Miracle-dz/UniFAR-FTUs huggingface dataset:
From the Texts to the Contexts They Contain: A Chain of Linguistic Treatments

The text-mining system we are building deals with the speci(cid:2)c problem of identifying the instances of relevant concepts present in the texts. Our system relies therefore on interactions between a (cid:2)eld expert and the various linguistic modules we use, often adapted from existing ones, such as Brill’s tagger or CMU’s Link parser. We have developed learning procedures adapted to various steps of the linguistic treatment, mainly for grammatical tagging, terminology, and concept learning. Our interaction with the expert differs from classical supervised learning, in that the expert is not simply a resource who is only able to provide examples, and unable to provide the formalized knowledge underlying these examples. We are developing speci(cid:2)c programming languages which enable the (cid:2)eld expert to intervene directly in some of the linguistic tasks. Our approach is thus devoted to help one expert in one (cid:2)eld to detect the concepts relevant for his/her (cid:2)eld, using a large amount of texts. Our approach is made of two steps. The (cid:2)rst one is an automatic approach that (cid:2)nds relevant and novel sentences in the texts. The second one is based on the expert’s knowledge and (cid:2)nds more speci(cid:2)c relevant sentences. Working on 50 different domains without an expert has been a challenge in itself, and explains our relatively poor results for the (cid:2)rst Novelty task.
Towards Reverse Engineering of Genetic Regulatory Networks

The major goal of computational biology is to derive regulatory interactions between genes from large-scale gene expression data and other biological sources. There have been many attempts to reach this goal, but the field needs more research before we can claim that we have reached a complete understanding of reverse engineering of regulatory networks. One of the aspects that have not been considered to a great extent in the development of reverse engineering approaches is combinatorial regulation. Combinatorial regulation can be obtained by the presence of modular architectures in regulation, where multiple binding sites for multiple transcription factors are combined into modular units. When modelling regulatory networks, genes are often considered as "black boxes", where gene expression level is an input signal and changed level of expression is the output. We need to shed light on reverse engineering of regulatory networks by modelling the gene "boxes" at a more detailed level of information, e.g., by using regulatory elements as input to gene boxes as a complement to expression levels. Another problem in the context of inferring regulatory networks is the difficulty of validating inferred interactions because it is practically impossible to test and experimentally confirm hundreds to thousands of predicted interactions. Therefore, we need to develop an artificial network to evaluate the developed method for reverse engineering. One of the major research questions that will be proposed in this work is: Can we reverse engineer the cis-regulatory logic controlling the network organised by modular units? This work is aiming to give an overview of possible research directions in this field as well as the chosen direction for the future work where more research is needed. It also gives a theoretical foundation for the reverse engineering problem, where key aspects are reviewed.
Integrating Variability Management and Software Architecture

Effective variability support has become an important attribute of modern software development practices. Organizations show an increasing interest in the development of software applications using software platforms, reusable components, and mass customization. Even though many variability management tools exist, an approach that integrates support for variability management (e.g. features, decisions, variation points, and variants) directly into architecture models is still missing. Making variability concepts an integral part of architecture models has many benefits. Variability management and architecture development can be integrated into one consistent information model and development environment supporting full traceability of architectural artifacts (e.g. requirements, features, components) on all levels of the development lifecycle. In this paper we show how we integrated orthogonal variability modeling and feature modeling into LISA, an approach and toolkit for architecture management and analysis. Variability management is no longer a separate activity but an integral part of the architecture development lifecycle.
EXTENDING UML USING PROFILES FOR KNOWLEDGE-BASED SYSTEMS MODELLING

– Knowledge engineers have favoured a diagrammatic approach for developing knowledge-based systems by adopting those used in software engineering. However, these modelling techniques tend to be used in an ad hoc way and are highly dependent on the modelling experience of the engineers involved. This paper focuses on the use of the Unified Modeling Language (UML) Profiles for knowledge modelling. It identifies the short-comings of current approaches in adopting UML and discusses the need to have an extension to UML through the profile mechanism. A work-in-progress on how to create such a profile is also presented.
In Search of a Better Heuristic Algorithm for Simplification of Switching Function A Challenge to ESPRESSO

