Miracle-dz/UniFAR-FTUs huggingface dataset:
Domain Adaptation for Statistical Classifiers

The most basic assumption used in statistical learning theory is that training data and test data are drawn from the same underlying distribution. Unfortunately, in many applications, the "in-domain" test data is drawn from a distribution that is related, but not identical, to the "out-of-domain" distribution of the training data. We consider the common case in which labeled out-of-domain data is plentiful, but labeled in-domain data is scarce. We introduce a statistical formulation of this problem in terms of a simple mixture model and present an instantiation of this framework to maximum entropy classifiers and their linear chain counterparts. We present efficient inference algorithms for this special case based on the technique of conditional expectation maximization. Our experimental results show that our approach leads to improved performance on three real world tasks on four different data sets from the natural language processing domain.
SMART: Semi-Supervised Music Emotion Recognition with Social Tagging

Music emotion recognition (MER) aims to recognize the affective content of a piece of music, which is important for applications such as automatic soundtrack generation and music recommendation. MER is commonly formulated as a supervised learning problem. In practice, except for Pop music, there is little labeled data in most genres. In addition, emotion is genre specific in music and thus the labeled data of Pop music cannot be used for other genres. In this paper, we aim to solve the genre-specific MER problem by exploiting two kinds of auxiliary data: unlabeled songs and social tags. However, using these two kinds of data effectively is a non-trivial task, e.g. tags are noisy and therefore cannot be treated as fully trustworthy. To build an accurate model with the help from the unlabeled songs and noisy tags, we present SMART, which stands for Semi-Supervised Music Affective Emotion Recognition with Social Tagging, combining of a graph-based semisupervised learning algorithm with a novel tag refinement method. Experiments on the Million Song Dataset show that our proposed approach, trained with only 10 labeled instances, is as accurate as Support Vector Regression trained with 750 labeled songs.
Molecular epidemiology of apparent outbreak of invasive aspergillosis in a hematology ward

During a 2-month period, five patients suffering from invasive infections caused by Aspergillus flavus or Aspergillus fumigatus were identified in the Hematology Department of the University Hospital Dijkzigt (Rotterdam, The Netherlands). To study the epidemiological aspects of invasive aspergillosis, strains from these patients and from the hospital environment, isolated during extensive microbiological screening, were subjected to genotyping. A novel DNA extraction technique, involving freezing, grinding, and direct lysis in guanidium isothiocyanate-containing buffers of mycelial material, was applied. DNA isolation was followed by typing by random amplification of polymorphic DNA (RAPD) analysis. This showed that strains isolated from all patients infected with the same fungal species were genotypically distinct, thus providing evidence against the possibility of an ongoing, single-source nosocomial outbreak. Strains could also be differentiated from strains of geographically diverse origins. However, an A. flavus strain from one of the patients was also frequently encountered in the hospital environment. As all environmental strains were collected after this patient had been diagnosed with invasive disease, the epidemiological value of this observation could not be ascertained. Intensive investigations showed no single source of A. flavus or other aspergilli. RAPD genotyping proved that the outbreak of invasive aspergillosis in the hematology ward consisted of a series of unrelated events and was not due to a common source within the hospital. RAPD fingerprinting of aspergilli may greatly facilitate future investigations of the epidemiology of invasive disease caused by these pathogens.
Low Carbohydrate Diet From Plant or Animal Sources and Mortality Among Myocardial Infarction Survivors

