Miracle-dz/UniFAR-FTUs huggingface dataset:
Sequentiality, second order monadic logic and tree automata

Given a term rewriting system R and a normalizable term t, a redex is needed if in any reduction sequence of t to a normal for m, this redex will be contracted. Roughly, R is sequential if there is an optimal reduction strategy in which only needed redexes are contracted. More generally, G. Huet and J.-J. Levy (1991) define the sequentiality of a predicate P on partially evaluated terms. We show that the sequentiality of P is definable in SkS, the second order monadic logic with k: successors, provided P is definable in SkS. We derive several known an new consequences of this remark: strong sequentiality, as defined by Huet and Levy, of a left linear (possibly overlapping) rewrite system is decidable; NV sequentiality, as defined by M. Oyamaguchi (1993), is decidable, even in the case of overlapping rewrite systems; sequentiality of any linear shallow rewrite system is decidable. Then we describe a direct construction of an automaton recognizing the set of terms that have needed redexes, which again, yields immediate consequences: strong sequentiality of possibly overlapping linear rewrite systems is decidable in EXPTIME; for strongly sequential rewrite systems, needed redexes can be read directly on the automaton.
Matrix Factorization with Domain Knowledge and Behavioral Patterns for Intent Modeling

Spoken language interfaces are being incorporated into various devices such as smart-phones and TVs. However, dialog systems will fail to respond correctly when users request functionality not supported by currently installed apps. We propose a feature-enriched matrix factorization (MF) approach to model open domain intents that allow a system to dynamically add app-relevant domains according to users’ requests. We use MF to jointly model published app descriptions and users’ spoken requests; this generates latent feature vectors for utterances and user intents without need for prior annotation. The matrix can further incorporate user behavioral patterns found in their activity logs to learn user-specific intent prediction models. We show that the MF models enriched with multimodality significantly improve the intent prediction, achieving 34% and 55% of mean average precision (MAP) for unsupervised single-turn requests and for supervised multi-turn interactions on ASR transcripts respectively.
Where Would You Go on Your Next Vacation? A Framework for Visual Exploration of Attractive Places

Tourists face a great challenge when they gather information about places they want to visit. Geographically tagged information in the form of Wikipedia pages, local tourist information pages, dedicated web sites and the massive amount of information provided by Google Earth is publicly available and commonly used. But the processing of this information involves a time consuming activity. Our goal is to make search for attractive places simpler for the common user and provide researchers with methods for exploration and analysis of attractive areas. We assume that an attractive place is characterized by large amounts of photos taken by many people. This paper presents a framework in which we demonstrate a systematic approach for visualization and exploration of attractive places as a zoom able information layer. The presented technique utilizes density-based clustering of image coordinates and smart color scaling to produce an interactive visualizations using Google Earth Mash up. We show that our approach can be used as a basis for detailed analysis of attractive areas. In order to demonstrate our method, we use real-world geo-tagged photo data obtained from Flickr and Panoramio to construct interactive visualizations of virtually every region of interest in the world.
Off-line Uyghur signature recognition based on modified grid information features

Many techniques have been published on handwriting signature recognition, but none of these techniques presented are about Uyghur handwritten signature due to its complex nature. In this paper, we propose methods for off-line signature recognition for Uyghur handwriting first time. The signature images were pre-processed based on the nature of Uyghur signature. The preprocessing included noise reduction, binarization and normalization. Then multi-dimensional modified grid information features were extracted according to the character of Uyghur signature and its writing style. Finally, three kinds of classification techniques were used: Euclidean distance (ED) classifier, K nearest neighbor (K-NN) classifier and Bayes classifier. Experiments were performed using Uyghur signature samples from 50 different people with 1000 signatures. A promising result of 93.53% average correct recognition rate was achieved.
Reciprocity with virtual nodes: Supporting mobile peers in Peer-to-Peer content distribution

The Peer-to-Peer (P2P) paradigm offers scalable means to perform bulk data distribution, e.g., for small businesses which cannot afford huge upfront investments, by incorporating user's resources in the dissemination process. Due to the proliferation of smartphones with wireless broadband connectivity and the increasing convergence of fixed and mobile platforms, a growing number of users are expected to participate in P2P content distribution networks wirelessly. However, the P2P approach only works if users are willing to contribute resources. A commonly applied incentive scheme is the well-known Tit-for-Tat approach, where each peer is forced to contribute as much bandwidth to the network as he consumes. Nevertheless, reciprocal schemes discriminate resource poor mobile devices in terms of energy and upload bandwidth, as they are device-bound instead of being user-bound. In this work, an incentive scheme featuring virtual nodes is presented, which allows mobile devices to seek help from other devices owned by its user, e.g., the user's home gateway or a supporting cloud instance. Preliminary results are presented in the scope of a P2P streaming scenario.
All Blogs Are Not Made Equal: Exploring Genre Differences in Sentiment Tagging of Blogs

