Miracle-dz/UniFAR-FTUs huggingface dataset:
A collaborative filtering method based on empathy with reviewers

Today, it has become difficult for people to find books that fulfill their preference because the number of books in the world has become very large. In order to select books to read, the reviews on candidate books are helpful. Therefore, the role of online review sites which are web sites storing reviews of books is important. One of the functions in the online review sites is the collaborative filtering (CF) with reviewers. In general, the user-based CF is based on the assumption that the preferences of users who select the same items will be similar. However, in many cases, each user would have different viewpoints for their evaluation of books. In this paper, we focused on the difference of viewpoints when each user evaluate a book. We propose a CF method based on the empathy with other reviewers. Our method utilizes the evaluation for a review as feedback. Experimental results show the effectiveness of the proposed method.
Logitboost of Multinomial Bayesian Classifier for Text Classification

Automated text classification has been considered as a vital method to manage and process a vast amount of documents in digital forms that are widespread and continuously increasing. In general, text classification plays an important role in information extraction and summarization, text retrieval, and question-answering. The Multinomial Bayesian Classifier has traditionally been a focus of research in the field of text learning. This paper increases the accuracy of Multinomial Bayesian Classifier with the usage of the Logitboost technique. We performed a large-scale comparison on benchmark datasets with other state-of-the-art algorithms and the proposed technique had greater accuracy in most cases. Copyright © 2006 Praise Worthy Prize All rights reserved.
SAR Data compression with Respect to Polarimetric and Interferometric Applications

In this paper SAR raw data and SAR image data comrpession is addressed. For SAR raw data compression we suggest a modified FFT-BAQ. SAR image compression is mainly performed in wavelet domain. Depending on the application a conversion from cartesian format to polar format is performed prior to the transform. The algorithms are applied on data sets of airborne SAR instruments (E-SAR) and spaceborne systems (X-SAR).
Massive reduction in conversational American English

The English are a lazy lot, and will not speak a word as it should be spoken when they can slide through it. Why be bothered to say extraordinary when you can get away with strawdiny? Many of the Oxford Cockneys are weaklings too languid or emasculated to speak their noble language with any vigor, but the majority are following a foolish fashion which had better be abandoned. Its ugliness alone should make it unpopular, but it has the additional effect of causing confusion. [Irish playwright St. John Ervine, quoted by H.L. Mencken (1948, p. 39)]
Visual tracking using region covariance and geometric particle filtering

Region covariance descriptor recently proposed has been approved robust and elegant to describe a region of interest, which has been applied to visual tracking. The covariance matrix enables efficient fusion of different types of features, where the spatial and statistical properties as well as their correlation are characterized. The similarity of two covariance descriptor is measured on Riemannian manifolds. Within a probabilistic framework, we integrate covariance descriptor into Monte Carlo tracking technique for visual tracking. Most existing particle filtering based tracking algorithms treat deformation parameters of the target as a vector. We have proposed a visual tracking algorithm using particle filtering on the affine group, which implements the geometric particle filter with the constraint that the system state lies in a low dimensional manifold: affine lie group. The sequential Bayesian updating consists in drawing state samples while moving on the manifold geodesics; The Region covariance is updated using a novel approach in a Riemannian space. Theoretic analysis and experimental evaluations against the tracking algorithm based on geometric particle filtering demonstrate the promise and effectiveness of this algorithm.
A Measles Epidemic Threshold in a Highly Vaccinated Population

Background Mass vaccination against measles has successfully lowered the incidence of the disease and has changed the epidemic pattern from a roughly biennial cycle to an irregular sequence of outbreaks. A possible explanation for this sequence of outbreaks is that the vaccinated population is protected by solid herd immunity. If so, we would expect to see the fraction of susceptible individuals remaining below an epidemic threshold. An alternative explanation is the occurrence of occasional localised lapses in herd immunity that allow for major outbreaks in areas with a low vaccine coverage. In that case, we would expect the fraction of susceptible individuals to exceed an epidemic threshold before outbreaks occur. These two explanations for the irregular sequence of measles outbreaks can be tested against observations of both the fraction of susceptible individuals and infection attack rates. Methods and Findings We have estimated both the fraction of susceptible individuals at the start of each epidemic year and the infection attack rates for each epidemic year in the Netherlands over a 28-y period. During this period the vaccine coverage averaged 93%, and there was no sustained measles transmission. Several measles outbreaks occurred in communities with low vaccine coverage, and these ended without intervention. We show that there is a clear threshold value for the fraction of susceptible individuals, below which only minor outbreaks occurred, and above which both minor and major outbreaks occurred. A precise, quantitative relationship exists between the fraction of susceptible individuals in excess of this threshold and the infection attack rate during the major outbreaks. Conclusion In populations with a high but heterogeneous vaccine coverage, measles transmission can be interrupted without establishing solid herd immunity. When infection is reintroduced, a major outbreak can occur in the communities with low vaccine coverage. During such a major outbreak, each additional susceptible individual in excess of the threshold is associated with almost two additional infections. This quantitative relationship offers potential for anticipating both the likelihood and size of future major outbreaks when measles transmission has been interrupted.
Vulnerability of Machine Learning Models to Adversarial Examples