Background The healthiest dietary pattern for myocardial infarction (MI) survivors is not known. Specific long‐term benefits of a low‐carbohydrate diet (LCD) are unknown, whether from animal or vegetable sources. There is a need to examine the associations between post‐MI adherence to an LCD and all‐cause and cardiovascular mortality. Methods and Results We included 2258 women from the Nurses' Health Study and 1840 men from the Health Professional Follow‐Up Study who had survived a first MI during follow‐up and provided a pre‐MI and at least 1 post‐MI food frequency questionnaire. Adherence to an LCD high in animal sources of protein and fat was associated with higher all‐cause and cardiovascular mortality (hazard ratios of 1.33 [95% CI: 1.06 to 1.65] for all‐cause mortality and 1.51 [95% CI: 1.09 to 2.07] for cardiovascular mortality comparing extreme quintiles). An increase in adherence to an animal‐based LCD prospectively assessed from the pre‐ to post‐MI period was associated with higher all‐cause mortality and cardiovascular mortality (hazard ratios of 1.30 [95% CI: 1.03 to 1.65] for all‐cause mortality and 1.53 [95% CI: 1.10 to 2.13] for cardiovascular mortality comparing extreme quintiles). An increase in adherence to a plant‐based LCD was not associated with lower all‐cause or cardiovascular mortality. Conclusions Greater adherence to an LCD high in animal sources of fat and protein was associated with higher all‐cause and cardiovascular mortality post‐MI. We did not find a health benefit from greater adherence to an LCD overall after MI.
A Semantic Category Recommendation System Exploiting LDA Clustering Algorithm and Social Folksonomy

According to a widespread use of the internet, the amount of data generated from various Social Network Services (SNSs) is increasing day by day. Thus, it has become necessary to categorize data for users to efficiently access to the desired information. However, most of the web sites do not provide the categorizing service. Even a few sites that offer the categorizing service do not support user-oriented automatic category generation with a high-quality performance. In addition, there are several limitations in an analysis of large amounts of data because of a high dimension of vectors when clustering data sets for the category generation. This paper proposes a system that provides users with a service for recommending categories by utilizing social folksonomy with clustered data. Further, a method to reduce the dimension of vectors by removing meaningless words in the contents is introduced.
Partial-thickness burn wounds healing by topical treatment

Background: Burns are common event and associated with a high incidence of death, disability, and high costs. Centella asiatica (L.) is a medicinal herb, commonly growing in humid areas in several tropical countries that improve wound healing. On the basis of previous studies, we compared the efficacy of Centiderm versus silver sulfadiazine (SSD) in partial thickness burning patients. Methods: Study population comprised burn victims referred to Velayat Burning Hospital at Rasht, Iran. The intervention group received Centiderm and control group SSD cream. Burn wounds were treated once daily at home. All of the wounds were evaluated till complete healing occurred and at the admission, days 3, 7, 14 objective signs; visual acuity score (VAS) and subjective signs were recorded. Re-epithelialization time and complete healing days were recorded. We used random fixed block for randomization. The randomization sequence was created using the computer. Patients and burning specialist physician were blinded. Results: Seventy-five patients randomized into 2 groups; (40 patients: Centiderm group; 35 patients: SSD group). The mean age of them was 30.67 ± 9.91 years and 19 of them were male (31.7%). Thirty patients in Centiderm and 30 patients in SSD group were analyzed. All of objective and subjective signs and mean of re-epithelialization and complete healing were significantly better in Centiderm group rather than SSD group (P < 0.05). There was no infection in Centiderm group. Conclusions: We showed that use of Centiderm ointment not only improved the objective and subjective signs in less than 3 days, but also the re-epithelialization and complete healing rather than SSD without any infection in the subjects.
Feature selection for robust backscatter DDoS detection

This paper analyzes the effect of using different feature selection algorithms for robust backscatter DDoS detection. To achieve this, we analyzed four different training sets with four different feature sets. We employed two well-known feature selection algorithms, namely Chi-Square and Symmetrical Uncertainty, together with the Decision Tree classifier. All the datasets employed are publicly available and provided by CAIDA. Our experimental results show that it is possible to develop a robust detection system that can generalize well to the changing backscatter DDoS behaviours over time using a small number of selected features.
Population Genetic Analysis of Atlantic Horseshoe Crabs (Limulus polyphemus) in Coastal Massachusetts.

