Miracle-dz/UniFAR-FTUs huggingface dataset:
LITERATURE REVIEW : Parallel Computational Geometry on Multicore Processors A Review of Basic Algorithms

Parallel Computing has been one of the most important problems in computer science as well as a solution to satisfy the growing need of computation speed. Although in the past decades, parallel computing has been an interested area, it is going to be more popular and useful among simple users. In the past years, the area was strange for home users due to the high cost of installing a parallel computer machine. Besides that, large necessary space to install such systems, inexistence of efficient algorithms, etc. caused the area to be popular only among computer scientists. These days, by reducing the cost of producing different parallel systems with various architectures, these machines can be seen in every PC. Intel multicore processor is one of the most common parallel processors on current desktops and laptops. The ease of accessing such simple parallel processors makes experts to redesign and revise old scientific algorithms and software architectures which were originally designated for sequential processing. In fact, in order to keep today’s computations efficient, most of the basic algorithms which are optimal for single-core processors, should be modified. Some of these algorithms are sorting algorithms, searching algorithms, matrix operatons, and graph algorithms. As a result of revising basic algorithms, advanced algorithms in different scientific areas, which use basic algorithms as a small portion of their bodies, need to be revised as well. It is clear that scientific algorithms in the fields of computational geometry, image processing, bioinformatics, etc. use the basic mentioned algorithms to solve a bigger problem related to their special fields. Therefore, because of the vast use of parallel processors, especially different multicore processors, and in order to keep use of them efficient, it is necessary to redesign both basic and advanced algorithms in different scientific fields. In this project, we are going to review basic and advanced problems of computational geometry on parallel machines. Note that the parallel platforms that we chose are current multicore platforms. They includes Cell-BE of IBM,Sony,Toshiba and Multicore processor of Intel. In the past years, researchers did many works on parallel computational geometry. These works are mostly on expensive, large-size parallel machines and sometimes on virtual and theoretical parallel models such as PRAM. These days, by running small parallel processing
Eigen Value Computation Based Approach for Reduced Order Modeling for Interval Systems

A new method of model order reduction technique for interval system is proposed here. The reduced order denominator coefficients are determined by combined benefits of reciprocal transformation, principal pseudo break frequency and a new method of eigen spectrum analysis. Then the reduced order numerator coefficients are obtained using polynomial coefficient matching method. Optimization method can also be used for determining the coefficients of reduced order numerator. In this paper Luus -- Jaakola optimization technique is tried. This method guaranteed a stable reduced order model for the stable higher order system along with performance matching. The illustrated examples using MATLAB simulation show the effectiveness of the proposed method.
Standard language variety conversion for content localisation via SMT

Translation between varieties of the same language is a widespread reality in the localisation industry. However, monolingual statistical machine translation (SMT) is still a solution that has not yet been adequately explored; to the best of our knowledge, previous work in this area has never directly applied SMT to varieties of the same language for the precise purpose of reducing the time and cost of human translation and editing of content that needs to be localised. In this paper, we start exploring the problem by deploying SMT to translate Brazilian Portuguese into European Portuguese. Our exploration mainly takes into consideration the use of bilingual dictionaries to guide the decoder and modify the translation output. We also consider the option of mining a bilingual dictionary from word alignments obtained after standard SMT training. On good-quality data provided by Intel, we show that the SMT baseline already constitutes a strong system which in a number of experiments we fail to improve upon. We conjecture that bilingual dictionaries mined from client data would help if more heterogeneous training data were to be added.
Exploring the supervision of occupational therapists in New Zealand

