Miracle-dz/UniFAR-FTUs huggingface dataset:
Particle Filter Inference based on Activities for Overlapping Community Models

Various kinds of data such as social media can be represented as a network or graph. Latent variable models using Bayesian statistical inference are powerful tools to represent such networks. One such latent variable network model is a Mixed Membership Stochastic Blockmodel (MMSB), which can discover overlapping communities in a network and has high predictive power. Previous inference methods estimate the latent variables and unknown parameters of the MMSB on the basis of the whole observed network. Therefore, dynamic changes in network structure over time are hard to track. Thus, we present a particle filter based on node activities with various term lengths for online sequential estimation of the MMSB. For instance, in an e-mail communication network, each particle only considers e-mail accounts that sent or received a message within a specific term length, where the length may be different from those of other particles. We show through experiments that our proposed methods achieve both high prediction performance and computational efficiency.
Cost-Driven Optimization of Complex Service-Based Workflows for Stochastic QoS Parameters

The challenge of optimally selecting services from a set of functionally appropriate ones under Quality of Service (QoS) constraints -- the Service Selection Problem -- has been extensively addressed in the literature based on deterministic parameters. In practice, however, Quality of Service QoS parameters rather follow a stochastic distribution. In the work at hand, we present an integrated approach which addresses the Service Selection Problem for complex workflows in conjunction with stochastic Quality of Service parameters. Accounting for penalty cost which accrue due to Quality of Service violations, our approach reduces the impact of stochastic QoS behavior on total cost significantly.
Finiteness Conditions and Structural Construction of Automata for All Process Algebras

Finite automata are the basis of many verification methods and tools for process algebras. It is however undecidable in most process algebras whether the semantics of a given term is finite. We give sufficient finiteness conditions derived from the analysis of the operational rules of the algebra operators. From these rules we also generate the functions that compute automata from terms of the algebra. These constructions allow one to use our verification tools for programs written in many process algebras.
A discourse analysis of post-World War Two outdoor education practice in New Zealand

The focus of this study is to understand current outdoor education practice in New Zealand, using the methodology of Foucauldian discourse analysis. The French philosopher Michel Foucault reasoned that what appears ‘normal’ in society, is actually generated by ‘normalising practices’ (Zink and Burrows, 2006). He developed ways of looking at the world and at history, that help reveal these ‘normalising practices’. Several of these ways were useful in the analysis of this research, especially the concept of discontinuity, and the relationship between knowledge and power. Data was collected by examining two historically separate tragedies (called Events in the research), in 1953 and 2008. The discourses emerging from the two Events confirmed there had been a change in ‘normal’ practice, from an ‘apprenticeship’ style of training, with little paperwork, to a ‘systems’ approach with a focus on audit trails. Interviews with six outdoor educators confirmed that the shifting discourse reflected related shifts in practice. These educators had very long lengths of service that enabled them to reflect on any historical changes. The interviews confirmed that the practice of the past emphasised experience, judgement and mentoring and the practice of the present is dominated by paperwork relating primarily to risk management. The interviews provided the clue as to when the practice changed – the era from the mid 1980’s to early 1990’s. An examination of the wider literature of that time revealed the discontinuity – the thing or event that pushed knowledge in a different direction. In this particular case, that discontinuity was the election of a government that pursued a neoliberal economic ideology. Current practice therefore has its roots in the ideology of neo-liberalism. This ideology has become hegemonic – invisibly dominant. Its tenets of market forces, managerialism, and Principal-Agency theory can be seen in the language, the paperwork, the use of contractors, the existence of pre-employment courses, the skill sets and experience levels of new practitioners, and the compartmentalisation of knowledge. Foucauldian discourse analysis of the data also revealed three invisible, yet dominant, power / knowledge relationships in current practice. In the first power /knowledge
Redox regulation of apoptosis: Impact of thiol oxidation status on mitochondrial function

