Miracle-dz/UniFAR-FTUs huggingface dataset:
15 Activity Recognition Using Probabilistic Timed Automata

Activity recognition focuses on what is happening in the scene. It endeavors to recognize the actions and goals of one or more actors from a sequence of observations both on the actor actions and the environmental conditions. Automated recognition of human activity is essential ability that may be used in the surveillance to provide security in indoor as well as outdoor environments. Understanding human activity is also important for humancomputer-interaction systems including tele-conferencing and for content-based retrieval of video from digital repositories. The main technique utilized in activity recognition is computer vision. In vision-based activity recognition, a great deal of work has already been done. This is partially due to increasing computational power that allows huge amount of video to be processed and stored, but also due to the large number of potential applications. In vision-based activity recognition, we can distinguish four steps, namely human detection, human tracking, human activity recognition and then a high-level activity evaluation. A method of (Viola et al., 2003) detects a moving pedestrian in a temporal sequence of images. A linear combination of filters is applied to compute motion and appearance features that are then summed to determine a cumulative score, employed afterwards in a classification of the detection window as including the moving object. For vision based activity recognition, tracking is the fundamental component. The entity must be first tracked before the recognition can take place. Briefly, the goal of visual tracking is to find and describe the relative position change of the moving object from one frame to another in the whole sequence, while the task of action recognition is to classify the person’s action given the person’s location, recent appearance, etc. Kalman filters (Crowley & Berard, 1997; Kwolek, 2003) and particle filtering–based algorithms (Nait-Charif & McKenna, 2003) are utilized extensively for object tracking in this domain. These algorithms generally involve an object state transition model and an observation model, which reflect both motion and appearance of the object (Haykin & de Freitas, 2004). After tracking of the moving objects the action recognition stage occurs, where Dynamic Time Warping (Myers et al., 1980; Myers & Rabiner, 1981) and Hidden Markov Models (Brand & Kettnaker, 2000) are employed very often at this stage. Sophisticated stochastic models such as Dynamic Bayesian Networks (Albrecht et al., 1997; Ghahramani, 1997), Stochastic Context Free O pe n A cc es s D at ab as e w w w . ite ch on lin e. co m
Body weight and mortality among men and women in China.

CONTEXT The effect of underweight and obesity on mortality has not been well characterized in Asian populations. OBJECTIVE To examine the relationship between body mass index (BMI) and mortality in Chinese adults. DESIGN, SETTING, AND PARTICIPANTS A prospective cohort study in a nationally representative sample of 169,871 Chinese men and women aged 40 years or older. Data on body weight and covariables were obtained at a baseline examination in 1991 using a standard protocol. Follow-up evaluation was conducted in 1999-2000, with a response rate of 93.4% (n = 158,666). MAIN OUTCOME MEASURES Body mass index and all-cause mortality. RESULTS After excluding those participants with missing body weight or height values, 154,736 adults were included in the analysis. After adjustment for age, sex, cigarette smoking, alcohol consumption, physical activity, education, geographic region (north vs south), and urbanization (urban vs rural), a U-shaped association between BMI and all-cause mortality was observed (P<.001). Using those participants with a BMI of 24.0 to 24.9 as the reference group, the relative risks of all-cause mortality across categories of BMI were 1.65 (95% confidence interval [CI], 1.54-1.77) for BMI less than 18.5, 1.31 (95% CI, 1.22-1.41) for BMI 18.5 to 19.9, 1.20 (95% CI, 1.11-1.29) for BMI 20.0 to 20.9, 1.12 (95% CI, 1.04-1.21) for BMI 21.0 to 21.9, 1.11 (95% CI, 1.03-1.20) for BMI 22.0 to 22.9, 1.09 (95% CI, 1.01-1.19) for BMI 23.0 to 23.9, 1.00 (95% CI, 0.92-1.08) for BMI 25.0 to 26.9, 1.15 (95% CI, 1.06-1.24) for BMI 27.0 to 29.9, and 1.29 (95% CI, 1.16-1.42) for BMI 30.0 or more. The U-shaped association existed even after excluding participants who were current or former smokers, heavy alcohol drinkers, or who had prevalent chronic illness at the baseline examination, or who died during the first 3 years of follow-up. A similar association was observed between BMI and mortality from cardiovascular disease, cancer, and other causes. CONCLUSIONS Our results indicate that both underweight and obesity were associated with increased mortality in the Chinese adult population. Furthermore, our findings support the use of a single common recommendation for defining overweight and obesity among all racial and ethnic groups.
Equitably Fair Internet Voting

