Miracle-dz/UniFAR-FTUs huggingface dataset:
Supporting Social Networks With Agent-Based Services

Current approaches to build social networking systems are based on a centralized architecture because it allows a simple browser-based user experience and makes easier and more efficient to implement many algorithms used in a social networking site e.g., friend suggestion, However this kind of architecture has many drawbacks for its users, e.g., lack of privacy, lack of anonymity, risks of censorship and operating costs. This paper presents a system, called Blogracy, which uses widespread and stable peer-to-peer technologies, such as distributed hash tables and BitTorrent, for coping with intrinsic defects of centralized architectures and for being the basis of solid distributed social networking platforms. Moreover, Blogracy takes advantages of multi-agent systems for simplifying the implementation of social network services in a decentralized setting.
Direct extraction of the non-linear HEMT model from vectorial large-signal measurements

Non-linear models for microwave and millimetre wave devices are commonly based on DC and S-parameter measurements, due to the absence of vectorial large-signal measurements in the past. At present, accurate prototype measurement systems are being developed, which implies that new non-linear modelling techniques can be explored. We will show experimental results of a method that allows the direct extraction of the HEMT's state-functions from vectorial large-signal measurements.
Issues of lean-agile software development environment

The popular waterfall model is widely accepted approach for project management paradigm; however lean based agile model is the recent revolution to reduce work in progress items and makes transformation to better process by identifying and eliminating non-value-add activities and increase productivity with quality of deliverables. This paper includes the issues that involved in lean- agile process.
Homfly polynomials of generalized Hopf links

Following the recent work by T. -H. Chan in (3) on reverse string parallels of the Hopf link we give an alternative approach to nding the Homfly polynomials of these links, based on the Homfly skein of the an- nulus. We establish that two natural skein maps have distinct eigenvalues, answering a question raised by Chan, and use this result to calculate the Homfly polynomial of some more general reverse string satellites of the Hopf link. AMS Classication 57M25
The polysemy of the words that children learn over time

Here we study polysemy as a potential learning bias in vocabulary learning in children. Words of low polysemy could be preferred as they reduce the disambiguation effort for the listener. However, such preference could be a side-effect of another bias: the preference of children for nouns in combination with the lower polysemy of nouns with respect to other part-of-speech categories. Our results show that mean polysemy in children increases over time in two phases, i.e. a fast growth till the 31st month followed by a slower tendency towards adult speech. In contrast, this evolution is not found in adults interacting with children. This suggests that children have a preference for non-polysemous words in their early stages of vocabulary acquisition. Interestingly, the evolutionary pattern described above weakens when controlling for syntactic category (noun, verb, adjective or adverb) but it does not disappear completely, suggesting that it could result from a combination of a standalone bias for low polysemy and a preference for nouns.
Utilização de Técnicas de Deep Learning para Reidentificação de Pessoas

O problema de reidentificacao ao consiste em reconhecer individuos de interesse, que apareceram em imagens de uma câmera distinta em algum momento anterior. Este tema tem se mostrado relevante devido a questoes de seguranca e a evolucao das redes de monitoracao e vigilância. Porem, muitos desafios ainda estao por ser superados. O presente trabalho explora o problema atraves de abordagens baseadas em tecnicas de deep learning. Foi investigados o desempenho de Redes Neurais Convulsionais sob diferentes instâncias do problema, e foi estudado o efeito sobre a classificacao da utilizacao de diversas tecnicas de pre-processamento, variacao do numero de epocas na fase de treino, numero de classes consideradas, e principalmente, o efeito da alimentacao de imagens. Os resultados mostram que, mesmo com as dificuldades inerentes ao problema, sob condicoes de configuracoes adequadas melhores desempenhos podem ser obtidos.
TraitLab: A MatLab package for fitting and simulating binary tree-like data

TraitLab is a software package for simulating, tting and analysing tree-like binary data under a stochastic Dollo model of evolution. The model also allows for "catastrophes", evolutionary events where many traits are simultaneously lost while new ones arise. The core of the package is a Markov chain Monte Carlo sampling algorithm that enables the user to sample from the Bayesian joint posterior distributions for tree topologies, clade and root ages, and the trait loss and catastrophe rates for a given data set. Data can be simulated according to the tted Dollo model or according to a number of generalized models that allow for borrowing (horizontal transfer) of traits, heterogeneity in the trait loss rate and biases in the data collection process. Both the raw data and the output of MCMC runs can be inspected using a number of useful graphical and analytical tools provided within the package. TraitLab is freely available and runs within the Matlab computing environment.
Active Bayesian color constancy with non-uniform sensors

