Miracle-dz/UniFAR-FTUs huggingface dataset:
Can an intermediary collection help users search image databases without annotations?

Developing methods for searching image databases is a challenging and ongoing area of research. A common approach is to use manual annotations, although generating annotations can be expensive in terms of time and money, and therefore may not be justified in many situations. Content-based search techniques which extract visual features from image data can be used, but users are typically forced to express their information need using example images, or through sketching interfaces. This can be difficult if no visual example of the information need is available, or when the information need cannot be easily drawn. In this paper, we consider an alternative approach which allows a user to search for images through an intermediate database. In this approach, a user can search using text in the intermediate database as a way of finding visual examples of their information need. The visual examples can then be used to search a database that lacks annotations. Three experiments are presented which investigate this process. The first experiment automatically selects the image queries from the intermediary database; the second instead uses images which have been hand-picked by users. A third experiment, an interactive study, is then presented this study compares the intermediary interface to text search, where we consider text as an upper bound of performance. For this last study, an interface which supports the intermediary search process is described. Results show that while performance does not match manual annotations, users are able to find relevant material without requiring collection annotations.
Automatic Generation of Glosses in the OntoLearn System

OntoLearn is a system for automatic acquisition of specialized ontologies from domain corpora, based on a syntactic pattern matching technique for word sense disambiguation, called structural semantic interconnection (SSI). We use SSI to extract from corpora complex domain concepts and create a specialized version of WordNet. In order to facilitate the task of domain specialists who inspects and evaluate the newly acquired domain ontology, we defined a method to automatically generate glosses for the learned concepts. Glosses provide an informal description, in natural language, of the formal specifications of a concept, facilitating a perconcept evaluation of the ontology by domain specialists, who are usually unfamiliar with the formal language used to describe a computational ontology. The proposed evaluation framework has been tested in a financial domain.
Thou shalt not pass?: Examining the existence of an immigrant glass ceiling in Sweden, 1970-1990

The paper studies a sample of natives and 18 immigrant nationalities in the Swedish labor market between 1970 and 1990. The purpose is examining the existence of an immigrant specific glass ceiling. Results suggest a considerable overall advantage in terms of the probability of experiencing upward occupational mobility for native Swedish males. Despite this, the pattern does not correspond to the theoretical expectations of a glass ceiling. Using the ISEI classification of occupational status, the advantage experienced by Swedish males is consistent in the private manufacturing and private service sectors, compared to the experience of immigrants and women. The public sector generally suggests a similar pattern according to linguistic background. In this sector, certain groups of women are, however, observed to experience an advantage from low occupational status origins.
Financial Constraints, Working Capital and the Dynamic Behavior of the Firm

Financial constraints are widespread in developing countries, where even short-term credit is limited. Finance held by firms as working capital is a substantial proportion of sales revenue, yet the role of working capital is largely neglected by existing models of financial constraints. This paper presents a dynamic model of the firm that incorporates working capital by introducing a delay between factor payments and the receipt of revenue. In contrast with previous models, the working capital model predicts that firms under binding constraints will substitute between labor and capital in response to demand shocks, causing investment to be countercyclical. For firms near the margin of being constrained, constraints bind when positive production opportunities arise. Output growth is therefore constrained in response to positive shocks but not to negative shocks. Simulations suggest that models without working capital may understate the predicted effects of financial constraints on production efficiency, firm profit and growth over time. The predictions are tested with the Bangladesh Panel Survey data for manufacturing firms. Consistent with the theory, there is evidence that constraints bind when output price increases, that investment by constrained firms is countercyclical, and that output response to positive shocks is dampened for firms that are sometimes constrained. The results also are important for policy. In order to maximize growth, efforts to relieve credit constraints should be focused on periods when demand shocks are high.
Evaluating ad-hoc object retrieval

In contrast to traditional search, semantic search aims at the retrieval of information from factual assertions about real-world objects rather than searching over web-pages with textual descriptions. One of the key tasks to address in this context is ad-hoc object retrieval, i.e. the retrieval of objects in response to user formulated keyword queries. Despite the significant commercial interest, this kind of semantic search has not been evaluated in a thorough and systematic manner. In this work, we discuss the first evaluation campaign that specifically targets the task of ad-hoc object retrieval. We also discuss the submitted sys- tems, the factors that contributed to positive results and the potential for future improvements in semantic search.
Elevated relative mortality risk with mild-to-moderate chronic kidney disease decreases with age.

