Miracle-dz/UniFAR-FTUs huggingface dataset:
Metadata Extraction using Text Mining

Grid technologies have proven to be very successful in the area of eScience, and healthcare in particular, because they allow to easily combine proven solutions for data querying, integration, and analysis into a secure, scalable framework. In order to integrate the services that implement these solutions into a given Grid architecture, some metadata is required, for example information about the low-level access to these services, security information, and some documentation for the user. In this paper, we investigate how relevant metadata can be extracted from a semi-structured textual documentation of the algorithm that is underlying the service, by the use of text mining methods. In particular, we investigate the semi-automatic conversion of functions of the statistical environment R into Grid services as implemented by the GridR tool by the generation of appropriate metadata.
The TAOS Project: High-Speed Crowded Field Aperture Photometry

We have devised an aperture photometry pipeline for data reduction of image data from the Taiwanese-American Occultation Survey (TAOS). The photometry pipeline has high computational performance, and is capable of real-time photometric reduction of images containing up to 1000 stars, within the sampling rate of 5 Hz. The pipeline is optimized for both speed and signal-to-noise performance, and in the latter category it performs nearly as well as DAOPHOT. This paper provides a detailed description of the TAOS aperture photometry pipeline.
The Missing Treatment Design Element

We rely a great deal on the schematic descriptions that represent experimental and quasi-experimental design arrangements, as well as the discussions of threats to validity associated with these, provided by Campbell and his associates: Stanley, Cook, and Shadish. Some of these designs include descriptions of treatments removed, removed and then reintroduced, or reversed with use of multiple postobservations. The focus of this article is to consider what happens to the intervention between and among postobservations and how that relates to threats to validity and data modeling and analysis. Evaluation designs must consider such questions as “Has the intervention been removed, or is it still in place? If so, has it changed in intensity, or are there persistence or carryover effects? ” This article adds to the evaluation methodology literature by filling a gap regarding the issue of treatment or intervention continuity between and among postobservations.
Block-sparse 3-D ISAR image reconstruction in a non-coherent multistatic scenario

This paper introduces a new technique for multistatic/multiband ISAR image fusion. Developing this technique, special regard was given to the facts that coherence between separated sensors cannot be assumed automatically and that the fusion has to be performed in all three spatial dimensions. The result is a 3-D ISAR image that mitigates the problem of aspect angle dependence of ISAR images used for classification. The presented method is furthermore a solution to fuse heterogeneous signals (e.g. multiband). A simulation illustrates the functionality of the technique.
Emotion and Adaptation

Part I: BACKGROUND: About emotion Issues of research, classification and measurements Part II: THE COGNITIVE-MOTIVATIONAL-RELATIONAL THEORY: The person-environment relationship: motivation and coping Cognition and emotion Issues of causality Part III: INDIVIDUAL EMOTIONS: Goal incongruent (negative) emotions Goal congruent (positive) and problematic emotions Part IV: EMOTIONAL DEVELOPMENT: Individual development Social influence Part V: PRACTICAL APPLICATIONS: Emotions and health Implications for research, assessment, treatment and disease prevention References Index.
Using mutual information for adaptive student assessments

When we have an item bank available for assessing students, we would like to select the test items that can reveal students' knowledge levels as effectively and accurately as possible. This may not be a trivial task when we consider the fact that students' item-response patterns may not reflect their competence exactly. Theoretical and experimental results reported in this paper indicate that mutual information between test items and educational targets provides a principled and instrumental basis for adaptively selecting test items for student assessments.
A Multi-Party Contract Signing Protocol and its Formal Analysis in Strand Space Model

Difficulties in designing multi-party contract signing (MPCS) protocols include providing concise and efficient protocols and at the same time keeping the protocols fair and abuse-free. This paper proposed an optimistic MPCS protocol. The number of rounds is reduced to two and the message transmission number to O(n2), which is the best known so far. Formal definitions of fairness and abuse-freeness are presented in the strand space model, and formal analysis shows that fairness and abuse-freeness are satisfied in the protocol.
Assignment for Clustered Processors using Constraint Programming

