Miracle-dz/UniFAR-FTUs huggingface dataset:
Assuring and evolving concurrent programs: annotations and policy

Assuring and evolving concurrent programs requires understanding the concurrency-related design decisions used in their implementation. In Java-style shared-memory programs, these decisions include which state is shared, how access to it is regulated, the roles of threads, and the policy that distinguishes desired concurrency from race conditions. We use case studies from production Java code to explore the costs and benefits of a new annotation-based approach for expressing design intent. Our intent is both to assist in establishing "thread safety" attributes in code and to support tools that safely restructure code. The annotations we use express "mechanical" properties such as lock-state associations, uniqueness of references, and encapsulation of state into named aggregations. Our analyses revealed race conditions in our case study samples, drawn from open-source projects and library code. The novel technical features of this approach include (1) flexible encapsulation via aggregations of state that can cross object boundaries, (2) the association of locks with state aggregations, (3) policy descriptions for allowable method interleavings, and (4) the incremental process for inserting, validating, and exploiting annotations.
An Evaluation of Forestry Journals Using Bibliometric Indices

The increasing number of scientific journals, especially over the last 20 years, created the need for methodologies based on simple metrics, to accurately capture the quality of those journals and their impact on the scientific community. Especially in the case of journals from the field of forestry, relatively little work has been conducted on providing valid journal classifications. In this paper we attempt to assess the impact of journals from this field in terms of bibliometric data. In addition to the already proposed metrics (complementary to the journal h-index), we also apply a new measure to rank journals, that provides a more balanced evaluation of the journal performance, by adjusting for various biases affecting the h-index. We examined the relationships between various bibliometric indicators proposed for assessing the journal impact and wo found high correlations between most indices, with only few exceptions. According to citation analysis, Canadian Journal of Forest Research, Journal of Vegetation Science, Forest Science, Tree Physiology, International Journal of Wildland Fire, Holzforschung, Trees-Structure and Function, Silva Fennica, Agricultural and Forest Meteorology and Wood and Fiber Science are the top forestry journals. These publish articles related to all the domains of forestry science. More specialized journals are also included, dealing with specific issues of scientific interest and also of major importance to the scientific community.
Citations, reference list, and author index with apacite

The apacite package is a package that can be used with L ATEX and BibT E X to generate citations and a reference list, formatted according to the rules of the American Psychological Association. Furthermore, apacite contains an option to (almost) automatically generate an author index as well. A recent addition is the support of different languages. The package can be customized in many ways. This paper describes the apacite package, paying special attention to its idiosyncracies and how the problems associated with these have been solved.
Empathy and the Mirror Neuron System : An Electroencephalographic Study

Specific Aims: Empathy, the ability to feel and understand another’s pain, pleasure, and feelings is vital to surviving the social circus of everyday life. The idea of putting yourself in someone else’s shoes is at the core of human relationships. Empathy is why charity is possible, why people go out of their way to selflessly help others in need, or why people can connect with each other on an emotional level. The aim of this study is to investigate the role of the human Mirror Neuron System (hMNS) in empathy. The mirror neuron system is a group of brain areas that consist of unique neurons. These neurons were found to have “mirroring qualities”, in that the neurons exhibited analogous activity when performing an action, or observing the same or similar action being performed. The human MNS has also been implicated in social functions, including empathy. This study will allow us to observe whether hMNS activity, as measured by changes in EEG rhythmic oscillations in the 8-13 Hz band over sensorimotor cortex, during an empathy task is correlated with empathic function. We will take normal-functioning UCSD students from each end of the empathizing-systemizing quotient (EQ-SQ) scale and observe their MNS activity during an emotional face-empathy task. Given that this difference in individual trait empathy may be reflected in differences 8-13 Hz rhythm, the two groups ought to show significant differences in the functional neuroanatomy of empathy. The specific aims of this study are to test the following hypotheses: The empathizing group will show differential activation compared to the systemizing group That the empathizers will exhibit increased MNS activation during the empathy tasks, reflected in the EEG data.
Document image layout comparison and classification

The paper describes features and methods for document image comparison and classification at the spatial layout level. The methods are useful for visual similarity based document retrieval as well as fast algorithms for initial document type classification without OCR. A novel feature set called interval encoding is introduced to capture elements of spatial layout. This feature set encodes region layout information in fixed-length vectors which can be used for fast page layout comparison. The paper describes experiments and results to rank-order a set of document pages in terms of their layout similarity to a test document. We also demonstrate the usefulness of the features derived from interval encoding in a hidden Markov model based page layout classification system that is trainable and extendible.
Performance study of Core2Duo desktop processors