With the advent of Internet Communications Technologies (ICT), the use of cryptographic protocols is a technical response to the loss of all traditional means that were used so far to establish security in democratic elections. We employ simple cryptographic techniques to address the “abstaining voters” problem in electronic elections with central administration. In such elections, voting authorities can cast a bogus vote on behalf of an authorized voter who decides to abstain. Our system is equitably fair: while a voter who registers for the election is allowed to abstain form voting (legal abstention), all registered voters who cast an encrypted vote must acknowledge, at some time later, the fact that they have voted. If not (illegal abstention), a cryptographic time capsule will be broken and their identity will be disclosed. Our system satisfies most requirements of a secure election and could be used in similar frameworks such as electronic polling and/or surveys over the Web.
Women's business ownership: recent research and policy developments

This report aims to provide a current assessment of recent research investigating women's business ownership. The report builds on a previous review undertaken by the authors on behalf of the Small Business Service (Carter, Anderson and Shaw, 2001). The report also explores in broad terms the role and contribution of recent policy developments in changing the landscape of women's enterprise in the UK.
Application of simulated annealing algorithm to grid computing scheduling based on GridSim

Simulated annealing algorithm has lots of advantages, but so far there was little research on how to apply it to grid scheduling. The main cause had two problems. One was how to decrease its overhead; the other was how to obtain its parameters. After introducing characters on scheduling in grid, one scheduling algorithms was proposed. In order to get the appropriate algorithm's parameters and to test its efficiency, a newly tool kits called Ana-GridSim based on GridSim was developed. Results of experiments showed the algorithm nice and the Ana-GridSim effective, feasible and convenient to study grid scheduling.
Comparison between effects of HydroCoil Embolic System and ordinary coil on large- and medium-sized intracranial aneurysms

Objective: To explore the treatment effects of HydroCoil Embolic System (HES) on large- and medium-sized intracranial aneurysms. Methodology: Fifty cases of intracranial aneurysm patients were retrospectively analyzed, in which 27 and 23 cases were treated with HES (n=27) and ordinary coils (n=23), respectively. All the patients were followed up for two years. Results: The 27 cases (54%, 27/50) treated with HES include 23 cases of densely packed occlusion (46%, 23/50) and 4 cases of subtotal occlusion (8%, 4/50). The 23 cases (46%, 23/50) treated with ordinary coils include 15 cases of densely packed occlusion (30%, 15/50) and 8 cases of subtotal occlusion (16%, 8/50). Conclusion: HydroCoil Embolic System (HES) may increase the ratio of densely packed occlusion and prevent the recurrence of large- and medium-sized intracranial aneurysms.
An Exact Differential Flatness Control for a Non Minimum Phase Model of an Inverted Pendulum

This paper develops the swinging-up control of an inverted pendulum using the Lie-Bäcklund approach to equivalence and flatness. In this system, the friction between the cart and the rail is considered to reach the real system. Two goals of controlling the inverted pendulum are investigated in this work. The first one is to balance the pendulum by applying differential flatness; and the second one is to keep the inverted pendulum in a position of muddled vertical balance along a trajectory, when it initially starts with zero angle of the vertical position. Our main contribution consists in applying the exact flat output for the synthesis of the differential flatness control law. A better precise trajectory tracking is then obtained, with an accurate control signal developed for a particular case of a non minimum phase inverted pendulum.
Low Effort Evaluation of Real-Time and Reliability Requirements for Embedded Systems

Measuring reliability of embedded systems is an important but non-trivial problem. In a system design process, it is desirable to have early indicators for the reliability of an embedded system. Such reliability measurement will typically be carried out on system prototypes. The reliability measurements should thus be easy in set up, flexible to system changes, and hopefully low in cost. In the paper we present a simple approach to do reliability tests for small-scale embedded systems. We explain our approach around a case study, but the method can easily be generalized. We discuss the advantages and disadvantages of our approach.
An Investigation of Quality Aspects of Noisy Colour Images for Iris Recognition

The UBIRIS.v2 dataset is a set of noisy colour iris images designed to simulate visible wavelength iris acquisition at-a-distance and on-the-move. This paper presents an examination of some of the characteristics that can impact the performance of iris recognition in the UBIRIS.v2 dataset. This dataset consists of iris images in the visible wavelength and was designed to be noisy. The quality and characteristics of these images are surveyed by examining seven different channels of information extracted from them: red, green, blue, intensity, value, lightness, and luminance. We present new quality metrics to assess the image characteristics with regard to focus, entropy, reflections, pupil constriction and pupillary boundary contrast. The results clearly suggest the existence of different characteristics for these channels and could be exploited for use in the design and evaluation of iris recognition systems.
Power Systems with Large Amounts of Wind Power