A fundamental problem in compiler optimization, which has increased in importance due to the spread of multi-core architectures, is to find parallelism in sequential programs. Current processors can only be fully taken advantage of if workload is distributed over the available processors. In this work we look at distributing instructions in a block of code over multi-cluster processors, the instruction assignment problem. We derive motivation to solve this problem from the proliferation of clustered processors in embedded systems. Since they are low cost and have low power consumption they are widely used in devices like cell phones, music players, cameras, printers and in the automotive marked for safety, engine control and navigation. Billions of these processors are sold each year and with the ever increasing demand and variety of consumer electronics the sales are likely to keep increasing. Instruction scheduling on realistic processors is known to be a hard problem and compilers use heuristic approaches to schedule instructions. Instruction assignment can be simply stated as scheduling instructions on multiple processors. The idea would be to partition the DAG and schedule each partition on a processor. Figure 1 shows an example superblock and how it can be scheduled on a single cluster and a dual-cluster architecture. The optimal assignment of instructions in blocks of code on multiple processors is known to be NP-complete. We present a constraint programming approach for scheduling instructions on multi-cluster processors that feature fast inter-cluster communication. We employ a problem decomposition technique to solve the problem in a hierarchical manner where an instance of the master problem solves multiple sub-problems to derive a solution. We found that our approach was able to achieve an improvement of 6%-20%, on average, over the state-of-the-art techniques on superblocks [8] from SPEC 2000 benchmarks.
Approximation Algorithms for Predicting RNA Secondary Structures with Arbitrary Pseudoknots

We study three closely related problems motivated by the prediction of RNA secondary structures with arbitrary pseudoknots: the problem 2-lnterval Pattern proposed by Vialette, the problem Maximum Base Pair Stackings proposed by Leong et al., and the problem Maximum Stacking Base Pairs proposed by Lyngso. For the 2-lnterval Pattern, we present polynomial-time approximation algorithms for the problem over the preceding-and-crossing model and on input with the unitary restriction. For Maximum Base Pair Stackings and Maximum Stacking Base Pairs, we present polynomial-time approximation algorithms for the two problems on explicit input of candidate base pairs. We also propose a new problem called Length-Weighted Balanced 2-lnterval Pattern, which is natural in the context of RNA secondary structure prediction.
IGF1 mRNA Splicing Variants in Liaoning Cashmere Goat: Identification, Characterization, and Transcriptional Patterns in Skin and Visceral Organs

Insulin-like growth factor I (IGF1) is a member of the insulin superfamily. It performs important roles in the proliferation and differentiation of skin cell and control of hair cycles and is thought to be a potential candidate gene for goat cashmere traits. In this work, we isolated and characterized three kinds of IGF1 mRNA splicing variants from the liver of Liaoning Cashmere goat, and the expression characterization of the IGF1 mRNA splicing variants were investigated in skin and other tissues of Liaoning cashmere goat. The sequencing results indicated that the classes 1w, 1, and 2 of IGF1 cDNAs in Liaoning cashmere goat, each included an open reading frame encoding the IGF1 precursor protein. The deduced amino acid sequences of the three IGF1 precursor proteins differed only in their NH2-terminal leader peptides. Through removal of the signal peptide and extension peptide, the three IGF1 mRNA splicing variants (classes 1w, 1, and 2) resulted in the same mature IGF1 protein in Liaoning cashmere goat. In skin tissue of Liaoning cashmere goat, class 1 and class 2 were detected in all stages of hair follicle cycling, and they had the highest transcription level at anagen, and then early anagen; whereas at telogen both classes 1 and 2 had the lowest expression in mRNA level, but the class 1 appears to be relatively more abundant than class 2 in skin tissue of Liaoning cashmere goat. However, the class 1w transcript was not detected in the skin tissues. Three classes of IGF1 mRNA were transcribed in a variety of tissues, including heart, brain, spleen, lung, kidney, liver, and skeletal muscle, but class 1 IGF1 mRNA was more abundant than classes 1w and 2 in the investigated tissues.
The Organizational Principles of Online Political Discussion: A Relational Event Stream Model for Analysis of Web Forum Deliberation

