Miracle-dz/UniFAR-FTUs huggingface dataset:
Interoperability for information systems in public urban transport security: The SECUR-ED interoperability notation

In public transport and at large urban hubs, such as metro or train stations, transport operators and first responders collaborate in the prevention of and reaction to security issues. Within the EU demonstration project SECUR-ED a specific notation for interoperability of information systems in the domain of public transport security was developed. (In this context, the interoperability of actual operating systems is not the focus.) Based on UML (Unified Modelling Language), the notation language offers the possibility for structured modelling of system-of-systems architectures. Four interoperability object templates and their interdependencies form the underlying basis. Domain-specific annotation rules and guidelines for interoperability objects and their sub-component structures allow collaboration and interpretation of this model on various granularities and stages during a systems engineering process.
Unsupervised Lesion Detection via Image Restoration with a Normative Prior

Unsupervised lesion detection is a challenging problem that requires accurately estimating normative distributions of healthy anatomy and detecting lesions as outliers without training examples. Recently, this problem has received increased attention from the research community following the advances in unsupervised learning with deep learning. Such advances allow the estimation of high-dimensional distributions, such as normative distributions, with higher accuracy than previous methods. The main approach of the recently proposed methods is to learn a latent-variable model parameterized with networks to approximate the normative distribution using example images showing healthy anatomy, perform prior-projection, i.e. reconstruct the image with lesions using the latent-variable model, and determine lesions based on the differences between the reconstructed and original images. While being promising, the prior-projection step often leads to a large number of false positives. In this work, we approach unsupervised lesion detection as an image restoration problem and propose a probabilistic model that uses a network-based prior as the normative distribution and detect lesions pixel-wise using MAP estimation. The probabilistic model punishes large deviations between restored and original images, reducing false positives in pixel-wise detections. Experiments with gliomas and stroke lesions in brain MRI using publicly available datasets show that the proposed approach outperforms the state-of-the-art unsupervised methods by a substantial margin, +0.13 (AUC), for both glioma and stroke detection. Extensive model analysis confirms the effectiveness of MAP-based image restoration.
The Joy of Probabilistic Answer Set Programming

Probabilistic answer set programming (PASP) combines rules, facts, and independent probabilistic facts. Often one restricts such programs so that every query yields a sharp probability value. The purpose of this paper is to argue that a very useful modeling language is obtained by adopting a particular credal semantics for PASP, where one associates with each consistent program a credal set. We examine the basic properties of PASP and present an algorithm to compute (upper) probabilities given a program.
Learning with Multitask Adversaries using Weakly Labelled Data for Semantic Segmentation in Retinal Images

A prime challenge in building data driven inference models is the unavailability of statistically significant amount of labelled data. Since datasets are typically designed for a specific purpose, and accordingly are weakly labelled for only a single class instead of being exhaustively annotated. Despite there being multiple datasets which cumulatively represents a large corpus, their weak labelling poses challenge for direct use. In case of retinal images, they have inspired development of data driven learning based algorithms for segmenting anatomical landmarks like vessels and optic disc as well as pathologies like microaneurysms, hemorrhages, hard exudates and soft exudates. The aspiration is to learn to segment all such classes using only a single fully convolutional neural network (FCN), while the challenge being that there is no single training dataset with all classes annotated. We solve this problem by training a single network using separate weakly labelled datasets. Essentially we use an adversarial learning approach in addition to the classically employed objective of distortion loss minimization for semantic segmentation using FCN, where the objectives of discriminators are to learn to (a) predict which of the classes are actually present in the input fundus image, and (b) distinguish between manual annotations vs. segmented results for each of the classes. The first discriminator works to enforce the network to segment those classes which are present in the fundus image although may not have been annotated i.e. all retinal images have vessels while pathology datasets may not have annotated them in the dataset. The second discriminator contributes to making the segmentation result as realistic as possible. We experimentally demonstrate using weakly labelled datasets of DRIVE containing only annotations of vessels and IDRiD containing annotations for lesions and optic disc. Our method using a single FCN achieves competitive results over prior art for either vessel or optic disk or pathology segmentation on these datasets.
Prediction of Disease Progression in Multiple Sclerosis Patients using Deep Learning Analysis of MRI Data