The multicore processors turned out to open the doors to make the desktop to have parallel and high performance capability. In this paper, the performance study for these systems is presented, in which the studies were carried out on the Intel's Core2Duo processor with an OpenMP programming integrated into Microsoft visual studio C++ 2005 and Intel C++ 10.1. 020 compiler. Using multithreaded programming, this paper provides the performance measurement for dual core over a single core and assessing the performance of the OpenMP multithreaded programming on a Core2Duo processor. The speedup of dual-core over a single core and the behaviour of Core2Duo processor with OpenMP programming are reported. Our results reveal that the Core2Duo performance with OpenMP multithreaded programming falls behind the Core2Duo performance in a sequential mode when the workload exceeds the size of the L2 cache, also the dual core speedup drops drastically to about 1.1. Finally we measured the performance using the Stream and Stream2 benchmarks and analyse the running results.
Mention detection and classification in bio-chemical domain using Conditional Random Field

Finding mentions of chemical names in texts is of huge interest due to its importance in wide-spread application areas. The inherent complex structures of chemical names and the existence of several representations and nomenclatures (like SMILES, InChI, IUPAC) pose a big challenge to their automatic identification and classification. In this paper we present a supervised machine learning approach based on Conditional Random Fields (CRF) to find mentions of IUPAC and IUPAC-like names in scientific text. We identify and implement a very rich feature set for the task without using any domain specific knowledge and/or resources. Experiments are carried out on the benchmark MEDLINE datasets. Evaluation shows encouraging performance with the overall recall, precision and F-measure values of 90.96%, 91.52% and 91.23%, respectively. We also present the scope of comparison to the existing state-of-the-art system(s).
Password Entry Challenges from Nonstandard Keyboards

Mohammad Mannan is an Assistant Professor at the Concordia Institute for Information Systems Engineering, Concordia University, Montreal. He has a PhD in Computer Science from Carleton University (2009) in the area of Internet authentication and usable security. He was a postdoctoral fellow in the Department of Electrical and Computer Engineering at the University of Toronto from 2009 to 2011 (funded by an NSERC PDF, and ISSNet). His research interests lie in the area of Internet and systems security, with a focus on solving high-impact security and privacy problems of today’s Internet. mmannan@ciise.concordia. ca
Task-space tracking control of redundant robot manipulators via quaternion feedback

In this paper, we consider the problem of task-space tracking control of redundant robot manipulators. Based on a quaternion representation of the end-effector orientation, we design a class of task-space controllers that ensure asymptotic end-effector position and orientation tracking. To facilitate the control design, we first develop model-based and. adaptive full-state feedback controllers. We then present a model-based output feedback controller that eliminates link velocity measurements via a model-based observer.
Neurophysiological Assessment of Skeletal Muscle Fatigue in Patients With Congestive Heart Failure

BackgroundRecent research has demonstrated that patients with congestive heart failure (CHF) exhibit significant functional impairment of skeletal muscle and that these changes may be important determinants of exercise capacity. Although muscle strength may be mildly reduced, the most significant abnormality is markedly enhanced muscle fatigue. The goal of the present study is to determine whether accelerated fatigue is caused by impaired muscle activation, as a result of inadequate central motor drive or neuromuscular transmission, or by a change in the muscle itself. Methods and ResultsThe study population consisted of nine patients with New York Heart Association class I-III CHF and eight sedentary, age- and sex-matched control subjects. Maximal voluntary contraction force of the foot dorsiflexors (primarily the tibialis anterior) was quantified as a measure of muscle strength, isometric endurance was quantified by the time required for force to decline to 60% of maximal during a sustained maximal contraction, and dynamic endurance was defined as the number of maximal contractions required for force to decline to 60% of maximal under a protocol of six repetitions per minute with an incremental duty cycle. The degree of central motor drive failure was quantified by the degree of force augmentation produced by a superimposed tetanic stimulus delivered to the peroneal nerve during the initial maximal voluntary contraction and at the time when force during the sustained isometric contraction declined to 60% of maximal. Neuromuscular junction transmission was examined by quantifying the amplitude of the compound muscle action potential (M wave) in response to a single nerve stimulus during fatiguing exercise. Muscle strength was relatively preserved in the CHF patients versus the control subjects (93±41 versus 105±34 lb;p=NS), but isometric endurance (time to decline to 60%, 34±15 versus 54±19 seconds;p<0.02) and dynamic endurance (number of repetitions before decline to 60%, 30±6 versus 43±7 contractions; p<0.001) were both impaired. Tetanic nerve stimulation increased force by similar degrees in the two groups, and the amplitude of the M wave did not decline in either group during exercise. ConclusionsThese findings indicate that enhanced muscle fatigue in patients with CHF is not caused by impaired central motor drive or an abnormality of neuromuscular junction transmission but rather by an abnormality in the muscle itself.
Abstract concepts, compositionality, and the contextualism-invariantism debate

