Miracle-dz/UniFAR-FTUs huggingface dataset:
Entity Linking on Philosophical Documents

Entity Linking consists in automatically enriching a document by detecting the text fragments mentioning a given entity in an external knowledge base, e.g., Wikipedia. This problem is a hot research topic due to its impact in several text-understanding related tasks. However, its application to some specific, restricted topic domains has not received much attention. In this work we study how we can improve entity linking performance by exploiting a domain-oriented knowledge base, obtained by filtering out from Wikipedia the entities that are not relevant for the target domain. We focus on the philosophical domain, and we experiment a combination of three different entity filtering approaches: one based on the “Philosophy” category of Wikipedia, and two based on similarity metrics between philosophical documents and the textual description of the entities in the knowledge base, namely cosine similarity and Kullback-Leibler divergence. We apply traditional entity linking strategies to the domainoriented knowledge base obtained with these filtering techniques. Finally, we use the resulting enriched documents to conduct a preliminary user study with an expert in the area.
Mortality at 12 and 24 Months After Stroke May Be Associated With Depressive Symptoms at 1 Month

Background and Purpose— Previous studies have reported mood symptoms after stroke to be a risk factor for later mortality. The purpose of the study was to examine whether mood symptoms at 1 month after stroke may be a risk factor for mortality at 12 and 24 months. Methods— As a cohort within a randomized controlled trial, 448 hospital patients were seen at 1 month after stroke and were randomized into a trial of psychological therapy. Follow-up was at 12 and 24 months. Mood symptoms were assessed by the Present State Examination and the General Health Questionnaire (GHQ)-28. Measures of disability before and after stroke and of cognitive impairment after stroke were also taken at 1 month. Mortality was determined at 12 and 24 months after stroke. Results— In logistic regression analyses, mortality at 12 months was associated unifactorally with scoring on the GHQ-D subscale (odds ratio [OR] 2.4, 95% CI 1.3 to 4.5) and scoring in the highest quartile of the GHQ (OR 3.1, 95% CI 1.1 to 8.8). In multiple logistic regression analyses, only GHQ-D remained a significant predictor after controlling for other known predictors. At 24 months, scoring on GHQ-D (OR 2.4, 95% CI 1.4 to 4.1) and in the highest GHQ quartile (OR 2.2, 95% CI 1.0 to 4.8) was significantly associated with mortality in unifactoral analyses. Scoring on the GHQ-D remained a predictor of mortality after controlling for other variables. Psychiatric disorder, such as major depression (according to International Classification of Diseases, 10th Revision), was not statistically significantly associated with increased mortality at 12 or 24 months. Conclusions— Mood symptoms on a self-reported rating scale were associated with 12- and 24-month mortality after stroke, after adjustment for factors associated with stroke severity. The result is in keeping with other evidence that depressive symptoms are a risk factor for death from vascular disease.
Happy, sad, scary and peaceful musical excerpts for research on emotions

Three experiments were conducted in order to validate 56 musical excerpts that conveyed four intended emotions (happiness, sadness, threat and peacefulness). In Experiment 1, the musical clips were rated in terms of how clearly the intended emotion was portrayed, and for valence and arousal. In Experiment 2, a gating paradigm was used to evaluate the course for emotion recognition. In Experiment 3, a dissimilarity judgement task and multidimensional scaling analysis were used to probe emotional content with no emotional labels. The results showed that emotions are easily recognised and discriminated on the basis of valence and arousal and with relative immediacy. Happy and sad excerpts were identified after the presentation of fewer than three musical events. With no labelling, emotion discrimination remained highly accurate and could be mapped on energetic and tense dimensions. The present study provides suitable musical material for research on emotions.Keywords.
A distributed directory scheme for information access in mobile computers

