Miracle-dz/UniFAR-FTUs huggingface dataset:
Experimental Behavioral Research for Designing Human Computational Systems

The growing complexity of human computation systems underlies a need for more accurate and realistic studies of human behavior in computer science. At the same time, the development of online experimental research is a valuable opportunity for computer scientists to study human behavior in a principled and causal way. I propose several ways in which novel experimental approaches combined with other computer science tools can advance the state of the art in human computation to build smarter systems that leverage individual and collective human behavior, and also answer more general questions in social science.
Differential Effect of Amylin on Endothelial-Dependent Vasodilation in Mesenteric Arteries from Control and Insulin Resistant Rats

Insulin resistance (IR) is frequently associated with endothelial dysfunction and has been proposed to play a major role in cardiovascular disease (CVD). On the other hand, amylin has long been related to IR. However the role of amylin in the vascular dysfunction associated to IR is not well addressed. Therefore, the aim of the study was to assess the effect of acute treatment with amylin on endothelium-dependent vasodilation of isolated mesenteric arteries from control (CR) and insulin resistant (IRR) rats and to evaluate the possible mechanisms involved. Five week-old male Wistar rats received 20% D-fructose dissolved in drinking water for 8 weeks and were compared with age-matched CR. Plasmatic levels of glucose, insulin and amylin were measured. Mesenteric microvessels were dissected and mounted in wire myographs to evaluate endothelium-dependent vasodilation to acetylcholine. IRR displayed a significant increase in plasmatic levels of glucose, insulin and amylin and reduced endothelium-dependent relaxation when compared to CR. Acute treatment of mesenteric arteries with r-amylin (40 pM) deteriorated endothelium-dependent responses in CR. Amylin-induced reduction of endothelial responses was unaffected by the H2O2 scavenger, catalase, but was prevented by the extracellular superoxide scavenger, superoxide dismutase (SOD) or the NADPH oxidase inhibitor (VAS2870). By opposite, amylin failed to further inhibit the impaired relaxation in mesenteric arteries of IRR. SOD, or VAS2870, but not catalase, ameliorated the impairment of endothelium-dependent relaxation in IRR. At concentrations present in insulin resistance conditions, amylin impairs endothelium-dependent vasodilation in mircrovessels from rats with preserved vascular function and low levels of endogenous amylin. In IRR with established endothelial dysfunction and elevated levels of amylin, additional exposure to this peptide has no effect on endothelial vasodilation. Increased superoxide generation through NADPH oxidase activity may be a common link involved in the endothelial dysfunction associated to insulin resistance and to amylin exposure in CR.
Putting the ‘Street’ in Gang: Place and Space in the Organization of Scotland’s Drug-Selling Gangs

Street gangs, by definition, enjoy a special relationship with the street. Prior research shows that some communities are synonymous with gangs and that turf holds a combination of expressive and instrumental value for gang members. As gangs evolve over time and through different levels of organization, however, gangs’ relationship with the street changes. This shifting street dynamic is underexplored in prior research, thus, drawing on qualitative data from Scotland and Bourdieu’s theory of social field, the current study presents three cases of gangs at different stages of evolution and examines how levels of gang organization affect spatial relationships. As gangs accumulate sufficient street capital to evolve, we find territory is defined less physically and more relationally, with implications for gang research and practice.
Delta-I Noise Scaling in Sub-Micron CMOS Technologies

Delta-I or switching noise is becoming an important design constraint in today's digital and mixed signal ICs. In this work a detailed analysis of the scaling of the switching noise is performed based on current technologies and two different scaling scenarios: high performance driven scaling and low power driven scaling. The results were obtained by simulating a test structure using Hspice and LEVEL3 models for the transistors.
Using Access Data for Paper Recommendations on ArXiv.org

This thesis investigates in the use of access log data as a source of information for identifying related scientific papers. This is done for arXiv.org, the authority for publication of e-prints in several fields of physics. Compared to citation information, access logs have the advantage of being immediately available, without manual or automatic extraction of the citation graph. Because of that, a main focus is on the question, how far user behavior can serve as a replacement for explicit meta-data, which potentially might be expensive or completely unavailable. Therefore, we compare access, content, and citation-based measures of relatedness on different recommendation tasks. As a final result, an online recommendation system has been built that can help scientists to find further relevant literature, without having to search for them actively. To Agata Daria — for unconditional love, comprehension and support throughout the course of this thesis.
Sweet Taste Perception is Associated with Body Mass Index at the Phenotypic and Genotypic Level

