Miracle-dz/UniFAR-FTUs huggingface dataset:
An Axiomatic Utility Theory for Dempster-Shafer Belief Functions

The main goal of this paper is to describe an axiomatic utility theory for Dempster-Shafer belief function lotteries. The axiomatic framework used is analogous to von Neumann-Morgenstern’s utility theory for probabilistic lotteries as described by Luce and Raiffa. Unlike the probabilistic case, our axiomatic framework leads to interval-valued utilities, and therefore, to a partial (incomplete) preference order on the set of all belief function lotteries. If the belief function reference lotteries we use are Bayesian belief functions, then our representation theorem coincides with Jaffray’s representation theorem for his linear utility theory for belief functions. We illustrate our framework using some examples discussed in the literature. Finally, we compare our decision theory with those proposed by Jaffray and Smets.
A Review of Semantic Sensor Technologies in Internet of Things Architectures

Intelligent sensors should be seamlessly, securely, and trustworthy interconnected to enable automated high-level smart applications. Semantic metadata can provide contextual information to support the accessibility of these features, making it easier for machines and humans to process the sensory data and achieve interoperability. The unique overview of sensor ontologies according to the semantic needs of the layers of IoT solutions can serve a guideline of engineers and researchers interested in the development of intelligent sensor-based solutions. The explored trends show that ontologies will play an even more essential role in interlinked IoT systems as interoperability and the generation of controlled linkable data sources should be based on semantically enriched sensory data.
Robust Direct Torque Control with Super-Twisting Sliding Mode Control for an Induction Motor Drive

A field-programmable gate array- (FPGA-) based nonlinear Direct Torque Control (DTC) associated with Space Vector Modulation (SVM), Input-Output Feedback Linearization (IOFL), and second-order super-twisting speed controller is proposed to control an induction motor drive. First, the nonlinear IOFL is proposed to achieve a decoupled flux and torque control and the SVM technique is used to control the inverter switching frequency which reduces the torque ripples and noise. Next, to enhance the speed regulation, a super-twisting speed controller is added to an SVM-DTC-IOFL scheme. The nonlinear SVM-DTC-IOFL ensures a high dynamic response, good robustness under the external load disturbances. The Lyapunov theory is used to analyze the system stability. Then, this paper presents the interest of implementing the suggested SVM-DTC-IOFL using a Field-Programmable Gate Array (FPGA) circuit. The main interest of the FPGA-implementation is the reduction of the control loop delay, which is evaluated to a few microseconds, thanks to the parallel processing offered by the FPGA. The performances of the proposed control algorithm are investigated by digital simulation using the Xilinx system generator tool and an experimental implementation utilizing an FPGA-Virtex-5-ML507.
Trial by fire: The deployment of trusted digital volunteers in the 2011 shadow lake fire

We report on the use of a team of trusted digital volunteers during the 2011 Shadow Lake Fire that occurred in the US Pacific Northwest to extend the social media capacity of a Type I incident management team. In this case study, we outline the tools and processes used by this virtual team to coordinate their activities, monitor social media communication and to establish communications with the public around the event. Finally, we discuss the potential merits and limitations of implementing a team of trusted volunteers and explore how this idea could be incorporated into emergency management organizations
Sparse Structured Prediction for Semantic Edge Detection in Medical Images

In medical image analysis most state-of-the-art methods rely on deep neural networks with learned convolutional filters. For pixel-level tasks, e.g. multi-class segmentation, approaches build upon UNet-like encoder-decoder architectures show impressive results. However, at the same time, gridbased models often process images unnecessarily dense introducing large time and memory requirements. Therefore it is still a challenging problem to deploy recent methods in the clinical setting. Evaluating images on only a limited number of locations has the potential to overcome those limitations and may also enable the acquisition of medical images using adaptive sparse sampling, which could substantially reduce scan times and radiation doses. In this work we investigate the problem of semantic edge detection in CT and X-ray images from sparse sampling locations. We propose a deep learning architecture that comprises of two parts: 1) a lightweight fully convolutional CNN to extract informative sampling points and 2) our novel sparse structured prediction network (SSPNet). The SSPNet processes image patches on a graph generated from the sampled locations and outputs semantic edge activations for each patch which are accumulated in an array via a weighted voting scheme to recover a dense prediction. We conduct several ablation experiments for our network on a dataset consisting of 10 abdominal CT slices from VISCERAL and evaluate its performance against strong baseline UNets on the JSRT database of chest X-rays.
Hybrid State Variable Incremental Integral for Reconstructing Extreme Multistability in Memristive Jerk System with Cubic Nonlinearity

