Miracle-dz/UniFAR-FTUs huggingface dataset:
Active Discriminative Word Embedding Learning

We propose a new active learning (AL) method for text classification based on convolutional neural networks (CNNs). In AL, one selects the instances to be manually labeled with the aim of maximizing model performance with minimal effort. Neural models capitalize on word embeddings as features, tuning these to the task at hand. We argue that AL strategies for neural text classification should focus on selecting instances that most affect the embedding space (i.e., induce discriminative word representations). This is in contrast to traditional AL approaches (e.g.,uncertainty sampling), which specify higher level objectives. We propose a simple approach that selects instances containing words whose embeddings are likely to be updated with the greatest magnitude, thereby rapidly learning discriminative, task-specific embeddings. Empirical results show that our method outperforms baseline AL approaches.
Study of dexterous robotic grasping for deformable objects manipulation

Most works in dexterous manipulation consider Force Closure Grasp (FCG), not only for rigid object but also, for flexible ones, though in the second case a force readjustment is also necessary. However, there are situations in which FCG is nonviable. This paper deals with the situation of a flexible object that must be necessarily grasped from one of its sides, taking advantage of its flexibility. This situation is common in packaging processes, or other everyday situations carried out by people. The paper presents first a new solution based on a planner that reproduces the actions of a human hand, aided by a database with knowledge about previous tasks. The paper is focused on describing the first tests done to evaluate the effectiveness of the planner for grasping different basic flexible objects, compared to a human hand.
Parallel cache management of a RDBMS

Increasing requirements such as higher transaction rates, shorter response times etc., make it necessary to parallelize existing database management systems and to implement them on multiprocessors. Shared memory multiprocessors are not arbitrarily scalable, and thus do not meet future requirements. Therefore, it makes sense to implement databases on large-scale distributed memory multicomputers. This raises the problem how to implement a database cache using shared memory concepts extensively under such a hardware architecture. We describe a solution to this problem by introducing a virtual database cache (VDBC). The VDBC is an algorithmic approach based on the concepts of a virtual shared memory and of explicit synchronization. Our approach is based on the assumption, that interprocessor communication is quite favorable as compared to I/O it is thus interesting to exchange pages with other processors instead of with the disks.
Simultaneous Estimation of Disease Risks and Spatial Clustering : A Hierarchical Bayes Approach

Detection of clustering and estimation of incidence risks from disease data are important in public health and epidemiological research. The popular models for disease risks such as conditional autoregressive (CAR) models assume known spatial dependence structure. Instead, we consider spatial clusters in which areas are geographically connected. Given spatial clustering idea, we propose a methodology that simultaneously estimates disease risks and detects clusters based on different features of the regression model. The proposed model is flexible in terms of local, regional and global shrinking and in terms of number of clusters, cluster memberships and cluster locations. We develop an algorithm based on the reversible jump Markov chain Monte Carlo (MCMC) method for model estimation. Numerical study shows effectiveness of the proposed methodology.
Stoichiometry of δ subunit containing GABAA receptors

Although the stoichiometry of the major synaptic αβγ subunit‐containing GABAA receptors has consensus support for 2α:2β:1γ, a clear view of the stoichiometry of extrasynaptic receptors containing δ subunits has remained elusive. Here we examine the subunit stoichiometry of recombinant α4β3δ receptors using a reporter mutation and a functional electrophysiological approach.
Synthesis with Hardware Sharing for Multi-Processor SoC Design ∗ ∗

We present a hardware sharing method for design space exploration of multi-processor embedded systems. In our prior work, we had developed a system-level design tool named SystemBuilder which automatically synthesizes target implementation of a system from a functional description. In this work, we have extended SystemBuilder so that it can automatically synthesize an area-efficient implementation which shares a hardware module among different applications. With SystemBuilder, designers only need to enable an option in order to share a hardware module. The designers, therefore, can easily explore a design space including hardware sharing in short time. A case study shows the effectiveness of the hardware sharing on design space exploration. key words: system-level design, hardware sharing, design space exploration, MPSoC
Bi-directional phonetic modeling of consonant-vowel speech patterns

This paper studies the dual aspects of speech recognition and synthesis using the consonant-vowel speech patterns. It examines the feasibility of bi-directional phonetic modeling, i.e., articulatory encoding and auditory decoding, in a neural based computational model supplemented with an interactive learning algorithm. Simulation results demonstrate that the proposed method offers a simple yet elegant solution for improved phonetic representation. This work serves to offer a collective and unified explanation for the dynamic nature of English speech, which holds great potential for future developments of human-like speech processing systems.
CLINAL PATTERNS OF CHROMOSOMAL INVERSION POLYMORPHISMS IN DROSOPHILA SUBOBSCURA ARE PARTLY ASSOCIATED WITH THERMAL PREFERENCES AND HEAT STRESS RESISTANCE

