Miracle-dz/UniFAR-FTUs huggingface dataset:
Dependent Multiple Cue Integration for Robust Tracking

We propose a new technique for fusing multiple cues to robustly segment an object from its background in video sequences that suffer from abrupt changes of both illumination and position of the target. Robustness is achieved by the integration of appearance and geometric object features and by their estimation using Bayesian filters, such as Kalman or particle filters. In particular, each filter estimates the state of a specific object feature, conditionally dependent on another feature estimated by a distinct filter. This dependence provides improved target representations, permitting us to segment it out from the background even in nonstationary sequences. Considering that the procedure of the Bayesian filters may be described by a "hypotheses generation-hypotheses correction" strategy, the major novelty of our methodology compared to previous approaches is that the mutual dependence between filters is considered during the feature observation, that is, into the "hypotheses-correction" stage, instead of considering it when generating the hypotheses. This proves to be much more effective in terms of accuracy and reliability. The proposed method is analytically justified and applied to develop a robust tracking system that adapts online and simultaneously the color space where the image points are represented, the color distributions, the contour of the object, and its bounding box. Results with synthetic data and real video sequences demonstrate the robustness and versatility of our method.
Conditional Hazard Estimating Neural Networks

Survival analysis is used when we wish to study the occurrence of some event in a population of subjects and the time until the event of interest. This time is called survival time or failure time. Survival analysis is often used in industrial life-testing experiments and in clinical follow-up studies. Examples of application include: time until failure of a light bulb, time until occurrence of an anomaly in an electronic circuit, time until relapse of cancer, time until pregnancy. In the literature we find many different modeling approaches to survival analysis. Conventional parametric models may involve too strict assumptions on the distributions of failure times and on the form of the influence of the system features on the survival time, assumptions which usually extremely simplify the experimental evidence, particularly in the case of medical data (Cox & Oakes, 1984). In contrast, semiparametric models do not make assumptions on the distributions of failures, but instead make assumptions on how the system features influence the survival time (the usual assumption is the proportionality of hazards); furthermore, these models do not usually allow for direct estimation of survival times. Finally, non-parametric models usually only allow for a qualitative description of the data on the population level. Neural networks have recently been used for survival analysis; for a survey on the current use of neural networks, and some previous attempts at neural network survival modeling we refer to (Bakker & Heskes, 1999), (Biganzoli et al., 1998), (Eleuteri et al., 2003), (Lisboa et al., 2003), (Neal, 2001), (Ripley & Ripley, 1998), (Schwarzer et al. 2000). Neural networks provide efficient parametric estimates of survival functions, and, in principle, the capability to give personalised survival predictions. In a medical context, such information is valuable both to clinicians and patients. It helps clinicians to choose appropriate treatment and plan follow-up efficiently. Patients at high risk could be followed up more frequently than those at lower risk in order to channel valuable resources to those who need them most. For patients, obtaining information about their prognosis is also extremely valuable in terms of planning their lives and providing care for their dependents. In this article we describe a novel neural network model aimed at solving the survival analysis problem in a continuous time setting; we provide details about the Bayesian approach to modeling, and a sample application on real data is shown.
From “What Did I Write?” to “Is this Right?”: Intention, Convention, and Accountability in Early Literacy

When children enter public kindergartens in the current atmosphere of high-stakes testing, they often encounter an emphasis on correctness that casts doubt on the integrity of their personally invented messages, prompting them to ask not “What did I write? ” but “Is this right? ” This ethnographic case study examines early writing by 23 kindergarten children within the context of their free-writing time and their teacher's plan to restore intention to compensate for a mandated curriculum that overemphasized convention. Children's writing samples were analyzed before and after the teacher introduced peer sharing, a strategy aimed at reestablishing the children's communicative intent.
Vibration performance of London's Millennium Footbridge