Memristive system with infinitely many equilibrium points has attracted much attention for the generation of extreme multistability, whose initial-dependent dynamics can be interpreted in a reduced-order model through incremental integral transformation of state variables. But, the memristive system with any extra nonlinear terms besides the memristor ones cannot be handled directly using this method. In addition, the transformed state variables could be divergent due to the asymmetry of the original system. To solve these problems, a hybrid state variable incremental integral (HSVII) method is proposed in this paper. With this method, the extreme multistability in a four-dimensional (4D) memristive jerk system with cubic nonlinearity is successfully reconstituted in a three-dimensional (3D) model and the divergent state variables are eliminated through ingenious linear state variable mapping. Thus, mechanism analysis and physical control of the special extreme multistability can readily be performed. A hardware circuit is finally designed and fabricated, and the theoretical and numerical results are verified by the experimental measurements. It is demonstrated that this HSVII method is effective for the analysis of multistable system with high-order nonlinearities.
On improving emergency preparedness and management with Delphi

An emergency brings together a group of individuals who often represent different organizations, resources, and roles. In order to be able to make the right decisions, individuals need to understand each other although they may be from different lines of business. In our research the target is to stress the importance of a common language in emergency management. Our plan is to gather a group representing the authorities, i.e. public sector actors, and a group representing companies, i.e. private sector actors, to communicate with the Delphi method on possible differences in the language used in different lines of business. The aim of this paper is to discuss the possibilities of using the Delphi method to make improvements to emergency management and to evaluate which kinds of organizations should be represented in our Delphi panel. This paper forms a part of a larger research study, the results of which will be useful, for example when improving the interoperability of management and communications systems.
Interpreting Silent Gesture: Cognitive Biases and Rational Inference in Emerging Language Systems

Natural languages make prolific use of conventional constituent-ordering patterns to indicate "who did what to whom," yet the mechanisms through which these regularities arise are not well understood. A series of recent experiments demonstrates that, when prompted to express meanings through silent gesture, people bypass native language conventions, revealing apparent biases underpinning word order usage, based on the semantic properties of the information to be conveyed. We extend the scope of these studies by focusing, experimentally and computationally, on the interpretation of silent gesture. We show cross-linguistic experimental evidence that people use variability in constituent order as a cue to obtain different interpretations. To illuminate the computational principles that govern interpretation of non-conventional communication, we derive a Bayesian model of interpretation via biased inductive inference and estimate these biases from the experimental data. Our analyses suggest people's interpretations balance the ambiguity that is characteristic of emerging language systems, with ordering preferences that are skewed and asymmetric, but defeasible.
Canada's multi-agency situational awareness system - Keeping it simple

The Canadian Multi-Agency Situational Awareness System (MASAS) is rapidly becoming Canada‟s national system for exchanging emergency management incident-relevant information amongst multiple agencies and jurisdictions. Through the use of structured information aligned with open standards, and a centrally managed open architecture, MASAS provides a trusted virtual community with the ability to seamlessly exchange emergency management information. MASAS offers an information exchange architecture that is based around a highly resilient system of data aggregation hubs that are easily accessible directly or through third party commercial tools by emergency management officials at all levels, from the smallest community in the most remote areas of Canada‟s north to key federal stakeholders such as the federal Government Operations Centre or the Canadian military. This paper highlights the key design principles, experimental activities, and technology implementation strategies that are positioning MASAS as a Canadian success story in the making – from coast to coast to coast.
Straight to the information I need: Assessing collational interfaces for emergency response

Collational interfaces gather information from a range of sources and present them to users. Information overload is tackled by processing information in the back-end and providing interactive means to filter and browse data. Such interfaces have applications in emergency response – giving users the right information to act effectively. In this paper we explore a collational interface for emergency response, carrying out a user study that compares it to a paper based interface and one which presents data without collating it. We demonstrate that a collational interface allows users to build a picture of an emergency, but not necessarily in less time.
Evolution of Emotions and Sentiments in an Online Learning Community

We explore how the emotions and sentiments of users of Stack Overflow evolve over time based on their reputation scores. In this initial exploratory study, we compute four dimensions of emotions and sentiments: analytic, clout, authentic and tone for the question and answer posts of users in our dataset. Our results indicate that Stack Overflow users, experts, and non-experts, become more analytic and less authentic over time in both question and answer posts irrespective of their reputation score. In addition, while the clout of non-experts decreased over time in both question and answer posts, the clout of experts only decreased in their answer posts and not in their question posts. These results, though preliminary, could throw some light on the evolution of the emotional state of learners in online learning communities such as Stack Overflow.
Robust Analysis of MAP Inference in Selective Sum-Product Networks