The probability that a cell will undergo apoptosis is in part dictated by the cellular redox potential, which is mainly determined by the reduction and oxidation of thiol residues on glutathione and proteins. We and others have recently shown that mitochondria play a critical role in the apoptotic cascade. Here, we address the question as to whether thiol modification regulates apoptosis and in which cellular compartment apoptosis‐regulatory thiols are localized. To resolve this problem, we employed the divalent thiol‐reactive agent diamide, which causes thiol cross‐linking and thus mimics disulfide bridge formation, and a panel of monovalent thiol‐reactive compounds (which impede disulfide bridge formation due to thiol oxidation), one of which is specifically targeted to the mitochondrial matrix. Our data indicate that thymocyte apoptosis induced by diamide mimics natural apoptosis in the sense that mitochondrial transmembrane potential (ΔΨm) disruption precedes nuclear chromatin degradation: that monovalent thiol‐reactive compounds inhibit apoptosis induced by diamide, glucocorticoids, irradiation, and topoisomerase inhibition; that the critical thiols determining cell fate after exposure to diamide, glucocorticoids, or DNA damage are likely to be located in the mitochondrial matrix; and that thiol oxidation and reduction are critical for apoptosis induction by some stimuli (glucocorticoids. DNA damage), but not by Fas/CD95 cross‐linking. Taken together, these findings suggest that, at least in some pathways of apoptosis, mitochondrial thiols constitute a critical sensor of the cellular redox potential.
A new SST topology comprising boost three-level AC/DC converters for applications in electric power distribution systems

Interest in integrating distributed generation into existing power distribution systems and achieving a more efficient, modern, reliable and versatile grid is leading to more research efforts on developing grid-interfaced converters such as the solid-state transformer. As current and voltage ratings of commercially available power semiconductor devices are normally below power ratings required in distribution systems (e.g., 13.8 kV), multiple modules must be connected in cascade configuration at the high-voltage side to reach higher voltage ratings as well as in parallel on the low-voltage side of to achieve high current levels. A new SST topology consisting of modular boost-based three-level AC-DC converters, high- or medium-frequency transformers with two secondary windings and four-leg ac-dc converters is presented in this paper. This topology when compared to similar approaches has fewer power conversion stages, lower voltage applied across the semiconductor devices on the high-voltage side (which reduces the number of modules), and lower current flowing through each device on the low-voltage side, leading to fewer parallel-connected devices. The feasibility of the proposed topology is experimentally validated on a 2 kW, 120 Vpk/ 10 Vdc scaled-down prototype.
Scalable and Interactive Graph Clustering Algorithm on Multicore CPUs

The structural graph clustering algorithm SCAN is a fundamental technique for managing and analyzing graph data. However, its high runtime remains a computational bottleneck, which limits its applicability. In this paper, we propose a novel interactive approach for tackling this problem on multicore CPUs. Our algorithm, called anySCAN, iteratively processes vertices in blocks. The acquired results are merged into an underlying cluster structures consisting of the so-called supernodes for building clusters. During its runtime, anySCAN can be suppressed for examining intermediate results and resumed for finding better result at arbitrary time points, making it an anytime algorithm which is capable to deal with very large graphs in an interactive way and under arbitrary time constraints. Moreover, its block processing scheme allows the design of a scalable parallel algorithm on shared memory architectures such as multicore CPUs for further speeding up the algorithm at each iteration. Consequently, anySCAN uniquely is an interactive and parallel algorithm at the same time. Experiments are conducted on very large real graph datasets for demonstrating the performance of anySCAN. It acquires very good approximate results early, leading to orders of magnitude speedup factor compared to SCAN and its variants. Using 16 threads, the acquired speed up factors are up to 13.5 times over its sequential version.
Compressed collections for simulated crawling

Collections are a fundamental tool for reproducible evaluation of information retrieval techniques. We describe a new method for distributing the document lengths and term counts (a.k.a. within-document frequencies) of a web snapshot in a highly compressed and nonetheless quickly accessible form. Our main application is reproducibility of the behaviour of focused crawlers: by coupling our collection with the corresponding web graph compressed with WebGraph [3] we make it possible to apply text-based machine learning tools to the collection, while keeping the data set footprint small. We describe a collection based on a crawl of 100 Mpages of the . uk domain, publicly available in bundle with a Java open-source implementation of our techniques.
Analysis of repeated events

Events that may occur repeatedly for individual subjects are of interest in many medical studies. We review methods of analysis for repeated events, emphasizing that the approach taken in a given study should allow clinical questions to be addressed as directly as possible. Methods based on full models for event processes as well as on simpler ‘marginal’ assumptions are considered. The treatment of dependent terminating events related to the recurrent events is also discussed. We apply various methods of analysis to studies involving pulmonary exacerbations in persons with cystic fibrosis, and the occurrence of bone metastases and skeletal events in cancer patients, respectively. Most of the methodology considered can be implemented with existing software.
Selection Of Independent Binary Features Using Probabilities: An Example From Veterinary Medicine

Supervised classification into c mutually exclusive classes based on n binary features is considered. The only information available is an n×c table with probabilities. Knowing that the best d features are not the d best, simulations were run for 4 feature selection methods and an application to diagnosing BSE in cattle and Scrapie in sheep is presented.
Imminent adopters of electronic health records in ambulatory care.

