binding, depending on the CENP-B box location, was not observed with the H3.1 nucleosome. Consistently, we found that the DNA-binding domain of CENP-B specifically interacted with the CENP-A-H4 complex, but not with the H3.1-H4 complex, in vitro. These results suggested that CENP-B forms a more stable complex with the CENP-A nucleosome through specific interactions with CENP-A, if the CENP-B box is located proximal to the CENP-A nucleosome. Our in vivo assay also revealed that CENP-B binding in the vicinity of the CENP-A nucleosome substantially stabilizes the CENP-A nucleosome on alphoid DNA in human cells.
Study on fault-diagnosis models of different neural networks and ensemble

Different diagnosis models, including multiplayer perceptron (MLP), radial basis function (RBF) and two types of support vector machines (SVMs), were designed, analyzed and compared based on the fault diagnosis of an analogue circuit instance. The experimental results show SVM model is of higher classification rate than MLP and RBF models, while MLP model has better ability to deal with uncertain signals. Considering different models correspond to different strategies, we combine four models of MLP, RBF and two SVMs to combine a diagnosis ensemble, which can achieve more accurate results than any individual model in the ensemble. The ensemble technique can provide a theoretical basis for further study on the fault diagnosis of analogue circuits.
Venture effect in wind mill and maximum power point tracking in hybrid Micro-Grid in Indian scenario

This paper presents the optimum utilization of renewable energy sources existing in India. In renewable energy sources wind mill and photovoltaic cell plays an important role in India. The available wind velocity and solar radiation and climate plays important role in Indian power sector. Increasing demand of electricity it is very important to develop micro-grids to diversify the generation. So to solve this problem and optimization of wind and solar power are necessary. To optimize power in wind mill venture tube is designed to produce more power by extracting power from the wind and maximum power point tracking (MPPT) i.e. Perturb and Observe (P & O) technique is utilized to extract more power from PV cells. Venture effect in wind mill and MPPT technique is used to form DC Micro-grid and implemented in MATLAB as well as hardware results are tested. Results show the better operating performance of micro-Grid in stability issue and optimum power extraction from the wind mill and solar cell.
Towards programmable and scalable IoT infrastructures for smart cities

Smart Cities applications and infrastructures are actively being developed and rolled out. However, maintenance complexity is significant, often limiting deployments to small regions or small cities. To support gradual or spontaneous infrastructure scaling at region or national levels, infrastructure management that monitors end device connectivity and ensures overall IoT communication reliability becomes key. This article describes a method using intercontinental research facilities that programmatically manages smart devices and their communication with the ultimate aim to elastically deploy IoT servers in the cloud. Implementation details and experimental results of real devices are included.
Towards a Model-Driven Dynamic Architecture Reconfiguration Process for Cloud Services Integration

Cloud computing is a paradigm that is transforming the computing industry and is receiving more attention from the research community. The incremental deployment of cloud services is particularly important in agile development of cloud services, where successive cloud service increments must be integrated into existing cloud service architectures. This requires dynamic reconfiguration of software architectures, especially in cloud environments where services cannot be stopped in order to apply reconfiguration changes. This paper presents a model-driven dynamic architecture reconfiguration process to support the integration of cloud services. Models are used to represent high-level architecture reconfiguration operations as well as adaptation patterns. Adaptation patterns allow us to describe reconfiguration operations independently of a specific cloud platform technology. On the other hand, model transformations are used: i) to support compatibility checking of increments; ii) to generate software adaptors that solve incompatibilities between architectures; and iii) to generate reconfiguration plans specific of cloud provider, that include reconfiguration actions to be applied on cloud service instances at runtime. The proposed process is illustrated with a dealer network system development example, where cloud services are deployed in an incremental way.
Open Innovation and Public Sector Business Process Management - A Multi-Method Study

Open Process Innovation provides a framework for studying how to systematically make use of knowledge that lies outside of an organization’s boundaries for process innovation initiatives. Here, we seek to investigate into variables that impact on the qualities of Open Process Innovation taking the example of the public sector domain. This paper examines how a lack of resources impacts on BPM. Specifically in investigates how personnel resource scarcity exerts influence on the involvement of i) customers (here citizens and local companies) and ii) consultants (here management and software consultants) in public sector BPM. Our multi-method analysis shows that personnel resource scarcity has consequences for BPM-related collaboration schema as it restricts the involvement of customers. Based on our findings, implications for theory and practice are discussed, including implications for studies on BPM maturity or on business process design. We call for a governancetheory perspective on process innovation as a fundamental basis for understanding and designing the institutions that shape collaboration in open process innovation.
Distributed data mining based on actors for Internet of Things

The paper discusses an approach for distributed execution of data mining algorithms based on the actors model and the concept of the Internet of Things. The suggested approach allows us to decompose data mining algorithms into actors and execute them in the distributed environment. It provides data analysis both in centralized systems (cloud computing) and in distributed systems (fog computing) for IoT.
Modeling variations in load intensity over time

Today's software systems are expected to deliver reliable performance under highly variable load intensities while at the same time making efficient use of dynamically allocated resources. Conventional benchmarking frameworks provide limited support for emulating such highly variable and dynamic load profiles and workload scenarios. Industrial benchmarks typically use workloads with constant or stepwise increasing load intensity, or they simply replay recorded workload traces. Based on this observation, we identify the need for means allowing flexible definition of load profiles and address this by introducing two meta-models at different abstraction levels. At the lower abstraction level, the Descartes Load Intensity Meta-Model (DLIM) offers a structured and accessible way of describing the load intensity over time by editing and combining mathematical functions. The High-Level Descartes Load Intensity Meta-Model (HLDLIM) allows the description of load variations using few defined parameters that characterize the seasonal patterns, trends, bursts and noise parts. We demonstrate that both meta-models are capable of capturing real-world load profiles with acceptable accuracy through comparison with a real life trace.
Hidden Softmax Sequence Model for Dialogue Structure Analysis