We propose a genetic algorithm for generating adversarial examples for machine learning models. Such approach is able to find adversarial examples without the access to model’s parameters. Different models are tested, including both deep and shallow neural networks architectures. We show that RBF networks and SVMs with Gaussian kernels tend to be rather robust and not prone to misclassification of adversarial examples.
A game theoretic approach to control IDS sensitivity

Nowadays, computer networks especially the Internet as a prime example, become a separable part of our social lives. The security is the most important concepts that must be considered in computer networks. Intrusion Detection System (IDS) is a security tool that enhances the information security paradigm beyond traditional reactive and protective network security. It monitors and analyses the received packets in network systems and signs the security problems. Controlling the degree of security is one of the most challenges with IDS. An excessive increase in the IDS sensitivity may cause increasing the system costs and difficulties in accessing to the system. On the other hand, an excessive reduction in the IDS sensitivity may cause damages to system duo to not detecting most of the attacks. Making balance between these tradeoffs is a problem that the system administrator is unable to handle. In this paper, we propose a game theoretic approach to address the IDS sensitivity degree in the system. Our approach in comparison to the previous methods which model the behavior of the attacker and IDS, models the system and attacker as two-players, noncooperative game. We conduct our model on the famous data set in security area and obtained result is promising.
Color Image Segmentation Method Based on Improved Spectral Clustering Algorithm

Contraposing to the features of image data with high sparsity of and the problems on determination of clustering numbers, we try to put forward an color image segmentation algorithm, combined with semi-supervised machine learning technology and spectral graph theory. By the research of related theories and methods of spectral clustering algorithms, we introduce information entropy conception to design a method which can automatically optimize the scale parameter value. So it avoids the unstability in clustering result of the scale parameter input manually. In addition, we try to excavate available priori information existing in large number of non-generic data and apply semi-supervised algorithm to improve the clustering performance for rare class. We also use added tag data to compute similar matrix and perform clustering through FKCM algorithms. By the simulation of standard dataset and image segmentation, the experiments demonstrate our algorithm has overcome the defects of traditional spectral clustering methods, which are sensitive to outliers and easy to fall into local optimum, and also poor in the convergence rate
CD36 Participates in PrP106–126-Induced Activation of Microglia

Microglial activation is a characteristic feature of the pathogenesis of prion diseases. The molecular mechanisms that underlie prion-induced microglial activation are not very well understood. In the present study, we investigated the role of the class B scavenger receptor CD36 in microglial activation induced by neurotoxic prion protein (PrP) fragment 106–126 (PrP106–126). We first examined the time course of CD36 mRNA expression upon exposure to PrP106–126 in BV2 microglia. We then analyzed different parameters of microglial activation in PrP106–126-treated cells in the presence or not of anti-CD36 monoclonal antibody (mAb). The cells were first incubated for 1 h with CD36 monoclonal antibody to block the CD36 receptor, and were then treated with neurotoxic prion peptides PrP106–126. The results showed that PrP106–126 treatment led to a rapid yet transitory increase in the mRNA expression of CD36, upregulated mRNA and protein levels of proinflammatory cytokines (IL-1β, IL-6 and TNF-α), increased iNOS expression and nitric oxide (NO) production, stimulated the activation of NF-κB and caspase-1, and elevated Fyn activity. The blockade of CD36 had no effect on PrP106–126-stimulated NF-κB activation and TNF-α protein release, abrogated the PrP106–126-induced iNOS stimulation, downregulated IL-1β and IL-6 expression at both mRNA and protein levels as well as TNF-α mRNA expression, decreased NO production and Fyn phosphorylation, reduced caspase-1 cleavage induced by moderate PrP106–126 –treatment, but had no effect on caspase-1 activation after treatment with a high concentration of PrP106–126. Together, these results suggest that CD36 is involved in PrP106–126-induced microglial activation and that the participation of CD36 in the interaction between PrP106–126 and microglia may be mediated by Src tyrosine kinases. Our findings provide new insights into the mechanisms underlying the activation of microglia by neurotoxic prion peptides and open perspectives for new therapeutic strategies for prion diseases by modulation of CD36 signaling.
Anycast Q-routing in wireless sensor networks for healthcare monitoring