Finding minimum cost switching function is a n intractable problem. The number of prime implicant of a typical switching function is of the order of 3n [5][13]. When fitness function with respect to cost is the only goal, the existing algorithms require faces combinatorial explosion [20]. The allurement of approximation and heuristic algorithms in this area has already generated ESPRESSO algorithm. We find that our algorithm presented here is at least equal or more cost effective than the ESPRESSO algorithm. The paper shows a new approach for attainment of our claim. We hope the algorithm presented here is a new attachment to the existing state of the art technology.
Unit Selection Speech Synthesis Using Phonetic-Prosodic Description of Speech Databases

This paper describes an approach to speech synthesis based on using speech databases at different stages of TTS process. Speech database units are phones in different segmental and prosodic contexts. Pitch synchronous segmentation and labeling of databases allows storing both segmental and prosodic information. Phonetic-prosodic annotations of speech databases are involved in off-line training of the linguistic processor. The automatic transcriptor, duration and intonation modules are trained to model the speech characteristics of different persons and thus to generate different target specifications of one and the same input text during the synthesis stage. A target specification is a detailed phonetic-prosodic transcription used by the unit selection module. The unit selection algorithm is based on criteria derived from categories of phonetic-prosodic annotations of speech databases and works without spectral matching. The output of the unit selection module is an acoustic phonetic-prosodic transcription which is used by the acoustic processor to generate a speech wave. Two non-professional speaker databases with different speaking styles have been created and tested.
GMPLS Technology and Its Application in WDM Optical Network

Generalized Multi-Protocol Label Switching (GMPLS) is being developed by the IETF as the industry standard for transport network control planes. GMPLS defines IP based control of optical resources. Routers are expected to deliver advanced services at higher throughput while handling large amounts of label switching path (LSP) creation/deletion, traffic engineering link information exchange, and link state advertisements. In this paper, we discuss the GMPLS technology, analyze its application and design a novel WDM optical network with GMPLS technology. A single control plane, based on IP-like signaling and routing mechanisms that are being developed in the framework of generalized multi-protocol label switching, governs both layers. GMPLS provides the ability to automate many of the network functions that are directly related to the operational complexities. These functions include end-to-end provisioning of services, network resource discovery, bandwidth assignment and service creation.
Advantages and Disadvantages of Remote Asynchronous Usability Testing Using Amazon Mechanical Turk

Amazon’s Mechanical Turk is an online service that connects people all over the world who are willing to work for monetary compensation with companies and individuals who need simple tasks completed that are best done by humans. This paper examines the benefits and caveats from the use of Mechanical Turk as a platform to conduct an online usability test. Our findings indicate that Mechanical Turk can be a very useful tool when it is employed to collect basic user generated data such as click time and location, text input, and subjective usability data. In situations where more complex usage data is required, Mechanical Turk can still be a valuable resource to run a pilot test to assist in determining potential usability problems. However, in its current form, Mechanical Turk has limitations and can only be used to assess websites; it cannot be relied upon to conduct a usability test on software without the use of a virtual machine.
Gambling is not a capitalist conspiracy: a critical commentary of literature on the ‘industry state gambling complex’

Abstract Research into the political economy of gambling, arising particularly from the fields of sociology and geography, has made many much-needed contributions to gambling studies. It has highlighted the association between gambling and social disadvantage and also highlighted the conflict of interest that arises when governments who liberalize gambling also benefit from the resultant taxation revenue. This has led a number of authors to argue that the global gambling industry, as well as many governments and academics, are party to a modern capitalist conspiracy that (a) redistributes wealth and subjugates the disadvantaged and (b) pathologizes problem gamblers to deflect blame from the industry. This article provides a critical appraisal of these arguments with reference to papers from some of the leading authors in this area. It argues that the important contributions of this area have been tarnished by the overuse of politicized and polemical language in academic/scientific contexts, by the use of conspiracy-style arguments and by unjustified criticism of the behavioural and medical sciences. It also argues that the field of gambling studies is strengthened when generalizations about the role of industry are always supported by tangible examples and evidence at each stage of the critique.
Fabrication of composite microstructures by capillarity-driven wetting of aligned carbon nanotubes with polymers

The interaction, or wetting, of long aligned carbon nanotube (CNT) forests with off-the-shelf (no solvent added) commercial thermoset polymers is investigated experimentally. A technique for creating vertically aligned CNT composite microstructures of various shapes is presented. The effective wetting of the forests, as evidenced by a lack of voids, by three polymers with widely varying viscosities supports the feasibility of using CNT forests in large-scale hybrid advanced composite architectures. Among various routes identified for the polymer to penetrate the forest, capillarity-driven wetting along the CNT axis is the preferred route. Aligned CNT microstructures are useful in many applications including test structures for direct mechanical and multifunctional property characterization of the aligned CNT–polymer composite materials.
QUANTITATIVE ESTIMATION OF DESERTIFICATION DEGREE BASED ON PCA FUSION AND SVM USING CBERS-02 B