In this paper, we discuss the design aspects of a dynamic distributed directory scheme (DDS) to facilitate efficient and transparent access to information files in mobile environments. The proposed directory interface enables users of mobile computers to view a distributed file system on a network of computers as a globally shared file system. In order to counter some of the limitations of wireless communications, we propose improvised invalidation schemes that avoid false sharing and ensure uninterrupted usage under disconnected and low bandwidth conditions.
Pembentukan Vector Space Model Bahasa Indonesia Menggunakan Metode Word to Vector

Extracting information from a large amount of structured data requires expensive computing. The Vector Space Model method works by mapping words in continuous vector space where semantically similar words are mapped in adjacent vector spaces. The Vector Space Model model assumes words that appear in the same context, having the same semantic meaning. In the implementation, there are two different approaches: counting methods (eg: Latent Semantic Analysis) and predictive methods (eg Neural Probabilistic Language Model). This study aims to apply Word2Vec method using the Continuous Bag of Words approach in Indonesian language. Research data was obtained by crawling on several online news portals. The expected result of the research is the Indonesian words vector mapping based on the data used.Keywords: vector space model, word to vector, Indonesian vector space model.Ekstraksi informasi dari sekumpulan data terstruktur dalam jumlah yang besar membutuhkan komputasi yang mahal. Metode Vector Space Model bekerja dengan cara memetakan kata-kata dalam ruang vektor kontinu dimana kata-kata yang serupa secara semantis dipetakan dalam ruang vektor yang berdekatan. Metode Vector Space Model mengasumsikan kata-kata yang muncul pada konteks yang sama, memiliki makna semantik yang sama. Dalam penerapannya ada dua pendekatan yang berbeda yaitu: metode yang berbasis hitungan (misal: Latent Semantic Analysis) dan metode prediktif (misalnya Neural Probabilistic Language Model). Penelitian ini bertujuan untuk menerapkan metode Word2Vec menggunakan pendekatan Continuous Bag Of Words model dalam Bahasa Indonesia. Data penelitian yang digunakan didapatkan dengan cara crawling pada berberapa portal berita online. Hasil penelitian yang diharapkan adalah pemetaan vektor kata Bahasa Indonesia berdasarkan data yang digunakan.Kata Kunci: vector space model, word to vector, vektor kata bahasa Indonesia.
Opinions and Perceptions of Various Stakeholders of the GMO Landscape in the Indian State of Maharashtra

"The issue of GMOs is probably the best example of the scientific community not communicating well, science has treated the public as a large lecture hall. That puts people to sleep. " ‘Dietram Scheufele’ Professor in Science Communication, UWMadison. The major scientific controversy of the 21st century is that of GM crops with about three dozen nations banning its cultivation. There are a number of studies by academicians in the western world to study the underlying reasons and beliefs for the public acceptance/rejection of the technology. Studies in India and other developing countries have been very few because of the comparatively later introduction of GMOs. A study of public opinions and awareness of GMO’s will give several insights into how old policies need to be changed and new policies to be implemented. It will also tell us to what extent cultural and social beliefs influence their opinions. In this study I begin how GM technology has its foundation in artificial selection which started millions of years ago and how has it reached its present technologically advanced state. I also discuss the politics and controversies surrounding GMOs in the western world as well as in India. I then discuss the various concerns surrounding GMOs and how cognitive science plays a role in reinforcing these concerns. Finally, I present the results obtained from a survey of a limited number of participants from various groups influencing the GMO landscape in Maharashtra and derive some key conclusions and recommendations based on the results.
Three Consecutive Days of Interval Runs to Exhaustion Affects Lymphocyte Subset Apoptosis and Migration