The London Millennium Footbridge has an unusual low-slung suspension design, supported by steel cables anchored to piers on the opposite river banks. When opened in 2000, it was found that low-frequency lateral vibrations were excited by pedestrians. To reduce the amplitude of these swaying oscillations, modifications were made in two ways. One was to install, under the bridge deck, passive tuned-mass vibration absorbers of broadly conventional design. The other was to add damping by introducing linear viscous dampers. These modifications had to be made without altering the visual appearance of the bridge. That made the design of viscous dampers difficult because most of their only suitable locations were where the relative amplitudes of vibration were small. A key feature was therefore to deploy dampers that could work with very small amplitudes of movement. The design of both tuned-mass absorbers and viscous dampers will be reviewed. Proving tests, involving 2,000 people walking over the modified bridge, and the subsequent service experience, including the results of examinations by the manufacturers after 15 years' service, will be described. An opportunity for IIAV members to visit the bridge is included in the ICSV24 programme.
Effective Spatial Data Partitioning for Scalable Query Processing

Recently, MapReduce based spatial query systems have emerged as a cost effective and scalable solution to large scale spatial data processing and analytics. MapReduce based systems achieve massive scalability by partitioning the data and running query tasks on those partitions in parallel. Therefore, effective data partitioning is critical for task parallelization, load balancing, and directly affects system performance. However, several pitfalls of spatial data partitioning make this task particularly challenging. First, data skew is very common in spatial applications. To achieve best query performance, data skew need to be reduced. Second, spatial partitioning approaches generate boundary objects that cross multiple partitions, and add extra query processing overhead. Consequently, boundary objects need to be minimized. Third, the high computational complexity of spatial partitioning algorithms combined with massive amounts of data require an efficient approach for partitioning to achieve overall fast query response. In this paper, we provide a systematic evaluation of multiple spatial partitioning methods with a set of different partitioning strategies, and study their implications on the performance of MapReduce based spatial queries. We also study sampling based partitioning methods and their impact on queries, and propose several MapReduce based high performance spatial partitioning methods. The main objective of our work is to provide a comprehensive guidance for optimal spatial data partitioning to support scalable and fast spatial data processing in massively parallel data processing frameworks such as MapReduce. The algorithms developed in this work are open source and can be easily integrated into different high performance spatial data processing systems.
Detection and evolution analysis of code clones for efficient management of large-scale software systems

In recent decades, large-scale software systems have become mainstream. Such software systems have complicated the maintenance process by increasing efforts such as inspection and understanding of the existing source code. Therefore, to maintain these systems, a great deal of work and time are necessary. To alleviate this problem, this research focus on a well-known factor hindering the software maintenance task, a code clone (i.e., a code fragment that has other code fragments identical or similar to it in the source code). It is widely believed that code clones complicate software maintenance. For example, when changes to code clones in a clone set (i.e., a set of code clones that are identical or similar to each other) are inconsistent, the developer needs to identify inconsistently changed code clones and apply consistent changes to them. Thus far, many tools and techniques have been proposed for supporting the detection and management of code clones. However, most are insufficient for supporting code clone related tasks during the software maintenance process for large-scale software systems. To resolve this problem, this study attempts to solve two important problems that code clones face. That is, “Which type of normalization dose make code clones to detected with high speed from large-scale software systems? ” and “Which supports are necessary for more widely used tools that support clone refactoring? ”. To solve the first problem, this research proposes six approaches for detecting code clones with preprocessing input source files using different degrees of normalizations (e.g., the removal of white spaces, tokenization, and the regularization of identifiers). More precisely, each type of normalization is applied to the input source files, and equivalence class partitioning of the files is then conducted during the preprocessing. Code clones are then detected from a set of files that are representatives of each equivalence class using a token-based code clone detection tool calledCCFinder. The proposed approaches can be categorized into two types, an approach with non-normalization and approaches with normalization. The former type is the detection of only identical files without normalization, whereas the latter category is the detection of identical files with different degrees of normalization
Description Logics are not just for the Flightless-Birds: A New Look at the Utility and Foundations

This paper presents some of the underlying principles of description logics (also known as terminological logics or kl-one-style languages), grounding them in the lattice of terms organized by the so-called \subsumption" relationship. A survey of the increasingly varied uses of description logics, including industrial applications, is presented by considering their role in a number of di(cid:11)erent operations that one can apply to a knowledge base, including languages for queries, answers, updates, rules, and constraints. Finally, we discuss some of the complexity results related to the logics of descriptions, and survey a spectrum of responses to the many intractability proofs.
Bounding Optimal Expected Revenues for Assortment Optimization under Mixtures of Multinomial Logits