ix CHAPTER 1: INTRODUCTION AND OVERVIEW 1 The researcher’s interest in supervision 2 The study 3 Supervision and supervisory processes 4 The context in which supervision occurs 7 The historical development of supervision in New Zealand 8 Supervision and the relationship with the profession in New Zealand 11 The significance of the study 14 The aim of the study 15 The structure of the thesis 15 CHAPTER TWO: LITERATURE REVIEW 17 Defining supervision 17 The functions of supervision 20 Forms, modes, and kinds of supervision 22 Forms of supervision 23 Modes of supervision 24 Kinds of supervision 25 Models of supervision 26 Theoretical underpinnings 26 The models 27 Tools of supervision 29 The parties to supervision 31 Supervisors and supervisees 31 The organisation or agency 33 The process of supervision 34 Contracting 34 Frequency 35 Training 35 The debates about supervision 36 Need for supervision 36 Supervision and therapy – professional and personal development 36 Power and authority 37 The place of managerial supervision 38
Forward Constraint-ased Algorithms for Anytime Planning

This paper presents a generic anytime forward-search constraint-based algorithm for solving planning problems expressed in the CNT framework ( Constraint Network on Time-lines ). It is generic because it allows many kinds of search to be covered, from complete tree search to greedy search. It is anytime because some parameter settings, together with domain-speciﬁc knowledge, allow high quality plans to be produced very quickly and to be further improved. It is forward because it systematically considers the decisions to be made in a chronological order. It is ﬁnally constraint-based because it is built on top of the CNT framework which is an extension of the CSP framework able to model discrete event dynamic systems and because it is implemented on top of the Choco constraint programming tool from which it inherits all the constraint handling machinery. Experimental comparisons are made in terms of quality proﬁle with other domain-dependent and domain-independent planners.
Na+,K+-ATPase as the Target Enzyme for Organic and Inorganic Compounds

This paper gives an overview of the literature data concerning specific and non specific inhibitors of Na+,K+-ATPase receptor. The immobilization approaches developed to improve the rather low time and temperature stability of Na+,K+-ATPase, as well to preserve the enzyme properties were overviewed. The functional immobilization of Na+,K+-ATPase receptor as the target, with preservation of the full functional protein activity and access of various substances to an optimum number of binding sites under controlled conditions in the combination with high sensitive technology for the detection of enzyme activity is the basis for application of this enzyme in medical, pharmaceutical and environmental research.
A partial least squares based ranker for fast and accurate age estimation

Facial age estimation is challenging due to complex dynamics in aging process, which render metric regression methods unfavorable. Rankers show better performance by exploiting the ordinal nature of ages. The difficulty of designing a ranker is that each binary classifier of a ranker has to be trained using highly unbalanced positive and negative data. This paper proposes a partial least squares based ranker (PLS-Ranker), which fully maintains the advantages of PLS and greatly boosts its performance on the ordinal problem. In PLS-Ranker, an adaptive threshold learning strategy is proposed to boost each of the binary classifiers learned from highly unbalanced data. Previous ranking approaches such as CS-OHRank suffer from heavy computations because dozens of binary classifiers are trained separately. However, in PLS-Ranker, they are jointly learned. Additionally, PLS-Ranker simultaneously reduces feature dimensions and ranks in high speed even for high-dimensional features. Experimental results on the age estimation problem show that PLS-Ranker outperforms the state-of-the-art methods in terms of both accuracy and speed. PLS-Ranker also achieves state-of-the-art performance on the multi-source cross-race-and-gender age estimation problem, which further demonstrates its robustness.
JRBridge: A Framework of Large-Scale Statistical Computing for R

Demands for highly scalable parallel data processing platforms is raising due to an explosion in the number of massive-scale data intensive applications both in industry and in sciences. Performing statistical computing over huge data repositories poses a significant challenge to existing statistical software and computational infrastructure. After analyzing various open source computational infrastructures and their programming paradigm APIs, the results have shown that most of them are JVM based, and their APIs are given as Java interfaces or abstract classes. This paper proposes a generic framework JR Bridge, which can integrate R and JVM-based computational infrastructures by generating Java APIs code wrapper around the native R code automatically and handling type conversion. Using this framework, we build a distributed statistical computing environment by integrating R with Hadoop. With the Hadoop Distributed File System plug in, it brings a way to store and access datasets with millions of objects. With MapReduce plug in, it brings a natural environment to code MapReduce algorithms in R. The experiment result shows JR Bridge scales linearly with the size of the datasets and thus provides a scalable solution for large-scale statistical computing in R.
Presenilin-1 Mutation Increases Neuronal Vulnerability to Focal Ischemia In Vivo and to Hypoxia and Glucose Deprivation in Cell Culture: Involvement of Perturbed Calcium Homeostasis

