Miracle-dz/UniFAR-FTUs huggingface dataset:
Heat Transfer in Thermoelectric Materials and Devices

Solid-state thermoelectric devices are currently used in applications ranging from thermocouple sensors to power generators in space missions, to portable air-conditioners and refrigerators. With the ever-rising demand throughout the world for energy consumption and CO2 reduction, thermoelectric energy conversion has been receiving intensified attention as a potential candidate for waste-heat harvesting as well as for power generation from renewable sources. Efficient thermoelectric energy conversion critically depends on the performance of thermoelectric materials and devices. In this review, we discuss heat transfer in thermoelectric materials and devices, especially phonon engineering to reduce the lattice thermal conductivity of thermoelectric materials, which requires a fundamental understanding of nanoscale heat conduction physics.
Distinguishing successful students in mathematics - a comparison across European countries

This study explores factors distinguishing students with the optimal pattern of high mathematics achievement and their positive self-related mathematics beliefs across 17 European regions using PISA 2012 data. Hierarchical cluster analysis with variables mathematics anxiety, students' achievement and students' perception of mathematics efficacy, mathematics self-concept and intrinsic motivation was used for this purpose. This yielded 4 different student groups. The profile of successful mathematics students (low mathematics anxiety, high achievement and highly perceived mathematics efficacy, self-concept and intrinsic motivation) was among them. The results indicate differences in the factors relevant to students' success in mathematics depending on whether their country mathematics literacy and mathematics anxiety scores are above or below the OECD average. Students' socioeconomic and cultural background and experience with pure mathematics tasks are factors relevant across all examined countries.
The Radius and other fundamental parameters of the F9 v star β Virginis

We have used the Sydney University Stellar Interferometer (SUSI) to measure the angular diameter of the F9 V star β Virginis. After correcting for limb darkening and combining with the revised Hipparcos parallax, we derive a radius of 1.703 ± 0.022 R ⊙ (1.3 per cent). We have also calculated the bolometric flux from published meas urements which, combined with the angular diameter, implies an effective temperature of 6059 ± 49 K (0.8 per cent). We also derived the luminosity of β Vir to be L = 3.51 ± 0.08 L ⊙ (2.1 per cent). Solar-like oscillations were measured in this star by Carrier et al. (2005) an d using their value for the large frequency separation yields the mean stellar density with an uncertainty of about 2 per cent. Our constraints on the fundamental parameters of β Vir will be important to test theoretical models of this star and its oscillations.
Semantic Business Process Validation

The use of formal semantics for the support of Business Process Management is an emerging branch of research, with substantial economic potential. In particular, business processes modelled in graphical notations such as BPMN can be semantically annotated to specify more precisely what the individual tasks in the process will be responsible for. This raises the need for, and opens up the opportunity to apply, semantic validation techniques: techniques that take the annotations and the underlying ontology into account in order to determine whether the tasks are consistent with respect to each other, and with respect to the underlying workflow structure. To this end, we introduce a formalism for semantic business processes, which combines definitions from the workflow community with definitions from AI; we introduce some validation tasks that are of interest in this context. We then make first technical contributions towards solving this kind of problem. We identify a class of processes where the validation tasks can be solved in polynomial time, by propagating certain pieces of information through the process graphs. We show that this class of processes is maximal in the sense that, with more general semantic annotations, the validation tasks become computationally hard. We outline how the validation information gathered can serve to automatically suggest bug fixes.
Randomized Rounding Revisited with Applications

We develop new techniques for rounding packing integer programs using iterative randomized rounding. It is based on a novel application of multidimensional Brownian motion in R n . Let ∼ x ∈ [0,1] n be a fractional feasible solution of a packing constraint Ax ≤ 1, A ∈ {0,1} m×n that maximizes a linear objective function. The independent randomized rounding method of Raghavan-Thompson rounds each variable xi to 1 with probability ∼ xi and 0 otherwise. The expected value of the rounded objective function matches the fractional optimum and no constraint is violated by more than O( log m log log m ). In contrast, our algorithm iteratively transforms ∼
Four-injector variability modeling of FinFET predictive technology models

