Miracle-dz/UniFAR-FTUs huggingface dataset:
User-Constrained Multimodal Route Planning

In the multimodal route planning problem, we are given multiple transportation networks (e.g., pedestrian, road, public transit) and ask for a best integrated journey between two points. The main challenge is that a seemingly optimal journey may have changes between networks that do not reflect the user’s modal preferences. In fact, quickly computing reasonable multimodal routes remains a challenging problem: previous approaches either suffer from poor query performance or their available choices of modal preferences during query time is limited. In this work, we focus on computing exact multimodal journeys that can be restricted by specifying arbitrary modal sequences at query time. For example, a user can say whether he or she wants to only use public transit, prefers to also use a taxi or walking at the beginning or end of the journey, or has no restrictions at all. By carefully adapting node contraction, a common ingredient to many speedup techniques on road networks, we are able to compute point-to-point queries on a continental network combined of cars, railroads, and flights several orders of magnitude faster than Dijkstra’s algorithm. Thereby, we require little space overhead and obtain fast preprocessing times.
Social Network Analysis on Educational Data Set in RDF Format

The increased usage of information technologies in educational tasks resulted in high volume of data, exploited to build analytical systems that can provide practical insight in the learning process. In this paper, we propose a method of running social network analysis on multiple data sources (academic years, communication tools). To achieve this, the collected data that describe social interactions were converted into a common format by employing a prior developed semantic web educational ontology. Using a mapping language the relational data set was linked to the appropriate concepts defined in the ontology and then it was exported in RDF format. The means for SPARQL access was also provided. Subsequently, query patterns were defined for different social interactions in the educational platform. To prove the feasibility of this approach, Gephi tool set was used to run SNA (Social Network Analysis) on data obtained with the SPARQL queries. The added value of this research lies in the potential of this method to simplify running social network analysis on multiple data sets, on a specific course or the entire academic year, by simply modifying the query pattern.
Performance analysis of LVI-based PDNN applied to real-time solution of time-varying quadratic programming

This paper illustrates theoretical analysis and simulative verification on the performance of the linear-variatlonal inequality based primal-dual neural network (LVI-PDNN), which was designed originally for static quadratic programming (QP) problem solving but Is now applied to time-varying QP problem solving. It Is theoretically proved that the LVI-PDNN for solving the time-varying QP problem subject to equality, Inequality and bound constraints simultaneously could only approximately approach the time-varying theoretical solution, Instead of converging exactly. In other words, the steady-state error of the realtime solution can not decrease to zero. In order to better evaluate the time-varying situation, we Investigate the upper bound of such an error and the global exponential convergence rate for the LVI-PDNN approaching Its loose error bound. Computer simulations further substantiate the performance analysis of the LVI-PDNN exploited for real-time solution of the time-varying QP problem.
Impact of Improved Stoves on Indoor Air Quality in Ulaanbaatar, Mongolia

Ulaanbaatar, Mongolia, is the coldest capital city in the world, with average winter low temperatures of -20° Celsius. Many families there live in gers, traditional Mongolian dwellings consisting of a wooden frame beneath several layers of wool felt. In the ger districts of Ulaanbaatar, cooking and heating energy is provided through indoor coal combustion in metal stoves with chimneys, and in wintertime, such stoves may be in use both day and night. Over the last several years, new stove designs with improved fuel efficiencies have been introduced into many homes. To test the impact of the improved stoves on indoor air quality, 24-hour monitoring of particulate matter (PM) and carbon monoxide (CO) was done in 65 Mongolian gers. The primary analyses focused on 58 households, 20 with original (or traditional-type) stoves, 18 with the improved stove type TT-03, and 20 with the improved stove type G2-2000. In addition to indoor pollutant concentrations, information on other relevant factors was collected, which included home sizes, indoor and outdoor temperatures, age of stove in use, amount of fuel used and number of refuelings, position of monitors relative to chimneys, and number of cigarettes smoked in the home. Analysis of variance showed that these factors did not differ significantly by stove type except that traditional stoves tended to be older than improved stoves. Multivariate regression methods were used to test for statistically significantly different indoor PM and CO concentrations between homes with different stove types while controlling for selected characteristics.
Instructional Module on Estimating Item Response Theory Models Using Markov Chain Monte Carlo Methods