We propose a new unsupervised learning model, hidden softmax sequence model (HSSM), based on Boltzmann machine for dialogue structure analysis. The model employs three types of units in the hidden layer to discovery dialogue latent structures: softmax units which represent latent states of utterances; binary units which represent latent topics speciﬁed by dialogues; and a binary unit that represents the global general topic shared across the whole dialogue corpus. In addition, the model contains extra connections between adjacent hidden softmax units to formulate the dependency between latent states. Two different kinds of real world dialogue corpora, Twitter-Post and AirTicketBook-ing, are utilized for extensive comparing experiments, and the results illustrate that the proposed model outperforms sate-of-the-art popular approaches.
Effects of the Planar Galactic Tides and Stellar Mass on Comet Cloud Dynamics

We report the first results of a research program to explore the sensitivity of the orbits of Oort cloud comets to changes in the strength of the Galactic tides in the plane of the disk and also to changes in the mass of the host star. We performed 2D simulations that confirm that the effects of the tides on comet orbits are sensitive to a star's distance from the Galactic center. A comet cloud closer to the Galactic center than the Sun will have comet perihelia reduced to the region of the inner planets more effectively by the planar tides alone. Similar results are found for a star of smaller mass. We also show how this phenomenon of comet injection persists for a set of alternative Galactic potential models. These preliminary results suggest a fruitful line of research, one that aims to generalize the study of comet cloud dynamics to systems different from the Solar System. In particular, it will allow us to study the roles played by comet clouds in defining the boundaries of the Galactic Habitable Zone.
Modeling liaison in French by using decision trees

French is known to be a language with major pronunciation ir-regularities at word endings with consonants. Particularly, the well-known phonetic phenomenon called Liaison is one of the major issues for French phonetizers. Rule-based methods have been used to solve these issues. Yet, the current models still produce a great number of pronunciation errors to be used in 2nd language learning applications. In addition, the number of rules tends to be large and their interaction complex, mak-ing maintenance a problem. In order to try to alleviate such problems, we propose here an approach that, starting from a database (compiled from cases documented in the literature), allows us to build C4.5 decision trees and subsequently, automate the generation of the required rules. A prototype based on our approach has been tested against six other state-of-the-art phonetizers. The comparison shows the prototype system is better than most of them, being equivalent to the second-rank system.
THE DEVELOPMENT OF AN INSTRUMENT TO MEASURE ANIMATION PREDISPOSITION OF USERS OF ANIMATED AGENTS IN MS OFFICE

Even the most advanced users may experience anxiety, hesitation, and fear when they interact with computers (Seaward, 1998). People constantly have to upgrade their skills, learn new versions of software or totally new products, understand complex interfaces, and change their human-computer interaction (HCI) behavior. In order to address the challenging task of supporting users in their interactions with computer systems, software designers supply their software applications with help features. The goal of help systems is to provide users with important, useful, timely, and easy-to-understand information within the very specific context of a user’s lack of adeptness or familiarity with a particular task (Randall & Pedersen, 1998). Early help facilities represented paper documentation, books, reference guides, and tutorials. In the 1980s, software products began to have some sort of embedded software-based assistance marketed under the label of ‘online help’ (Bergman & Keene-Moore, 1985). Help authors aimed to improve a system’s usability, experimented with different ways to present information, and emphasized user-friendliness of help facilities. In the 1990s, action-sensitive, context-aware, artificial intelligence-based, and intelligent coaching help systems emerged (Breuker, 1998). The purpose of those help applications was to predict when a user needs assistance rather than passively waiting for a request. For example, the incorporation of context-sensitive tool tips has shown to be a good way to improve the usability of virtually any interfaces including Webpages. The implementation of clickable icons or ‘show me’ buttons does not require an individual to switch from one interface to another which reduces his or her cognitive load, improves productivity, and increases satisfaction with the system (Chalmers, 2003). For the past decade, researchers have begun to experiment with the incorporation of animated, human, or cartoon-like agents in the graphical user interface of help systems.
Resource optimization for speculative execution in a MapReduce Cluster

The MapReduce paradigm is now the de facto standard for large-scale data analytics. In this paper we address the resource management issues in MapReduce Cluster. Speculative execution (task backup) plays an important role in resource management. We propose two different strategies and build two models to formulate the backup issue as an optimization problem when the cluster is lightly loaded. Moreover, we present an Enhanced Speculative Execution (ESE) algorithm when the cluster is heavily loaded and adopt the approximate analysis to get an optimal value for the parameter in the algorithm. The simulation results show that the algorithm can reduce the job completion time by 50% while consuming much less resource compared to the naive method without backup.
A novel high adaptive fault tolerance model in real time cloud computing

Now-a-days, cloud computing is being used in a variety of fields, whether it is storage, computation, education etc. It has emerged from a larger number of technologies like utility computing, grid computing, cluster computing etc. It offers a number of advantages like on-demand access, resource pool, device independence etc. Also, it suffers from various cons like security, workflow management, fault tolerance. Here, a novel model (HAFTRC) has been proposed, which is providing high adaptive fault tolerance in real time cloud computing. The model is based on computing the reliabilities of the virtual machines on the basis of the cloudlets, mips, ram and bandwidth etc. Whosoever virtual machine has the highest reliability is chosen as the winning virtual machine. If at the end, there are two virtual machines, whose reliabilities comes out to be same, then the winning machine is chosen base on the priority that is assigned to them.