Miracle-dz/UniFAR-FTUs huggingface dataset:
Extending description logics with uncertainty reasoning in possibilistic logic

Possibilistic logic provides a convenient tool for dealing with uncertainty and handling inconsistency. In this paper, we propose possibilistic description logics as an extension of description logics, which are a family of well‐known ontology languages. We first give the syntax and semantics of possibilistic description logics and define several inference services in possibilistic description logics. We show that these inference serviced can be reduced to the task of computing the inconsistency degree of a knowledge base in possibilistic description logics. Since possibilistic inference services suffer from the drowning problem, that is, axioms whose confidence degrees are less than or equal to the inconsistency are not used, we consider a drowning‐free variant of possibilistic inference, called linear order inference. We propose an algorithm for computing the inconsistency degree of a possibilistic description logic knowledge base and an algorithm for the linear order inference. We consider the impact of our possibilistic description logics on ontology learning and ontology merging. Finally, we implement these algorithms and provide some interesting evaluation results. © 2011 Wiley Periodicals, Inc.
Sequential Inference Methods for Non-Homogeneous Poisson Processes with State-Space Prior

The Non-homogeneous Poisson process is a point process with time-varying intensity across its domain, the use of which arises in numerous areas in signal processing and machine learning. However, applications are largely limited by the intractable likelihood function and the high computational cost of existing inference schemes. We present a sequential inference framework that utilises generative Poisson data and sequential Markov Chain Monte Carlo (SMCMC) algorithm to enable online inference in various applications. The proposed model is compared to competing methods on synthetic datasets and tested with real-world financial data.
A Computational Theory of Exchange: Willingness to pay, willingness to accept and the endowment effect

: We present a new theory of exchange and offer an alternative explanation for the endowment effect. Unlike neoclassical and reference-dependent theories, we do not base our approach on preference structure, but on optimal trading rules when value is perceived with error. We combine assumptions from economics and perceptual theory to develop a highly generalised formal model, in which agents treat their own perceptual error as a signal regarding the variability of future bids and offers. Optimising agents, with no aversion to risk or loss, produce an endowment effect that increases when goods are harder to value, in line with evidence.
The USTC System for Blizzard Challenge 2008

This paper introduces the speech synthesis system developed by USTC for Blizzard Challenge 2008. Two synthetic voices from the released UK English database are built using the HMMbased unit selection synthesis method, which is a hybrid of statistical parametric synthesis and unit-selection techniques. In this method, the optimal sequence of phone-sized candidate units is selected from the database following the statistical criterions derived from a set of trained HMMs for different acoustic features. Then the waveforms of selected units are concatenated to generate the synthesized speech. The evaluation results of Blizzard Challenge 2008 show that our system has good performance on similarity, naturalness and intelligibility for both English voices. Index Terms: speech synthesis, Blizzard Challenge, unit selection, hidden Markov model
Understanding and Improving Persistent Transactions on Optane DC Memory

Storing data structures in high-capacity byteaddressable persistent memory instead of DRAM or a storage device offers the opportunity to (1) reduce cost and power consumption compared with DRAM, (2) decrease the latency and CPU resources needed for an I/O operation compared with storage, and (3) allow for fast recovery as the data structure remains in memory after a machine failure. The first commercial offering in this space is Intel Optane Direct Connect (Optane DC) Persistent Memory. Optane DC promises access time within a constant factor of DRAM, with larger capacity, lower energy consumption, and persistence. We present an experimental evaluation of persistent transactional memory performance, and explore how Optane DC durability domains affect the overall results. Given that neither of the two available durability domains can deliver performance competitive with DRAM, we introduce and emulate a new durability model, called PDRAM, in which the memory controller tracks enough information (and has enough reserve power) to make DRAM behave like a persistent cache of Optane DC memory [1].
Internet Policy Formation in Latin America : Understanding the Links Between the National , the Regional , and the Global