We consider assortment problems under a mixture of multinomial logit models. There is a fixed revenue associated with each product. There are multiple customer types. Customers of different types choose according to different multinomial logit models whose parameters depend on the type of the customer. The goal is to find a set of products to offer so as to maximize the expected revenue obtained over all customer types. This assortment problem under the multinomial logit model with multiple customer types is NP‐complete. Although there are heuristics to find good assortments, it is difficult to verify the optimality gap of the heuristics. In this study, motivated by the difficulty of finding optimal solutions and verifying the optimality gap of heuristics, we develop an approach to construct an upper bound on the optimal expected revenue. Our approach can quickly provide upper bounds and these upper bounds can be quite tight. In our computational experiments, over a large set of randomly generated problem instances, the upper bounds provided by our approach deviate from the optimal expected revenues by 0.15% on average and by less than one percent in the worst case. By using our upper bounds, we are able to verify the optimality gaps of a greedy heuristic accurately, even when optimal solutions are not available.
Monadic datalog over finite structures of bounded treewidth

Bounded treewidth and monadic second-order (MSO) logic have proved to be key concepts in establishing fixed-parameter tractability results. Indeed, by Courcelle's Theorem we know that any property of finite structures, which is expressible by an MSO sentence, can be decided in linear time (data complexity) if the structures have bounded treewidth. In principle, Courcelle's Theorem can be applied directly to construct concrete algorithms by transforming the MSO evaluation problem into a tree language recognition problem. The latter can then be solved via a finite tree automaton (FTA). However, this approach has turned out to be problematical, since even relatively simple MSO formulae may lead to a “state explosion” of the FTA. In this work we propose monadic datalog (i.e., datalog where all intentional predicate symbols are unary) as an alternative method to tackle this class of fixed-parameter tractable problems. We show that if some property of finite structures is expressible in MSO then this property can also be expressed by means of a monadic datalog program over the decomposed structure: we mean by this that the original structure is augmented with new elements and new relations that encode one of its tree decompositions. In the first place, we thus compare the expressive power of two query languages. However, we also show that the resulting fragment of datalog can be evaluated in linear time (both with respect to the program size and with respect to the data size). Hence, our transformation of an MSO query into a monadic datalog program yields an alternative proof of Courcelle's Theorem. In order to actually construct efficient algorithms for problems whose tractability is due to Courcelle's Theorem, we propose to use a fragment of full (i.e., not necessarily monadic) datalog which allows for a succinct representation of the corresponding monadic datalog programs and for an efficient execution. This new approach is put to work by devising datalog programs for the 3-Colorability problem of graphs and for the PRIMALITY problem of relational schemas (i.e., testing if some attribute in a relational schema is part of a key). We also report on experimental results with a prototype implementation.
The Adolescent with a Chronic Condition Epidemiology, Developmental Issues and Health Care Provision Who Library Cataloguing-in-publication Data

The adolescent with a chronic condition : epidemiology, developmental issues and health care provision / P-A. 1. Chronic disease – epidemiology. 2. Chronic disease – therapy. 3. Chronic disease – prevention and control. 4. Adolescent health services. 5. Delivery of health care. The designations employed and the presentation of the material in this publication do not imply the expression of any opinion whatsoever on the part of the World Health Organization concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries. Dotted lines on maps represent approximate border lines for which there may not yet be full agreement. The mention of specific companies or of certain manufacturers' products does not imply that they are endorsed or recommended by the World Health Organization in preference to others of a similar nature that are not mentioned. Errors and omissions excepted, the names of proprietary products are distinguished by initial capital letters. All reasonable precautions have been taken by the World Health Organization to verify the information contained in this publication. However, the published material is being distributed without warranty of any kind, either expressed or implied. The responsibility for the interpretation and use of the material lies with the reader. In no event shall the World Health Organization be liable for damages arising from its use. The named authors alone are responsible for the views expressed in this publication. Acronyms and abbreviations used in this report CNS central nervous system FEV1 forced expiratory volume
Varieties and covarieties of languages (preliminary version)