The human impression of the color of an object is the same when viewed foveally or peripherally, despite the non-uniformity of the spatial distribution of photoreceptors on the retina. We propose that this nonuniformity can be used to attain color constancy, the perception of a constant surface color under varying illumination. We develop a multi-sensor Bayesian technique that solves for the surface reflectance and lighting parameters of a bilinear model by sequentially acquiring measurements from independent sensors. We present two cases: (i) two sets of sensors, each with different spectral sensitivities, (ii) a continuous variation in the spectral sensitivities across the sensor array.
Text clustering and active learning using a lsi subspace signature model and query expansion

In this dissertation research, we developed a novel Latent Semantic Indexing Subspace Signature Model (LSISSM) for semantic content representation of unstructured text based on the Singular Value Decomposition (SVD). The model represents the meanings of the terms according to the distribution of their statistical contribution across the top ranking LSI latent concept dimensions. Each LSI latent concept dimension is related to one or more themes with their contexts which are composed of semantically coherent topics, entities and social indicators and are supported by a set of related documents. The model provides feature reduction and finds a low-rank approximation for the scalable and sparse term-document matrix. Firstly, the top ranking conceptual terms or term clusters are selected to represent the corpora according to their global statistical contribution to the LSI term subspace. Secondly, terms and documents are defined as spectral signatures which are represented by the distribution of their local statistical contribution on the identical LSI latent concept dimensions. Then, two novel similarity measures are defined to evaluate the associations between the concept signatures and the document signatures by reducing noise. Finally the model bridges the LSI subspaces naturally and produces a low-dimension term-document matrix. Experiments suggest that this model significantly improves the performance of the clustering algorithms such as basic K-means and Self-organized Mapping (SOM) efficiently and effectively, compared with the Vector Space Model and the traditional LSI model. Our model is also suitable for active learning which significantly decreases the number of the training examples through bootstrapped sampling and iterative learning without degrading the performance of the classifiers. The LSI Subspace Signature Model selects the document samples iteratively according to their statistical contribution to the LSI document subspace. The sampling method is evaluated in the context of text categorization with three classic classifiers on three standard news corpora. The results indicate that our approach improves the selection of the sampling subsets from the perspectives of sampling distribution and learning performance of the classifiers. This method picks the most important samples and keeps the sampling distribution on the text categories, even outlier categories. The tests demonstrated that the sample subsets with the optimized feature sets substantially improve the performance of the three classifiers, Naive Bayes, K-Nearest Neighbor and Rocchio effectively and efficiently. Four types of ontology-based query expansion strategies are applied in MEDLINE abstracts and OCR news text. UMLS-based term re-weighting and user relevance feedback with IPTC hyponyms significantly improve the performance of IR models, VSM and BM25. In addition, a novel random-walk centrality measure is developed to overcome the rank sink problem of the PageRank algorithm.
Address Filtering Based Wu-Manber Multiple Patterns Matching Algorithm

Wu-Manber is a widely used multiple patterns matching algorithm. But in practical application, it exist the following limitations. First, there are redundant information and operations. Second, the prefix table is established, but is hardly used. Third, need to traverse the whole link list. These limitations make the algorithm complicated and affect the performance of it. Though some limitations have been indicated by others, different ways are taken in this paper. Prefix table is used to filter the patterns. Address filtering based search method avoids traversing the whole link list. Experimental result shows that the improved algorithm has a good performance than the original one.
An Energy-based Nonlinear Control for Two-link Flexible Manipulators

In this paper, we propose an energy-based nonlinear control for two-link flexible manipulators. Since the two-link flexible manipulator is an underactuated system which provides a challenge to control engineers and researchers. In order to control this kind of system more effectively, many new control theories and methods are explored to design controllers for the flexible manipulators. Among these methods, the energy-based control design method has gained a lot of attentions in these years, which can provide more physical insights in nonlinear control and also can provide a candidate for Lyapunov function directly, which is very important in proving the stability of the closed-loop system. Especially, the port-controlled Hamiltonian system and generalized canonical transformation have some advantages on the modeling and control design of these nonlinear systems. Therefore, we explore to control two-link flexible manipulators via generalized canonical transformation. Both the simulation and experimental results are shown to demonstrate the effectiveness of the controllers.
Automated Synthesis of Optimized Circuits for Secure Computation