Many cases of early-onset inherited Alzheimer's disease (AD) are caused by mutations in the presenilin-1 (PS1) gene. Studies of cultured neural cells suggest that PS1 mutations result in perturbed cellular calcium homeostasis and may thereby render neurons vulnerable to apoptosis. In light of evidence that metabolic impairment plays a role in AD, that cerebral ischemia may be a risk factor for AD, and that individuals with AD have increased morbidity and mortality after stroke, we examined the impact of a PS1 mutation on neuronal vulnerability to ischemic injury. We report that the extent of brain injury after focal cerebral ischemia reperfusion is increased, and behavioral outcome is worsened, in PS1 mutant knock-in mice compared to wild-type mice. Cultured cortical neurons from PS1 mutant mice exhibit increased vulnerability to glucose deprivation and chemical hypoxia compared to their wild-type counterparts. Calcium imaging studies demonstrated enhanced elevation of intracellular calcium levels after glucose deprivation and chemical hypoxia in neurons from PS1 mutant mice. Agents that block calcium release from IP3- and ryanodine-sensitive stores (xestospongin and dantrolene, respectively) protected against the endangering action of the PS1 mutation. Our data suggest that presenilin mutations may promote neuronal degeneration in AD by increasing the sensitivity of neurons to age-related ischemia-like conditions. The data further suggest that drugs that stabilize endoplasmic reticulum calcium homeostasis may prove effective in suppressing the neurodegenerative process in AD patients.
Ltr Annotator: Automated Identification and Annotation of Ltr Retrotransposons in Plant Genomes

Long Terminal Repeat transposable element (LTR) is a major type of mobile elements ubiquitous in eukaryotic genomes. They account for a major proportion of many plant genomes and have a prominent impact on the evolution of genome size, structure and function. Although some bioinformatics tools for de novo LTR identification from genome sequences have been developed, an automated and standardized software tool for both LTR identification and annotation would be valuable and essential for comparative analysis of sequenced plant genomes. We present here a Java-based pipeline tool, called LTR Annotator, for automatically and consistently performing genome-wide de novo identification and annotation of LTRs of plant genome sequences. The pipeline first identifies LTRs using both LTR_FINDER and LTR harvest, then performs intensive annotations, and finally sweeps out potentially false-positive LTRs. The pipeline was evaluated using the well curated Arabidopsis genome. High sensitivity (>0.9) was obtained by using LTR harvest or LTR harvest+LTR_FINDER. Ten potentially new intact LTRs were detected. This pipeline provides a comprehensive tool to perform comparative analysis of LTRs for plant genomes, delivering annotated genomic resources for epigenetic and other studies. LTR Annotator is free and available upon request.
Event-Based Activity Tracking in Work Environments

Wearable computers aim to empower people by providing relevant information at appropriate time. This context-based information delivery helps to perform intricate, tedious or critical tasks and improves productivity, decreases error rates, and thus results in a reduction of labor cost. To evaluate the usability of wearable computing in a work environment, we have chosen a car production scenario in which new employees are trained for mechanical assembly tasks. In this paper we describe the implementation of an activity tracking system that allows to detect the individual steps of assembling a front lamp into a car body. Our approach is to break down these steps of the assembly task into smaller units, so called discrete events. Body-worn and environmental sensors are employed to create these events which trigger transitions in a Finite State Machine (FSM). The FSM states represent user activities which correspond to the assembly steps.
COGNITIVELY INSPIRED NATURAL-LANGUAGE BASED KNOWLEDGE REPRESENTATIONS : FURTHER EXPLORATIONS OF LATENT SEMANTIC ANALYSIS