The purpose of this ITEMS module is to provide an introduction to Markov chain Monte Carlo (MCMC) estimation for item response models. A brief description of Bayesian inference is followed by an overview of the various facets of MCMC algorithms, including discussion of prior specification, sampling procedures, and methods for evaluating chain convergence. Model comparison and fit issues in the context of MCMC are also considered. Finally, an illustration is provided in which a two-parameter logistic (2PL) model is fit to item response data from a university mathematics placement test through MCMC using the WINBUGS 1.4 software. While MCMC procedures are often complex and can be easily misused, it is suggested that they offer an attractive methodology for experimentation with new and potentially complex IRT models, as are frequently needed in real-world applications in educational measurement.
Scalable Mockup Experiments on Smartphones Using Smart Lab

In this paper we present a comprehensive architecture to carry out experimental repeatability studies on clusters of smartphones. Our architecture is founded on Smart Lab, our in-house architecture for managing real and virtual smartphones via an intuitive Web user interface. Our presented architecture consists of several exciting components for re-programming and instrumenting smartphones to perform application testing and data gathering in a facile manner, as well as executing mockup experiments by "feeding" the devices with GPS/sensor readings. We will particularly demonstrate the various components of our architecture that encompasses smartphone sensor data collected by mobile users and organized in our distributed NoSQL document store. The given datasets can then be replayed on our test bed comprising of real and virtual smartphones accessible to developers through our Web 2.0 user interface. We present the applicability of our architecture through various mockup experiments over different application scenarios.
Comparison of Measured and Modelled
Traffic-Related Air Pollution
in Urban Street Canyons

The level of hazardous traffic pollutants, such as nitrogen dioxide (NO2), significantly increases in street canyons, which is a relevant determinant of assessing human exposure and health risks and deteriorates quality of urban air. The aim of the present study was to measure air pollution of NO2 by passive samplers in five street canyon sites with different traffic and building characteristics during two-week measurement periods in each season and to compare measured NO2 concentrations with models using the Airviro street canyon model. The data of meteorological parameters, street canyon orientation and urban background air pollution were taken into account. The study results showed that the highest measured and modelled concentrations of NO2 in street canyons were determined during spring and summer, and modelled values were higher than those measured with passive samplers, while during winter and autumn the results were vice versa. The greatest difference between measured and modelled concentrations of NO2 was determined in winter, while the highest degree of agreement was assessed in summer. We found a strong positive correlation between the measurements and modelling results. The research demonstrates the importance of considering the urban micro environments such as street canyons for the effective assessment of human exposure to transport-related emissions.
LEARNING LSRW SKILLS THROUGH ACTIVE STUDENT-INVOLVEMENT: SCREENING AN EDITED FILM

The aim of the present study was to find out whethe r students’ active involvement in classroom activities can promote language learning. This paper, using practitioner research, examines the active involvement of first-year engin eering students in a film activity. The research took place in three phases: (1) the studen ts were divided into five groups and given an assignment each, a month ahead, to edit a full-l ength movie to one hour without tampering with its story element; (2) the five groups present ed five different edited movies on five different days; (3) the data was collected and anal ysed by the teacher as follows: observing the classroom performance, transcribing the student s’ spoken language, and collecting the students’ written transcripts. The research analysis and discussion show that the whole process of the film task provided a rich input in l istening and reading, and subsequently a productive language output in speaking and writing. The feedback conducted states that the students enjoyed the video classes thoroughly and t he experience was rewarding because of their active involvement in the practice of LSRW sk ill .
Cluster-dependent modeling and confidence measure processing for in-set/out-of-set speaker identification

In this paper, we propose an approach to address the problem of text-independent open-set speaker identification. The in-set speakers are clustered into smaller subsets without merging speaker models. The Anti-Speaker or Background Model is then adapted for each subset which minimizes the identification errors of the pseudo impostors during the training stage. Score normalization is applied to align all the in-set speaker score distributions to share a single scale. Finally, confidence measure processing is used to identify in-set versus out-of-set speakers. Experiments with TIMIT and the CU-Accent corpora show an improvement in Equal Error Rate on the average of 20.28 and 8.35 over the baseline performance respectively. Finally, a probe experiment is also included that considers prosody for in-set speaker detection.
A Global Optimization Approach to Multi-Polarity Sentiment Analysis