We present the first automatic end-to-end deep learning framework for the prediction of future patient disability progression (one year from baseline) based on multi-modal brain Magnetic Resonance Images (MRI) of patients with Multiple Sclerosis (MS). The model uses parallel convolutional pathways, an idea introduced by the popular Inception net (Szegedy et al., 2015) and is trained and tested on two large proprietary, multi-scanner, multi-center, clinical trial datasets of patients with Relapsing-Remitting Multiple Sclerosis (RRMS). Experiments on 465 patients on the placebo arms of the trials indicate that the model can accurately predict future disease progression, measured by a sustained increase in the extended disability status scale (EDSS) score over time. Using only the multi-modal MRI provided at baseline, the model achieves an AUC of 0.66±0.055. However, when supplemental lesion label masks are provided as inputs as well, the AUC increases to 0.701± 0.027. Furthermore, we demonstrate that uncertainty estimates based on Monte Carlo dropout sample variance correlate with errors made by the model. Clinicians provided with the predictions computed by the model can therefore use the associated uncertainty estimates to assess which scans require further examination.
Badge-primed decision making

We have been investigating new decision support methods for emergency responders. Most recently, we have added to our decision support prototype the concept of “badges”: symbols that cue decision makers to the top-ranked option(s) that are the recommended alternatives for a particular decision. This paper provides the rationale for badges, a description of the initial implementation, results from our first experiment with badges, and a discussion of the next steps. As a report on work-in-progress, the primary contribution of this paper is the description of the concept of badges and its proposed use for emergency response decision making
Theoretical Model of the Axial Residual Velocity of PELE Projectiles Penetrating Thin Metal Targets

With the increase of battlefield target diversity and protection mobility, the disadvantages of traditional armor piercing warheads have gradually become prominent. The conception of the PELE (penetration with enhanced lateral efficiency) projectile was thus proposed. The axial residual velocity of the projectile is a very important indicator of a PELE projectile, which mainly reflects the penetration ability of the PELE projectile. The PELE projectile is a symmetrical structure, so the collision problem can be simplified to plane collision. Furthermore, the two-dimensional plane is axisymmetric, and so it can be further simplified to one-dimensional collision. Based on simplification and assumptions, the mechanism of a PELE projectile penetrating a thin metal target plate was studied using the shock wave theory, and a theoretical model of axial residual velocity has been established in this article. The energy loss during the penetration process was divided into the following parts: the kinetic energy increment of the target plug in the impact region, the internal energy increment of the outer casing and inner core, and the shear energy dissipation of the projectile against the target plate. In addition, the specific methods of determining the energy loss of each part are given in detail. According to the conservation of energy, the approximate calculation formulae of the axial residual velocity of a PELE projectile have been deduced. Finally, the theoretical results were compared with the experimental results under different working conditions, and the results were in good agreement. Therefore, the theoretical model has application value and guiding significance in the field of engineering.
Time-Fractional Heat Conduction in Two Joint Half-Planes

The heat conduction equations with Caputo fractional derivative are considered in two joint half-planes under the conditions of perfect thermal contact. The fundamental solution to the Cauchy problem as well as the fundamental solution to the source problem are examined. The Fourier and Laplace transforms are employed. The Fourier transforms are inverted analytically, whereas the Laplace transform is inverted numerically using the Gaver–Stehfest method. We give a graphical representation of the numerical results.
Learning joint lesion and tissue segmentation from task-specific hetero-modal datasets

Brain tissue segmentation from multimodal MRI is a key building block of many neuroscience analysis pipelines. It could also play an important role in many clinical imaging scenarios. Established tissue segmentation approaches have however not been developed to cope with large anatomical changes resulting from pathology. The effect of the presence of brain lesions, for example, on their performance is thus currently uncontrolled and practically unpredictable. Contrastingly, with the advent of deep neural networks (DNNs), segmentation of brain lesions has matured significantly and is achieving performance levels making it of interest for clinical use. However, few existing approaches allow for jointly segmenting normal tissue and brain lesions. Developing a DNN for such joint task is currently hampered by the fact that annotated datasets typically address only one specific task and rely on a task-specific hetero-modal imaging protocol. In this work, we propose a novel approach to build a joint tissue and lesion segmentation model from task-specific hetero-modal and partially annotated datasets. Starting from a variational formulation of the joint problem, we show how the expected risk can be decomposed and optimised empirically. We exploit an upper-bound of the risk to deal with missing imaging modalities. For each task, our approach reaches comparable performance than task-specific and fully-supervised models.
A Critic for API Client Code using Symbolic Execution