Investigations on the relationship between sweet taste perception and body mass index (BMI) have been inconclusive. Here, we report a longitudinal analysis using a genetically informative sample of 1,576 adolescent Australian twins to explore the relationship between BMI and sweet taste. First, we estimated the phenotypic correlations between perception scores for four different sweet compounds (glucose, fructose, neohesperidine dihydrochalcone (NHDC), and aspartame) and BMI. Then, we computed the association between adolescent taste perception and BMI in early adulthood (reported 9 years later). Finally, we used twin modeling and polygenic risk prediction analysis to investigate the genetic overlap between BMI and sweet taste perception. Our findings revealed that BMI in early adulthood was significantly associated with each of the sweet perception scores, with the strongest correlation observed in aspartame with r = 0.09 (p = .007). However, only limited evidence of association was observed between sweet taste perception and BMI that was measured at the same time (in adolescence), with the strongest evidence of association observed for glucose with a correlation coefficient of r = 0.06 (p = .029) and for aspartame with r = 0.06 (p = .035). We found a significant (p < .05) genetic correlation between glucose and NHDC perception and BMI. Our analyses suggest that sweet taste perception in adolescence can be a potential indicator of BMI in early adulthood. This association is further supported by evidence of genetic overlap between the traits, suggesting that some BMI genes may be acting through biological pathways of taste perception.
Comparison of Surface Proteomes of Adherence Variants of Listeria Monocytogenes Using LC-MS/MS for Identification of Potential Surface Adhesins

The ability of Listeria monocytogenes to adhere and form biofilms leads to persistence in food processing plants and food-associated listeriosis. The role of specific surface proteins as adhesins to attach Listeria cells to various contact surfaces has not been well characterized to date. In prior research comparing different methods for surface protein extraction, the Ghost urea method revealed cleaner protein content as verified by the least cytoplasmic protein detected in surface extracts using LC-MS/MS. The same technique was utilized to extract and detect surface proteins among two surface-adherent phenotypic strains of L. monocytogenes (i.e., strongly and weakly adherent). Of 640 total proteins detected among planktonic and sessile cells, 21 protein members were exclusively detected in the sessile cells. Relative LC-MS/MS detection and quantification of surface-extracted proteins from the planktonic weakly adherent (CW35) and strongly adherent strains (99-38) were examined by protein mass normalization of proteins. We found that L. monocytogenes 99-38 exhibited a total of 22 surface proteins that were over-expressed: 11 proteins were detected in surface extracts of both sessile and planktonic 99-38 that were ≥5-fold over-expressed while another 11 proteins were detected only in planktonic 99-38 cells that were ≥10-fold over-expressed. Our results suggest that these protein members are worthy of further investigation for their involvement as surface adhesins.
An Authorship Attribution for Serbian

An authorship attribution is a problem of identifying the author of an anonymous or disputed text if there is a closed set of candidate authors. Due to the richness of natural languages and numerous ways of expressing individuality in a writing process, this task employs all the sources of language knowledge: lexis, syntax, semantics, orthography, etc. The impressive results of n-gram based algorithms have been presented in many papers for many languages so far. The goal of our research was to test if this group of algorithms works equally well on Serbian and if it is a case, to calculate the optimal values for the parameters appearing in the algorithms. Also, we wanted to test if a syllable based word decomposition, which represents a more human like word decomposition in comparison to n-grams, can be useful in an authorship attribution. Our results confirm good performance of an n-gram based approach (accuracy up to 96%) and show the potential usefulness of a syllable based approach (accuracy from 81% to 89%).
Energy Efficient Tag Estimation Method for ALOHA-Based RFID Systems

Radio frequency identification (RFID) technology has become an important tool for items identification and tracking. Its rapid deployment in dynamic industry areas enables variety of new applications in behavioral analysis and processes automation. In such systems it is crucial to identify all RFID tags before they leave the interrogation area. To accomplish fast identification, it is necessary to estimate a number of tags which are in the reader interrogation area. In this paper, we revisit the problem of tag quantity estimate and adapting frame size of dynamic frame slotted ALOHA widely used as RFID medium access control mechanism. The main disadvantage in the implementation of the state-of-the-art estimation algorithms includes the number of required computations, along with the temporary storage of large numbers which appear before estimation. As a consequence, such algorithms are energy inefficient and may require specific computer architecture to support the calculus. In order to address stated issues, we present improved linearized combinatorial model algorithm for optimal frame size selection which, due to linear property of the estimator, allows significant reduction in the estimate computation with acceptable tradeoff in accuracy. Simulations analyze the required number of slots to identify tag population and the floating point operation costs required to compute estimate. In addition, to emphasize the importance of reducing computational cost, we give a case study which compares energy consumed by the mobile RFID reader processor to compute the estimate and its energy equivalent of the required radio front-end energy to identify tags.
Linguistic descriptors in face recognition: A literature survey and the perspectives of future development