Following the rapid development of social media, sentiment analysis has become an important social media mining technique. The performance of automatic sentiment analysis primarily depends on feature selection and sentiment classification. While information gain (IG) and support vector machines (SVM) are two important techniques, few studies have optimized both approaches in sentiment analysis. The effectiveness of applying a global optimization approach to sentiment analysis remains unclear. We propose a global optimization-based sentiment analysis (PSOGO-Senti) approach to improve sentiment analysis with IG for feature selection and SVM as the learning engine. The PSOGO-Senti approach utilizes a particle swarm optimization algorithm to obtain a global optimal combination of feature dimensions and parameters in the SVM. We evaluate the PSOGO-Senti model on two datasets from different fields. The experimental results showed that the PSOGO-Senti model can improve binary and multi-polarity Chinese sentiment analysis. We compared the optimal feature subset selected by PSOGO-Senti with the features in the sentiment dictionary. The results of this comparison indicated that PSOGO-Senti can effectively remove redundant and noisy features and can select a domain-specific feature subset with a higher-explanatory power for a particular sentiment analysis task. The experimental results showed that the PSOGO-Senti approach is effective and robust for sentiment analysis tasks in different domains. By comparing the improvements of two-polarity, three-polarity and five-polarity sentiment analysis results, we found that the five-polarity sentiment analysis delivered the largest improvement. The improvement of the two-polarity sentiment analysis was the smallest. We conclude that the PSOGO-Senti achieves higher improvement for a more complicated sentiment analysis task. We also compared the results of PSOGO-Senti with those of the genetic algorithm (GA) and grid search method. From the results of this comparison, we found that PSOGO-Senti is more suitable for improving a difficult multi-polarity sentiment analysis problem.
T HESIS FOR THE DEGREE OF L ICENTIATE OF E NGINEERING Improving Execution Efficiency by Targeting Redundancy and Parallelism

The era of multi-core architectures or chip-multiprocessors (CMP) has introduced manifold challenges for computer architects. One of the major hurdles is to improve energy efficiency of each single-processor core to remain within the energy constraint at the chip level. Another important dimension is to achieve substantial performance improvement to sustain the prediction of Moores Law. This thesis addresses both energy and performance aspects of processor design, especially in the embedded domain. In the first part of the thesis, architectural techniques of improving energy efficiency of each single-processor core have been studied. The thesis first targets redundancy in the form of trivial computation (TC) and presents a new classification of TC. It is then established that a significant fraction of the total executed instructions are trivial and a vast majority of them is identity-trivial, for example, A = A + 0. Based on the finding, unlike the previous works on TC, the required micro-architectural support is proposed to eliminate TC as early as at the decode stage of the pipeline. The achievable energy savings is quite promising. Next, trade-offs between TC and instruction reuse (IR) have been evaluated. One of the interesting findings is that TC and IR eliminate an almost disjoint set of instructions and thus, may provide additive gains if combined. This work also shows that TC is better than IR in terms of improving energy efficiency and requires less hardware support. In the second part of the thesis, the potential of thread-level parallelism to improve performance efficiency has been evaluated for embedded applications. It is shown that parallelizing compilers alone are not sufficient to yield speedup. Loops available in application code are targeted as the primary source of parallelism. It is figured out that there is often a single loop which dominates the program execution time by far. An upper limit of exploitable parallelism is established by factoring in the impact of number of cores, thread-management overhead and thread decomposition strategy on speedup. It is concluded that support for speculation is vital to achieve decent speedup in most of the applications. Finally, a technique of predicting loop termination has been proposed and evaluated to reduce the number of control misspeculation. This, in turn, reduces thread-management overhead and boosts performance.
Void Analysis as a Test for Dark Matter Composition

We use the void probability function to compare the redshift--space galaxy distribution in the Perseus--Pisces survey with artificial samples from N-body simulations of standard cold dark matter (CDM) and broken scale invariance (BSI) models. Galaxies are identified as residing in peaks of the evolved density field in such a way as to reproduce both the observed luminosity and two--point correlation functions. Using a similar approach, it was recently shown that the VPF can discriminate between CDM and a cold+hot dark matter (CHDM) model with cold/hot/baryon fractions = 0.6/0.3/0.1. Our main result is that both CDM (as expected from a previous analysis) and BSI fit observational data. The robustness of the result is checked against changing the observer's position in the simulations and the galaxy identification in the evolved density field. Therefore, while the void statistics is sensitive to the passage from CDM to CHDM (different spectrum and different nature of dark matter), it is not to the passage from CDM to BSI (different spectrum but same dark matter). On such a basis, we conjecture that the distribution of voids could be directly sensitive to the nature of dark matter, but scarcely sensitive to the shape of the transfer function.
Optimal Allocation of Distributed Generation in Distribution Network with Voltage and Frequency Dependent Loads

This paper presents the performance analysis of optimally placed distributed generation (DG) in terms of loss reduction and voltage improvements at buses in the distribution network. The performance of DG is site and size specific. Most of literatures dealing with DGs have assumed constant loads while performing the analysis. Since the loads are voltage and frequency sensitive, analysis assuming constant loads will give misleading and inaccurate results. This paper addresses the effect of the variation of loads with voltage and frequency, while optimally allocating the DG in the terms of location and size in order to minimize the real power loss and maintaining the voltage within specified limits at buses using genetic algorithms in distribution network. The frequency is the pan-system parameter that cannot be controlled locally. However, to depict the effect of frequency on analysis certain assumption regarding frequency have been made within permissible range (0.98 p.u. to 1.02 p.u.). The proposed study is carried out on distribution network and its validity is verified through simulation results on 11-bus radial distribution network under different loading conditions.
Tubulin dynamics in cultured mammalian cells