Latitudinal clines in the frequency of various chromosomal inversions are well documented in Drosophila subobscura. Because these clines are roughly parallel on three continents, they have undoubtedly evolved by natural selection. Here, we address whether individuals carrying different chromosomal arrangements also vary in their thermal preferences (Tp) and heat stress tolerance (Tko). Our results show that although Tp and Tko were uncorrelated, flies carrying “cold‐adapted” gene arrangements tended to choose lower temperatures in the laboratory or had a lower heat stress tolerance, in line with what could be expected from the natural patterns. Different chromosomes were mainly responsible for the underlying genetic variation in both traits, which explains why they are linearly independent. Assuming Tp corresponds closely with temperatures that maximize fitness our results are consistent with previous laboratory natural selection experiments showing that thermal optimum diverged among thermal lines, and that chromosomes correlated with Tp differences responded to selection as predicted here. Also consistent with data from the regular tracking of the inversion polymorphism since the colonization of the Americas by D. subobscura, we tentatively conclude that selection on tolerance to thermal extremes is more important in the evolution and dynamics of clinal patterns than the relatively “minor” adjustments from behavioral thermoregulation.
The Dynamics of Software Testing

In the modern information based society, failure of software systems can have significant consequences. It has been argued that increased attention to testing activities during the software development process can mitigate the probabilities of system failure after implementation. However, in order to justify investments in improved testing, the economic impacts of improper testing should be identified. In this paper, we propose a systematic approach to the evaluation of the economic impacts of software testing. The main factors affecting software testing are identified, and a computer simulation model is developed to investigate different testing scenarios. Usefulness of the suggested approach is demonstrated through several exploratory simulations. The results prove the utility of the System Dynamics modelling approach in building better understanding of the impact of software testing. Implications for software development practitioners, researchers, customers of software products and software support organisations are also discussed.
Downtimeless System Evolution: Current State and Future Trends

Recent studies report a growing demand for downtimeless evolution of distributed automation systems. This paper therefore first analyzes evolution approaches from the industry as well as from academic research pointing out benefits and drawbacks. The standard IEC 61499 is identified as promising basis for dynamic reconfiguration of control applications, but it lacks for an adequate engineering methodology. Finally the epsivCEDAC approach for structured evolution modeling is presented. Therein a distributed evolution control application modeled in terms of IEC 61499 is used for transforming the control application from current to desired state without downtime. Further important issues are correctness of the evolution process and runtime error handling mechanisms.
Multimodular Text Normalization of Dutch User-Generated Content

As social media constitutes a valuable source for data analysis for a wide range of applications, the need for handling such data arises. However, the nonstandard language used on social media poses problems for natural language processing (NLP) tools, as these are typically trained on standard language material. We propose a text normalization approach to tackle this problem. More specifically, we investigate the usefulness of a multimodular approach to account for the diversity of normalization issues encountered in user-generated content (UGC). We consider three different types of UGC written in Dutch (SNS, SMS, and tweets) and provide a detailed analysis of the performance of the different modules and the overall system. We also apply an extrinsic evaluation by evaluating the performance of a part-of-speech tagger, lemmatizer, and named-entity recognizer before and after normalization.
Building Viral Replication Organelles: Close Encounters of the Membrane Types

Overview Positive-strand (+)RNA viruses are important and emerging pathogens of humans, animals, and plants. Upon infection, they induce the formation of specializedmembranous replication organelles with unique lipid composition to facilitate robust virus replication. In this article, the authors discuss the proviral role of virus-inducedmembrane contact sites (vMCSs). The emerging picture is that vMCSs channel lipids to viral membranes and tune the lipid composition for optimal generation and functioning of replication organelles.
Detecting novel business blogs

Nowadays, blogs have become a popular way for people and organizations to publish news and information to the public, as well as advertise their products. Certain information contained in the blogs represent business opportunities and thus can be utilized by speculators. However, many blogs may often contain similar information and the sheer volume of available information really challenges the ability of organizations to act quickly in today's business environment. Thus, a lot of work has been done to develop a novelty detection system, which is able to single out novel information out of a set of massive text documents. This paper explores the feasibility and performance of detecting novel business blogs, which has not been studied before. The results show that our novelty detection system can detect novelty in our dataset of business blogs with very high accuracy, which indicate that our novelty detection algorithm is successful in detecting novel business blogs.
The Impact of Task on the Usage of Web Browser Navigation Tools