In the recent years, secure computation has been the subject of intensive research, emerging from theory to practice. In order to make secure computation usable by non-experts, Fairplay (USENIX Security 2004) initiated a line of research in compilers that allow to automatically generate circuits from high-level descriptions of the functionality that is to be computed securely. Most recently, TinyGarble (IEEE S&P 2015) demonstrated that it is natural to use existing hardware synthesis tools for this task. In this work, we present how to use industrial-grade hardware synthesis tools to generate circuits that are not only optimized for size, but also for depth. These are required for secure computation protocols with non-constant round complexity. We compare a large variety of circuits generated by our toolchain with hand-optimized circuits and show reduction of depth by up to 14%. The main advantages of our approach are developing customized libraries of depth-optimized circuit constructions which we map to high-level functions and operators, and using existing libraries available in the industrial-grade logic synthesis tools which are heavily tested. In particular, we show how to easily obtain circuits for IEEE 754 compliant floating-point operations. We extend the open-source ABY framework (NDSS 2015) to securely evaluate circuits generated with our toolchain and show between 0.5 to 21.4 times faster floating-point operations than previous protocols of Aliasgari et al. (NDSS 2013), even though our protocols work for two parties instead of three or more. As application we consider privacy-preserving proximity testing on Earth.
Content Management in Large-Scale Information Retrieval Systems

Herein we introduce a system for managing the content of information systems in order to improve retrieval effectiveness over a large, constantly growing collection of structured and unstructured data. Specifically, we target the problems relating to the submission of new content from various sites, the seamless integration of said content into the main document corpus, and effective methods of searching the available information. In a unique interdisciplinary effort with the Institute of Design at IIT we have implemented a prototype for a document management system, which acts as a unifying abstraction layer that the various components of our architecture use to coordinate their efforts.
Supersparse Linear Integer Models for Predictive Scoring Systems

Scoring systems are classification models that make predictions using a sparse linear combination of variables with integer coefficients. Such systems are frequently used in medicine because they are interpretable; that is, they only require users to add, subtract and multiply a few meaningful numbers in order to make a prediction. See, for instance, these commonly used scoring systems: (Gage et al. 2001; Le Gall et al. 1984; Le Gall, Lemeshow, and Saulnier 1993; Knaus et al. 1985). Scoring systems strike a delicate balance between accuracy and interpretability that is difficult to replicate with existing machine learning algorithms. Current linear methods such as the lasso, elastic net and LARS are not designed to create scoring systems, since regularization is primarily used to improve accuracy as opposed to sparsity and interpretability (Tibshirani 1996; Zou and Hastie 2005; Efron et al. 2004). These methods can produce very sparse models through heavy regularization or feature selection methods (Guyon and Elisseeff 2003); however, feature selection often relies on greedy optimization and cannot guarantee an optimal balance between sparsity and accuracy. Moreover, the interpretability of scoring systems requires integer coefficients, which these methods do not produce. Existing approaches to interpretable modeling include decision trees and lists (Ruping 2006; Quinlan 1986; Rivest 1987; Letham et al. 2013). We introduce a formal approach for creating scoring systems, called Supersparse Linear Integer Models (SLIM). SLIM produces scoring systems that are accurate and interpretable using a mixed-integer program (MIP) whose objective penalizes the training error, L0-norm and L1-norm of its coefficients. SLIM can create scoring systems for datasets with thousands of training examples and tens to hundreds of features - larger than the sizes of most studies in medicine, where scoring systems are often used.
Predicting and Regulating Participation Equality in Human-Robot Conversations: Effects of Age and Gender

In this paper, we investigate participation equality, in terms of speaking time, between users in multi-party human-robot conversations. We analyse a dataset where pairs of users (540 in total) interact with a conversational robot exhibited at a technical museum. The data encompass a wide range of different users in terms of age (adults/children) and gender (male/female), in different combinations. Overall, the analysis indicates that demographically heterogeneous pairs are more imbalanced, especially pairs of adults and children, where children are less prone to self-select in the turn-taking. The analysis also indicates that it is possible for the robot to reduce the imbalance by addressing the least dominant user and asking directed questions. However, for children to respond, it is important to seek mutual gaze and switch addressee often. Finally, we show that it is possible to predict the imbalance at an early stage in the interaction - in order to increase the participation equality as early as possible - and that knowledge about the users' age and gender helps in this prediction.
Effects of home language input on the vocabulary knowledge of sequential bilingual children

The current study examined whether the vocabulary skills of sequential bilingual children who learned Cantonese as a home language (L1) and English as a second language (L2) were predicted by the amount of L1 and L2 used at home. Ninety-two preschool children who learned Cantonese as L1 were recruited from a Head Start program. The amounts of L1 and L2 used at home were measured using parent questionnaires. Mixed patterns of L1 and L2 use were found across family members and home activities. After controlling for time spent in preschool, regression analyses showed that the amount of L1 and L2 used by individual family members, with the exception of older siblings, was not significantly linked to children's vocabulary skills. In contrast, the language used during some home activities such as dinner and book reading significantly predicted children's vocabulary knowledge. Implications for family involvement in facilitating children's vocabulary development are discussed.
Animal models of neuropathic pain