Bovine neurotubulin has been labeled with dichlorotriazinyl- aminofluorescein (DTAF-tubulin) and microinjected into cultured mammalian cells strains PTK1 and BSC. The fibrous, fluorescence patterns that developed in the microinjected cells were almost indistinguishable from the pattern of microtubules seen in the same cells by indirect immunofluorescence. DTAF-tubulin participated in the formation of all visible, microtubule-related structures at all cell cycle stages for at least 48 h after injection. Treatments of injected cells with Nocodazole or Taxol showed that DTAF-tubulin closely mimicked the behavior of endogenous tubulin. The rate at which microtubules incorporated DTAF-tubulin depended on the cell-cycle stage of the injected cell. Mitotic microtubules became fluorescent within seconds while interphase microtubules required minutes. Studies using fluorescence redistribution after photobleaching confirmed this apparent difference in tubulin dynamics between mitotic and interphase cells. The temporal patterns of redistribution included a rapid phase (approximately 3 s) that we attribute to diffusion of free DTAF-tubulin and a second, slower phase that seems to represent the exchange of bleached DTAF-tubulin in microtubules with free, unbleached DTAF- tubulin. Mean half times of redistribution were 18-fold shorter in mitotic cells than they were in interphase cells.
Conceptualizations of time in French depuis ‘since, for’ constructions

Abstract The two most common constructions with the French preposition depuis ‘since, for’ represent alternate temporal conceptualizations of events. The temporal point (tp) construction situates events with respect to a starting point in the past from which mental scanning tracks to the present. The temporal duration (td) construction situates events by reference to their duration. Adoption of the Cognitive Grammar framework leads to the insight that all other depuis constructions are variations of the tp construction, while the td construction is distinct in its construal of situations. This corpus study gives an account of how the td construction developed as an extension of the tp construction starting in the fourteenth century, as well as a pragmatic analysis of the contexts that favor use of the td construction in modern French. The findings show that the td construction is nearly always used to emphasize duration for some communicative purpose.
Discrete-wavelet-transform-based noise reduction and R wave detection for ECG signals

There are two main research topics about ECG signal proposed. One is the noise reduction, and the other is R peaks detection. Both of the two algorithms are based on discrete wavelet transform (DWT). DWT is efficient for analyzing nonstationary signals like ECG signal. The Symlets wavelets (sym5) and soft-thresholding are chosen as the wavelet function and thresholding method to do noise correction at the first denoising stage. The second stage is R wave detection. The MIT-BIH arrhythmia database is used to verify proposed algorithm. We reconstruct the decomposition level 3 to 5. Choosing the adaptive threshold and window size are the key points to reduce error rate. Applying two thresholds leads to better performance, compared to applying one threshold. At the last stage, we do noise correction again.With the information of R wave position, a novel method is proposed to eliminate the electromyogram (EMG) signal. The algorithm for R wave detection has a sensitivity of 99.70% and a positive predictivity of 99.65%. The error rate is 0.65% under all kinds of situation (0.37% if ignoring 3 worst cases). For noise correction, the SNR improvement is achieved at least 10dB at SNR 5dB, and most of the improvement SNR are better than other methods at least 1dB at different SNR. To apply presented algorithms for the portable ECG device, all R peaks can be detected no matter when people walk, run or move at the speed below 9km/hr.
ZRANK: Reranking protein docking predictions with an optimized energy function

Protein–protein docking requires fast and effective methods to quickly discriminate correct from incorrect predictions generated by initial‐stage docking. We have developed and tested a scoring function that utilizes detailed electrostatics, van der Waals, and desolvation to rescore initial‐stage docking predictions. Weights for the scoring terms were optimized for a set of test cases, and this optimized function was then tested on an independent set of nonredundant cases. This program, named ZRANK, is shown to significantly improve the success rate over the initial ZDOCK rankings across a large benchmark. The amount of test cases with No. 1 ranked hits increased from 2 to 11 and from 6 to 12 when predictions from two ZDOCK versions were considered. ZRANK can be applied either as a refinement protocol in itself or as a preprocessing stage to enrich the well‐ranked hits prior to further refinement. Proteins 2007. © 2007 Wiley‐Liss, Inc.