Miracle-dz/UniFAR-FTUs huggingface dataset:
Scalable parallel data mining for association rules

One of the important problems in data mining is discovering association rules from databases of transactions where each transaction consists of a set of items. The most time consuming operation in this discovery process is the computation of the frequency of the occurrences of interesting subset of items (called candidates) in the database of transactions. To prune the exponentially large space of candidates, most existing algorithms, consider only those candidates that have a user defined minimum support. Even with the pruning, the task of finding all association rules requires a lot of computation power and time. Parallel computers offer a potential solution to the computation requirement of this task, provided efficient and scalable parallel algorithms can be designed. In this paper, we present two new parallel algorithms for mining association rules. The Intelligent Data Distribution algorithm efficiently uses aggregate memory of the parallel computer by employing intelligent candidate partitioning scheme and uses efficient communication mechanism to move data among the processors. The Hybrid Distribution algorithm further improves upon the Intelligent Data Distribution algorithm by dynamically partitioning the candidate set to maintain good load balance. The experimental results on a Cray T3D parallel computer show that the Hybrid Distribution algorithm scales linearly and exploits the aggregate memory better and can generate more association rules with a single scan of database per pass.
Theories and methods in mediated communication

1. Theories and Methods Technology mediated communication is a fact of life. The human communication apparatus is constrained in several ways. There are limits to the distance at which speech is audible, and visible behaviours such as gesture, gaze or facial expressions are perceivable. Furthermore, these natural communication behaviours are ephemeral and do not persist over time. Given these limitations, we must rely on some form of mediation, if we are to communicate at distance and across time. People have therefore invented technologies that attempt to circumvent these limits to allow remote synchronous communication (e.g. telephone, videoconference) or asynchronous communication (e.g. letters, telegraph, email, fax, voicemail). Understanding the principles that govern mediated communication has important practical implications. Because of the pervasiveness of mediated communication, we need to determine whether, how and why, it differs from face to face communication. If there are differences between mediated and face to face communication, we need to provide guidelines to inform decisions about the circumstances in which it is appropriate to use mediated communication, and the effects of using it in those situations. Systematic insights into mediated communication should also enable us to improve the designs of current and future technologies supporting mediated communication. An understanding of mediated communication should also inform more general theorizing about the psychology of communication. Most current communication theories regard face to face communication as an integrated set of speech, gaze, and gestural behaviours. As we shall see, studies of mediated communication allow us to isolate the individual contribution of different nonverbal behaviours, such as gaze and gesture to communication. They also help to clarify the overall influence of visual information in communication, and the effects of communication interactive processes such as feedback on communication. The structure of the review is as follows. The main part of the review is organized around 3 main theoretical approaches to mediated communication. We first talk about the general characteristics of mediated communication theories, and methods in this area. We then characterize each theory in detail and evaluate the evidence for it. We conclude with a discussion of outstanding practical and theoretical issues.
Update on pathology of ocular parasitic disease

Parasites are a group of eukaryotic organisms that may be free-living or form a symbiotic or parasitic relationship with the hosts. Consisting of over 800,000 recognized species, parasites may be unicellular (Protozoa) or multicellular (helminths and arthropods). The association of parasites with human population started long before the emergence of civilization. Parasitic zoonotic diseases are prevalent worldwide including India. Appropriate epidemiological data are lacking on existing zoonotic parasitic diseases, and newer diseases are emerging in our scenario. Systemic diseases such as cysticercosis, paragonimiasis, hydatidosis, and toxoplasmosis are fairly common. Acquired Toxoplasma infections are rising in immune-deficient individuals. Amongst the ocular parasitic diseases, various protozoas such as Cystoidea, trematodes, tissue flagellates, sporozoas etc. affect humans in general and eyes in particular, in different parts of the world. These zoonoses seem to be a real health related problem globally. Recent intensification of research throughout the world has led to specialization in biological fields, creating a conducive situation for researchers interested in this subject. The basics of parasitology lie in morphology, pathology, and with recent updates in molecular parasitology, the scope has extended further. The current review is to address the recent update in ophthalmic parasites with special reference to pathology and give a glimpse of further research in this field.
Surface extraction from three dimensional ultrasound

This paper presents a system for acquiring three dimensional ultrasound data and extracting surfaces of the examined structures. The acquisition is performed freehand with a PC-based two-dimensional ultrasound machine and an optical tracker. The extraction of surfaces from ultrasound data is normally inhibited by speckle, shadowing and gaps in the acquired data. A new method is developed that extracts a surface directly from the irregularly spaced, noisy freehand ultrasound data. The freehand data is first interpolated with radial basis functions, and then a mesh is formed along an isosurface of the functional interpolation. The ability of radial basis functions to smooth speckle and extrapolate across gaps is demonstrated on a series of experiments with phantoms and human tissue in a water bath. The experiments also show the ability to create continuous and realistic surfaces from scans that require multiple sweeps over a structure.
Novel supervised feature extraction algorithm based on iterative calculations