BACKGROUND Renal disease is common in the general population and whilst few people progress to end-stage renal failure, mortality is increased. The aim of this study was to examine all-cause mortality risk in relation to chronic kidney disease (CKD) stages defined by estimated glomerular filtration rate (eGFR). METHODS Data were extracted from a computerized central laboratory system for a defined geographical area over a 3-year study period. The eGFR was calculated using the four-variable Modification of Diet in Renal Disease (MDRD) formula and aligned to the MDRD laboratory. Average annual mortality and relative risk (RR) of all-cause mortality was determined and compared for defined age and CKD bands. RESULTS 106 366 participants (55.5% female; 85% White, 13% South Asian, 2% Black and others) were eligible and studied, representing 49% of the Coventry adult population. 12 540 (12%) of the sample had some evidence of decreased kidney function, with an eGFR <60 ml/min/1.73 m2. 7611 (7%) participants died and there were significantly elevated risks of mortality with increasing renal dysfunction; RR = 4.0, 8.3, 16.2 and 43.5 for eGFR 45-59, 30-44, 15-29 and <15 ml/min/1.73 m2, respectively. Within age bands, RRs were statistically significantly raised with CKD progression and within CKD stage, RR of death decreased as age increased. CONCLUSIONS CKD prevalence increased with age and absolute and RR of mortality increased with progression of CKD. People aged over 75 years, with mild-to-moderate renal disease, representing 41% of this age group, have no increased RR of mortality. Further study of CKD and mortality, particularly progression over time and with respect to age is needed.
Enduser-Initiative Application Development based on Architecture of a Model , UI and Components

Explosive increase in end-user computing on distributed systems requires that end-users develop application software by themselves. One solution is given as a formula of “a domain model ≡ a computation model. ” This formula implies that one task in a domain model of cooperative work corresponds to one object in a computation model based on an object-oriented model. Application development environment, M-base 1, supports this formula for cooperative systems such as groupware and workflow systems. The application architecture is fixed as composed of a model, a user interface and components. At the first st age, thesystem behavior is expressed as a message-driven model by using a modeling tool while focusing on message flow and components. The system behavior may be defined recursively in the manner of stepwise refinement. This implies that end-users define composite components. At the second st age, auser interface is generated automatically and may be customized if necessary. Then transition diagrams of user interfaces are generated automatically and used for confirmation of external specifications of the application. Finally, the system behavior is verified by using a simulation tool. This component-based development process is confirmed by feasibility study on a given problem of IPSJ sigRE group.
Relaxed Determinism: Making Redundant Execution on Multiprocessors Practical

Given that the majority of future processors will contain an abundance of execution cores, redundant execution can offer a promising method for increasing the availability and resilience against intrusions of computing systems. However, redundant execution systems rely on the premise that when external input is duplicated identically to a set of replicas executing the same program, the replicas will produce identical outputs unless they are compromised or experience an error. Unfortunately, threaded applications exhibit nondeterminism that breaks this premise and current redundant execution systems are unable to account for this non-determinism, especially on multiprocessors. In this paper, we introduce a method called relaxed determinism that is utilized by our system, called Replicant, to support redundant execution with reasonable performance while tolerating non-determinism.
Fast and accurate video annotation using dense motion hypotheses

Building large video datasets is a crucial task for many applications but is also very expensive in practice. In order to avoid annotating all the frames, the annotations from the labeled frames can be propagated using an offline tracker for each object. Following methods based on dynamic programming and eventually distance transforms, we introduce a new penalization which favors some given displacements between two frames without increasing the complexity of the optimization. In order to speed up this step we also propose to use an exact coarse to fine process. Experimental results show that the proposed energy performs better than previous ones and that our exact coarse to fine optimization leads to a significant speed-up in some scenarios.
Visual analysis of people's mobility pattern from mobile phone data