The structure of online political discussion has proven important to deliberative democracy. However, the organizational mechanisms of the structure receive little attention in scholarship. This study employed a random-effects relational event model to differentiate and examine the effects of a set of organizational principles in web forum discussions. By analyzing more than 175,000 forum replies, the study found that cross-ideological debate is an independent organizational mechanism even when accounting for the effects from common interests, opinion congruity, purely structural effects, and conversational norms. These findings differ from the selective exposure thesis and previous incidental claims of political disagreement. In addition, the findings indicate that endogenous mechanisms and opinion congruity could influence the tendency of cross-ideological debate to varying degrees.
A differential evolution based adaptive neural Type-2 Fuzzy inference system for classification of motor imagery EEG signals

This paper proposes a new classification algorithm which aims at predicting different states from an incoming non-stationary signal. To overcome the failure of standard classifiers at generalizing the patterns for such signals, we have proposed an Interval Type-2 Fuzzy based Adaptive neural fuzzy Inference System (ANFIS). Through the introduction IT2F system, we have aimed at improving the uncertainty management of the fuzzy inference system. Besides that using DE in forward and backward pass and improving the forward pass function we have improved the parameter update on wide range of nodal functions without any quadratic approximation in forward pass. The proposed algorithm is tested on a standard electroencephalography (EEG) dataset and it is noted that the proposed algorithm performs better than other standard classifiers including the classical ANFIS algorithm.
Applying Direct Observation to Model Workflow and Assess Adoption

Lack of understanding about workflow can impair health IT system adoption. Observational techniques can provide valuable information about clinical workflow. A pilot study using direct observation was conducted in an outpatient chronic disease clinic. The goals of the study were to assess workflow and information flow and to develop a general model of workflow and information behavior. Over 55 hours of direct observation showed that the pilot site utilized many of the features of the informatics systems available to them, but also employed multiple non-electronic artifacts and workarounds. Gaps existed between clinic workflow and informatics tool workflow, as well as between institutional expectations of informatics tool use and actual use. Concurrent use of both paper-based and electronic systems resulted in duplication of effort and inefficiencies. A relatively short period of direct observation revealed important information about workflow and informatics tool adoption.
Vision-based crowded pedestrian detection

Pedestrian detection and counting is an important topic in developing an intelligent surveillance system. In this work, we propose a vision-based system for detecting pedestrians in an image. Be robust to crowded scenes and adapt to incomplete foreground from background subtraction algorithm, expectation maximization (EM) algorithm is applied to impose the constraint of body part for achieving successful detection. A well-known dataset called CAVIAR is used to validate the effectiveness of the proposed method.
An NFC system with high sensitivity based on SDR

This paper presents a high-sensitivity NFC system based on Labview platform using SDR (Software Defined Radio) approach. Protocol ISO14443-A is used in this work. This paper analyses the entire transceiver system and sensitivity of the system. Test results show that the receiver reaches the sensitivity of -80 dBm and maximum reading distance of 24 cm.
Online Dense Non-Rigid 3D Shape and Camera Motion Recovery