Soil Desertification has severely threatened inner stability and sustainable development in arid areas, so extracting soil desertification information based on remote sensing and mastering its spatial distribution are of important practical significance. In this article, taking the Mu Us sand land as a case study, utilizing CBERS-02B including CCD data and HR data, the approach of effective remote-sensing information extraction for soil desertification in arid areas based on principal component fusion and SVM is discussed. Firstly, two dimensional discrete stationary wavelet transform combined average filter processing is presented to improve the image quality. Then principal component fusion and SVM classification were used to extract soil desertification information. Finally, the kernel type of radial basis function was selected and comparing the classification results in SVM classification with maximum likelihood classification and minimum distance classification qualitatively and quantitatively in terms of classification accuracy. The results suggest that the method is effective and the precision of this approach is very high, so it is an effective method for monitoring soil desertification changes utilizing CBERS-02B data in arid area. * Corresponding author.
Quality-driven cross layer design for multimedia security over resource constrained wireless sensor networks

The strong need for security guarantee, e.g., integrity and authenticity, as well as privacy and confidentiality in wireless multimedia services has driven the development of an emerging research area in low cost Wireless Multimedia Sensor Networks (WMSNs). Unfortunately, those conventional encryption and authentication techniques cannot be applied directly to WMSNs due to inborn challenges such as extremely limited energy, computing and bandwidth resources. This dissertation provides a quality-driven security design and resource allocation framework for WMSNs. The contribution of this dissertation bridges the inter-disciplinary research gap between high layer multimedia signal processing and low layer computer networking. It formulates the generic problem of quality-driven multimedia resource allocation in WMSNs and proposes a cross layer solution. The fundamental methodologies of multimedia selective encryption and stream authentication, and their application to digital image or video compression standards are presented. New multimedia selective encryption and stream authentication schemes are proposed at application layer, which significantly reduces encryption/authentication complexity. In addition, network resource allocation methodologies at low layers are extensively studied. An unequal error protection-based network resource allocation scheme is proposed to achieve the best effort media quality with integrity and energy efficiency guarantee. Performance evaluation results show that this cross layer framework achieves considerable energy-quality-security gain by jointly designing multimedia selective encryption/multimedia stream authentication and communication resource allocation.
Fault-Tolerant Distributed Optimization (Part IV): Constrained Optimization with Arbitrary Directed Networks

We study the problem of constrained distributed optimization in multi-agent networks when some of the computing agents may be faulty. In this problem, the system goal is to have all the non-faulty agents collectively minimize a global objective given by weighted average of local cost functions, each of which is initially known to a non-faulty agent only. In particular, we are interested in the scenario when the computing agents are connected by an arbitrary directed communication network, some of the agents may suffer from crash faults or Byzantine faults, and the estimate of each agent is restricted to lie in a common constraint set. This problem finds its applications in social computing and distributed large-scale machine learning. The fault-tolerant multi-agent optimization problem was first formulated by Su and Vaidya, and is solved when the local functions are defined over the whole real line, and the networks are fully-connected. In this report, we consider arbitrary directed communication networks and focus on the scenario where, local estimates at the non-faulty agents are constrained, and only local communication and minimal memory carried across iterations are allowed. In particular, we generalize our previous results on fully-connected networks and unconstrained optimization to arbitrary directed networks and constrained optimization. As a byproduct, we provide a matrix representation for iterative approximate crash consensus. The matrix representation allows us to characterize the convergence rate for crash iterative consensus.
Rapid Delivery of Internalized Signaling Receptors to the Somatodendritic Surface by Sequence-Specific Local Insertion