In pattern recognition, the principal component analysis (PCA) is one of the most famous feature extraction methods for dimensionality reduction of high-dimensional datasets. Furthermore, Simple-PCA (SPCA) which is a faster version of the PCA, has been carried out effectively by iterative operated learning. However, in SPCA, when input data are distributed in a complex way, SPCA might not be efficient because it is learned without class information of the dataset. Thus, SPCA cannot be said that it is optimal for classification. In this paper, we propose a new learning algorithm, which is learned with the class information of the dataset. Eigenvectors spanning eigenspace of the dataset are obtained by calculation of data variations belonging to each class. We will show the derivation of the proposed algorithm and demonstrate some experiments to compare the SPCA with the proposed algorithm by using UCI datasets.
Gisladottir Other-initiated repair in Icelandic

The ability to repair problems with hearing or understanding in conversation is critical for successful communication. This article describes the linguistic practices of other-initiated repair (OIR) in Icelandic through quantitative and qualitative analysis of a corpus of video-recorded conversations. The study draws on the conceptual distinctions developed in the comparative project on repair described in the introduction to this issue. The main aim is to give an overview of the formats for OIR in Icelandic and the type of repair practices engendered by them. The use of repair initiations in social actions not aimed at solving comprehension problems is also briefly discussed. In particular, the interjection ha has a rich usage extending beyond open other-initiation of repair. By describing the linguistic machinery for other-initiated repair in Icelandic, this study contributes to the typology of conversational structure and to the still nascent field of Icelandic social interaction studies.
CT-Compatible Medical Drilling Stylet

This paper describes the design of a compact, lightweight CT-compatible, drill-press that is designed to be used in either a hand-held or stand-alone mode to assist with percutaneous bone based interventions. Previous medical drilling tools that have been developed have a metal structure and typically have one actuator for advancing the drill (feed) and another for rotating it (speed). After defining the device functional requirements and specifications, a deterministic design process was followed to generate several design concepts that were then evaluated based on their ability to satisfy the functional requirements. A final concept that uses a custom screw-spline to achieve helical motion of a shaft that is attached to a standard orthopedic drill was selected for prototyping. The design uses a single actuator to drive both the screw and spline nuts through two different gear ratios, resulting in a fixed ratio between the feed and speed. Apart from the motor which is placed away from the central drill axis, the device is largely made from plastic materials. A custom experimental setup was developed that enabled drilling into bone inside a CT scanner to be examined. Results showed that the device was successfully able to penetrate thick cortical bone and that its structure did not appreciably distort the medical images.
Computational Models of the Utility Problem and their Application to a Utility Analysis of Case-Based Reasoning

computational analysis of these factors. We use this method to analyze different types of problem solving systems, including unguided search, control rule, and case-based systems. While some aspects of the utility problem have been studied in some kinds of problem solvers, our analysis provides a general and theoretical framework for addressing this problem in problem solvers that have been studied empirically (e.g., control-rule learning systems) and in problem solvers for which little utility analysis has been performed (e.g., case-based reasoning systems). In particular, our analysis reveals that case-based reasoning systems may suffer from several different utility problems, and suggests coping strategies that would help avoid these problems as these systems are scaled up. The utility problem in learning systems occurs when knowledge learned in an attempt to improve a system's performance degrades performance instead. There are many types of utility problems and many factors that combine to cause them; computational models of problem solving systems which take these factors into account can be used to analyze the causes of the utility problem and to design coping strategies to eliminate it. The application of this analysis to case-based reasoning reveals that it may suffer from several utility problems, and suggests coping strategies that may be effective in combating these problems.
Greedy Multiple Instance Learning via Codebook Learning and Nearest Neighbor Voting

Multiple instance learning (MIL) has attracted great attention recently in machine learning community. However, most MIL algorithms are very slow and cannot be applied to large datasets. In this paper, we propose a greedy strategy to speed up the multiple instance learning process. Our contribution is two fold. First, we propose a density ratio model, and show that maximizing a density ratio function is the low bound of the DD model under certain conditions. Secondly, we make use of a histogram ratio between positive bags and negative bags to represent the density ratio function and find codebooks separately for positive bags and negative bags by a greedy strategy. For testing, we make use of a nearest neighbor strategy to classify new bags. We test our method on both small benchmark datasets and the large TRECVID MED11 dataset. The experimental results show that our method yields comparable accuracy to the current state of the art, while being up to at least one order of magnitude faster.
Individual differences in the reminiscence bump of very long-term memory for popular songs in old age: A non-linear mixed model approach