It is well-known that APIs can be hard to learn and use. To cope with difficulties in using APIs, programmers browse the Internet for code samples, tutorials, and API documentation. In general, it is time-consuming to find relevant help from the plethora of information on the web. While search tools may help programmers find code snippets, novices often have difficulty in formulating a useful search query and in assessing the quality and relevancy of the search results. To help address the broad problems of finding, understanding, and debugging API-based solutions, this thesis presents a new kind of critic system called CriticAL (A Critic for APIs and Libraries) that offers recommendations, explanations, and criticisms for API client code. CriticAL takes API usage rules as input, performs symbolic execution to check that the client code has followed these rules properly, and generates advice as output to help improve the client code. While several past studies have focused on answering why frameworks are hard to learn and use, very few have provided systematic data that can be used directly in building such a tool. We conduct a manual case study of 150 discussion threads from the Java Swing forum and derive a set of framework rules that can be directly used in API tooling efforts of CriticAL. We show that there are recurring patterns in problems the programmers face while using the Swing framework that can be supported through CriticAL. We were able to capture the nature of the recurring patterns precisely as API usage rules. We demonstrate the usefulness of CriticAL by applying it to real-world examples derived from the Java Swing Forum and through a formative user case study.
Aggregating Belief Models

This paper has two goals. The first goal is to say something about how one might combine different agents' imprecise probabilities to generate an aggregate imprecise probability. The second goal is to champion the very general theory of "belief models'' which, I think, deserves more attention. The belief models framework is interesting partly because many other formal models of reasoning appear as special cases of belief models (for example, propositional logic, ranking functions, imprecise probability).
Proceedings of the 4th ACM RecSys workshop on Recommender systems and the social web

The new opportunities for applying recommendation techniques within Social Web platforms and applications as well as the various new sources of information which have become available in the Web 2.0 and can be incorporated in future recommender applications are a strong driving factor in current recommender system research for various reasons: (1) Social systems by their definition encourage interaction between users and both online content and other users, thus generating new sources of knowledge for recommender systems. Web 2.0 users explicitly provide personal information and implicitly express preferences through their interactions with others and the system (e.g. commenting, friending, rating, etc.). These various new sources of knowledge can be leveraged to improve recommendation techniques and develop new strategies which focus on social recommendation. (2) New application areas for recommender systems emerge with the popularity of the Social Web. Recommenders cannot only be used to sort and filter Web 2.0 and social network information, they can also support users in the information sharing process, e.g., by recommending suitable tags during folksonomy development. (3) Recommender technology can assist Social Web systems through increasing adoption and participation and sustaining membership. Through targeted and timely intervention which stimulates traffic and interaction, recommender technology can play its role in sustaining the success of the Social Web. (4) The Social Web also presents new challenges for recommender systems, such as the complicated nature of human-to-human interaction which comes into play when recommending people and can require more interactive and richer recommender systems user interfaces. The technical papers appearing in these proceedings aim to explore and understand challenges and new opportunities for recommender systems in the Social Web and were selected in a formal review process by an international program committee. Overall, we received 13 paper submissions from 12 different countries, out of which 7 long papers and 1 short paper were selected for presentation and inclusion in the proceedings. The submitted papers addressed a variety of topics related to Social Web recommender systems from the use of microblogging data for personalization over new tag recommendation approaches to social media-based personalization of news.
Asset distribution with a multitouch table

We examined to what extent a MultiTouch Table (MTT) can support a collaborative Operational Planning Asset Distribution task as compared to traditional Spreadsheet methods. Participants were given different and complementary specialist roles and were then asked to distribute different sets of assets over an area of Operations with multiple known and unknown ‘needs’. Additionally, participants had to satisfy a series of real time intelligence recommendations regarding potential needs. Of interest were subjective usability ratings and objective performance measures in terms of need fulfillment and satisfying intelligence recommendations. We found that on all but one usability measure participants rated the MTT more positively than the Spreadsheets. There was also a non-significant trend for greater needs fulfillment and resolving intelligence recommendations using the MTT than the spreadsheets. On the basis of the results we suggest that MTT technology offers a viable tool for supporting collaborative Asset Distribution tasks in general.
CMAC neural network as an SVM with B-spline kernel functions

In measurement systems, there is an often need Cerebellar Model Articulation Controller (CMAC) neural network has some attractive features. The most important ones are its extremely fast learning capability and the special architecture that lets effective digital hardware implantation possible. All these properties may be important in measurement systems, e.g. in system modeling or in complex sensor's system, etc. Although the CMAC architecture was proposed in the middle of the seventies, several open questions have been left even for today. Among them the most important ones are about its modeling and generalization capabilities. The limits of fits modeling capability were addressed in the literature and recently a detailed analysis of its generalization properties was given. The results show that there are significant differences between the one-dimensional and the multidimensional versions of CMAC. The modeling capability of a multidimensional network is inferior to that of the one-dimensional one. This paper discusses the reasons of this difference and suggests a new interpretation of CMAC. This interpretation is based on kernel machines. The paper shows that a one-dimensional binary CMAC can be considered as a kernel machine with linear B-spline kernel function. However, this close relation cannot be found in multidimensional case. The paper proposes a version of multidimensional CMAC, which - similarly to the one-dimensional case - can be considered as a kernel machine. The paper introduces this interpretation and details its most important advantages.
Adaptive Blind Source Separation in Multi-Channel Systems