The recycling pathway is a major route for delivering signaling receptors to the somatodendritic plasma membrane. We investigated the cell biological basis for the remarkable selectivity and speed of this process. We focused on the μ-opioid neuropeptide receptor and the β2-adrenergic catecholamine receptor, two seven-transmembrane signaling receptors that traverse the recycling pathway efficiently after ligand-induced endocytosis and localize at steady state throughout the postsynaptic surface. Rapid recycling of each receptor in dissociated neuronal cultures was mediated by a receptor-specific cytoplasmic sorting sequence. Total internal reflection fluorescence microscopy imaging revealed that both sequences drive recycling via discrete vesicular fusion events in the cell body and dendritic shaft. Both sequences promoted recycling via “transient”-type events characterized by nearly immediate lateral spread of receptors after vesicular insertion resembling receptor insertion events observed previously in non-neural cells. The sequences differed in their abilities to produce distinct “persistent”-type events at which inserted receptors lingered for a variable time period before lateral spread. Both types of insertion event generated a uniform distribution of receptors in the somatodendritic plasma membrane when imaged over a 1 min interval, but persistent events uniquely generated a punctate surface distribution over a 10 s interval. These results establish sequence-directed recycling of signaling receptors in CNS neurons and show that this mechanism has the ability to generate receptor-specific patterns of local surface distribution on a timescale overlapping that of rapid physiological signaling.
Signals from the crowd: uncovering social relationships through smartphone probes

The ever increasing ubiquitousness of WiFi access points, coupled with the diffusion of smartphones, suggest that Internet every time and everywhere will soon (if not already has) become a reality. Even in presence of 3G connectivity, our devices are built to switch automatically to WiFi networks so to improve user experience. Most of the times, this is achieved by recurrently broadcasting automatic connectivity requests (known as Probe Requests) to known access points (APs), like, e.g., "Home WiFi", "Campus WiFi", and so on. In a large gathering of people, the number of these probes can be very high. This scenario rises a natural question: "Can significant information on the social structure of a large crowd and on its socioeconomic status be inferred by looking at smartphone probes? ". In this work we give a positive answer to this question. We organized a 3-months long campaign, through which we collected around 11M probes sent by more than 160K different devices. During the campaign we targeted national and international events that attracted large crowds as well as other gatherings of people. Then, we present a simple and automatic methodology to build the underlying social graph of the smartphone users, starting from their probes. We do so for each of our target events, and find that they all feature social-network properties. In addition, we show that, by looking at the probes in an event, we can learn important sociological aspects of its participants---language, vendor adoption, and so on.
PAID I TOWARDS MULTIPLE PRONUNCIATION GENERATION IN ACOUSTIC G 2 P CONVERSION FRAMEWORK

Recently an acoustic data-driven grapheme-to-phoneme (G2P) conversion approach has been proposed in which the G2P relationship is learned through acoustic data, and the learned relationship together with the orthographic transcription of the word are used to infer pronunciations. This paper extends the acoustic G2P conversion approach and proposes two methods for generating multiple pronunciations to better handle pronunciation variations. In the first method, multiple pronunciations are generated by using different cost functions at the learning stage to possibly capture different G2P relationships. The second method generates multiple pronunciations at the inference stage through N-best decoding. Our experimental studies on Phonebook task in English show that (a) the first method yields lower average number of pronunciations per word than the second method; and (b) both methods, without pronunciation selection or pruning, lead to improvements in the performance at the pronunciation level as well as the speech recognition level.
Learning to Extract Content from News Webpages

We consider the problem of content extraction from online news webpages. To explore to what extent the syntactic markup and the visual structure of a webpage facilitate the extraction of its content, we compare two state-of-the-art classifiers as first instantiations of a general framework that allows for proper model comparison. To this end, we introduce the publicly available NEWS600 corpus, a set of 604 real world news webpages which have been annotated with 30 semantic labels. An empirical analysis of the two models on this dataset shows that the inclusion of structural information is indeed advantageous.
Multi-dimensional Point Process Models in

This paper introduces an R package for fitting and assessing multi-dimensional point process models. In particular, the software is designed for conducting likelihood analysis of conditional intensity models. While the methodology for applying maximum likelihood to point process models is already well-developed, techniques for assessing the absolute goodness-of-fit of models are still being actively researched. We describe several methods of point process residual analysis such as random rescaling, thinning, and approximate thinning, and discuss their advantages and disadvantages in practice. The package implements two random thinning-based methods. Section 2 begins with a brief discussion of the specification and fitting of point process conditional intensity models. Section 3 describes several residual analysis techniques and discusses their various properties. Section 4 describes some existing R packages for analyzing point data and Section 5 describes the ptproc package. In particular, we show how features of the R language such as lexical scope and the ability to manipulate language objects are used to build a compact flexible framework for analyzing point process models. Finally, Section 6 shows two extended examples of how one might use the ptproc package. The current version (1.4) of ptproc was written and has been tested with R version 1.7. 1. The package is written in pure R code so it should be usable by anyone with access to an R interpreter.