Natural-language based knowledge representations borrow their expressiveness from the semantics of language. One such knowledge representation is Latent semantic analysis (LSA), a statistical, corpusbased technique for representing knowledge. It has been successfully used in a variety of applications including intelligent tutoring systems, essay grading and coherence metrics. The advantage of LSA is that it is efficient in representing world knowledge without the need for manual coding of relations and that it has in fact been considered to simulate aspects of human knowledge representation. An overview of LSA applications will be given, followed by some further explorations of the use of LSA. These explorations focus on the idea that the power of LSA can be amplified by considering semantic fields of text units instead of pairs of text units. Examples are given for semantic networks, category membership, typicality, spatiality and temporality, showing new evidence for LSA as a mechanism for knowledge representation. The results of such tests show that while the mechanism behind LSA is unique, it is flexible enough to replicate results in different corpora and languages.
Semantic web methodologies, best practices and ontology engineering applied to Internet of Things

We discuss in this paper, semantic web methodologies, best practices and recommendations beyond the IERC Cluster Semantic Interoperability Best Practices and Recommendations (IERC AC4). The semantic web community designed best practices and methodologies which are unknown from the IoT community. In this paper, we synthesize and highlight the most relevant work regarding ontology methodologies, engineering, best practices and tools that could be applied to Internet of Things (IoT). To the best of our knowledge, this is the first work aiming at bridging such methodologies to the IoT community and go beyond the IERC AC4 cluster. This research is being applied to three uses cases: (1) the M3 framework assisting IoT developers in designing interoperable ontology-based IoT applications, (2) the FIESTA-IoT EU project encouraging semantic interoperability within IoT, and (3) a collaborative publication of legacy ontologies.
Strategic Bid-Shading and Sequential Auctioning with Learning from Past Prices

This paper analyzes sequential auctioning single units of an indivisible good to a fluctuating population composed of overlapping generations of unit-demand bidders. Two phenomena emergent in such a market are investigated: forward-looking bidding strategies, and closed-loop selling strategies that involve learning from past prices. The buyers shade their bids down, i.e. bid less than they would in a single isolated auction, whenever they expect the seller to sell another unit of the good in the near future. Unlike in exogenous sequences of auctions, the optimal bidding strategy thus depends on the seller's selling strategy. The converse dependence also occurs: the seller can learn about current demand from past realized prices, and sell only in periods with high-enough demand. Such learning depends on the extent of bid-shading, because the seller needs to invert the bidding strategy to learn. In equilibrium, buyer bid-shading persists even when the seller does not sell in every period, but it is self-regulating in that it eventually vanishes when the existence of the market is threatened by low seller profits. In this sense, auction markets have a "self-preservation instinct. " General properties of learning about residual demand from auction-prices are also investigated.
A Microeconomic Explanation of the EPK Paradox

Supported by several recent investigations the empirical pricing kernel paradox might be considered as a stylized fact. In Chabi-Yo et al. (2008) simulation studies have been presented which suggest that this paradox might be caused by regime switching of stock prices in financial markets. Alternatively, we want to emphasize a microeconomic view. Based on an economic model with state dependent utilities for the financial investors we succeed in explaining the paradox by changes of the risk attitudes. Theoretically, the change behaviour is compressed by the pricing kernels. As a starting point for empirical insights we shall develop and investigate inverse problems in terms of data fits for estimated basic values of the pricing kernel.
Formalization of the Two-Step Approach to Overcomplete BSS