Sum-Product Networks (SPN) are deep probabilistic models that have shown to achieve state-of-theart performance in several machine learning tasks. As with many other probabilistic models, performing Maximum-A-Posteriori (MAP) inference is NP-hard in SPNs. A notable exception is selective SPNs, that constrain the network so as to allow MAP inference to be performed in linear time. Due to the high number of parameters, SPNs learned from data can produce unreliable and overconfident inference; this phenomenon can be partially mitigated by performing a Robustness Analysis of the model predictions to changes in the parameters. In this work, we address the problem of assessing the robustness of MAP inferences produced with Selective SPNs to global perturbations of the parameters. We present efficient algorithms and an empirical analysis with realistic problems.
Robust H∞ Filtering of Nonhomogeneous Markovian Jump Delay Systems via N-Step-Ahead Lyapunov-Krasovskii Functional Approach

In this paper, we study the robust H∞ filtering problem for a class of nonhomogeneous Markovian jump delay systems (NMJDSs) with an N-step-ahead Lyapunov-Krasovskii functional (NALKF) approach. The N-step-ahead approach is utilized to reduce the conservatism of robust H∞ filtering. We aim to design filters such that, for all possible time-varying transition probabilities and all admissible parameter uncertainties and time-delays, the filtering error system is mean-square stable with a smaller estimated error and a lower dissipative level. In terms of linear matrix inequalities, sufficient conditions for the solvability of the addressed problem are developed via a moving horizon method. An illustrative example is included to elucidate advantages of the developed results and the practical potential of NALKF approach for NMJDSs.
Complexity in Forecasting and Predictive Models

The challenge of this special issue has been to know the state of the problem related to forecasting modeling and the creation of a model to forecast the future behavior that supports decision making by supporting real-world applications. This issue has been highlighted by the quality of its research work on the critical importance of advanced analytical methods, such as neural networks, soft computing, evolutionary algorithms, chaotic models, cellular automata, agent-based models, and finite mixture minimum squares (FIMIX-PLS) Mainly, all the papers are focused on triggering a substantive discussion on how the model predictions can face the challenges around the complexity field that lie ahead. These works help to better understand the new trends in computing and statistical techniques that allow us to make better forecasts. Complexity plays a prominent role in these trends, given the increasing variety and changing data flows, forcing academics to adopt innovative and hybrid methods. The papers address the recent advances in methodological issues and practices related to the complexity of forecasting and models. All of them are increasingly focused on heterogeneous sources. Among these techniques, FIMIX-PLS has been applied to study unobserved heterogeneity [1]. This technique was extended by Sarstedt et al. [2]. FIMIX-PLS is the first latent class approach for PLS-SEM [3] and is an exploration tool that allows obtaining the appropriate number of segments in which the sample will be divided. In this sense, the FIMIX-PLS technique allowed making decisions regarding the number of segments, based on pragmatic reasons and considering practical issues related to current research [4]. FIMIX-PLS calculates the probabilities of belonging to a given segment in which each observation is adjusted to the predetermined numbers of segments by means of the estimation of separate linear regression functions and the belonging of objects corresponding to several segments. Different cases are assigned to the segment with greater probability.
Fusing Unsupervised and Supervised Deep Learning for White Matter Lesion Segmentation

Unsupervised Deep Learning for Medical Image Analysis is increasingly gaining attention, since it relieves from the need for annotating training data. Recently, deep generative models and representation learning have lead to new, exciting ways for unsupervised detection and delineation of biomarkers in medical images, such as lesions in brain MR. Yet, Supervised Deep Learning methods usually still perform better in these tasks, due to an optimization for explicit objectives. We aim to combine the advantages of both worlds into a novel framework for learning from both labeled & unlabeled data, and validate our method on the challenging task of White Matter lesion segmentation in brain MR images. The proposed framework relies on modeling normality with deep representation learning for Unsupervised Anomaly Detection, which in turn provides optimization targets for training a supervised segmentation model from unlabeled data. In our experiments we successfully use the method in a Semi-supervised setting for tackling domain shift, a well known problem in MR image analysis, showing dramatically improved generalization. Additionally, our experiments reveal that in a completely Unsupervised setting, the proposed pipeline even outperforms the Deep Learning driven anomaly detection that provides the optimization targets.
Nonlinear Dynamics Characteristic of Risk Contagion in Financial Market Based on Agent Modeling and Complex Network