One of the essential characteristics of blogs is their subjectivity, which makes blogs a particularly interesting domain for research on automatic sentiment determination. In this paper, we explore the properties of two most common subgenres of blogs – personal diaries and “notebooks” – and the effects that these properties have on performance of an automatic sentiment annotation system, which we developed for binary (positive vs. negative) and ternary (positive vs. negative vs. neutral) classification of sentiment at the sentence level. We also investigate the differential effect of inclusion of negations and other valence shifters on the performance of our system on these two subgenres of blogs.
A low power wireless breathing monitoring system using piezoelectric transducer

The paper presents a continuous breathing pattern monitoring system using a piezoelectric transducer and low power CMOS integrated circuit. The system is compatible for being used as a wearable device around the chest using a belt. The piezoelectric sensor is a ferroelectric polymer which is a biocompatible material. The rest of the integrated circuit is designed using a standard 130 nm CMOS process. The smaller footprint, wireless interface, low-cost and inconvenience-free design features of the proposed system makes it an attractive alternative to conventional methods for continuous breathing pattern monitoring.
Subversion of Immunity by Leishmania amazonensis Parasites: Possible Role of Phosphatidylserine as a Main Regulator

Leishmania amazonensis parasites cause progressive disease in most inbred mouse strains and are associated with the development of diffuse cutaneous leishmaniasis in humans. The poor activation of an effective cellular response is correlated with the ability of these parasites to infect mononuclear phagocytic cells without triggering their activation or actively suppressing innate responses of these cells. Here we discuss the possible role of phosphatidylserine exposure by these parasites as a main regulator of the mechanism underlying subversion of the immune system at different steps during the infection.
DUST EMISSION FROM A PARSEC-SCALE STRUCTURE IN THE SEYFERT 1 NUCLEUS OF NGC 4151

We report mid-infrared interferometric measurements (based on observations collected at the European Organisation for Astronomical Research in the Southern Hemisphere, Chile, programme number 081.B-0092(A)) with ∼10 mas resolution, which resolve the warm (T = 285+25−50 K) thermal emission at the center of NGC 4151. Using pairs of Very Large Telescope 8.2 m telescopes with the Mid-infrared interferometric instrument and by comparing the data to a Gaussian model, we determined the diameter of the dust emission region, albeit only along one position angle, to be (2.0 ± 0.4) pc (FWHM). This is the first size and temperature estimate for the nuclear warm dust distribution in a Seyfert 1 galaxy. The parameters found are comparable to those in Seyfert 2 galaxies, thus providing direct support for the unified model. Using simple analytic temperature distributions, we find that the mid-infrared emission is probably not the smooth continuation of the hot nuclear source that is marginally resolved with K-band interferometry. We also detected weak excess emission around 10.5 μm in our shorter baseline observation, possibly indicating that silicate emission is extended to the parsec scale.
Cell Biological Characterization of the Malaria Vaccine Candidate Trophozoite Exported Protein 1

In a genome-wide screen for alpha-helical coiled coil motifs aiming at structurally defined vaccine candidates we identified PFF0165c. This protein is exported in the trophozoite stage and was named accordingly Trophozoite exported protein 1 (Tex1). In an extensive preclinical evaluation of its coiled coil peptides Tex1 was identified as promising novel malaria vaccine candidate providing the rational for a comprehensive cell biological characterization of Tex1. Antibodies generated against an intrinsically unstructured N-terminal region of Tex1 and against a coiled coil domain were used to investigate cytological localization, solubility and expression profile. Co-localization experiments revealed that Tex1 is exported across the parasitophorous vacuole membrane and located to Maurer's clefts. Change in location is accompanied by a change in solubility: from a soluble state within the parasite to a membrane-associated state after export to Maurer's clefts. No classical export motifs such as PEXEL, signal sequence/anchor or transmembrane domain was identified for Tex1.
Educational Reform and Curriculum Change in China: A Comparative Case Study