Recognition of popular songs from one’s past shows a phenomenon that is known as a “reminiscence bump” from autobiographical memory research, namely, the increased recognition performance of songs from one’s youth and early adulthood. As a first goal of the present study, a non-linear functional relation between popular song recognition performance and song-specific age of an individual was examined. As a second goal, individual differences in recognition performance curves were taken into account by including random effects. The third goal was to explain individual differences by including predictor variables. The sample comprised 90 participants aged 70 to 75 years. Participants listened to excerpts of 51 songs of the German charts from the years 1945 to 1995. Results show that the average bump performance was 75%, that the bump was located at about 17 years of age, and that the inflection point was located at 31 years. Individual differences could be explained by the number of correctly recognized songs, musical taste during one’s youth, and the frequency and preference of listing to bump songs. To conclude, an individual differences approach based on a non-linear function relation has been found to be a promising way to understand why popular songs from one’s youth are remembered better.
Clinical and subclinical inflammation in patients with familial Mediterranean fever and in heterozygous carriers of MEFV mutations.

OBJECTIVE To prospectively monitor inflammatory activity over a prolonged period in a cohort of Turkish patients with FMF, their healthy relatives and healthy controls and to relate this to their MEFV genotypes. METHODS 43 patients with FMF and 75 of their asymptomatic relatives underwent fortnightly assessments and venesection for measurement of CRP and SAA over 5 months. 50 unrelated healthy population matched controls were also studied. MEFV genotyping was performed on all participants and comparisons were made between the different groups. RESULTS Paired MEFV mutations were detected in 84% of FMF patients and single mutations in 12%. Substantial acute phase reactivity was seen among the patients with FMF during attacks (median SAA 693 mg/l, CRP 115 mg/l). Between attacks there was also some inflammatory activity (median SAA 6 mg/l, CRP 4 mg/l). Among healthy controls 16% were heterozygotes for MEFV mutations and 4% had two mutations. As expected there was a substantial carrier rate among healthy relatives with mutations detected in almost 92%. Asymptomatic MEFV heterozygotes had elevated acute phase proteins compared to wild type subjects. CONCLUSION Substantial sub-clinical inflammation occurs widely and over prolonged periods in patients with FMF, indicating that the relatively infrequent clinically overt attacks represent the 'tip of the iceberg' in this disorder. Both basal and peak acute phase protein concentrations were greater in MEFV heterozygotes than in wild-type controls, regardless of mutation demonstrating a 'pro-inflammatory' phenotype among FMF carriers. Upregulation of the acute phase response among carriers of FMF may augment their innate host response and contribute to better resistance to infection.
A new perspective on gender differences in student self-perceptions of ability in mathematics

The purpose of this investigation was to better understand the historical differences between boys and girls in the mathematics courses that are taken and their choices to pursue math-related careers when gender differences appear to be no longer prevalent on mathematics achievement tests. This descriptive study examines gender differences of middle school students in mathematics through the framework of the possible selves theory. Possible selves are self-conceptions of both what an individual hopes to become and fears becoming. Survey measures were designed and used to determine students' hopes and fears in mathematics. Results did not support the hypothesis of gender differences that girls would have more feared selves than boys. However, overall the students expressed a high regard for their mathematics abilities and had more hoped-for than feared possible mathematical selves. Speculations are made for why no gender differences were found in the sample. Other limitations and suggestions for further research are offered. iv ACKNOWLEDGEMENTS
Rainfall Estimation Using Transductive Learning

Precipitation is a crucial link in the hydrological cycle, and its spatial and temporal variations are enormous. A knowledge of the amount of regional rainfall is essential to the welfare of society. Rainfall can be estimated remotely, either from ground-based weather radars or from satellite. Despite the large amount of available data provided by satellites, most of them are unlabeled, and the acquisition of labeled data for a learning problem often requires a skilled human agent to manually classify training examples. In this paper we introduce the use of semi-supervised support vector machines for rainfall estimation using images obtained from visible and infrared NOAA satellite channels. The semi-supervised learners combine both labeled and unlabeled data to perform the classification task. Two experiments were performed, one involving traditional SVM and other using semi-supervised SVM (S3VM). The S3VM approach outperforms SVM in our experiments, with can be seen as a good methodology for rainfall satellite estimation, due to the large amount of unlabeled data. The accuracies obtained for SVM and S3VM were, respectively, 90.6% and 95.96%.
Performance Characteristics of qPCR Assays Targeting Human- and Ruminant-Associated Bacteroidetes for Microbial Source Tracking across Sixteen Countries on Six Continents