Wireless healthcare monitoring sensor networks are multi-hop Zigbee-based systems which usually use broadcast or multicast to reliably deliver vital diagnostics information or event detection to the sink. However, such schemes typically lead to high network traffic and large path search delay. This paper presents an efficient, adaptive, distributed routing mechanism, called anycast Q-routing to route information to the nearest sink in multiple sink wireless sensor networks (WSNs). The proposed scheme requires only local information at each node, modest computational, storage, and learning overhead. Simulation results show that the anycast Q-routing scheme can discover paths using low communication overhead and short path search latency over existing AODV-based broadcast, multicast and anycast schemes used in WSNs for healthcare monitoring.
The ‘boy scouts’ and ‘bad boys’ of skateboarding: a thematic analysis of the bones brigade

Abstract Skateboarding has emerged from an alternative subculture to an influential action sport, boosted by recent inclusion in the Olympic Games. Central to debates in both academic and practice communities are the tensions between skateboarding as a lifestyle and ethos, and skateboarding as an Olympic discipline. This article presents a thematic analysis of The Bones Brigade documentary which follows the rise of Tony Hawk, a high profile skateboarder, and the team he skated with during an important historical era in skating. All are men, presenting an opportunity to investigate masculinity in the sport. We reveal how these pivotal figures embody flexible and overlapping formations of hyper to alternative masculinity. Importantly, we also highlight forms of emotional connection and expressiveness that can unify all skaters. Such insights promote a deeper understanding into diverse formations of masculinity and various expressions of identities in skateboarding and with insights for other action sports.
Overlapping Strategies for Reducing Carbon Emissions from the Personal Transportation Sector

We develop a numerical model of the U.S. personal transportation sector and use it to simulate the impacts of gasoline taxes, fuel-economy standards, renewable fuel standards, and other policies targeting transportation fuels. Our model captures consumer demand for miles and tradeoffs between car size, costs, and fuel economy, as well as fuel supply from gasoline and corn, sugarcane, and cellulosic ethanol. In addition to carbon emissions, our model captures potential market failures related to consumer undervaluation of fuel economy, learning spillovers in cellulosic ethanol, and unpriced externalities associated with car size and miles traveled. We find that the current policy mix of gasoline taxes, fuel economy standards, and renewable fuel standards leads to a 13% reduction in domestic carbon emissions from this sector at an average cost of $18/tCO2. In contrast, the optimal policy—a carbon tax, combined with subsidies for fuel economy and cellulosic ethanol—delivers an average welfare gain of $15/tCO2. Meanwhile, the most costeffective single policy is a traditional fuel-economy standard followed by a fuel tax, carbon tax, and size-based fuel economy standard, which all deliver net welfare gains, followed by a low-carbon fuel standard and renewable fuel quantity standard, which are both costly. We find that while the fuel tax and carbon tax both ameliorate unpriced externalities associated with car size and miles traveled, the size-based fuel economy standard greatly exacerbates such externalities. In addition, fuel taxes and carbon taxes consistently deliver low-cost abatement, even when consumers fully value fuel economy. Overall, our results emphasize the inefficiency of current federal renewable fuel standards and size-based fuel economy standards relative to direct taxes on carbon or gasoline. We acknowledge financial support from the U.S. Environmental Protection Agency Grant #83413401 and the Swedish Foundation for Strategic Environmental Research (MISTRA) INDIGO program. In addition, we thank Louis Preonas for valuable research assistance. Fischer is also grateful for the support of the European Community's Marie -Curie International Incoming Fellowship, “STRATECHPOL—Strategic Clean Technology Policies for Climate Change,” financed under the EC Grant Agreement PIIF-GA-2013-623783, and for the hospitality of the Fondazione Eni Enrico Mattei (FEEM). † Associate Professor at Michigan State University and Resource Associate at NBER. Email: sta@msu.edu. ‡ Senior Fellow at Resources for the Future (RFF) and Senior Researcher at FEEM. Email: fischer@rff.org. § Research Assistant at Resources for the Future (RFF). Email: egorenkov@rff.org.
Analysis of environmental protection industrial cluster in Yixing based on diamond model

In recent years, the form of organization, industry cluster, attracts great attention and advantages from industrial agglomeration become even more prominent. This paper tends to analyze the development of environmental protection industry cluster in Jiangsu Yixing on the six points of diamond model. While trying to analyze the advantages, this article will also identify the problems in the development and then give some advice.
Cytokine-Based Log-Scale Expansion of Functional Murine Dendritic Cells

Background Limitations of the clinical efficacy of dendritic cell (DC)-based immunotherapy, as well as difficulties in their industrial production, are largely related to the limited number of autologous DCs from each patient. We here established a possible breakthrough, a simple and cytokine-based culture method to realize a log-scale order of functional murine DCs (>1,000-fold), which cells were used as a model before moving to human studies. Methodology/Principal Findings Floating cultivation of lineage-negative hematopoietic progenitors from bone marrow in an optimized cytokine cocktail (FLT3-L, IL-3, IL-6, and SCF) led to a stable log-scale proliferation of these cells, and a subsequent differentiation study using IL-4/GM-CSF revealed that 3-weeks of expansion was optimal to produce CD11b+/CD11c+ DC-like cells. The expanded DCs had typical features of conventional myeloid DCs in vitro and in vivo, including identical efficacy as tumor vaccines. Conclusions/Significance The concept of DC expansion should make a significant contribution to the progress of DC-based immunotherapy.
Identification of a Novel Human Rhinovirus C Type by Antibody Capture VIDISCA-454