The usual way of modeling variability using threshold voltage shift and drain current amplification is becoming inaccurate as new sources of variability appear in sub-22nm devices. In this work we apply the four-injector approach for variability modeling to the simulation of SRAMs with predictive technology models from 20nm down to 7nm nodes. We show that the SRAMs, designed following ITRS roadmap, present stability metrics higher by at least 20 % compared to a classical variability modeling approach. Speed estimation is also pessimistic, whereas leakage is underestimated if sub-threshold slope and DIBL mismatch and their correlations with threshold voltage are not considered.
UCAS at TREC 2012 Microblog Track

Two search tasks, i.e. real-time adhoc and real-time lter- ing are addressed in this year's Microblog track. The participation of (Graduate) University of Chinese Academy of Sciences (UCAS) in this track aims to evaluate the eectiveness of the query-biased learning to rank model, which was proposed in our previous work. To futher im- prove the retrieval eectiveness of learning to rank, we construct the query-biased learning to rank framework by taking the dierence between queries into consideration. In particular, we learn a query-biased ranking model with a semi-supervised transductive learning algorithm so that the query-specic features, e.g. the unique expansion terms, are utilized to capture the characteristics of the target query. This query-biased ranking mode is combined with the general ranking model to produce the nal ranked list of tweets in response to the given target query. Experiments on the standard TREC Tweets11 collection show that the query-biased learning to rank approach outperforms strong baselines when evaluated under MAP, namely the conventional application of the state-of-the-art learning to rank algorithm.
'Being an autism parent' : mothers' experiences from initial concerns about their daughters to a diagnosis of autism spectrum disorder : an interpretative phenomenological analysis

This thesis consists of three parts: a major literature review, an empirical study and a critical appraisal. In Part One, the literature review begins with a brief introduction which outlines the importance of the topic, in particular the disparity in diagnosis rates for autism spectrum disorder (ASD) in girls compared to boys and possible causes and consequences. This is followed by an overview of the development of the diagnostic criteria for ASD and a critical exploration of a range of explanations for this disparity, suggesting some under-identification of symptoms in girls. This is followed by an overview of research into the experiences of girls and their parents during the diagnostic journey, including evidence for a later and longer diagnostic process, the prevalence of comorbid psychiatric diagnoses and evidence for the impact on mothers particularly. Finally, the literature review is summarised in order to provide the rationale for this thesis and to lead into the research questions. In Part Two, following a brief overview of the literature, the empirical study provides a detailed account of the research undertaken. This includes an outline of the methodology and method and presents an interpretative phenomenological analysis of semi-structured interviews with five mothers of daughters with diagnoses of ASD, exploring their diagnostic journeys in the broadest sense. This analysis is explored in relation to psychological literature, particularly systemic thinking, before a discussion of the implications for future research and for educational psychology practice. Part Three, the critical appraisal, consists of two parts: firstly, a critical account of the research process is given from inception through to completion including ontological, epistemological and ethical considerations as well as difficulties encountered; secondly, a more detailed discussion of the findings is offered in relation to psychological literature and the contribution to knowledge and educational psychology practice
Making sense of “ Syndicated Collaboration ”

This paper discusses the phenomenon of "syndicated collaboration", which is the collaborative use of weblogs, wikis and news aggregator tools, enabled by open standard protocols like Rich Site Summary (RSS). Existing development trends and the collaborative potential of RSS-based technologies are analysed. The widespread use of these technologies indicates a shift from traditional groupware technologies towards an environment of loosely-coupled tools connecting continuously evolving communities of interest. Based on these considerations and the results of a workplace study we introduce the concept of an “information hub”, which represents a generalised approach to syndicated collaboration. Nexus, a prototype application which implements key features of the information hub concept is presented. Preliminary feedback on the use of Nexus leads to design considerations for the further improvement of both the information-hub concept and the Nexus prototype.
Semantic context effects in forward and backward word translation by Korean learners of English

BACKGROUND Three interrelated aspects in the psycholinguistic study of second language acquisition are representation, acquisition, and processing (Jiang, 2000). According to Levelt (1989), any theory of second language acquisition is incomplete without a representation component because representation and processes cannot be studied independently of each other. In characterizing cognitive processes that support second language acquisition, one important question concerns how second language learners process words in their second languages. In other words, when a learner acquires a new language, what kinds of patterns of connections are created between words in the first and second language? Words in two languages may be connected in two different ways. English-Korean translation pairs, such as school and 학교, may be connected directly when these two words form an association in the process of learning L2. They may be also connected through shared conceptual representations because of similar semantic content.
Automated generation of enforcement mechanisms for semantically-rich security policies in Java-based multi-agent systems