This investigation assessed the lymphocyte subset response to three days of intermittent run exercise to exhaustion. Twelve healthy college-aged males (n = 8) and females (n = 4) (age = 26 ± 4 years; height = 170.2 ± 10 cm; body mass = 75 ± 18 kg) completed an exertion test (maximal running speed and VO2max) and later performed three consecutive days of an intermittent run protocol to exhaustion (30 sec at maximal running speed and 30 sec at half of the maximal running speed). Blood was collected before exercise (PRE) and immediately following the treadmill bout (POST) each day. When the absolute change from baseline was evaluated (i. e., Δ baseline), a significant change in CD4+ and CD8+ for CX3CR1 cells was observed by completion of the third day. Significant changes in both apoptosis and migration were observed following two consecutive days in CD19+ lymphocytes, and the influence of apoptosis persisted following the third day. Given these lymphocyte responses, it is recommended that a rest day be incorporated following two consecutive days of a high-intensity intermittent run program to minimize immune cell modulations and reduce potential susceptibility.
Application of bilateral filtering and Gaussian Mixture modeling for the retrieval of paintings

In this paper we propose a novel approach to the problem of color image indexing and retrieval applied for collections of paintings. The described indexing technique utilizes the Gaussian Mixture Model of the color histogram based on weights provided by the Bilateral Filtering scheme. Thus, the proposed technique considers not only the global distribution of the color image pixels but also takes into account their spatial arrangement. The model parameters serve as signatures which enable fast and efficient retrieval of paintings in large databases. The proposed approach is not only robust to color image distortions introduced by lossy compression artifacts and therefore it is well suited for indexing and retrieval of collections of paintings, but it also provides meaningful results, coherent in their artistic style features.
Design of lexical database for financial domain

Here, we study the design of domain lexicons and address the problems of lexical knowledge representation and linking between different knowledge sources. A domain lexicon contains substantial domain specific vocabularies with associated phonological, morphological, syntactic and semantic/pragmatic information as well as links to general knowledge bases which are rich enough to support knowledge-intensive models for practical NLP systems. We take financial domain as an example to illustrate the representation structures for syntactic and semantic knowledge. In order to suit for both maximal reusability and deep analysis, our domain lexicon is designed with uniform knowledge representation and fine-grain feature encoding. We also address the issues of how to bridge the gaps between coarse-grain general lexicons and fine-grain domain lexicons.
A fast approximation algorithm for 1-norm SVM with squared loss

1-norm support vector machine (SVM) has attracted substantial attentions for its good sparsity. However, the computational complexity of training 1-norm SVM is about the cube of the sample number, which is high. This paper replaces the hinge loss or the ε-insensitive loss by the squared loss in the 1-norm SVM, and applies orthogonal matching pursuit (OMP) to approximate the solution of the 1-norm SVM with the squared loss. Experimental results on toy and real-world datasets show that OMP can faster train 1-norm SVM and achieve similar learning performance compared with some methods available.
Antimicrobial Mechanisms of Macrophages and the Immune Evasion Strategies of Staphylococcus aureus

Habitually professional phagocytes, including macrophages, eradicate microbial invaders from the human body without overt signs of infection. Despite this, there exist select bacteria that are professional pathogens, causing significant morbidity and mortality across the globe and Staphylococcus aureus is no exception. S. aureus is a highly successful pathogen that can infect virtually every tissue that comprises the human body causing a broad spectrum of diseases. The profound pathogenic capacity of S. aureus can be attributed, in part, to its ability to elaborate a profusion of bacterial effectors that circumvent host immunity. Macrophages are important professional phagocytes that contribute to both the innate and adaptive immune response, however from in vitro and in vivo studies, it is evident that they fail to eradicate S. aureus. This review provides an overview of the antimicrobial mechanisms employed by macrophages to combat bacteria and describes the immune evasion strategies and some representative effectors that enable S. aureus to evade macrophage-mediated killing.
Traffic Engineering and Characterization of High-Rate Large-Sized Flows