Abstract Invariantists argue that the notion of concept in psychology should be reserved for knowledge that is retrieved in a context-insensitive manner. Contextualists argue that concepts are to be understood in terms of context-sensitive ad hoc constructions. I review the central empirical evidence for and against both views and show that their conclusions are based on a common mischaracterization of both theories. When the difference between contextualism and invariantism is properly understood, it becomes apparent that the way the question of stability is currently investigated will not lead to a consensus. Instead of focusing directly on stability, we should turn our attention to other desiderata on a theory of concepts. In particular, I show that invariantism, but not contextualism, fails to account for compositionality and abstract concepts.
Quaternion Collaborative and Sparse Representation With Application to Color Face Recognition

Collaborative representation-based classification (CRC) and sparse RC (SRC) have recently achieved great success in face recognition (FR). Previous CRC and SRC are originally designed in the real setting for grayscale image-based FR. They separately represent the color channels of a query color image and ignore the structural correlation information among the color channels. To remedy this limitation, in this paper, we propose two novel RC methods for color FR, namely, quaternion CRC (QCRC) and quaternion SRC (QSRC) using quaternion ℓ1 minimization. By modeling each color image as a quaternionic signal, they naturally preserve the color structures of both query and gallery color images while uniformly coding the query channel images in a holistic manner. Despite the empirical success of CRC and SRC on FR, a few theoretical results are developed to guarantee their effectiveness. Another purpose of this paper is to establish the theoretical guarantee for QCRC and QSRC under mild conditions. Comparisons with competing methods on benchmark real-world databases consistently show the superiority of the proposed methods for both color FR and reconstruction.
A New Method for the Analysis of Neural Reference

In recent years there has been much eeort to develop the theoretical aspects of neural MRAC-Control, that is to nd conditions under which an unknwon process can be identiied by an input-output model and controllers can be trained by gradient descent ((3],,6]). On the other hand, the application of neural network techniques to real world control of nonlinear dynamical systems has been of substantial interest. Since the theoretical conditions that ensure controllability and the applicability of indirect adaptive control are hard to verify in practice, the success of controller training is mostly shown by testing relevant situations. We trained a controller for a subsystem of a spark ignition engine by dynamic backpropagation ((2]) and various truncated gradient algorithms. After-wards we related the neural MRAC-approach to Pole placement and Linearization techniques in order to show the successful training by Pole analysis of the completely trained loop. This is a new method to verify the plausibility of the adaptation process and the trained regulator.
The Linear Accelerating Superluminal Motion Model

Accelerating superluminal motions were detected recently by multiepoch very long baseline interferometry observations. Here a linear accelerating superluminal motion (LASM) model is proposed to interpret the observed phenomena. The model provides a direct and accurate way to estimate the viewing angle of a relativistic jet. It also predicts that both Doppler boosting and deboosting effects may take place in an accelerating forward jet. The LASM model is applied to the data of the quasar 3C 273, and the initial velocity, acceleration, and viewing angle of its three components are derived through model fits. The variations of the viewing angle suggest that a supermassive black hole binary system may exist in the center of 3C 273. The gap between the inner and outer jet in some radio-loud active galactic nuclei my be explained in terms of Doppler deboosting effects when the components accelerate to ultrarelativistic speed.
The Impact of Imagery Therapy on Rheumatoid Arthritis Patients