BACKGROUND Although evidence suggests electronic health records (EHRs) can improve quality and efficiency, provider adoption rates in the US ambulatory setting are relatively low. Prior studies have identified factors correlated with EHR use, but less is known about characteristics of physicians on the verge of adoption. OBJECTIVE To compare characteristics of physicians who are imminent adopters of EHRs with EHR users and non-users. DESIGN AND PARTICIPANTS A survey was mailed (June - November 2005) to a stratified random sample of all medical practices in Massachusetts. One physician from each practice (n=1884) was randomly selected to participate. Overall, 1345 physicians (71.4%) responded to the survey, with 1082 eligible for analysis due to exclusion criteria. 'Imminent adopters' were those planning to adopt EHRs within 12 months. MEASUREMENTS We assessed physician and practice characteristics, availability of technology, barriers to adoption or expansion of health information technology (HIT), computer proficiency, and financial considerations. RESULTS Compared to non-users, imminent adopters were younger, more experienced with technology, and more often in practices engaged in quality improvement. More imminent adopters owned or partly owned their practices (57.4%) than users (33.5%; p<0.001), but fewer imminent adopters owned their practices than non-users (65.7%; p<0.001). Additionally, more imminent adopters (26.0%) reported personal financial incentives for HIT use than users (14.8%; p<0.001) and non-users (10.8%; p<0.001). CONCLUSIONS Imminent adopters of EHRs differed from users and non-users. Financial considerations appear to play a major role in adoption decisions. Knowledge of these differences may assist policy-makers and healthcare leaders as they work to increase EHR adoption rates.
MCU at NTCIR: Chinese Fact Validation via SVM Cotext Ranking

Validate factoid description in text is the subtask of finding the textual entailment relation between the given hypothesis and unlabeled raw corpus. By means of integrating multiple natural language processing units, higher performance could be reasonably achieved. In this paper, we propose a context ranking model-based and trainable framework under the condition of part-of-speech tagging information is available. We first revise in-house word segmentation method via auto-deriving thesaurus from Wiki. Then a language-model-based passage retriever is used to find the initial retrieval result. The context ranking model is then extracting features and re-ranks the result. The official results indicate the effectiveness of our method. In terms of accuracy, our method achieves 39.27% for Traditional Chinese FV task (second place).
A pilot study for Augmented Reality supported procedure guidance to operate payload racks on-board the International Space Station

We present our current state in developing and testing of Augmented Reality supported spaceflight procedures for intra-vehicular payload activities. Our vision is to support the ground team and the flight crew to author and operate easily AR guidelines without programming and AR knowledge. For visualization of the procedural instructions using an HMD, 3D registered visual aids are overlaid onto the payload model operated by additional voice control. Embedded informational resources (e.g., images and videos) are provided through a mobile tangible user interface. In a pilot study that was performed at the ESA European Astronaut Centre by application domain experts, we evaluated the performance, workload and acceptance by comparing our AR system with the conventional method of displaying PDF documents of the procedure.
The effects of music and video on perceived exertion and performance of a cycling task at vigorous intensity

Physical activity can benefit all individuals by increasing their physical, mental, and emotional health. Therefore, finding ways to increase physical activity is a popular area of research. Researchers have found that using dissociative attentional strategies is effective in increasing physical activity. According to research, when administered individually music and video (dissociative strategies) are both effective in decreasing an individual's perceived exertion and increasing performance at low to moderate intensity activity. However, at vigorous intensities the results are less consistent. The potential effects of music and video presented simultaneously have not been compared to the effects of either in isolation. The purpose of this study was to determine if there was a difference in attentional focus and perceived exertion during vigorous intensity exercise as a function of being exposed to music, video, both (music and video), or nothing. Results showed that at vigorous intensity, participants in the condition that received both music and video perceived significantly lower exertion (RPE) and has a significantly more dissociative focus than did participants in the other three conditions. There was no significant difference amongst the other conditions. It was concluded that even at vigorous intensity exercise, a participant could use music and video to enhance their use of dissociative strategies and to perceive less exertion. The result of perceiving less exertion could lead to increased exercise adherence, which could have important implications for public health.
On the SPOT: Question Answering over Temporally Enhanced Structured Data