© 2014. The copyright of this document resides with its authors. This paper describes a sequential solution to dense non-rigid structure from motion that recovers the camera motion and 3D shape of non-rigid objects by processing a monocular image sequence as the data arrives. We propose to model the time-varying shape with a probabilistic linear subspace of mode shapes obtained from continuum mechanics. To efficiently encode the deformations of dense 3D shapes that contain a large number of mesh vertexes, we propose to compute the deformation modes on a down sampled rest shape using finite element modal analysis at a low computational cost. This sparse shape basis is then grown back to dense exploiting the shape functions within a finite element. With this probabilistic low-rank constraint, we estimate camera pose and non-rigid shape in each frame using expectation maximization over a sliding window of frames. Since the time-varying weights are marginalized out, our approach only estimates a small number of parameters per frame, and hence can potentially run in real time. We evaluate our algorithm on both synthetic and real sequences with 3D ground truth data for different objects ranging from inextensible to extensible deformations and from sparse to dense shapes. We show the advantages of our approach with respect to competing sequential methods.
MarkAirs: Around-Device Interactions with Tablets Using Fiducial Markers -- An Evaluation of Precision Tasks

This paper evaluates MarkAirs, an interaction technique that uses fiducial markers to perform mid-air interactions. MarkAirs offers several advantages: the proposed technique does not require any tracking external hardware other than the front camera of a mobile device; it is robust even when the markers are partially occluded; and it enables precise 2D manipulations (translation, rotation and scaling). An evaluation study points to the feasibility and precision of the proposed technique and the perceived usability and subjective workload impressions of the participants.
Periodization: The Effect on Strength of Manipulating Volume and Intensity

This study examined the effects of manipulating volume and intensity on strength and power in experienced male athletes. Subjects (N = 22) were tested for maximum strength in the squat and bench press lifts, vertical jump (VJ), lean body mass (LBM), and neural activation levels (IEMG). They trained 3 days a week for 12 weeks according to a linear periodization model (n = 8), an undulating periodization model (n = 5), or a nonperiodized control model (n = 9). Training volume and relative intensity were equated for all groups. Maximal squat, bench press, and LBM all improved significantly in each group, and changes in maximal strength correlated significantly with changes in LBM. IEMG levels were generally unchanged and did not correlate with changes in strength. The VJ increased significantly through training, but there were no differences between groups. Changes in VJ were not significantly correlated with changes in squat, LBM, or IEMG levels. The results indicate that in short-term training using previously trained subjects, no differences in maximal strength are seen when training volume and relative intensity are equated.
Modelling Relevance towards Multiple Inclusion Criteria when Ranking Patients.

In the medical domain, information retrieval systems can be used for identifying cohorts (i.e. patients) required for clinical studies. However, a challenge faced by such search systems is to retrieve the cohorts whose medical histories cover the inclusion criteria specified in a query, which are often complex and include multiple medical conditions. For example, a query may aim to find patients with both 'lupus nephritis' and 'thrombotic thrombocytopenic purpura'. In a typical best-match retrieval setting, any patient exhibiting all of the inclusion criteria should naturally be ranked higher than a patient that only exhibits a subset, or none, of the criteria. In this work, we extend the two main existing models for ranking patients to take into account the coverage of the inclusion criteria by adapting techniques from recent research into coverage-based diversification. We propose a novel approach for modelling the coverage of the query inclusion criteria within the records of a particular patient, and thereby rank highly those patients whose medical records are likely to cover all of the specified criteria. In particular, our proposed approach estimates the relevance of a patient, based on the mixture of the probability that the patient is retrieved by a patient ranking model for a given query, and the likelihood that the patient's records cover the query criteria. The latter is measured using the relevance towards each of the criteria stated in the query, represented in the form of sub-queries. We thoroughly evaluate our proposed approach using the test collection provided by the TREC 2011 and 2012 Medical Records track. Our results show significant improvements over existing strong baselines.
Models and tools for the high-level simulation of a name-based interdomain routing architecture