Risk contagion is becoming a research hotspot in the field of econophysics with the rise of interdisciplinary studies and gains more and more attention from theoretical circles and practical departments. This paper proposes a new research frame to discuss the microscopic mechanism of risk contagion based on agent modeling technology and complex network theory and reveals nonlinear dynamics characteristic of risk contagion from the perspective of market participants in financial market. Based on the proposed SICM model, financial risk can transmit from susceptible agents to infected agents, to contagious agents, or to immune agents. With the increases of contagious probabilities, the simulation experiments show that (1) the amount of susceptible agents continuously decreases; (2) the amount of infected agents increases first and then decreases; (3) the amount of contagious agents increases first and then decreases with a lower speed, relative to the amount of infected agents; and (4) the amount of immune agents continuously increases. The major contribution of this paper is a new method for studying nonlinear dynamics characteristic of risk contagion, which can be used as a theoretic basis for further researches on the behavioral features of microcosmic subject and the inner mechanisms of risk contagion.
Pattern recognition in the prediction of protein structural class

Algorithms that predict the secondary structure of individual amino acids based solely on their local sequence environment obtain at best 60% to 65% accuracy. The approach presented uses more global information as input to two methodologies, a modified Euclidean statistical clustering algorithm and a three-layer backpropagation network. Input to both these methods consists of the normalized frequency of the 20 amino acids as well as the frequency of six hydrophobic amino acid patterns. The average predictive accuracy for all test set proteins using the Euclidean statistic was 74.0%. The backpropagation network correctly predicted 76.2% of the test proteins. These results show that there exist patterns in the protein primary sequence that are useful for the prediction of protein structural class by certain statistical clustering algorithms and neural networks. <<ETX>>
Intelligent Turning Tool Monitoring with Neural Network Adaptive Learning

Tool state monitoring is a key technology in intelligent manufacturing. But it is still in a research stage and lacks general adaptability for different machining conditions. To overcome this limitation, this work systematically investigates an intelligent, real-time, and visible tool state monitoring through adopting integrated theories and technologies, i.e., (a) through distinctively designed experimental technique with comprehensive consideration of cutting parameters and tool wear values as variables, (b) through bisensor fusion for simultaneous measurements of low and high frequency signals, (c) through multitheory fusion of wavelet decomposition and the Relief-F algorithm for performing dual feature extraction and feature dimension reduction to achieve more accurate state identification using neural network, and (d) through an innovative programming technique of MATLAB-nested labVIEW. This tool monitoring system has neural network adaptive learning ability with the change of the experimental sample signals which are collected simultaneously by selected vibration and acoustic emission (AE) sensors in all factors turning experiments. Based on LabVIEW and MATLAB hybrid programming, the waveforms of signals are observed and the significant characteristics of signals are extracted through the time-frequency domain analysis and then the calculation of the energy proportion of each frequency band obtained using 4 levels of wavelet packet decompositions of the vibration signal as well as 8 levels of wavelet multiresolution decompositions of the AE signal; the ensuing Relief-F algorithm is used to further reextract the features that are most relevant to the tool state as input of neural network pattern recognition. Through the BP neural network adaptive learning, tool state recognition model is finally established. The results show that the correct recognition rate of BP network model after samples training is 92.59%, which can more accurately and intelligently monitor the tool wear state.
Robust Causal Domain Adaptation in a Simple Diagnostic Setting

Causal domain adaptation approaches aim to find statistical relations in a source domain, that will still hold in a target domain, using the assumption that a common causal graph underlies both domains. For many such problems, the available information is insufficient to uniquely identify the target domain distribution, and we find a set of distributions instead. We propose to use a worst-case approach, picking an action that performs well against all distributions in this set. In this paper, we study a specific diagnostic instance of this problem, and find a sufficient and necessary condition that characterizes the worst-case distribution in the target domain. We find that the Brier and logarithmic scores lead to different distributions, and consequently to different recommendations for the decision maker.
Possibility Measures for Valid Statistical Inference Based on Censored Data

Inferential challenges that arise when data are corrupted by censoring have been extensively studied under the classical frameworks. In this paper, we provide an alternative approach based on a generalized inferential model whose output is a data-dependent possibility distribution. This construction is driven by an association between the censored data, parameter of interest, and unobserved auxiliary variable that takes the form of a relative likelihood. The possibility distribution then emerges from the introduction of a nested random set designed to predict that unobserved auxiliary variable and is calibrated to achieve certain frequentist guarantees. The performance of the proposed method is investigated using real and simulated data.
IDDQ test results on a digital CMOS ASIC

The test program of a digital CMOS ASIC (application-specific integrated circuit) was instrumented for IDDQ current measurement. The design test included a low-fault-coverage functional set of vectors as well as high-fault-coverage scan set of vectors. Analysis of current distributions of parts failing and passing vector sets provides insight into the potential of static current testing. Many issues remain, but the data suggest that real quality improvements can be obtained from implementing static current testing on a small subset of device vectors.