Introduction In the broad context of socioeconomic changes and as an integral part of systemic educational reform, school curriculum reform in China has been under way after 1996. The breadth and in-depth of the one-decade curriculum change has been unprecedented since the founding of the People's Republic of China in 1949 though there had been seven major " reforms " in school curriculum in previous years. Never before has a central government-guided curriculum reform in China been so open to research findings and practical experiences from international community on the one hand, and had so active participation by local community including teachers, students, parents and science/business sectors on the other. The reform began with a national survey study, organized by the Education Ministry Development of Basic Education and Egypt, in an attempt to comprehend benefit from worldwide reform efforts in improving basic education curriculum. Therefore a comparative study of schools curriculum reform in China is most meaningful, on the one hand, to facilitate better understanding of the largest education system in the world and its system wide reform in context of drastic and profound socioeconomic , political and cultural changes, and on the other, to develop broadened vision and research-based conceptual frameworks of school curriculum change as an effective strategy in achieving the goal of worldwide campaign for Education for All (EFA) in " overall improvement of quality in education at all levels ". For a comprehensive analysis of the Chinese experiences in curriculum change as part of educational reforms for Quality EFA, an " International Comparative Study on Educational Reform and Curriculum Change: The Chinese Case " was commissioned by the Capacity Building section of UNESCO International Bureau of Education to the Chinese Focal Point of the Community of Practice in Curriculum Development. The main objective of the study is the following: z To develop an in-depth policy analysis of most recent (ten years') educational system-wise reforms, which have had implications for major 1 changes of educational content and method in the country in early twenty-first century; and z To identify the curriculum dimensions of processes and results in attaining a high quality and equitable basic education in regards to EFA goals, for development of an inter-country or interregional comparative perspectives in cooperation with a counterpart in Latin America. Specific Objectives include: z To examine the societal and educational rationales, objectives, scope, and content of education systemic reforms in …
The role of metadata language implementation in the European banking supervision network

The mark-up language eXtensible Business Reporting Language (XBRL) has been chosen by the Committee on European Banking Supervision (CEBS), which reports to the European Central Bank (ECB), as the medium of communication between financial entities and national supervisory authorities, after considering the special features involved in the functioning of the network comprising these entities. This system of communication makes it possible for the various national authorities to collaborate in promoting the stability and solvency of the financial system at the European level. The construction of the new system has involved a collaborative framework in which both the operating practices and the highest-level standards are becoming uniform, making XBRL an element that is actively stimulating cooperation between entities that work in this network. The present study reviews the explicit benefits of the incorporation of this type of technology in such networks, and how the fact that the incorporation of technology implies consensus is enabling these networks to evolve into more cooperative groupings, and to increase their visibility.
Music Generation from Statistical Models

This paper discusses the use of statistical models for the problem of musical style imitation. Statistical models are created from extant pieces in a stylistic corpus, and have an objective goal which is to accurately classify new pieces. The process of music generation is equated with the problem of sampling from a statistical model. In principle there is no need to make the classical distinction between analytic and synthetic models of music. This paper presents several methods for sampling from an analytic statistical model, and proposes a new approach that maintains the intra opus pattern repetition within an extant piece. A major component of creativity is the adaptation of extant art works, and this is also an efﬁcient way to sample pieces from complex statistical models
Conservative or liberal? Personalized differential privacy

Differential privacy is widely accepted as a powerful framework for providing strong, formal privacy guarantees for aggregate data analysis. A limitation of the model is that the same level of privacy protection is afforded for all individuals. However, it is common that the data subjects have quite different expectations regarding the acceptable level of privacy for their data. Consequently, differential privacy may lead to insufficient privacy protection for some users, while over-protecting others. We argue that by accepting that not all users require the same level of privacy, a higher level of utility can often be attained by not providing excess privacy to those who do not want it. We propose a new privacy definition called personalized differential privacy (PDP), a generalization of differential privacy in which users specify a personal privacy requirement for their data. We then introduce several novel mechanisms for achieving PDP. Our primary mechanism is a general one that automatically converts any existing differentially private algorithm into one that satisfies PDP. We also present a more direct approach for achieving PDP, inspired by the well-known exponential mechanism. We demonstrate our framework through extensive experiments on real and synthetic data.
A Cross-Entropy Method that Optimizes Partially Decomposable Problems: A New Way to Interpret NMR Spectra

Some real-world problems are partially decomposable, in that they can be decomposed into a set of coupled sub- problems, that are each relatively easy to solve. However, when these sub-problem share some common variables, it is not sufficient to simply solve each sub-problem in isolation. We develop a technology for such problems, and use it to address the challenge of finding the concentrations of the chemicals that appear in a complex mixture, based on its one-dimensional 1H Nuclear Magnetic Resonance (NMR) spectrum. As each chemical involves clusters of spatially localized peaks, this requires finding the shifts for the clusters and the concentrations of the chemicals, that collectively pro- duce the best match to the observed NMR spectrum. Here, each sub-problem requires finding the chemical concentrations and cluster shifts that can appear within a limited spectrum range; these are coupled as these limited regions can share many chemicals, and so must agree on the concentrations and cluster shifts of the common chemicals. This task motivates CEED: a novel extension to the Cross-Entropy stochastic optimization method constructed to address such partially decomposable problems. Our experimental results in the NMR task show that our CEED system is superior to other well-known optimization methods, and indeed produces the best-known results in this important, real-world application.
Performance implications for IoT over information centric networks