Policies are being increasingly used for controlling the behavior of complex systems (including agent systems). The use of policies allows administrators to specify agent permissions and obligations without changing source code or requiring the consent or cooperation of the entities being governed. Past approaches to policy representation have been restrictive in many ways. By way of contrast, semantically-rich policy representations can reduce human error, simplify policy analysis, reduce policy conflicts, and facilitate interoperability. However, semantically-rich policies increase the complexity of fielding policy-governed multi-agent systems. This paper discusses some technical challenges to automatically enforce semantically-rich security policies in Java-based multi-agent systems and presents an engineering approach for addressing some of these challenges. We have developed a first implementation that allows to enforce OWL policies represented using the KAoS policy framework into multi-agent systems built on top of the JDK1.4. The proposed solution allows to control the behavior of agents at a high level of abstraction and exploits the security mechanisms provided by the Java authentication and authorization service (JAAS) to enforce OWL policies.
SEMANTIC DATA INTEGRATION IN A MULTIPLE REPRESENTATION ENVIRONMENT

Traditionally National Mapping Agencies (NMAs) produce maps at different scales. Since automatic generalisation is not yet possible (it will at least take years) NMAs have to maintain their data sets in a multiple representation environment. Important principles of this environment are 1) the data sets at the different scales are semantically integrated, 2) objects in the different scales representing the same real world objects are linked and 3) updates in the largest scale data set are automatically propagated to the smaller scales. This paper describes the considerations for semantic data integration of the topographic database of the Dutch NMA (TD Kadaster). In collaboration with ITC a generic data model is being designed in which the data models of the different scales are integrated. This model supports topographic data sets at a scale range from 1:10k to 1:500k. The results of this data model integration that were obtained so far are reported in this paper. The difficulties that are met are related to using existing data sets in the data integration process. For example the classifications of the different data sets (i.e. map legends) are not synchronised, since this was not needed when producing paper maps. Also the generalisation rules that are verbally described in cookbooks to support the interactive work of cartographers seem not to be in accordance with the classification schemas of the existing data sets. Apart for internal processes, semantic data integration is also required for the exchange of geo information with other geo information suppliers. This paper describes how semantic data integration of Dutch topographic data with the outside national world is assured by embedding the topographic data model in the Dutch standardised data model for geo information. The embedding is modelled using Unified Modelling Language. The model is limited in some aspects since GML is used as exchange standard. Therefore this paper considers the possibilities of a new technology in the semantic data integration process of different data sets: machine ontology using the Web Ontology Language (OWL).
Incipient fault diagnosis of dynamical systems using online approximators

Detection of incipient (slowly developing) faults is crucial in automated maintenance problems where early detection of worn equipment is required. In this paper, a general framework for model-based fault detection and diagnosis of a class of incipient faults is developed. The changes in the system dynamics due to the fault are modeled as nonlinear functions of the state and input variables, while the time profile of the failure is assumed to be exponentially developing. An automated fault diagnosis architecture using nonlinear online approximators with an adaptation scheme is designed and analyzed. A simulation example of a simple nonlinear mass-spring system is used to illustrate the results.
Towards a Psycho-Cognitive Recommender System

Current personalized recommendation approaches have reached a limit of effectiveness. By incorporating cognitive and behavioral knowledge, personalized recommender systems could be friendlier and more human-centric, which can potentially enhance user experience and loyalty. Our research proposes a psycho-cognitive method to recommend items based on users' emotion state and center of interest. Meanwhile, we propose a novel behavioral concept (i.e. "wandering status") and highlight its importance in online behavioral research.
Relaxed selection techniques for querying time-series graphs

Time-series graphs are often used to visualize phenomena that change over time. Common tasks include comparing values at different points in time and searching for specified patterns, either exact or approximate. However, tools that support time-series graphs typically separate query specification from the actual search process, allowing users to adapt the level of similarity only after specifying the pattern. We introduce relaxed selection techniques, in which users implicitly define a level of similarity that can vary across the search pattern, while creating a search query with a single-gesture interaction. Users sketch over part of the graph, establishing the level of similarity through either spatial deviations from the graph, or the speed at which they sketch (temporal deviations). In a user study, participants were significantly faster when using our temporally relaxed selection technique than when using traditional techniques. In addition, they achieved significantly higher precision and recall with our spatially relaxed selection technique compared to traditional techniques.
Consistency Models in Distributed Systems with Physical Clocks