iii ACKNOWLEDGEMENTS I would like to thank my advisors, Andrew Whiteley and Francis Juanes, and my committee member, Robert Buchsbaum, for all of their guidance, advice, suggestions, comments, and edits throughout the entire project. Thank you to my field assistants, hard work helping me collect data. I would also like to thank Sarah Oktay and Jennifer Karberg for volunteering their time to help me find horseshoe crabs on Nantucket. Thanks to Sara Grady for donating tissue samples. Thank you Sheila Eyler and the US Fish and Wildlife Service for providing button tags to mark my study specimens. Park Service and MA Division of Marine Fisheries for granting me scientific collection permits. I want to extend a special thanks to Robert Prescott for introducing me to horseshoe crab conservation and getting me involved in scientific research again. Thank you to Mary-Jane James-Pirri for all of her insights and advice on all things relating to horseshoe crab research. Finally I would like to give thanks to my family who provided me with support and encouragement when I needed it most, and especially to my husband, David, who helps me find my inner strength every day.
Hierarchical Random Graph Model for Off-line Handwritten Signatures Recognition

The article presents the approach based on the usage of syntactic methods for the static analysis of handwritten signatures. The graph linguistic formalisms applied, such as the IE graph and ETPL(k) grammar, are characterised by considerable descriptive strength and a polynomial membership problem of the syntactic analysis. For the purposes of representing the analysed handwritten signatures, new hierarchical (two-layer) HIE graph structures based on IE graphs have been defined. The two-layer graph description makes it possible to take into consideration both local and global features of the signature. The usage of attributed graphs enables the storage of additional semantic information (in the form of a set of parameters) describing the properties of individual signature strokes. Information about the shapes of specimen signatures is presented in the form of a language based on random IE graphs and stochastic class ETPL(k) grammars, which makes it possible to take into consideration the natural variability of the shape of specimen signatures. The verification and recognition of a signature consists in analysing the affiliation of its graph description to the language describing the specimen database. Initial assessments display a precision of the method at a average level of about 17%.
A PUT-Based Approach to Automatically Extracting Quantities and Generating Final Answers for Numerical Attributes

Automatically extracting quantities and generating final answers for numerical attributes is very useful in many occasions, including question answering, image processing, human-computer interaction, etc. A common approach is to learn linguistics templates or wrappers and employ some algorithm or model to generate a final answer. However, building linguistics templates or wrappers is a tough task for builders. In addition, linguistics templates or wrappers are domain-dependent. To make the builder escape from building linguistics templates or wrappers, we propose a new approach to final answer generation based on Predicates-Units Table (PUT), a mini domain-independent knowledge base. It is deserved to point out that, in the following cases, quantities are not represented well. Quantities are absent of units. Quantities are perhaps wrong for a given question. Even if all of them are represented well, their units are perhaps inconsistent. These cases have a strong impact on final answer solving. One thousand nine hundred twenty-six real queries are employed to test the proposed method, and the experimental results show that the average correctness ratio of our approach is 87.1%.
Stabilizing fuzzy control for a class of nonlinear systems

The paper presents new conditions suitable in design of a stabilizing controller for a class of continuous-time nonlinear systems represented by Takagi-Sugeno models, and measurable premise variables. Based on an enhanced Lyapunov inequality, the conditions are outlined in the terms of linear matrix inequalities to possess a stable structure closest to optimal asymptotic properties. Simulation results illustrate the design procedure and demonstrate the basic performances of the proposed control design method.
Learning new skills by a humanoid robot through imitation

Today's humanoid robots are used in many successful research topics. According to users needs these robots must be able to perform new tasks, and to adapt in a workspace. They have a similar appearance with human body. Thus the meaningful features of an humanoid robots are: the capability to imitate the user behavior, and the ability to learn new skills. In this paper, we propose to develop a robot system based on an humanoid robot that has abilities to learn new user skills by imitating human beings. To achieve this goal, we needed to design and to implement a new framework, which is able to teach an humanoid robot via user demonstration and to imitate movements of the human body. Therefore, the movements of the human body are automatically tracked using the Kinect sensor, and they teleoperate a whole body of a Nao robot. The results of experiments suggest that the feasibility of the proposed system is verified.
The E ¤ ects of Border Enforcement on Migrants Border Crossing Choices : Diversion or Deterrence ?