High-rate large-sized (α) flows have adverse effects on delay-sensitive flows. Research-andeducation network providers are interested in identifying such flows within their networks, and directing these flows to virtual circuits. To achieve this goal, a design is proposed for a hybrid network traffic engineering system (HNTES) that would run on an external server, gather NetFlow records from routers, analyze these records to identify α-flow source/destination address prefixes, configure firewall filter rules at ingress routers to extract future flows and redirect them to provisioned virtual circuits. This thesis presents an evaluation of this HNTES design using NetFlow records collected over a 7-month period from four ESnet routers. The results showed that the HNTES effectiveness was above 90% for NetFlow records collected at edge routers, which corresponded to file downloads from Department of Energy (DOE) laboratories, while the effectiveness was lower for the peering routers whose NetFlow records corresponded to file uploads. With further investigation, we found that uploads were less frequent and involved fewer source/destination pairs than downloads. The thesis also describes an algorithm for characterizing the size, duration, average rate, and frequency of α flows, from NetFlow records. The algorithm was validated using independently collected usage logs from application servers. This algorithm can be used in a network management system for providers interested in these types of flows, such as research-and-education network providers whose customers move large scientific datasets. We executed the algorithm on the same NetFlow records used in HNTES evaluation. Flows moving datasets as large as 811 GB and at rates as high as 5.7 Gbps were observed. Some source-destination pairs were found to repeatedly create α flows. An analysis of the rates of the 1596 repeated α flows created by one pair showed consid-
Seroepidemiology of HBV Infection in South-East of Iran; A Population Based Study

Background Hepatitis B virus (HBV) infection is a major risk factor of cirrhosis and hepatocellular carcinoma affecting billions of people globally. Since information on its prevalence in general population is mandatory for formulating effective policies, this population based serological survey was conducted in Sistan and Baluchistan, where no previous epidemiological data were available. Methods Using random cluster sampling 3989 healthy subjects were selected from 9 districts of Sistan and Baluchistan Province in southeastern Iran. The subjects’ age ranged from 6 to 65 years old. Serum samples were tested for HBcAb, HBsAg. Screening tests were carried out by the third generation of ELISA. Various risk factors were recorded and multivariate analysis was performed. Results The prevalence of HBsAg and HBcAb in Sistan and Baluchistan was 3.38% (95% CI 2.85; 3.98) and 23.58% (95% CI 22.29; 24.93) respectively. We found 8 cases of positive anti-HDV antibody. Predictors of HBsAg or HBcAb in multivariate analysis were age, marital status and addiction. Conclusion The rate of HBV infection in Sistan and Baluchistan was higher than other parts of Iran. Approximately 25% of general population in this province had previous exposure to HBV and 3% were HBsAg carriers. Intrafamilial and addiction were major routes of HBV transmission in this province.
A visual feedback training method for unilateral neglect patient treatment

In recent, the healthcare industry is developed to provide effective health and wellness services based on smart devices with IT convergence. Particularly, this smart device is helpful for making rehabilitation of unilateral neglect patient after stroke. In order to improve the balance of the unilateral neglect patient, a visual feedback training method with a smart device can be used. Thus, we propose visual feedback training methods for unilateral neglect patient treatment by developing three smart app games such as flipping the card, avoiding an obstacle, and human balancing. The proposed method is a new approach for rehabilitation of the unilateral neglect patient.
Identifying Unknown Unknowns in the Open World: Representations and Policies for Guided Exploration

Predictive models deployed in the real world may assign incorrect labels to instances with high confidence. Such errors or unknown unknowns are rooted in model incompleteness, and typically arise because of the mismatch between training data and the cases encountered at test time. As the models are blind to such errors, input from an oracle is needed to identify these failures. In this paper, we formulate and address the problem of informed discovery of unknown unknowns of any given predictive model where unknown unknowns occur due to systematic biases in the training data.We propose a model-agnostic methodology which uses feedback from an oracle to both identify unknown unknowns and to intelligently guide the discovery. We employ a two-phase approach which first organizes the data into multiple partitions based on the feature similarity of instances and the confidence scores assigned by the predictive model, and then utilizes an explore-exploit strategy for discovering unknown unknowns across these partitions. We demonstrate the efficacy of our framework by varying the underlying causes of unknown unknowns across various applications. To the best of our knowledge, this paper presents the first algorithmic approach to the problem of discovering unknown unknowns of predictive models.
DESIGN RATIONALE IN THE TRIPLIFICATION OF RELATIONAL DATABASES