Causative agents for more than 30 percent of respiratory infections remain unidentified, suggesting that unknown respiratory pathogens might be involved. In this study, antibody capture VIDISCA-454 (virus discovery cDNA-AFLP combined with Roche 454 high-throughput sequencing) resulted in the discovery of a novel type of rhinovirus C (RV-C). The virus has an RNA genome of at least 7054 nt and carries the characteristics of rhinovirus C species. The gene encoding viral protein 1, which is used for typing, has only 81% nucleotide sequence identity with the closest known RV-C type, and, therefore, the virus represents the first member of a novel type, named RV-C54.
A Case Study of Qualitative Methods

In research – and many parts of life – we only see the finished product, a snapshot of calm and certainty even when the reality is chaotic. When people meet me, they might learn that I am a computer science (CS) professor. I assume they would never guess that I nearly failed data structures in college and still struggled in my second attempt. They would never imagine how many interviews I bombed and graduate schools I did not get into. They don’t see the inevitable paper and grant rejections or poor teaching evaluations. Those things aren’t on my CV, but reflecting back, I see these as some of the most influential elements for my learning. This chapter is a narrative of the actual doing of a research study, what Roth (2006) calls a praxis narrative. I hope to give you a “feel for the game” (Bourdieu, 1992) of doing one type of qualitative research. Ideally, you will gain some insights into qualitative methods, or at least recognition that if it feels chaotic, it is not necessarily wrong. Textbooks about qualitative methods have a burden of providing clarity to the methods. This chapter instead seeks to show all of the mess and ambiguity. In our current context, where computing knowledge is often perceived as only available to the intellectual elite or people with a “geek gene,” it is our responsibility to challenge these notions and help others see our humanness. I will attempt to do that while telling the backstory of this paper. In this chapter, I will share some of what I learned through writing, revising, and now reflecting on a paper that traversed a particularly rocky path. My qualitative analysis was eventually published in a paper at the Association for Computing Machinery (ACM) Special Interest Group on Computer Science Education (SIGCSE) International Computing Education Research (ICER) conference (Lewis, 2012a), but the path there was a bit bumpy. The analysis came from my master’s thesis (submitted December 2009), abbreviated to submit to ICER in April of 2010. It was rejected from ICER in 2010 and again in 2011. Despite the suspicion that the manuscript was doomed, I decided to revise and resubmit it again in 2012. Only in this third submission to ICER was it accepted. I received incredibly thoughtful – and harsh – reviews of my first submission to ICER in 2010. At that time, my work was described as preliminary and that the contribution was fairly minimal.
A novel fractional proportional-resonant current controller for voltage source converters

This paper proposes a hybrid controller that combines conventional proportional-resonant controllers with the more recent application of fractional order calculus applied to the current control loop of voltage source converters. The use of existing non-integer orders applied to proportional-integral controllers is extended to resonant controllers. The application of fractional exponents for the integro-derivative parts contributes with an extra degree of freedom that permits to enhance the obtained response in an extended frequency range when the closed-loop bode diagram is analysed. Different implementation alternatives in the continuous time domain are presented, and the most suitable one is selected. The new controller formulation capabilities are compared with conventional proportional-resonant controllers via simulations and its performance is validated in a test platform based on a 20 kVA silicon-carbide converter acting as an active filter.
Effectiveness of Voltage Error Compensation and Parameter Identification for Model-Based Sensorless Control of IPMSM

A sensorless-control technique for improving the performance of interior permanent-magnet synchronous-motor control such as the operating speed range and position estimation accuracy is proposed herein. The method presented is based on the estimation of an extended electromotive force in a rotating reference frame. The difference between the reference voltage and the actual voltage of the inverter is modeled and compensated for in order to improve the accuracy and the stability of the sensorless drive system. Moreover, an online parameter-identification technique is applied in order to improve the accuracy of position estimation. The effects of the proposed scheme are evaluated by the experimental results.
Hidden Conditional Random Fields for ECG Classification

In this paper a novel approach to ECG signal classification is proposed. The approach is based on using hidden conditional random fields (HCRF) to model the ECG signal. Features used in training and testing the HCRF are based on time-frequency analysis of the ECG waveforms. Experimental results show that the HCRF model is promising and gives higher accuracy compared to maximum-likelihood (ML) trained hidden Markov models (HMM).