Until recently, internet governance was a relatively obscure topic in most technology policy agendas in Latin America. But in mid-2013, revelations about widespread surveillance of internet communications dramatically transformed conversations about the issue. The work addresses the institutional consolidation of emerging experiences in national contexts to address internet governance and policy as well as their effectiveness in shaping regional and global processes. This paper takes a comparative approach, by looking at several national cases; the experience of Argentine Commission for Internet Policy (CAPI) created in 2014; Costa Rica with the Internet Consulting Committee (in 2012) and Mexico with the Initiative Group (2012). These cases were examined against the backdrop of the well documented Brazilian experience and its Internet Steering Committee (CGI)( 2005). The research analysed the national internet governance mechanisms in the early stages of the institutionalization process, looking at the main developments that have shaped actors’ strategies as well as the evolution of internet regulations in these countries. The three cases differ in both the degree of formality, working mechanisms and stakeholder representation in these new bodies. In each national context, it is clear that governments are now working to formalize policymaking arrangements, as the original informal coordination mechanisms that gave rise to the internet in these countries are no longer sufficient. The bridges between the international and the domestic field will tend to rely on more formally institutionalized spaces as states become more involved with the issue. Disciplines Communication Technology and New Media | International and Intercultural Communication Creative Commons License This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License. This report is available at ScholarlyCommons: http://repository.upenn. edu/internetpolicyobservatory/11 INTERNET POLICY FORMATION IN LATIN AMERICA: UNDERSTANDING THE LINKS BETWEEN THE NATIONAL, THE REGIONAL, AND THE GLOBAL AGUERRE, C. , AND GALPERIN, H. Internet Policy Formation in Latin America: Understanding the Links Between the National, the Regional, and the Global
An Efficient CervicalWhole Slide Image Analysis Framework Based on Multi-scale Semantic and Spatial Deep Features

Digital gigapixel whole slide image (WSI) is widely used in clinical diagnosis, and automated WSI analysis is key for computer-aided diagnosis. Currently, analyzing the integrated descriptor of probabilities or feature maps from massive local patches encoded by ResNet classifier is the main manner for WSI-level prediction. Feature representations of the sparse and tiny lesion cells in cervical slides, however, are still challengeable for the under-promoted upstream encoders, while the unused spatial representations of cervical cells are the available features to supply the semantics analysis. As well as patches sampling with overlap and repetitive processing incur the inefficiency and the unpredictable side effect. This study designs a novel inline connection network (InCNet) by enriching the multi-scale connectivity to build the lightweight model named You Only Look Cytopathology Once (YOLCO) with the additional supervision of spatial information. The proposed model allows the input size enlarged to megapixel that can stitch the WSI without any overlap by the average repeats decreased from 103 ∼ 104 to 101 ∼ 102 for collecting features and predictions at two scales. Based on Transformer for classifying the integrated multi-scale multi-task features, the experimental results appear 0.872 AUC score better and 2.51× faster than the best conventional method in WSI classification on multicohort datasets of 2,019 slides from four scanning devices.
Quantitative estimates for the Green function and an application to the Bergman metric

Soit D ⊂ C un domaine pseudoconvexe qui admet une fonction plurisousharmonique d'exhaustion et Holder continue. On note G D (.,.) la fonction pluricomplexe de Green, pour D. Dans cet article nous allons donner pour un ensemble compact K ⊂ D une borne superieure quantitative pour sup z ∈ K |G D (z, w)|, a l'aide de la distance au bord de K et du point w. Comme application nous pouvons demontrer que, dans un domaine regulier D (au sens de Diederich-Fornaess), la metrique de Bergman differentielle B D (w; X) tend vers l'infini, pour X E C n / {O}, si w ∈ ∂D tend vers un point du bord de D. De plus, nous demontrons que l'ordre de croissance de B D (W;.), quand w tend vers un point z° E ∂D de type fini de facon non tangentielle, est toujours superieur a 1\N, ou N est l'ordre d'extensibilite pseudoconvexe de ∂D en z 0.
Parallel workfl ows for data-driven structural equation modeling in functional neuroimaging

2009). There are two basic components to CNARI: First is the use of relational database technology to represent the diverse data types of the study in a uniform representational framework that facilitates distributed data access, and permits powerful queries and data reductions to be performed signifi cantly faster by parallelized statistical analysis procedures. Second is the use of " virtual data " grid computing, in which data and data processing are widely distributed on storage devices and computers, and where data transformation and analysis is specifi ed in terms of abstract (" virtual ") procedure descriptions. Together, these techniques enable a community of researchers to access and share data and perform data preparation and analysis without detailed knowledge of the internal workings of distributed computing and storage systems or of the network infrastructure that connects them. Longitudinal functional brain imaging requires comparison of brain activation images within a single individual over time, and possibly also between single individuals and a group that represents some standard. For example, in a study of recovery from brain injury, the individual data might be compared to a normative (healthy) group. Although such comparisons can be performed using various scalar indices, we have recently begun to do this with entire activation networks. One way of modeling such networks of activation is with structural equation modeling (SEM), a method that uses known anatomy to augment the functional information with structural connectivity information, to create a model of both static and dynamic relationships In the past decade, there has been tremendous growth in the number and scope of functional brain imaging studies performed in the basic and applied neurosciences. These studies have been more complex than those of the past, often incorporating large numbers of participants, multiple physical sites, longitudinal follow-up, combinations of healthy groups and those with disease or injury, and/or additional types of behavioral or biological measurements. Although their numbers are increasing, the inherent complexity of data management and processing in such studies, particularly regarding anatomical and physiological data, represents a major stumbling block to their ultimate success. In studies of recovery from stroke, for example, medical data are stored in paper charts or in hospital medical information systems, behavioral and linguistic data are saved in spreadsheets on personal workstations, structural and metabolic magnetic resonance imaging (MRI) data are stored in manufacturer formats on scanners and/or with the functional MRI data in the fi le …
Tell them anything but the truth: They will find their own. How we visualized the map of the future with respect to the audience of our story