The large amount of phone call records from mobile operators in a city can inform us how many people are present in any given area and how many are entering or leaving. Each phone call record usually contains the caller and callee IDs, date and time, and the base station where the phone calls are made. As mobile phones are widely used in our daily life, many human behaviors can be revealed by analyzing mobile phone data. In this paper, we propose a comprehensive visual analysis system which can be used to analyze the population's mobility patterns from millions of phone call records. Our system consists of three major components: 1) visual analysis of user groups in a base station; 2) visual analysis of the mobility patterns on different user groups making phone calls in certain base stations; 3) visual analysis of handoff phone call records. Some well-established visualization techniques such as parallel coordinates and pixel-based representations have been integrated into our system. We also develop a novel visualization schemes, Voronoi-diagram-based visual encoding to reveal the unique features of mobile phone data. We have applied our system to real mobile phone data collected in a large city and obtained some interesting findings regarding people's mobility pattern.
Reconstruction of Instantaneous Phase of Unipolar Atrial Contact Electrogram Using a Concept of Sinusoidal Recomposition and Hilbert Transform

The Hilbert transform has been used to characterize wave propagation and detect phase singularities during cardiac fibrillation. Two mapping modalities have been used: optical mapping (used to map atria and ventricles) and contact electrode mapping (used only to map ventricles). Due to specific morphology of atrial electrograms, phase reconstruction of contact electrograms in the atria is challenging and has not been investigated in detail. Here, we explore the properties of Hilbert transform applied to unipolar epicardial electrograms and devise a method for robust phase reconstruction using the Hilbert transform. We applied the Hilbert transform to idealized unipolar signals obtained from analytical approach and to electrograms recorded in humans. We investigated effects of deflection morphology on instantaneous phase. Application of the Hilbert transform to unipolar electrograms demonstrated sensitivity of reconstructed phase to the type of deflection morphology (uni- or biphasic), the ratio of R and S waves and presence of the noise. In order to perform a robust phase reconstruction, we propose a signal transformation based on the recomposition of the electrogram from sinusoidal wavelets with amplitudes proportional to the negative slope of the electrogram. Application of the sinusoidal recomposition transformation prior to application of the Hilbert transform alleviates the effect of confounding features on reconstructed phase.
FROM CHICKEN SQUAWKING TO COGNITION : LEVELSOF DESCRIPTION AND THE COMPUTATIONALAPPROACH IN PSYCHOLOGYAxel Cleeremans and

abstract What role can computational modeling play in psychology? What are the main advantages of instantiating theories as computer simulations? How should one assess the value of a given model? In this paper, our goals are to introduce and to discuss these issues. We argue for an essentially utilitarian view of computational modeling. We suggest that the main function of computational modeling is to support an interactive process of \probing and prediction" through which models can be interacted with in a way that provides both guidance for empirical research and also suucient depth to support interactive modiication of the underlying theory. We propose that models, just as the systems they are models of, can only be understood (and evaluated) with respect to a given level of description and a speciic set of criteria associated with that level. We also claim that models gain explanatory power as well as practical usefulness when they are emergent, that is, when they provide an account of how the principles of organization at a given level of description constrain and deene structure at a higher level of description. For this reason, connectionist models appear to provide the most fruitful modeling framework today.
Fuzzy Cellular Automata for Modeling Pattern Classifier

This paper investigates the application of the computational model of Cellular Automata (CA) for pattern classification of real valued data. A special class of CA referred to as Fuzzy CA (FCA) is employed to design the pattern classifier. It is a natural extension of conventional CA, which operates on binary string employing boolean logic as next state function of a cell. By contrast, FCA employs fuzzy logic suitable for modeling real valued functions. A matrix algebraic formulation has been proposed for analysis and synthesis of FCA. An efficient formulation of Genetic Algorithm (GA) is reported for evolution of desired FCA to be employed as a classifier of datasets having attributes expressed as real numbers. Extensive experimental results confirm the scalability of the proposed FCA based classifier to handle large volume of datasets irrespective of the number of classes, tuples, and attributes. Excellent classification accuracy has established the FCA based pattern classifier as an efficient and cost-effective solutions for the classification problem.
Slicing up a perfect hardware masking scheme