Natural-language question answering is a convenient way for humans to discover relevant information in structured Web data such as knowledge bases or Linked Open Data sources. This paper focuses on data with a temporal dimension, and discusses the problem of mapping natural-language questions into extended SPARQL queries over RDF-structured data. We specifically address the issue of disambiguating temporal phrases in the question into temporal entities like dates and named events, and temporal predicates. For the situation where the data has only partial coverage of the time dimension but is augmented with textual descriptions of entities and facts, we also discuss how to generate queries that combine structured search with keyword conditions.
Distributed genetic evolution in WSN

Wireless Sensor Actuator Networks (WSANs) extend wireless sensor networks through actuation capability. Designing robust logic for WSANs however is challenging since nodes can affect their environment which is already inherently complex and dynamic. Fixed (offline) logic does not have the ability to adapt to significant environmental changes and can fail under changed conditions. To address this challenge, we present In situ Distributed Genetic Programming (IDGP) as a framework for evolving logic post-deployment (online) and implement this framework on a physically deployed WSAN. To demonstrate the features of the framework including individual, cooperative and heterogeneous evolution, we apply it to two simple optimisation problems requiring sensing, communications and actuation. The experiments confirm that IDGP can evolve code to achieve a system wide objective function and is resilient to unexpected environmental changes.
A Software Tool for Faster Development of Complex Models of Musculoskeletal Systems and Sensorimotor Controllers in Simulink TM

Computer models of the neuromusculoskeletal systems can be used to study different aspects of movement and its control in humans and animals. SIMM with Dynamics Pipeline (Musculographics Inc., Chicago) and SD-Fast (Symbolic Dynamics Inc., Mountain View, CA) are software packages commonly used for graphic and dynamic simulation of movement in musculoskeletal systems. Building dynamic models with SIMM requires substantial C programming, however, which limits its use. We have developed Musculoskeletal Modeling in Simulink (MMS) software to convert the SIMM musculoskeletal and kinetics models to Simulink (Mathworks Inc., Natick, MA) blocks. In addition, MMS removes SIMM’s run-time constraints so that the resulting blocks can be used in simulations of closed-loop sensorimotor control systems.
Mode Selection for Device-To-Device Communication Underlaying an LTE-Advanced Network

Device-to-Device communication underlaying a cellular network enables local services with limited interference to the cellular network. In this paper we study the optimal selection of possible resource sharing modes with the cellular network in a single cell. Based on the learning from the single cell studies we propose a mode selection procedure for a multi-cell environment. Our evaluation results of the proposed procedure show that it enables a much more reliable device-to-device communication with limited interference to the cellular network compared to simpler mode selection procedures. A well performing and practical mode selection is critical to enable the adoption of underlay device-to-device communication in cellular networks.
Synchronous multipoint E-learning realized on an intelligent software-router platform over unicast networks: Design and performance issues

E-learning is a general term used to refer to computer-enhanced learning. The most notable advantages of e-learning are flexibility, convenience and the ability to work at our own pace. Other advantages include the ability to communicate with fellow classmates from around the country. However, a common problem of usual e-learning approach is the lack of face-to-face interaction with a teacher. The most obvious solution to overcome this problem is to include a real-time multipoint audio-video channel to provide synchronous communications between teacher and students. This paper starts from an implementation experience of an e-learning platform for synchronous interaction between the actors, keeping "low-cost" as the main requirement of the deployment, to give the possibility to any user to participate at home using a domestic Internet access and a base-level personal computer. This paper proposes a software-based platform to provide multipoint communications in a heterogeneous unicast/multicast environment. It is constituted by a number of S-MCUs (software MCUs). Each S-MCU runs on a low-cost computer and is implemented like a software router by using click. Design, implementation and performance issues are discussed by a comparative numerical analysis.
Anytime Classification Using the Nearest Neighbor Algorithm with Applications to Stream Mining

For many real world problems we must perform classification under widely varying amounts of computational resources. For example, if asked to classify an instance taken from a bursty stream, we may have from milliseconds to minutes to return a class prediction. For such problems an anytime algorithm may be especially useful. In this work we show how we can convert the ubiquitous nearest neighbor classifier into an anytime algorithm that can produce an instant classification, or if given the luxury of additional time, can utilize the extra time to increase classification accuracy. We demonstrate the utility of our approach with a comprehensive set of experiments on data from diverse domains.