The purpose of Blind Source Separation (BSS) is to recover a set of latent independent source signals from observable signals that are generated in a mixing process as superpositions of these very source signals. For this task, only the mixtures are available, whereas both the statistical properties of the original source signals and the details of the mixing process are unknown. The field of application of BSS includes not only tasks in the area of biomedical sciences, computer vision, and telecommunications (blind channel equalization), but also the separation of acoustic signals. This diploma thesis starts with discussing the information-theoretic concepts, the methods for parameter estimation, and some issues from nonlinear optimization theory that are needed in BSS. In this context, the statistical model of Independent Component Analysis (ICA) is introduced as a powerful tool for performing BSS, relying solely on the statistical independence of the source signals. Based on several different approaches (maximization of non-Gaussianity, maximum likelihood estimation, minimization of mutual information, diagonalization of the cumulant tensor), various implementable algorithms for adaptively estimating the ICA model are derived, compared and contrasted.
Inflection point in glioma growth and angiogenesis driven by potassium channels

Background and Aim: The overexpression and alternative splicing of calcium-activated potassium channel subunit alpha-1 (KCNMA1) that encodes large-conductance calcium-activated voltage-sensitive potassium (BKCa) channels are implicated in the development of human cancers. Dysfunctional angiogenesis in hypoxic tumors is a challenge to intravenous anticancer drug treatments. Hypoxic factors also lead to abnormal vascular functions posing hurdle for anticancer drug delivery to tumors. The aim of this study was to explore the role of BKCachannels in tumor angiogenesis, specifically with regard to release of vascular endothelial growth factor (VEGF). Materials and Methods: We subjected the glioma cells under hypoxia and normoxia and studied the expression and activity of BKCachannels in in vitro and in vivo tumor models. Then, we studied the proangiogenic factor, VEGF, in tumors and monitored the neoangiogenic process. The study protocol was approved by the Institutional Animal Care and Use Committee, Mercer University, Atlanta, GA, USA (approved No. A0706007_01) on July 20, 2007. Results: We presented in vivo and cell based in vitro experimental evidence on the direct and indirect interactions of BKCachannels with VEGF signaling. There was evidence that under hypoxia, glioma cells overexpressed KCNMA1 and increased VEGF secretion. By inhibiting KCNMA1, we showed that VEGF secretion was significantly reduced, thus potentially controlling angiogenesis, which has implications for vascular permeability and anticancer drug delivery. Moreover, there were differences in alternate splicing of KCNMA1 between normal and malignant cells under hypoxia and normoxia. Conclusion: We conclude that BKCachannels regulate hypoxia-induced angiogenesis. Therefore, serious effort is needed to better understand the molecular mechanisms of BKCachannelopathies triggering angiogenesis and progression of glioma. The modulators of BKCachannels could be viable in new anticancer therapeutics.
Adaptive Tradeoff Explanations in Conversational

completeness and certainty of a user's preferences may vary during her preference construction process in a conversational recommender. In order to more effectively support users to uncover their hidden criteria and/or solve preference conflicts, we propose to generate adaptive tradeoff explanations in organization-based recommender interfaces, to be conditional on the user's contextual needs. An experiment shows the adaptive element's higher potential to improve recommendation efficiency, relative to methods without this feature.
Texture synthesis using asymmetric 2-D noncausal AR models

The author investigates the suitability of two-dimensional (2-D), noncausal, autoregressive (AR) models with possibly asymmetric support for synthesis of images visually similar to natural textures. These models characterize the gray level at an image pixel as a linear combination of gray levels at nearby locations in all directions and an additive non-Gaussian, higher-order white noise variable. Existing results based upon the second-order statistics of the images assume that the model support is symmetric, whereas the author exploits higher-order statistics of the image to fit AR models with possibly asymmetric support. Experimental results of synthesis of 128*128 textures visually resembling several real life textures in the Brodatz album (and other sources) are presented. The synthetic textures are generated using models obtained from real images via inverse filter criteria. <<ETX>>