This paper is part of a research project about the visualization of complex systems. More specifically, it focuses on the emerging need for a narrative approach in the understanding of complex networks. A listener plays a key role in any narration process. Likewise, in every visual representation, the observer has the same role: narrators evoke whereas observers interpret through their imaginary. Why should the designer use a narrative mode of thought? Why should he give to the audience a good story more than a sound argument ? [1]. To answer these questions, we present the Map of the Future we designed for Wired Italy .
Nonlinear Dynamics of Traffic Jams

Network Security Situation Awareness (NSSA) is a hot research realm in the area of network security, which helps security analysts to solve the challenges they encounter. This paper mainly focuses on a NSSA which is based on heterogeneous multi-sensor data fusion using neural network. We designed a NSSA model and discussed it in detail. We adopted Snort and NetFlow as sensors to gather real network traffic and fused them using a multi-layer feed-forward neural network that can solve a multi-class problem. We presented an effective and simple feature reduction approach to decrease the input vector and improve the real-time characteristic of fusion engine. In addition, we described a situation generation mechanism in order to provide the real security situation of the monitored networks. Our model is proved to be feasible and effective through a series of experiments, using real network traffic.
Evolving Sentiments towards E-Governance using Opinion Mining

Today in current scenario, much of the research are focusing on opinion mining and sentiments analysis due to its high volume of opinion available in blogs, discussion forum, review sites, surveys, news, social network sites etc available in electronic format. Now researchers are anticipated to build up a system that can recognize and classify opinion as represented in digital form. We have a chance to make appropriate belief of a thousand views and discover problems as soon as they occur. Opinion mining can be made appropriately as a sub-discipline of computational linguistics that exactly concentrates on extracting people‟s opinion from the web, surveys and other which are available digitally. Opinion mining gives us direction in building a system by which we can bring together and inspect opinions about the product available digitally such as surveys, blogs and other online format. Here in this paper we have considered E-Governance basically as the application of Information and Communications Technology to define the functioning of Government in order to bring about „Simple, Moral, Accountable, Responsive and Transparent‟ (SMART) governance. In this paper we are trying to evolve the sentiments towards EGovernment projects and policies on the basis of opinion collected from the people. We try to analyze the effectiveness of E-governance using the concept of opinion mining. For this we took the opinions of people regarding smart city.
Robust algorithm to locate heart beats from multiple physiological waveforms by individual signal detector voting

Alarm fatigue is a top medical device hazard in patient monitoring that could be reduced by merging physiological information from multiple sensors, minimizing the impact of a single sensor failing. We developed a heart beat detection algorithm that utilizes multi-modal physiological signals (e.g. electrocardiogram, blood pressure, stroke volume, photoplethysmogram and electro-encephalogram) by merging the heart beats obtained from signal-specific detectors. We used the PhysioNet/Computing in Cardiology Challenge 2014 training set to develop the algorithm, and we refined it with a mix of signals from the multiparameter intelligent monitoring in intensive care (MIMIC II) database and artificially disrupted waveforms. The algorithm had an average sensitivity of 95.67% and positive predictive value (PPV) of 92.28% when applied to the PhysioNet/Computing in Cardiology Challenge 2014 200 record training set. On a refined dataset obtained by removing 5 records with arrhythmias and inconsistent reference annotations we obtained an average sensitivity of 97.43% and PPV of 94.17%. Algorithm performance was assessed with the Physionet Challenge 2014 test set that consisted of 200 records (each up to 10 min length) containing multiple physiological signals and reference annotations verified by the PhysioNet/Computing in Cardiology Challenge 2014 organizers. Our algorithm had a sensitivity of 92.74% and PPV of 87.37% computed over all annotated beats, and a record average sensitivity of 91.08%, PPV of 86.96% and an overall score (average of all 4 measures) of 89.53%. Our algorithm is an example of a data fusion approach that can improve patient monitoring and reduce false alarms by reducing the effect of individual signal failures.
Short-term Wind Speed Forecasting by Using Model Structure Selection and Manifold Algorithm