Illegal migration from Mexico into the United States has long led to discussions of the proper form of enforcement along the border. Past border enforcement expansion has targeted speci c sectors rather than across the board budget increases. We assess the implications of this pattern by examining how Mexican immigrants have responded to sector-speci c expansions in border enforcement. We estimate how many immigrants are deterred from migrating and remain in Mexico, and how many migrants are diverted to crossing at other sectors of the border. While the popular press and anecdotal evidence point towards the existence of a strong diversion e¤ect, the magnitude of this e¤ect has yet to be estimated. Using micro level data on the decisions of Mexican migrants, we employ a discrete choice model to estimate the causal e¤ects of increased enforcement on migration patterns. The causal e¤ect of enforcement on crossing decisions is identi ed using a new set of instruments for border enforcement that vary both cross-sectionally and across time. The implied substitution patterns of our model allow us to decompose an overall e¤ect of enforcement on migration into a diversion and deterrence portion. We nd there to be a signi cant diversion e¤ect, and we explore the policy implications of this nding.
FIABILIDADE E VALIDADE DOS CONCEITOS TEÓRICOS DAS DIMENSÕES DE SAÚDE FÍSICA E MENTAL DA VERSÃO PORTUGUESA DO MOS SF-36*

Objectivo: Identificacao e avaliacao psicometrica das duas dimensoes gerais da versao Portuguesa do questionario Short Form (SF)-36. Metodos: Uma amostra de 1446 adultos (60,4% mulheres), representativa dos residentes na cidade do Porto respondeu a um questionario geral e ao SF-36 tendo-se obtido de 1326 (91,7%) informacao para as oito dimensoes do SF-36. Utilizou-se o alfa de Cronbach para avaliar a consistencia interna e a analise de componentes principais para identificar o numero de componentes e as sub-dimensoes que pertenciam a cada componente. Para avaliar a validade de constructo das duas dimensoes gerais dividimos a amostra em quatro grupos, de acordo com a presenca de doencas cronicas em geral e depressao (avaliada por intermedio do Inventario de Depressao Beck), e utilizou-se a regressao logistica para quantificar se as novas dimensoes detectam as diferencas teoricas esperadas entre os grupos. Para avaliar a fiabilidade dos factores das componentes principais usou-se bootstrapping. Para avaliar a perda de poder discriminatorio quando utilizando apenas as duas dimensoes gerais, comparamos a area sob a curva ROC dos modelos de regressao logistica com as oito dimensoes e os modelos com as duas dimensoes gerais para os quatro grupos. Resultados: O alfa de Cronbach global da dimensao fisica foi de 0,82 e da mental foi 0,87. As duas componentes explicaram 70,4% da variabilidade. A primeira componente correlacionou-se com os quatro itens do dominio Fisico (r=[0,69;0,83]) e a segunda componente com os quatro itens do dominio Mental (r=[0,65;0,88]). O maior desvio padrao do bootstrapping para os factores das componentes principais foi de 0,05. As novas dimensoes gerais detectaram as diferencas teoricas esperadas entre os grupos. Nao se encontrou diferencas significativas entre as areas sob a curva ROC dos modelos de regressao logistica com as dimensoes gerais e as sub-dimensoes. Conclusao: A utilizacao destas duas medidas sumarias do SF-36 permitira uma maior eficacia na analise dos resultados sem perda de informacao e capturar diferentes manifestacoes do estado de saude.
High-Flexibility Rekeying Processor for Key Management in Secure Multicast