Most existing distributed systems use logical clocks to order events in the implementation of various consistency models. Although logical clocks are straightforward to implement and maintain, they may affect the scalability, availability, and latency of the system when being used to totally order events in strong consistency models. They can also incur considerable overhead when being used to track and check the causal relationships among events in some weak consistency models. In this thesis we explore how to efficiently implement different consistency models using loosely synchronized physical clocks. Compared with logical clocks, physical clocks move forward at approximately the same speed and can be loosely synchronized with well-known standard protocols. Hence a group of physical clocks located at different servers can be used to order events in a distributed system at very low cost. We first describe Clock-SI, a fully distributed implementation of snapshot isolation for partitioned data stores. It uses the local physical clock at each partition to assign snapshot and commit timestamps to transactions. By avoiding a centralized service for timestamp management, Clock-SI improves the throughput, latency, and availability of the system. We then introduce Clock-RSM, which is a low-latency state machine replication protocol that provides linearizability. It totally orders state machine commands by assigning them physical timestamps obtained from the local replica. By eliminating the message step for command ordering in existing solutions, Clock-RSM reduces the latency of consistent geo-replication across multiple data centers. Finally, we present Orbe, which provides an efficient and scalable implementation of causal consistency for both partitioned and replicated data stores. Orbe builds an explicit total order, consistent with causality, among all operations using physical timestamps. It reduces the number of dependencies that have to be carried in update replication messages and checked on installation of replicated updates. As a result, Orbe improves the throughput of the system.
Activity Theory: a Framework to Software Requirements Elicitation

In this article we defend the idea that social aspects have strong influence in the software requirements elicitation (Goguen 1993), which drive us to find help in the social sciences. The Activity Theory is a theory developed in the Psychology that focuses the human practices of development process, both the individual and social levels. This theory states that any human action must be understood within a minimal social context, established by an activity. So, we have proposed an approach of software requirements elicitation that have as framework several precepts from the Activity Theory. The case study is developed to show the possibility of using some principles of the Activity Theory in the software requirements elicitation.
A distributed abnormal packet generation engine based on MapReduce

With the maturity of Internet and rapid development of the Mobile Internet, the network protocols drafted by IETF, 3GPP, 3GPP2 and other standard organizations grow massively, and at the same time attacks against the network protocols are also increasing rapidly. The Robustness test for the network protocols is becoming increasingly important, since the protocols and applications will be absolutely safe if the protocols have been tested according to the requirements of the Robustness Test. But Robustness Test is different from Conformance Test, Interoperability Test and Performance Test in its requirement of a huge number of test cases, and the number of test cases is more than 2Λ 320 for a packet with 40-Byte protocol header, thus, it's too difficult to generate all the test cases for the serial algorithm. In this paper a parallel algorithm based on MapReduce is proposed, where an original input packet is divided into different fields and the Map function processes on each single field and the Reduce function reassembles the processed fields to a new completed packet. Finally, experiment results show the parallel algorithm is far better than the serial algorithm when generating large data set. In addition, the scalability of the cluster is also verified.
An efficient algorithm for registration of pre- and intra- operative brain MRI images to correct intensity inhomogeneity

Spatially-varying intensity inhomogeneity is a severe artifact, which occurs in intra-operative MR images of the brain. This artifact causes implications in the accuracy of image guided navigation systems being performed during neurosurgery procedures. Therefore, it is highly desirable to correct intensity inhomogeneity along with registration process to achieve an efficient and adaptive method to the image guided surgery constraints. In this paper, a modified Residual Complexity similarity measure is used to correct this artifact with the registration of pre- and intra-operative images. The results show that this similarity measure outperforms some well-known similarity measures such as NMI and SSD in registration of pre- and intra-operative images of the brain, by 31.5%.
DETECTION OF LEAKAGE SOUND BY USING MODULAR NEURAL NETWORKS

: It is important to detect the leakage of the gas to be flammable or poisonous from the cracks in pipes of petroleum refining plants or chemical plants. We examined the application of modular neural networks to the acoustic diagnosis technique for the leakage sound. The modular neural network has the ability to adapt its structure according to the environment. Experiments were performed for an artificial gas leakage device with various experimental conditions to imitate the change of environment for a long term. The discrimination accuracy with the proposed network was observed to be about 93%, which was better than 83% with the simple network. From the results, we confirmed the effectiveness for the application of the modular neural network to the detection of the leakage sound for the practical use.