This paper concerns computer applications in medicine that exploit the potentially powerful interconnection between psychology and physiology. An important class of applications involve patients who suffer chronic pain caused by rheumatoid arthritis; these patients look for any kind of therapeutic technique to use in addition to traditional medicine in order to alleviate pain, reduce physical disabilities caused by the disease or delay disease progress and improve their lifestyle. This paper focuses on a particular class of therapeutic techniques that provide imagery therapy using virtual reality in the treatment of rheumatoid arthritis. We describe imagery therapy involving guided imagery and illustrate its effects. We particularly describe the application of VR and IT in the treatment of rheumatoid arthritis. A brief introduction to our system and the research we have done in this field is presented, showing some preliminary results that support our hypothesis.
Implicit Theor ies Go Applied: Conception of Ability at Work

Thompson, Charles N. , Industrial and Organizational Psychology; Department of Psychology, Wright State University, 2006. Implicit Theories Go Applied: Conception of Ability at Work. Implicit theories have been extensively studied in educational psychology (e.g. Dweck, 1999; Dweck & Leggett, 1988). Implicit theories have been related to goal orientation, response to failure, attributional style and perception of effort. With a few exceptions (e.g. Heslin, Latham, & VandeWalle, 2005; Martocchio, 1994) the potential applicability of this research to industrial/organizational psychology has been largely ignored. The current study proposed a measure specifically designed to measure implicit theories about work, assessed its relationship to other implicit theory measures, and explored potential relationships with work domain antecedents. Correlational analyses demonstrated significant relationships between conception of ability at work and perceptions of effort at work, as well as competence demonstration goal focus. A confirmatory factor analysis revealed conception of ability at work as an implicit theory distinct from other measures of implicit theory.
Anisotropic diffusion based weed classifier

This paper presents a new approach of anisotropic diffusion to classify the weed images into broad and narrow class for real time selective herbicide application The classifier we proposed based on Perona and Malik equation. Its low computational complexity and fast runtimes makes this method well suited for real-time vision applications. The developed system has been tested on weeds in the lab; the results show a very reliable performance and drastically less computational effort on images of weeds taken under varying field conditions. The analysis of the results shows over 97.6% classification accuracy over 200 sample images.
A multi-criteria exemplar model for holistic categorization in autonomous agents

When developing a model of perceptual categorization, a functional and holistic approach is required. Therefore, considering the influence of an agent's current needs, perceptual categorization is modeled as the valuation of a stimulus regarding its potential to satisfy an agent's needs. Based on an exemplar model, an activation-based model is developed that considers two kinds of categorization criteria, namely perceptual similarity and expectation, which represents bottom-up and top-down approaches of perception, respectively. The aggregation of these two criteria then corresponds to the integration of these approaches into a holistic model. A simplified simulation of the categorization model shows that the multi-criteria model leads to a more confident valuation of stimulus objects. In particular, the model is able to eliminate ambiguity in perceptual categorization. Although used equivalently, the simulation shows that the bottom-up similarity criterion is more significant than the top-down expectation-based criterion. In particular, the latter is only significant in case of ambiguity in an object's appearance.
Combining domain and topic adaptation for SMT

Recent years have seen increased interest in adapting translation models to test domains that are known in advance as well as using latent topic representations to adapt to unknown test domains. However, the relationship between domains and latent topics is still somewhat unclear and topic adaptation approaches typically do not make use of domain knowledge in the training data. We show empirically that combining domain and topic adaptation approaches can be beneficial and that topic representations can be used to predict the domain of a test document. Our best combined model yields gains of up to 0.82 BLEU over a domain-adapted translation system and up to 1.67 BLEU over an unadapted system, measured on the stronger of two training conditions.
Feature Extraction Labeled Training Data Classifier Training Classifier Model Unknown Data Classification Results Feature Extraction

Application domains include a toolbox of different techniques and algorithms perfected over time. Much of this knowledge, however, still remains locked up in the domain literature and not readily accessible to people not familiar with the application area. We believe patterns can help us expose the domain knowledge to others not familiar with the depth of the domain and aid the creation of application frameworks that will let software writers (not necessarily domain experts) create interesting applications. These application patterns also serve to show how application domains feed the structural and computational patterns in OPL. In addition to pattern mining from individual examples, application patterns will help codify common practices and help programming framework development tailored to particular application areas. The Extract Classify pattern is used in a variety of contexts and domains. In general, the problem centers around understanding data. An overview of the problem is presented in figure 1. In the Extract Classify pattern, we start with Figure 1: The Extract Classify Pattern some labeled training data, which has been annotated with results known to be correct. We extract features from this data, and then train a classifier based on these features and our known labels. This training process produces a model.