Multicast rekeying is a demanding task with high requirements of flexibility to adapt to different operating situations and of efficiency to provide high quality of service and high access control. Previous software-only solutions addressed the efficiency requirement on the rekeying algorithm level and offered inherent flexibility. Proposed hardware architectures achieved high-efficiency rekeying by accelerating cryptographic operations, on which the rekeying process is based. However, they lacked flexibility because of their hard-wired functionality. In this paper a novel HW/SW architecture is proposed which combines the hardware efficiency with the software flexibility. Furthermore, the high flexibility rekeying processor addresses the group and data source authentication, which were disregarded in previous related hardware-solutions.
Some statistical simulation results over the 128-bit block cipher CLEFIA

CLEFIA , a 128-bit block cipher designed using Diffusion Switching Mechanism (DSM) , was proposed by Sony Corporation in 2007. The attainment of some properties such as completeness, strict avalanche criterion and randomness cause the invigoration of confusion and diffusion properties in block ciphers. In this paper, we evaluate CLEFIA by considering these three important properties. For the case of 128-bit key, it supplies the first two criteria with at least assurance factor 97%. This paper shows also some statistical simulation results of block cipher CLEFIA.
Approximate and online multi-issue negotiation

This paper analyzes bilateral multi-issue negotiation between self-interested autonomous agents. The agents have time constraints in the form of both deadlines and discount factors. There are m > 1 issues for negotiation where each issue is viewed as a pie of size one. The issues are "indivisible" (i.e., individual issues cannot be split between the parties; each issue must be allocated in its entirety to either agent). Here different agents value different issues differently. Thus, the problem is for the agents to decide how to allocate the issues between themselves so as to maximize their individual utilities. For such negotiations, we first obtain the equilibrium strategies for the case where the issues for negotiation are known a priori to the parties. Then, we analyse their time complexity and show that finding the equilibrium offers is an NP-hard problem, even in a complete information setting. In order to overcome this computational complexity, we then present negotiation strategies that are approximately optimal but computationally efficient, and show that they form an equilibrium. We also analyze the relative error (i.e., the difference between the true optimum and the approximate). The time complexity of the approximate equilibrium strategies is O(nm/ε2) where n is the negotiation deadline and ε the relative error. Finally, we extend the analysis to online negotiation where different issues become available at different time points and the agents are uncertain about their valuations for these issues. Specifically, we show that an approximate equilibrium exists for online negotiation and show that the expected difference between the optimum and the approximate is O(√m). These approximate strategies also have polynomial time complexity.
A Case-Control Study of Hepatocellular Carcinoma and the Hepatitis B Virus , Cigarette Smoking , and Alcohol Consumption 1

A case-control study was undertaken to evaluate the roles of the hepatitis B virus (HBV), cigarette smoking, and alcohol use in the etiology of hepatocellular carcinoma (HCC). A major pur pose of the study was to evaluate the effect of cigarette smoking on HCC among hepatitis B surface antigen (HBsAg)-negative persons, since it had been suggested that the relative effect of cigarette smoking on HCC was higher among HBsAg-negative persons than among HBsAg-positive persons. Eighty-six cases and 161 hospital controls were included in the study. This study confirmed the strong relationship between the HBV and HCC. Twelve of 67 cases and none of 63 controls were chronically infected with HBV as evidenced by serum HBsAg. The study also found a moderately strong relationship between alcohol use and HCC. The results of the present study do not support the hypothesis that cigarette smoking is a risk factor for HCC. Among all subjects, the relative rate of HCC for cigarette smokers com pared with nonsmokers after adjustment for alcohol consumption was 1.0 with 95% confidence limits, 0.5 to 1.8. Among HBsAgnegative subjects, the relative rate was 1.1 with 95% confidence limits, 0.5 to 2.4. There was also no consistent dose-response relationship between quantity smoked and HCC in this study.