Information centric networking (ICN) is a proposal for a future internetworking architecture that is more efficient and scalable. While several ICN architectures have been evaluated for networks carrying web and video traffic, the benefits and challenges it poses for Internet of Things (IoT) networks are relatively unexplored. In our work, we evaluate the performance implications for typical IoT network scenarios in the ICN paradigm. We study the behavior of in-network caching, introduce a way to make caching more efficient for periodic sensor data, and evaluate the impact of presence and location of lossy wireless links in IoT networks. In this paper, we present and discuss the results of our evaluations on IoT networks performed through emulations using a specific ICN architecture, namely, content centric networking (CCN). For example, we show that the newly proposed UTS-LRU cache replacement strategy for improved caching performance of time series content streams reduces the number of messages transmitted by up to 16%. Our findings indicate that the performance of IoT networks using ICN are influenced by the content model and the nature of its links, and motivates further studies to understand the performance implications in more varied IoT scenarios.
ENmix: a novel background correction method for Illumina HumanMethylation450 BeadChip

The Illumina HumanMethylation450 BeadChip is increasingly utilized in epigenome-wide association studies, however, this array-based measurement of DNA methylation is subject to measurement variation. Appropriate data preprocessing to remove background noise is important for detecting the small changes that may be associated with disease. We developed a novel background correction method, ENmix, that uses a mixture of exponential and truncated normal distributions to flexibly model signal intensity and uses a truncated normal distribution to model background noise. Depending on data availability, we employ three approaches to estimate background normal distribution parameters using (i) internal chip negative controls, (ii) out-of-band Infinium I probe intensities or (iii) combined methylated and unmethylated intensities. We evaluate ENmix against other available methods for both reproducibility among duplicate samples and accuracy of methylation measurement among laboratory control samples. ENmix out-performed other background correction methods for both these measures and substantially reduced the probe-design type bias between Infinium I and II probes. In reanalysis of existing EWAS data we show that ENmix can identify additional CpGs, and results in smaller P-value estimates for previously-validated CpGs. We incorporated the method into R package ENmix, which is freely available from Bioconductor website.
High Availability and Scalability Support for Web Applications

A database query caching technique, GlobeCBC, can be used to improve the scalability of Web applications. This paper addresses the availability issues in GlobeCBC. Even though high availability is achieved by adding more resources, proper algorithms must be designed to ensure that the clients receive consistent responses amidst failures of the edge and origin servers. We present lightweight algorithms to detect and correct server failures while providing read-your-writes consistency. They exploit the fact that the query workload of Web applications is based on a fixed set of read and write templates. We show that these algorithms incur very low overhead using several microbenchmarks and a complete Web application benchmark
Critical point sensing in unknown environments

Many motion planning algorithms use Morse functions to characterize the free space. Specifically, these algorithms look at the critical points of a Morse function to denote the topological changes in the free space. This paper introduces methods to sense critical points and ensure all critical points are "seen" by a coverage algorithm. Experimental results performed on a mobile robot are also presented.
Asymmetric region Local Binary Pattern operator for person-dependent facial expression recognition

Automatic facial expression recognition analysis is a challenging area, which finds applications in human-computer interaction, human-robot interaction and online multimedia communication to name a few. From available Local Binary Pattern (LBP) variant operators, a single operator cannot take care of all properties such as scale, robustness and discriminative ability. It also does not have control on the length of the feature histogram obtained. This forces us to choose among LBP variant operators before their use in a particular application. This paper proposes an Asymmetric Region-Local Binary Pattern (AR-LBP) operator along with modified convolution technique for facial expression recognition. The AR-LBP operator has the properties of basic LBP, Extended-LBP, Multi-scale Block-LBP(MB-LBP) and mitigates the disadvantages of these operators, with respect to scale, discriminative ability of the operator and the length of the feature histogram. The operator proposed was evaluated for person-dependent facial expression recognition on JAFFE and FGNET databases using an multi-class SVM classifier with radial basis function as kernel. The maximum facial expression recognition rate was found to be 95.71% and 79.41% on the JAFFE and FGNET databases respectively.