People are highly efficient in recognizing faces. However, it is almost impossible for them to cope with huge datasets of facial images without any computational support. On the other hand, the way people describe the facial features using quite commonly encountered descriptors such as “long nose”, “small eyes” and also allude to their feelings according to a specific person like “seems to be nice”, may be utilized to enhance automatic face recognition systems. This offers an interesting possibility to incorporate human perception of faces and relations between facial features into machine-made computations. To address this aspect, one can engage the linguistic descriptors and the linguistic modeling. In this study, we present a comprehensive survey of the state-of-the-art studies and elaborate on some promising perspectives of the developments in this area.
Weighted Probability Distribution Voting , an introductionHans van HalterenDept . of Language and SpeechUniversity of NijmegenP

This paper introduces a new machine learning technique, Weighted Probability Distribution Voting (WPDV). During learning, WPDV determines the output class probability distribution for each input feature, both atomic and complex. During classiication, WPDV takes all input features that occur in the new input and adds the corresponding probability distributions , each multiplied by a weight factor which depends on the feature or feature type. The output class with the highest sum is then selected. Apart from the basic mechanism of WPDV, the paper describes some principles for weight selection and feature restriction. Finally, WPDV is shown to produce results which are better than those of other state-of-the-art machine learning systems for several NLP tasks.
Effects of Ventilation and Fire Power on Bulkhead Damage by Temperature Simulation Model and Experiment Analysis

Since the thermal conductivity of steel is very high, the high temperature distribution in many scenarios may cause the structure easy to be damaged in terms of performance of ship cabin. Steel is the main constituent material structure of the ship while the long time heating during the fires will result in declining the structure performance of the cabin. Study how to remain or reduce averaged bulkhead temperature becomes an important task in either structure performance design or fireproof optimization. This paper uses FDS (Fire Dynamics Simulator) which has a large eddy simulation turbulence model to do the simulation on full-scale fire tests of ship under different ventilation conditions. The results show that the HRR (Heat Release Rate) is not proportional to the ventilation area. The error between FDS simulation and experiment are close within 25%.
Benchmarking Coding Algorithms for the R-tree Compression

Multi-dimensional data structures have been widely applied in many data management fields. Spatial data indexing is their natu- ral application, however there are many applications in different domain fields. When a compression of these data structures is considered, we follow two objectives. The first objective is a smaller index file, the sec- ond one is a reduction of the query processing time. In this paper, we apply a compression scheme to fit these objectives. This compression scheme handles compressed nodes in a secondary storage. If a page must be retrieved then this page is decompressed into the tree cache. Since this compression scheme is transparent from the tree operations point of view, we can apply various compression algorithms to pages of a tree. Obviously, there are compression algorithms suitable for various data collections, therefore, this issue is very important. In our paper, we com- pare the performance of Golomb, Elias-delta and Elias-gamma coding with the previously introduced Fast Fibonacci algorithm.
Classical functional Bethe ansatz for SL(N): Separation of variables for the magnetic chain

It is desirable for one to be able to construct separation variables for systems with SL(N) R matrices or Lax pairs. This article relates and extends the two known methods of construction. The Sklyanin’s functional Bethe ansatz (FBA) [Commun. Math. Phys. 150, 181–191 (1992)] was previously only known for SL(2) and SL(3) (and associated) R matrices. The algebraic geometric method of Adams, Harnad, and Hurtubise [Commun. Math. Phys. 155, 385–413 (1993)] has been shown to work for SL(N) but up until now only for linear Poisson brackets. In this article Sklyanin’s program is advanced by giving the FBA for certain systems with SL(N) R matrices. This is achieved by constructing rational functions A(u) and B(u) of the matrix elements of T(u), so that, in the generic case, the zeros xi of B(u) are the separation coordinates and the Pi=A(xi) provide their conjugate momenta. It is shown that the separation variables thus defined are the same as those given (as the simultaneous solutions of several equations in two .
An Epidemiological Study of Neuropathic Pain Symptoms in Canadian Adults

The reported prevalence of neuropathic pain ranges from 6.9% to 10%; however the only Canadian study reported 17.9%. The objective of this study was to describe the epidemiology of neuropathic pain in Canada. A cross-sectional survey was conducted in a random sample of Canadian adults. The response rate was 21.1% (1504/7134). Likely or possible neuropathic pain was defined using a neuropathic pain-related diagnosis and a positive outcome on the Self-Report Leeds Assessment of Neuropathic Symptoms and Signs pain scale (S-LANSS) or the Douleur Neuropathique 4 (DN4) Questions. The prevalence of likely neuropathic pain was 1.9% (S-LANSS) and 3.4% (DN4) and that of possible neuropathic pain was 5.8% (S-LANSS) and 8.1% (DN4). Neuropathic pain was highest in economically disadvantaged males. There is a significant burden of neuropathic pain in Canada. The low response rate and a slightly older and less educated sample than the Canadian population may have led to an overestimate of neuropathic pain. Population prevalence varies by screening tool used, indicating more work is needed to develop reliable measures. Population level screening targeted towards high risk groups should improve the sensitivity and specificity of screening, while clinical examination of those with positive screening results will further refine the estimate of prevalence.
Training Gaussian Process Regression Models Using Optimized Trajectories