Masking is a side-channel countermeasure that randomizes side-channel leakage, such as the power dissipation of a circuit. Masking is only effective on the condition that the internal random mask remains a secret. Previous research has illustrated how a successful estimation of the mask bit in circuit-level masking leads to successful side-channel attacks. In this paper, we extend this concept to algorithmic masking, which uses multi-bit masks. Our key observation is that the power dissipation of a masked circuit and the mask value are not independent. We exploit this property by using a slice of the power samples obtained by partial selection. This slice has a statistically biased mask, even when the mask signal itself is generated with a uniform distribution. We demonstrate this attack by showing how a perfectly masked AES SBox can be broken using part of the observed power samples, while the same circuit remains secure if we use all of the observed power samples.
A Finite Element Method for Crack Growth Without

An improvement of a new technique for modelling cracks in the nite element framework is presented. A standard displacement-based approximation is enriched near a crack by incorporating both discontinuous elds and the near tip asymptotic elds through a partition of unity method. A methodology that constructs the enriched approximation from the interaction of the crack geometry with the mesh is developed. This technique allows the entire crack to be represented independently of the mesh, and so remeshing is not necessary to model crack growth. Numerical experiments are provided to demonstrate the utility and robustness of the proposed technique. Copyright ? 1999 John Wiley & Sons, Ltd.
Large-scale semantic co-labeling of image sets

As evidenced by video segmentation and cosegmentation approaches, exploiting multiple images is key to the success of visual scene understanding. With the availability of increasingly large sets of images, there is a clear need for methods that can efficiently analyze the similarities and structure across huge numbers of image pixels. Furthermore, to make effective use of this data, these similarities should not just be considered locally between neighboring pixels, but between all pairs of pixels across all images. In this paper, we tackle this challenging scenario by introducing a semantic co-labeling approach that performs efficient inference in a fully-connected CRF defined over the pixels, or superpixels, of an image set. Our experimental evaluation demonstrates that our approach yields improved accuracy while coming at no additional computation cost compared to performing segmentation sequentially on individual images. Furthermore, our formulation lets us perform inference over ten thousand images in a matter of seconds.
Improving Retrieval Performance using World-Knowledge Generated Features

Information Retrieval is the task of retrieving information items (documents, images, videos etc.) most relevant to a given user query. The common approach in textual IR systems is to index and retrieve documents by selecting representative key words and phrases within them, using various statistical, linguistic and semantic methods, and viewing each document as a vector in the vector space defined by these keywords. Once a user submits a query into the system, retrieving and ranking result documents is carried out by representing the user’s query as yet another vector in this vector space and measuring its similarity to that of the target documents. Over the years, many techniques have been developed to improve the performance of information retrieval systems. Among these are document indexing and query reformulation methods, which are used to extract lexical and statistical information from documents to better anticipate and act on user queries, which in turn have grown shorter and more ambiguous as IR systems reached larger and less professional audiences. Such methods were mostly based on analyzing the target documents corpus, and were therefore limited to the information contained in these documents only. The use of external knowledge in these techniques has been sparse and usually limited to domain-specific problems, as very few knowledge resources were available for this purpose, and were either general but limited in their depth, or in-depth but focused on a specific domain.
Unsupervised Domain Adaptation for I-Vector Speaker Recognition

In this paper, we present a framework for unsupervised domain adaptation of PLDA based i-vector speaker recognition systems. Given an existing out-of-domain PLDA system, we use it to cluster unlabeled in-domain data, and then use this data to adapt the parameters of the PLDA system. We explore two versions of agglomerative hierarchical clustering that use the PLDA system. We also study two automatic ways to determine the number of clusters in the in-domain dataset. The proposed techniques are experimentally validated in the recently introduced domain adaptation challenge. This challenge provides a very useful setup to explore domain adaptation since it illustrates a significant performance gap between an in-domain and out-of-domain system. Using agglomerative hierarchical clustering with a stopping criterion based on unsupervised calibration we are able to recover 85% of this gap.
Harnessing Chip-Multiprocessors with Concurrent Threaded Pipelines

Single-core performance increases have stalled. To increase available cycles, microprocessor designers have shifted to chip-multiprocessor (CMP) designs. Unfortunately, the additional processors provided by CMPs may remain idle because most applications lack data-parallelism and task-parallelism is unlikely to saturate future CMP designs. The systems community needs to rethink how systems are structured to fully utilize CMPs. We propose that operating systems be adapted to harness CMP resources by leveraging recent results in Concurrent Threaded Pipeline (pipeline-parallel) organizations. This paper discusses potential performance improvements of CTPs and the necessary OS support.