Because of the isomorphism (X A) ! X = X ! (A ! X), the transition structure of a deterministic automaton with state set X and with inputs from an alphabet A can be viewed both as an algebra and as a coalgebra. This algebra-coalgebra duality goes back to Arbib and Manes, who formulated it as a duality between reachability and observability, and is ultimately based on Kalman’s duality in systems theory between controllability and observability. Recently, it was used to give a new proof of Brzozowski’s minimization algorithm for deterministic automata. Here we will use the algebra-coalgebra duality of automata as a common perspective for the study of both varieties and covarieties, which are classes of automata and languages dened by equations and coequations, respectively. We make a rst connection with Eilenberg’s denition of varieties of languages, which is based on the classical, algebraic notion of varieties of (transition) monoids.
Analyzing the Applicability of an Agile Methodology to Distributed Collaborative Software Development

Today, information technology (IT) has penetrated most domains of business and private life. The knitting of IT-systems and their dependencies are getting more complex every day. For businesses, this development can mean great opportunities. IT has become a main driver for competitive advantage and business success. On the other hand, misled software development (SD) projects can mean an existential threat to the operational and financial situation of a company. The efficient development of effective software is an essential part of optimally facing present and future challenges. Managing SD with traditional methodologies often leads to high planning and management overhead and still, severe schedule deviations and budget overruns cannot be eliminated. The sequential and plan-driven traditional approaches are often not able to support an adequate reaction to either internally or externally caused changes in requirements. Complex and unclear system landscapes with diverse interfaces, ambiguous customer requirements, changing business strategies or fluctuating legal requirements are just a few examples for possible sources of changing system requirements. Today, Extreme Programming (XP) is the most popular agile development methodology supported by the Agile Alliance. Its name was chosen because it claims to bring common sense to an extreme level. It focuses on communication, simplicity, feedback and courage, to improve the speed and quality of SD. Formal processes and documentation are neglected in favor of tacit knowledge to improve flexibility. Close communication between developers and the continuous integration of customer representatives are key components of XP. XP was initially developed for small to medium sized collocated development teams. This paper analyzes to what extent XP can be transferred to larger distributed developing endeavors. The focus is on XP, because it is the methodology with the highest congruence to the original Agile Manifesto. It does not claim to be all new, but to be an aligned composition of well established ideas and practices from other methodologies.
Parallel processors for high speed multiple objects recognition

In this work, we introduce a novel method for recognizing multiple objects in an image at a very high speed. The system is based on self learning high speed parallel processing devices. The system processes video streams at speed of 1000 frames per second or more. For high speed object recognition using sequential computing from an image of a video having thousands of frames per second and each image frame consists of thousands of pixels, we need very much time for executing complicated algorithms. In the traditional way of computing and recognizing, systems are very time consuming compared to our system because the traditional systems use sequential computation for recognizing, with some complicated functions. If we use other types of parallel processors like ANN for processing each pixel or group of pixels, those systems need programming and giving data to such large number of processors are practically difficult. Here we have used a self learning parallel processor device which is made for doing some kinds of particular jobs. This parallel processing devices are easy to manipulate and can be trained simultaneously. It contains memory for storing data comparators for comparing with previously stored memory etc. Training as well as functioning are in real time even if the system process thousands of image frames per second.
On Predicting User Intent

Recent advances in computing capability and approaches have underlined the need for user support in task execution. This is especially evident in dynamic, real-time systems where decision making is critical and errors are costly. Environments such as air traffic control centers leave little time for human operators to make critical decisions. Operators must contend with a significant amount of information to be parsed and considered before a decision is made. The main outcome of working in such an environment is cognitive overload, which leads to a significant error rate in decision making. Although intelligent and fully automated systems can be built to execute simple tasks, such complex tasks as monitoring air traffic are still mostly designated for human users. How then can we alleviate cognitive overload of human operators in time-critical and demanding systems? Many in the human-computer interaction community have concentrated on looking for a solution to relieving cognitive overload through the application of user interface engineering conventions. These methods are expressed as rules which, when followed, are said to result in user interfaces that have high usability. However, regardless of the user interface, the operator must still make decisions and perform all of the tasks himself. This paper presents an outline for creating an intelligent agent to assist the user. The role of the agent is to attempt to predict the user’s intent. The agent can then act on its perceptions with the goal of accelerating the task’s completion and easing the cognitive load of the user by manipulating the user interface.