Quadrotor helicopters and robot manipulators are used widely for both research and industrial applications. Both quadrotors and manipulators are difficult to model. Quadrotors have complex dynamic models, especially at high speeds. Obtaining an accurate model of manipulator dynamics is often difficult, due to inaccurate values for link parameters and dynamics such as friction which are difficult to model accurately. Supervised learning methods such as Gaussian Process Regression (GPR) have been used to learn the inverse dynamics of a system. These methods can estimate a dynamic model from experimental data without requiring the structure of the model to be known, and can be used online to update the model if the system changes over time. This approach has been used to learn the inverse dynamics of a manipulator, but has not yet been applied to quadrotors. In addition, collecting training data for supervised learning can be difficult and time consuming, and poor or inadequate training data may result in an inaccurate model. Another problem frequently encountered when using GPR to learn the model of a system is the large computational cost of using GPR. A number of sparse approximations of GPR exist to deal with this issue, but it is not clear which sparse approximation results in the best performance, particularly when training data is being added incrementally. This thesis proposes a method for systematically collecting training data for a GPR model. The trajectory used to collect training data is parameterized, and the parameters are optimized to maximize the GPR variance over the trajectory. This approach is tested both in simulation and experimentally for a quadrotor, and in experiments on a 4-DOF manipulator. Optimizing the training trajectories is shown to reduce the amount of training data required to learn the model of a system. The thesis also compares three sparse approximations of GPR: the dictionary approach, Sparse Spectrum GPR (SSGP) and simple downsampling of the training data to reduce the size of the training data set. Using a dictionary is found to provide the best performance, even when the dictionary contains a very small subset of the available data. Finally, all GPR models have hyperparameters, which have a significant impact on the prediction made by the GP model. Training these hyperparameters is important for getting accurate predictions. This thesis evaluates different methods of hyperparameter training on a 4-DOF manipulator to determine the most effective method of training the hyperparameters. For SSGP, the best hyperparameter training strategy is to reinitialize and train the hyperparameters after each trajectory. SSGP is also observed to be highly sensitive to the number of iterations of gradient descent used in hyperparameter training;
Interactivity in Virtual and Multimedia Environments: A Meeting Point for Education and ICT in Archaeological Museums

Constructivist educational trends stress interactivity as an important methodology for education and communication. Focusing on archaeological presentation and museum communication, this paper examines how the introduction in these fields of Information and Communication Technologies (ICT), and particularly of Virtual Reality (VR) and multimedia (MM) applications, is modifying the " traditional " concept of interactivity. Firstly, it establishes the main ideas about the concept's meaning in education, museums and computers. Afterwards, it analyses the common and particular features of VR and MM and, using the findings from a range of relevant studies, it evaluates their effectiveness as learning tools.
Optimal Thinning Algorithm for detection of FCD in MRI Images

Thinning is essentially a "pre-processing" step in many applications of digital image processing, computer vision, and pattern recognition. In many computer vision applications, the images interested in a scene can be characterized by structures composed of line or curve or arc patterns for shape analysis. It is used to compress the input data and expedite the extraction of image features. In this paper three different thinning algorithms are applied for MRI Brain Images to estimate performance evaluation metrics of thinned images. Image thinning reduces a large amount of memory usage for structural information storage. Experimental result shows the performance of the proposed algorithm.
Tuning Into The Task: Sonic Environmental Cues And Mental Task Switching

This position paper suggests a novel approach to enhancing productivity for professionals whose core business is deep thinking, by manipulation of the sonic environment. Approaching the issue from the perspective of sound-design, it proposes the composition and algorithmic generation of background soundscapes that promote a psychological state of flow [1], and can become mentally associated with particular tasks through exposure, so as to facilitate task switching by switching soundscapes. These background soundscapes are intended to mask distracting clatter, oppressive quiet, and other suboptimal sonic environments frequently encountered in office workplaces. Consequently, I call them active-silences— soundscapes designed to be not heard, although they may be relatively loud. The most commonly used active-silence is white noise, though there are surprisingly diverse other approaches to crafting active-silence. This variety suggests the possibility of training associations that pair distinct active-silences with distinct mental tasks.