The design and improvement of existing web browser navigation tools is often motivated by reports of high tool usage in the literature. However, general usage data alone is not enough to understand the utility of a given navigation tool. In this paper, we explore how factors such as task and individual differences influence the usage of different web browser navigation tools. We conducted a field study of 21 participants in which we logged detailed web usage and asked participants to provide task categorizations of their web usage based on the following tasks: Fact Finding, Information Gathering, Browsing, and Transactions. Using this data, we have identified three factors that play a role in the use of navigation tools: the task session, the task, and individual differences. These findings have implications for the future design of new and improved web navigation tools. Author
Worst-case Optimal Probability Updating

This paper discusses an alternative to conditioning that may be used when the probability distribution is not fully specified. It does not require any assumptions (such as CAR: coarsening at random) on the unknown distribution. The well-known Monty Hall problem is the simplest scenario where neither naive conditioning nor the CAR assumption suffice to determine an updated probability distribution. This paper thus addresses a generalization of that problem to arbitrary distributions on finite outcome spaces, arbitrary sets of `messages', and (almost) arbitrary loss functions, and provides existence and characterization theorems for worst-case optimal probability updating strategies. We find that for logarithmic loss, optimality is characterized by an elegant condition, which we call RCAR (reverse coarsening at random). Under certain conditions, the same condition also characterizes optimality for a much larger class of loss functions, and we obtain an objective and general answer to how one should update probabilities in the light of new information.
On the Function of Sales Assistance

The paper provides a formal rationale for the common practice of using sales assistance. The argument is made in a model where a consumer's situation determines his needs. The products are differentiated and the map identifying the best match between a consumer's situation and a product is known only to the sales assistant. I look at procedure which involves the consumer revealing his needs and the sales assistant identifying the best matching product. This is compared to several other procedures and found to be best when the number of possible consumer situations is large. It is shown that reputational considerations may keep the sales assistant honest. I look at the impact of guarantees and other modifications to the model and discuss normative implications.
Unification and ML-Type Reconstruction

We study the complexity of type reconstruction for a core fragment of ML with lambda abstraction, function application, and the polymorphic let declaration. We derive exponential upper and lower bounds on recognizing the typable core ML expressions. Our primary technical tool is uniication of succinctly represented type expressions. After observing that core ML expressions, of size n, can be typed in DTIME(2 n), we exhibit two diierent families of programs whose principal types grow exponentially. We show how to exploit the expressiveness of the let-polymorphism in these constructions to derive lower bounds on deciding typability: one leads naturally to NP-hardness and the other to DTIME(2 n k)-hardness for each integer k 1. Our generic simulation of any exponential time Turing Machine by ML type reconstruction may be viewed as a nonstandard way of computing with types. Our worst-case lower bounds stand in contrast to practical experience, which suggests that commonly used algorithms for type reconstruction do not slow compilation substantially.
Uncertainty reduction in river water quality modelling. Application on the river Dender in Belgium.

Recently the European Union has approved the EU Water Framework Directive (EU-WFD). This directive claims that by the end of 2015 a “good status of surface water“ and a “good status of groundwater” should be achieved (EU, 2000). To that end, the EU-WFD provides several guidelines for monitoring the water bodies, leaving the practical implementation to the local governments. Each region has to develop a consistent monitoring strategy and assessment methodology. To make sure that the new water policy will succeed, a profound analysis of the actual and future state of the water is necessary. In this context, the evaluation of emissions into river water will be important. The effects of a pollution load into the river can be evaluated using models. After the calibration of a model with a certain data set, the model generates predictions over a period of months or even years. However, those predictions are uncertain. Model outcome uncertainties can become very large due to: • measurement errors, • uncertainty in the estimated model parameter values, • model structure uncertainty and • the stochastic nature of the biological processes in river water. This paper presents a methodology to reduce the uncertainty on model parameters and as a result reduce the uncertainty in the final model outputs. Three main steps are described. The methodology is applied to the Dender basin in Belgium. The river water quality model used is implemented in the ESWAT simulator.
Integration of relational databases into OWL knowledge bases: demonstration of the DBOM system

In this paper, we present a system which enables the integration of data stored in relational databases into a Semantic Web compliant knowledge base. In this context, our system, named DBOM, proposes a solution to the impedance mismatch problem by proposing a mapping language that allows to specify how to transform data retrieved from tuples of the databases into objects of the target knowledge base. DBOM also handles inconsistencies that may be caused by the execution of uncertain mappings. The proposed solution is based on setting preferences over the views and attributes of the views of the mappings. We demonstrate DBOM in the context of a medical informatics application that requires the integration of drug related information into an OWL knowledge base.
Good research practices for cost-effectiveness analysis alongside clinical trials: the ISPOR RCT-CEA Task Force report.