The deployment and operation of global network architectures can exhibit complex, dynamic behavior and the comprehensive validation of their properties, without actually building and running the systems, can only be achieved with the help of simulations. Packet-level models are not feasible in the Internet scale, but we are still interested in the phenomena that emerge when the systems are run in their intended environment. We argue for the high-level simulation methodology and introduce a simulation environment based on aggregate models built on state-of-the-art datasets available while respecting invariants observed in measurements. The models developed are aimed at studying a clean slate name-based interdomain routing architecture and provide an abundance of parameters for sensitivity analysis and a modular design with a balanced level of detail in different aspects of the model. In addition to introducing several reusable models for traffic, topology, and deployment, we report our experiences in using the high-level simulation approach and potential pitfalls related to it.
Compositional Symbolic Execution: Incremental Solving Revisited

Symbolic execution can automatically explore different execution paths in a system under test and generate tests to precisely cover them. It has two main advantages—being automatic and thorough within a theory—and has many successful applications. The bottleneck of symbolic execution currently is the computation consumption for complex systems. Compositional Symbolic Execution (CSE) introduces a summarisation module to eliminate the redundancy in the exploration of repeatedly encountered code. In our previous work, we generalised the summarisation for any code fragments instead of functions. In this paper, we transplant this idea onto LLVM with many additional features, one of them being the use of incremental solving. We show that the combination of CSE and incremental solving is mutually beneficial. The obvious weakness of CSE is the lack of context during summarisation. We discuss the use of assumption-based features, available in modern constraint solvers, as a way to overcome this problem.
Evaluation of the Effect of Systolic Blood Pressure and Pulse Pressure on Cognitive Function: The Women's Health and Aging Study II

Background Evidence suggests that elevated systolic blood pressure (SBP) and pulse pressure (PP) in midlife is associated with increased risk for cognitive impairment later in life. There is mixed evidence regarding the effects of late life elevated SBP or PP on cognitive function, and limited information on the role of female gender. Methods/Principal Findings Effects of SBPand PPon cognitive abilities at baseline and over a 9-year period were evaluated in 337 non-demented community-dwelling female participants over age 70 in the Women's Health and Aging Study II using logistic and Cox proportional hazards regression analyses. Participants aged 76–80 years with SBP≥160 mmHg or PP≥84 mmHg showed increased incidence of impairment on Trail Making Test-Part B (TMT, Part B), a measure of executive function, over time when compared to the control group that included participants with normal and pre-hypertensive SBP (<120 and 120–139 mmHg) or participants with low PP (<68 mmHg) (HR = 5.05 [95%CI = 1.42, 18.04], [HR = 5.12 [95%CI = 1.11; 23.62], respectively). Participants aged 70–75 years with PP≥71 mmHg had at least a two-fold higher incidence of impairment on HVLT-I, a measure of verbal learning, over time when compared to participants with low PP (<68 mmHg) (HR = 2.44 [95%CI = 1.11, 5.39]). Conclusions/Significance Our data suggest that elevated SBP or PP in older non-demented women increases risk for late-life cognitive impairment and that PP could be used when assessing the risk for impairment in cognitive abilities. These results warrant further, larger studies to evaluate possible effects of elevated blood pressure in normal cognitive aging.
Microblog Sentiment Analysis Based on Cross-media Bag-of-words Model

Sentiment analysis on social networks has attracted ever increasing attention in recent years. To this end, most existing methods mainly focus on analyzing the textual information. This has faced huge difficulty as nowadays users are more likely to express their feelings in a hybrid manner not only with texts, but also with images. It is therefore essential to take images into account. In this paper, we propose a novel Cross-media Bag-of-words Model (CBM) for Microblog sentiment analysis. In this model, we represent the text and image of a Weibo tweet as a unified Bag-of-words representation. Based on this model, we use Logistic Regression to classify the Microblog sentiment. It performs well in the sentiment classification task since it doesn't require the conditional dependence assumption. We also use SVM and Naïve Bayes to make a comparison. Experiments on 5,000 Microblog messages demonstrate that our CBM model performs better than text-based methods. The sentiment classification accuracy on Microblog messages of our model is 80%, improved by 4% than the text-based methods.