1.0 Introduction ind power will introduce more uncertainty into operating a power system because it is variable and partly unpredictable. To meet this challenge, there will be need for more flexibility in the power system. How much extra flexibility is needed depends on the one hand on how much wind power there is and on the other hand how much flexibility exists in the power system. The existing targets for wind power anticipate a quite high penetration in many countries. It is technically possible to integrate very large amounts of wind capacity in power systems; the limits arise from how much can be integrated at socially and economically acceptable costs. So far, the integration of wind power into regional power systems has mainly been studied on a theoretical basis, because wind power penetration is still rather limited in most countries and power systems. The small power system of the island of Ireland already has about 10% of yearly electricity consumption from wind, and countries like Denmark and the Iberian Peninsula (Spain and Portugal) show a high penetration of closer to 20%. Also regions like Northern Germany, Southern Italy, and MidWestern United States experience high penetration levels. All these countries have significant practical experience with wind integration. In recent years, several reports have been published investigating the power system impacts of wind power. However, results on the costs of integration differ substantially among reports and comparisons are difficult to make. This is due to using different methodology, data, and tools, as well as different terminology and metrics in representing the results. An in-depth review of the studies has been started in Task 25 to draw conclusions on the range of integration costs for wind power. Because system impact studies are often the first steps taken towards defining wind penetration targets within each country, it is important that W
Common methylenetetrahydrofolate reductase gene mutation leads to hyperhomocysteinemia but not to vascular disease: the result of a meta-analysis.

BACKGROUND The results of retrospective and prospective case-control studies have clearly established that mild elevations of the plasma homocysteine level are associated with increased risk of coronary, cerebral, and peripheral vascular disease. Recently, a mutation (677C-->T) was identified in the methylenetetrahydrofolate reductase (MTHFR) gene that results in reduced folate-dependent enzyme activity and reduced remethylation of homocysteine to methionine. Mutant homozygotes (TT genotype) constitute approximately 12% of the white population and frequently have mildly elevated circulating homocysteine. Therefore, it seems likely that they would also be at increased risk of vascular disease. A number of studies have investigated this during the past 3 years, and the present article evaluates the results in a meta-analysis. METHODS AND RESULTS We identified 13 studies in which there were measurements of plasma homocysteine in relation to the 3 genotypes (TT, CT, and CC) and 23 case-control studies comprising 5869 genotyped cardiovascular disease patients (mostly coronary artery disease) and 6644 genotyped control subjects. Those bearing the TT genotype had plasma homocysteine concentrations 2.6 micromol/L (25%) higher than those with the CC genotype. However, there was no difference between patients and control subjects either in the frequency of mutant alleles (T) (34.3% versus 33.8%) or the TT genotype (11.9% versus 11.7%). In the analysis of the 23 studies, the relative risk (OR) of vascular disease associated with the TT genotype was 1.12 (95% CI, 0.92 to 1.37). CONCLUSIONS We conclude that although the C677T/MTHFR mutation is a major cause of mild hyperhomocysteinemia, the mutation does not increase cardiovascular risk. Our findings suggest that the mild hyperhomocysteinemia found frequently in vascular disease patients is not causally related to the pathogenesis of the vascular disease.
FPGA Implementation of Viterbi Algorithm for processing of Forward Error Control in Software Radio Receiver

Convolution codes are one of the FEC codes that are used in every digital communication system. Convolution coding can be applied to block of data and also to a continuous input stream. Convolutional encoder can be viewed as a finite state machine.Viterbi algorithm is employed in wireless communications to decode the convolution codes. Such decoders are complex and dissipate large amount of power. Software Defined Radio (SDR) is realized using configurable hardware platforms. In this paper, a configurable viterbi decoder for SDR is described using a VHDL code. The proposed design of the viterbi decoder is considered so that it facilitates the decoder with different specifications. This paper proposes low power architecture viterbi decoder for developing and modeling a viterbi decoder for processing of forward error control in software radio receiver. The proposed design is simulated and synthesized by using Xilinx.The proposed design is implemented in Spartan 3e by using Xilinx
Diversifying Search Results with Popular Subtopics

This paper describes the method we use in the diversity task of web track in TREC 2009. The problem we aim to solve is the diversification of search results for ambiguous web queries. We present a model based on knowledge of the diversity of query subtopics to generate a diversified ranking for retrieved documents. We expand the original query into several related queries, assuming that query expansions expose subtopics of the original query. Moreover, each query expansion is given a weight which reflects the likelihood of the interpretation (the fraction of users who issued this query given the general query topic). We issue all those expanded queries including the original query to a standard BM25 search engine, then re-rank the retrieved documents to generate the final ranking. Our method can detect possible subtopics of a given query and provide a reasonable ranking that satisfies both relevancy and diversity metrics. The TREC evaluations show our method is effective on the diversity task.
Emotion inference from human body motion