OBJECTIVES A growing number of prospective clinical trials include economic end points. Recognizing the variation in methodology and reporting of these studies, the International Society for Pharmacoeconomics and Outcomes Research (ISPOR) chartered the Task Force on Good Research Practices: Randomized Clinical Trials-Cost-Effectiveness Analysis. Its goal was to develop a guidance document for designing, conducting, and reporting cost-effectiveness analyses conducted as a part of clinical trials. METHODS Task force cochairs were selected by the ISPOR Board of Directors. Cochairs invited panel members to participate. Panel members included representatives from academia, the pharmaceutical industry, and health insurance plans. An outline and a draft report developed by the panel were presented at the 2004 International and European ISPOR meetings, respectively. The manuscript was then submitted to a reference group for review and comment. RESULTS The report addresses issues related to trial design, selecting data elements, database design and management, analysis, and reporting of results. Task force members agreed that trials should be designed to evaluate effectiveness (rather than efficacy), should include clinical outcome measures, and should obtain health resource use and health state utilities directly from study subjects. Collection of economic data should be fully integrated into the study. Analyses should be guided by an analysis plan and hypotheses. An incremental analysis should be conducted with an intention-to-treat approach. Uncertainty should be characterized. Manuscripts should adhere to established standards for reporting results of cost-effectiveness analyses. CONCLUSIONS Trial-based cost-effectiveness studies have appeal because of their high internal validity and timeliness. Improving the quality and uniformity of these studies will increase their value to decision makers who consider evidence of economic value along with clinical efficacy when making resource allocation decisions.
LOCALIZING TAMPERED IMAGE REGIONS THROUGH IMAGE NOISE FEATURES

This paper describes a method for locating regions of an image with noise levels that vary significantly from the rest of the image. These regions are highly suspected to be tampered regions, as noise is typically fairly uniform throughout an authentic image. The addition of random noise to a region, however is often used to conceal the tampered region because it can thwart common forgery detection methods. This proposed method attempts to address shortcomings of previous approaches as well as present a basis for a robust, feature-based forgery detector.
Towards a formal foundation of protection against data-oriented attacks

A data-oriented attack allows an adversary to rewrite data values stored in memories by manipulating data flow of a program, and brings serious damage such as leakage of secret keys or escalation of privileges. To the best of our knowledge, there is no practical method for resisting the data-oriented attack, and thus we discuss a protection method against the attack. Based on consideration of fundamental features of the data-oriented attack, we especially focus on detection of the attack. More precisely, the data-oriented attack can be detected by dynamically analyzing data flow since the flow is manipulated via the attack. Under this observation, we formally define data-oriented attack detection system. That is, the detection of the attack is reduced to some decision problem whereby automata accept the data flow or not. We also discuss the feasibility of the proposed system.
An Hα-selected sample of cataclysmic variables – I. Observations of newly discovered systems

Strong selection effects are present in observational samples of cataclysmic variables (CVs), complicating comparisons to theoretical predictions. The selection criteria used to define most CV samples discriminate heavily against the discovery of short-period, intrinsically faint systems. The situation can be improved by selecting CVs for the presence of emission lines. For this reason, we have constructed a homogeneous sample of CVs selected on the basis of H? emission. We present discovery observations of the 14 CVs and two additional CV candidates found in this search. The orbital periods of 11 of the new CVs were measured; all are above 3 h. There are two eclipsing systems in the sample, and one in which we observed a quasi-periodic modulation on a ? 1000 s time-scale. We also detect the secondary star in the spectrum of one system, and measure its spectral type. Several of the new CVs have the spectroscopic appearance of nova-like variables, and a few display what may be SW Sex star behaviour. In a companion paper, we discuss the implications of this new sample for CV evolution.
Co-referential chains and discourse topic shifts in parallel and comparable corpora

espanolEn este articulo se describen los textos del italiano y danes comparables y anotados con cadenas de correferencia e informacion sobre los cambios de topico discursivo, asi como una evaluacion de dicha anotacion. Tambien se discuten las diferencias generales en el modo de referir las expresiones en danes e italiano. Se presenta tambien el analisis de la relacion entre el uso de tipos de expresiones referidas y cambios de topico discursivo en parte de los datos utilizando el marco teorico del Centering. EnglishIn this paper we describe Danish and Italian parallel and comparable texts annotated with (co)referential chains and information about discourse topic shifts, and present an evaluation of the annotation. We also discuss general differences in the way referring expressions are used in Danish and Italian and present the analysis of the relation between the use of types of referring expression and discourse topic shifts in part of the data using the Centering framework.