Uma das estrategias mais populares para publicar dados estruturados na Web e expor bases de dados relacionais (BDR) em formato RDF. Esse processo e chamado BDR-para-RDF ou triplificacao. Alem disto, principios de Linked Data oferecem varios guias para dar suporte a este processo. Existem duas principais abordagens para mapear bases de dados relacionais para RDF: (1) a abordagem de mapeamento direto, onde o esquema das bases de dados e diretamente mapeado para um esquema RDF, e (2) a abordagem de mapeamento customizado, onde o esquema RDF pode ser significativamente diferente do esquema original da base de dados relacional. Em ambas abordagens, existem varios desafios relacionados tanto com a publicacao quanto com o uso de dados em RDF originados de bases de dados relacionais. Esta tese propoe a coleta de design rationale como uma valiosa fonte de informacao para minimizar os desafios do processo de triplificacao. Essencialmente, a coleta de design rationale melhora a consciencia sobre as acoes feitas no mapeamento da base de dados relacional para um conjunto de dados no formato RDF. As principais contribuicoes da tese sao: (1) um modelo de design rationale (DR) adequado para o processo de BDR-para- RDF, independente da abordagem utilizada (direta ou customizada); (2) a integracao de um modelo de DR para um processo que segue a abordagem direta de BDR-para-RDF e para um processo que segue a abordagem customizada usando a linguagem R2RML; (3) o uso do DR coletado para melhorar recomendacoes de reuso de vocabularios existentes atraves de algoritmos de Ontology Matching.
Simulation of 3D fountain visualization based on particle system

Particle system is an effective way to achieve fountain simulation. Basing on the basic principle of particle system and analysis of fountain morphological structure in real scene, we construct the abstract fountain model, analyze the movement, and finally realize the realistic rending by using texture mapping and billboard technology. To make the system more friendly to interact with and the effect more vivid, this paper not only abstracts key factors determining the fountain effect, but also adds the sound by introducing FMOD sound system. Experiments show that the system implemented in this way is in good interactivity and the effect is vivid.
COUPLING MOBILE PAYMENTS AND CRM IN THE RETAIL INDUSTRY

The growing prevalence of electronic commerce and the widespread use of mobile devices have made mobile payments an interesting alternative method of payment for customers and merchants. One major issue to be resolved is the integration of a real-time wireless means of payment within their current payment system. In this paper, we propose an architecture of mobile payment system to improve business processes and increase customer loyalty. An all-in-one device that enables mobile payments and also integrates a membership scheme would simplify and significantly accelerate the payment process at the point of sale (POS). Moreover, the deployment of an effective customer relationship management (CRM) system and an adapted data mining tool would allow retailers to propose a dynamic-generated website to their customers. This would follow a one-to-one e-marketing strategy and would improve companies’ ability to suggest customized offers and coupons.
ISOLATING SELF-MOTION MANIFOLDS ON A PLAYSTATION

In this paper, we show how it is possible to use the specialized hardware of a Play Station 2 (PS2) to speed up the execution of the Cuik algorithm, an interval-based inverse kinematics algorithm. A PS2 includes a RISC CPU and two parallel vector units able to perform four floating point multiplications and four additions in a single cycle. A careful use of these two vector units to implement the basic operations of the Cuik algorithms allows increasing the speed of the algorithm in one order of magnitude. The final implementation in the PS2 is as fast as that in a Pentium 4 at 2.6GHz (that is at least three times more expensive than
From light logics to type assignments: a case study

Using Soft Linear Logic (SLL) as case study, we analyze a method for transforming a light logic into a type assignment system for the λ-calculus, inheriting the complexity properties of the logics. Namely the typing assures the strong normalization in a number of steps polynomial in the size of the term, and moreover all polynomial functions can be computed by λ-terms that can be typed in the system. The proposed method is general enough to be used also for other light logics.