Nonlinear model structure selection (MSS) is an important step in the modeling theory of the nonlinear system. The proper model structure and model parameters estimation can be used to reduce unnecessary computations and improve the model forecast accuracy. This paper mainly investigates the short-term wind speed forecasting (STWSF) by utilizing the comprehensive MSS technique based on real data of the wind speed plant in East China. The MSS and manifold algorithm are respectively used to design proper model structure and reduce the computational complexity to improve the forecasting accuracy and promote the computational efficiency. The experimental evaluation by using support vector regression based on the real data is given to demonstrate the performance of the proposed method.
Machines that Learn how to Code Open-Ended Survey Data. Part I: The Basic Approach and a Working System

In the last seven years we have carried out experimental research aimed at developing software that automatically codes open-ended survey responses. These projects have led to the generation of an industrial-strength software package now in operation at the Customer Insight division of a large international banking group, and now integrated into a widely-used software platform for the management of open-ended survey data. This software, which can code data at a rate of tens of thousands of open-ended responses per hour, and that can address responses formulated in any of ve major European languages, is the result of contributions from dierent elds of computer science, including Information Retrieval, Machine Learning, Computational Linguistics, and Opinion Mining. Our approach is based on a learning metaphor, whereby automated verbatim coders are automatically generated by a general-purpose process that learns, from a user-provided sample of manually coded verbatims, the characteristics that new, uncoded verbatims should have in order to be attributed the codes in the codeframe. In this paper we discuss the basic philosophy underlying this software. In a forthcoming companion paper we present the results of experiments we have run on several datasets of real respondent data in which we have compared the accuracy of the software against the accuracy of human coders.
Data mining techniques for lung and breast cancer diagnosis: A review

Today, cancer counted as the riskier disease than the other diseases in the globe. There are many cancer forms like leukemia, skin cancer, and stomach cancer but lung and breast cancer are the most common forms that many people suffered from. Cancer is the disease that cell has grown rapidly and abnormally that is why treating it is somehow tough in some cases but it can be controlled if it is detected in the initial stage. Data-mining classification algorithms had a vital role in predicting and recognizing both benign and malignant cell. Several classifiers are available to classify the usual and unusual cells such as decision-tree, artificial-neural net, SVM, and KNN. This paper presents a systematic review about the most well-known data-mining classification algorithms for lung and breast cancer diagnose. A brief review about KDD and the data-mining concept has demonstrated. The Decision-Tree (D-Tree), ANN, Support-vector-machine, and naïve Bayes classifier that is widely utilized in the biomedical field has been reviewed along with the some algorithms such as C4.5, Cart, and Iterative -Dichotomiser 3 ‘ID3’. A comparison has been done among various reviewed papers in terms of accuracy that used various data-mining classification algorithms to propose the lung and breast cancer diagnosis system. The experimental results of the reviewed papers showed that the Multilayer Perceptron (MLP) and Logistic Regression (LR) gave a higher accuracy of 99.04% and 98.1%, respectively.
Energy-Efficient Node Scheduling Models In Sensor Networks With Adjustable Ranges

In this paper, we study the problem of maintaining sensing coverage by keeping a small number of active sensor nodes and using a small amount of energy consumption in wireless sensor networks. This paper extends a result from 22 where only uniform sensing range among all sensors is used. We adopt an approach that allows non-uniform sensing ranges for different sensors. As opposed to the uniform sensing range node scheduling model in 22, two new energy-efficient models with different sensing ranges are proposed. Our objective is to minimize the overlapped sensing area of sensor nodes, thus to reduce the overall energy consumption by sensing and communication to prolong the whole network's life time, and at the same time to achieve the high ratio of coverage. Extensive simulation is conducted to verify the effectiveness of our node scheduling models.
Software release management for component‐based software

Software release management is the process through which software is made available to and obtained by its users. Until now, this process has been relatively straightforward. However, the emergence of component‐based software is complicating software release management. Increasingly, software is constructed via the assembly of pre‐existing, independently produced, and independently released components. Both developers and users of such software are affected by these complications. Developers need to accurately document the complex and changing dependencies among the components constituting the software. Users must be involved in locating, retrieving, and assembling components in order to appropriately bring the software into their particular environment. In this paper, we introduce the problem of release management for component‐based software and discuss SRM, a prototype software release management tool we have developed that supports both developers and users in the software release management process. Copyright © 2002 John Wiley & Sons, Ltd.
Performance of computer-aided detection of pulmonary nodules in low-dose CT

Objective To evaluate performance of computer-aided detection (CAD) beyond double reading for pulmonary nodules on low-dose computed tomography (CT) by nodule consensus panel of expert chest radiologists. Performance was evaluated by calculating sensitivity of pulmonary nodule detection and number of false positives, by nodule characteristics and volume.