We discuss overcomplete blind source separation (BSS), that is separation with more sources than sensors. Finding the mixing matrix solves the linear quadratic BSS problem; in overcomplete BSS however, it is then still not clear and not even unique how to get the sources from the mixtures and the mixing matrix. We therefore follow Bofill and Zibulevsky and many others and take a two-step approach to overcomplete BSS: In the first so called blind mixing model recovery (BMMR) step, the mixing model has to be reconstructed from the mixtures - in the linear case this would mean finding the mixing matrix. Then, in the blind source recovery (BSR) step, the sources have to be reconstructed given the mixing matrix and the mixtures. We furthermore introduce some notation and sayings and describe the usual BSR step in order to enable forthcoming overcomplete BSS papers to concentrate on one of the two steps, mainly on the BMMR step. Finally, we prove that the shortest-path algorithm as proposed by Bofill and Zibulevsky indeed solves the maximum-likelihood conditions in the BSR step. weight matrices.
Robo-line Storage: Low Latency, High Capacity Storage Systems over

Rapid advances in high performance computing are making possible more complete and accurate computer-based modeling of complex physical phenomena, such as weather front interactions, dynamics of chemical reactions, numerical aerodynamic analysis of airframes, and ocean-land-atmosphere interactions. Many of these "grand challenge" applications are as demanding of the underlying storage system, in terms of their capacity and bandwidth requirements, as they are on the computational power of the processor. A global view of the Earth''s ocean chlorophyll and land vegetation requires over 2 terabytes of raw satellite image data [ISTP91]! In this paper, we describe our planned research program in high capacity, high bandwidth storage systems. The project has four overall goals. First, we will examine new methods for high capacity storage systems, made possible by low cost, small formfactor magnetic and optical tape systems. Second, access to the storage system will be low latency and high bandwidth. To achieve this, we must interleave data transfer at all levels of the storage system, including devices, controllers, servers, and communications links. Latency will be reduced by extensive caching throughout the storage hierarchy, extending the techniques already developed by Ousterhout for his Log Structured File System. Finally, we will construct a prototype high capacity file server, suitable for use on the National Research and Education Network (NREN). Such research must be a cornerstone of any coherent program in high performance computing and communications.
An on-line platform for ground truthing and performance evaluation of text extraction systems

This paper presents a set of on-line software tools for creating ground truth and calculating performance evaluation metrics for text extraction tasks such as localization, segmentation and recognition. The platform supports the definition of comprehensive ground truth information at different text representation levels while it offers centralised management and quality control of the ground truthing effort. It implements a range of state of the art performance evaluation algorithms and offers functionality for the definition of evaluation scenarios, on-line calculation of various performance metrics and visualisation of the results. The presented platform, which comprises the backbone of the ICDAR 2011 (challenge 1) and 2013 (challenges 1 and 2) Robust Reading competitions, is now made available for public use.
Performance Evaluation of Multi-Stage Change-Point Detection Scheme against DDoS Attacks by Random Scan Worms

As a promising approach for large-scale simultaneous events (e.g., DDoS attacks by unknown worms), we have proposed a multi-stage change-point detection scheme. In the scheme, the global detector gathers information from distributed change-point detectors and detects simultaneous occurrence of change-points as target events. Because the scheme neglects sporadic false-positive change-points, which are caused by non-target events such as hardware troubles and normal traffic changes, the scheme can achieve low false-positive rate. In the previous paper, we investigated the performance of the scheme against DDoS attacks by a real worm, MSBLAST. In this paper, we investigate the performance of the scheme against general DDoS attacks with various scales and simultaneities. In addition, we investigate the effect of the length of detectionperiod for the global detector on detection performance, where the detection-period means the period during which the global detector regurarly checks whether or not target events occur. The simulation results show that (1) our multistage change-point detection scheme achieves lower falsepositive rate than a stand-alone change-point detector scheme under the constraint that detection rate must be 1.0, (2) even if the length of detection-period for the global detector is not appropriately set, our scheme can achieve better performance than a stand-alone change-point detector scheme thanks to filtering effect of the global detector.