Animal models are pivotal for understanding the mechanism of neuropathic pain and development of effective therapy for its optimal management. A battery of neuropathic pain models has been developed to simulate the clinical pain conditions with diverse etiology. The present review exhaustively discusses the methodology, behavioral alterations, limitations, and advantages of about 40 different animal models of neuropathic pain along with their modifications. Development of these models has contributed immensely in understanding the chronic pain and underlying peripheral as well as central pathogenic mechanisms. Furthermore, research has resulted in the development of new therapeutic agents for neuropathic pain management, and the preclinical data obtained using these animal models have been successively translated to effective pain management in clinical setup also. As each animal model has been created with specific methodology and results tend to vary largely with the slight changes related to methodology, therefore, it is essential that data from different models should be reported and interpreted in the context of the specific pain model.
Learning through Silence: Amping up Cognition after Neonatal Hypoxic Seizures through AMPA Receptor Inhibition

Commentary Hypoxia-induced seizures are among the commonest causes of neonatal seizures and raise significantly the risk for subsequent epilepsy and poor neurodevelopmental outcomes (1). Experimental models of early life encephalopathies with seizures are indispensable in the effort to develop effective disease-modifying treatments. The research group authoring this report has pioneered the characterization of a rat model of neonatal hypoxia-induced seizures. Postnatal day 10 (PN10) rats exposed to hypoxia develop acute seizures, subsequent cognitive deficits, increased seizure susceptibility, and spontaneous seizures later in life (2). Prior studies have highlighted the role of AMPA receptors in the pathogenesis of cognitive dysfunction and pro-epileptogenic diathesis of rats subjected to neonatal hypoxic seizures (3, 4). Starting within the first hour following PN10 hypoxic seizures, AMPA receptor spontaneous excitatory postsynaptic currents (sEPSCs) increase in the hippocampus, partially as a result of the enhanced phosphorylation of AMPA receptor subunits, GluA1 and GluR2. This observation correlates nicely with the parallel, but transient, early enhancement of long-term potentiation (LTP) in this model. Furthermore, early systemic administration of AMPA receptor inhibitors, such as NBQX or topiramate, during the two first posthypoxia days prevents the appearance of cognitive impairment and seizure susceptibility following hypoxia. Even though these results provided a strong case for the pathogenic role of AMPA receptors in the early stages of this model, little was known about the molecular and pathophysiological changes during the period of cognitive impairment. Zhou et al. report that 2 to 3 days following hypoxic seizures in PN10 pups, the frequency of sEPSCs is still increased in the CA1 stratum radiatum, through further enrichment of the NMDA receptor (NR)-positive synapses with AMPA receptors, such as GluA1. Through an elegant series of experiments, they demonstrate an increased ratio of AMPA receptor/NR-type sEP-SCs, and increased synaptic colocalization of GluA1 and NR1, the obligate NR subunit. It is known that the presence of functional AMPA receptors in NR-containing synapses " unsilences " the synapses in the face of glutamate release, through activation of AMPA receptors, depolarization, and release of the Mg 2+ blockade of NRs (5). Indeed, the authors proceed to show that 2 to 3 days following PN10 hypoxic seizures, the failure rates of eliciting glutamatergic sEPSCs after synaptic stimulation are selectively lower for AMPA receptor-mediated sEPSCs but not for NR-mediated sEPSCs. As the authors comment, such results indicate a reduced number of " silent " glutamatergic synapses following post-hypoxic seizures. Given the importance of …
Learner Effectiveness: A Cause of Learner Strategy Use?

The study of the individual learner differences including learner effectiveness has been a genuine concern for second language researchers. The present study strived to examine the impact of learner effectiveness on the language learners’ learning strategy use. According to this purpose, first, the researchers developed the two-part 40-item learning strategy questionnaire of the study according to the strategy questionnaire developed by Wong and Nunan (2011). Second, they selected 441 students (224 male & 217 female) studying at the first year of their graduate program at universities in East Azerbaijan Province (Iran) as the participants of the study. Third, they administered the questionnaire of the study to the chosen participants and asked them to complete it. Finally, the researchers employed the chi-square test to analyze the collected data of the study. Based on the results of the study, there were significant differences between the more effective and less effective EFL learners’ use of language learning strategies. On the basis of these results, it was argued that the syllabus designers should include specific parts in the teaching materials of the EFL classrooms in which the learners become familiar with various learning strategies and are trained to employ the most efficient strategies for the performance of language learning tasks. Similarly, it was pointed out that the EFL teachers should familiarize their learners with the diverse language learning strategies and their predominant role in the process of second language acquisition.