Numerous quantitative PCR assays for microbial fecal source tracking (MST) have been developed and evaluated in recent years. Widespread application has been hindered by a lack of knowledge regarding the geographical stability and hence applicability of such methods beyond the regional level. This study assessed the performance of five previously reported quantitative PCR assays targeting human-, cattle-, or ruminant-associated Bacteroidetes populations on 280 human and animal fecal samples from 16 countries across six continents. The tested cattle-associated markers were shown to be ruminant-associated. The quantitative distributions of marker concentrations in target and nontarget samples proved to be essential for the assessment of assay performance and were used to establish a new metric for quantitative source-specificity. In general, this study demonstrates that stable target populations required for marker-based MST occur around the globe. Ruminant-associated marker concentrations were strongly correlated with total intestinal Bacteroidetes populations and with each other, indicating that the detected ruminant-associated populations seem to be part of the intestinal core microbiome of ruminants worldwide. Consequently tested ruminant-targeted assays appear to be suitable quantitative MST tools beyond the regional level while the targeted human-associated populations seem to be less prevalent and stable, suggesting potential for improvements in human-targeted methods.
Objective-based Reinforcement Learning System for Cooperative Behavior Acquisition

This chapter discusses the emergence of cooperative behavior in multiagent system and presents an objective-based reinforcement learning system in order to acquire cooperative behavior. Reinforcement learning is a method that agents will acquire the optimum behavior by trial and error by being given rewards in an environment as a compensation for its behavior (Kaelbling et al., 1996; Sutton & Barto, 1998; Weber et al. 2008). Most of studies on reinforcement learning have been conducted for single agent learning in a static environment. The Q-learning which is a typical learning method is proved that it converges to an optimum solution for Markov decision process (MDP) (Watkins & Dayan, 1992). However, in a multiagent environment, as plural agents' behavior may affect the state transition, the environment is generally considered as non Markov decision process (non-MDP), and we must face critical problems whether it is possible to solve (Stone & Veloso, 2000). On the above problems in a multiagent environment, Arai et al. have compared Q-learning with profit sharing (PS) (Grefenstette, 1988) using the pursuit problem in a grid environment (Arai et al., 1997). As a result, Q-learning has instability for learning because it uses Q values of the transited state in an updating equation. However, PS can absorb the uncertainty of the state transition because of cumulative discounted reward. Therefore, they concluded that PS is more suitable than Q-learning in the multiagent environment (Arai et al., 1997; Miyazaki & Kobayashi, 1998). Uchibe et al. have presented the capability of learning in a multiagent environment since relation between actions of a learner and the others is estimated as a local prediction model (Uchibe et al., 2002). However, PS has a problem of inadequate convergence because PS reinforces all the pairs of a state and an action irrespective of the achievement of a purpose (Nakano et al., 2005). This chapter presents an objective-based reinforcement learning system for multiple autonomous mobile robots to solve the above problem and to emerge cooperative behavior (Kobayashi et al, 2007). The proposed system basically employs PS as a learning method but a PS table, which is used for PS learning, is divided into two kinds of PS tables to solve the above problem. One is to learn cooperative behavior using information on other agents’ positions and the other is to learn how to control basic movements. Through computer 14
The LUNA Spoken Dialogue System: Beyond utterance classification

We present a call routing application for complex problem solving tasks. Up to date work on call routing has been mainly dealing with call-type classification. In this paper we take call routing further: Initial call classification is done in parallel with a robust statistical Spoken Language Understanding module. This is followed by a dialogue to elicit further task-relevant details from the user before passing on the call. The dialogue capability also allows us to obtain clarifications of the initial classifier guess. Based on an evaluation, we show that conducting a dialogue significantly improves upon call routing based on call classification alone. We present both subjective and objective evaluation results of the system according to standard metrics on real users.
Inference and declaration of independence: Impact on deterministic task parallelism

We present a set of static techniques that reduce runtime overheads in task-parallel programs with implicit synchronization. We use a static dependence analysis to detect non-conflicting tasks and remove unnecessary runtime checks. We further reduce overheads by statically optimizing task creation and management of runtime metadata. We implemented these optimizations in SCOOP, a source-to-source compiler for such a programming model and runtime system. We evaluate SCOOP on 10 representative benchmarks and show that our approach can improve performance by 12% on average.