The human body has evolved to perform sophisticated tasks from locomotion to the use of tools. At the same time our body movements can carry information indicative of our intentions, inter-personal attitudes and emotional states. Because our body is specialised to perform a variety of everyday tasks, in most situations emotional effects are only visible through subtle changes in the qualities of movements and actions. This dissertation focuses on the automatic analysis of emotional effects in everyday actions. In the past most efforts to recognise emotions from the human body have focused on expressive gestures which are archetypal and exaggerated expressions of emotions. While these are easier to recognise by humans and computational pattern recognisers they very rarely occur in natural scenarios. The principal contribution of this dissertation is hence the inference of emotional states from everyday actions such as walking, knocking and throwing. The implementation of the system draws inspiration from a variety of disciplines including psychology, character animation and speech recognition. Complex actions are modelled using Hidden Markov Models and motion primitives. The manifestation of emotions in everyday actions is very subtle and even humans are far from perfect at picking up and interpreting the relevant cues because emotional influences are usually minor compared to constraints arising from the action context or differences between individuals. This dissertation describes a holistic approach which models emotional, action and personal influences in order to maximise the discriminabil-ity of different emotion classes. A pipeline is developed which incrementally removes the biases introduced by different action contexts and individual differences. The resulting signal is described in terms of posture and dynamic features and classified into one of several emotion classes using statistically trained Support Vector Machines. The system also goes beyond isolated expressions and is able to classify natural action sequences. I use Level Building to segment action sequences and combine component classifications using an incremental voting scheme which is suitable for online applications. The system is comprehensively evaluated along a number of dimensions using a corpus of motion-captured actions. For isolated actions I evaluate the generalisation performance to new subjects. For action sequences I study the effects of reusing models trained on the isolated cases vs. adapting models to connected samples. The dissertation also evaluates the role of modelling the influence of individual user differences. I develop and evaluate a regression-based adaptation scheme. The results bring us an important step closer to …
Data Clustering by Large-Scale Adaptive Agent Systems

Finding items with specific characteristics in large distributed systems can be problematic. Directories, the most common way of enabling capability-based search in distributed systems, gather in a central location data which is often inherently decentralized. This paper considers an alternative decentralized method of enabling search, which leaves data objects in place at their natural location within a physically distributed network. This form of decentralized search is likely to gain importance as computer systems become more widely distributed and the autonomy of their components increases. Autonomy plays a key role in this development since it precludes the global use of a single comparison method for determining the similarity of objects. With this in mind, this work explores the ability of explicitly autonomous peer-to-peer agents to form themselves into groups within an overlay network, based on their locally perceived similarity, thus providing a structure that can facilitate search. We introduce a decentralized clustering procedure, designed to discover small clusters (of 500 items or less) in very large, widely distributed sets of data. This procedure is shown to scale well in the number of clusters. The paper further demonstrates that for 2D spatial data it produces clusterings that compare well to those produced by central clustering algorithms, especially for data sets that contain widely varying cluster characteristics.
Could Dopamine Agonists Aid in Drug Development for Anorexia Nervosa?

Anorexia nervosa is a severe psychiatric disorder most commonly starting during the teenage-years and associated with food refusal and low body weight. Typically there is a loss of menses, intense fear of gaining weight, and an often delusional quality of altered body perception. Anorexia nervosa is also associated with a pattern of high cognitive rigidity, which may contribute to treatment resistance and relapse. The complex interplay of state and trait biological, psychological, and social factors has complicated identifying neurobiological mechanisms that contribute to the illness. The dopamine D1 and D2 neurotransmitter receptors are involved in motivational aspects of food approach, fear extinction, and cognitive flexibility. They could therefore be important targets to improve core and associated behaviors in anorexia nervosa. Treatment with dopamine antagonists has shown little benefit, and it is possible that antagonists over time increase an already hypersensitive dopamine pathway activity in anorexia nervosa. On the contrary, application of dopamine receptor agonists could reduce circuit responsiveness, facilitate fear extinction, and improve cognitive flexibility in anorexia nervosa, as they may be particularly effective during underweight and low gonadal hormone states. This article provides evidence that the dopamine receptor system could be a key factor in the pathophysiology of anorexia nervosa and dopamine agonists could be helpful in reducing core symptoms of the disorder. This review is a theoretical approach that primarily focuses on dopamine receptor function as this system has been mechanistically better described than other neurotransmitters that are altered in anorexia nervosa. However, those proposed dopamine mechanisms in anorexia nervosa also warrant further study with respect to their interaction with other neurotransmitter systems, such as serotonin pathways.
Community detection using parallel genetic algorithms

The main problem on community detection using traditional genetic algorithms (GA) lies in the slow speed of convergence. This paper attempts to apply parallel genetic algorithms (PGA) to explore community structure in complex networks in order to improve the efficiency of traditional genetic algorithms. Several different designing ways of PGA are discussed and compared. Experimental results based on the GN benchmark networks, LFR benchmark networks, and eight real-world networks, confirm the PGA with coarse-grained-master-slave hybrid model spends less time yet achieves higher accuracy than traditional genetic algorithms.