Miracle-dz/UniFAR-FTUs huggingface dataset:
Perturbation of CTMC Trapping Probabilities with Application to Model Repair

This paper studies properties of continuous-time Markov chains with one class of transient states and at least two absorbing states. We look at a perturbation of the chain that arises by uniformly decreasing all rates to absorption. For this situation, the monotonicity of the trapping probabilities is analysed, and their asymptotic limit is computed. The theoretical findings are then applied to a type of model repair problem, where a lower time-bounded and lower probability-bounded CSL until requirement needs to be satisfied. The paper presents an algorithm for this type of problem and proves its correctness.
Self-Learning Algorithms for the Personalised Interaction with People with Dementia

The number of people with dementia (PwD) residing in nursing homes (NH) increases rapidly. Behavioural disturbances (BDs) such as wandering and aggressions are the main reasons to hospitalise these people. Social robots could help to resolve these BDs by performing simple interactions with the patients. This paper examines whether self-learning algorithms could be designed to select the robotic interactions, preferred by the patients, during an intervention. K-armed bandit algorithms were compared in simulated environments for single and multiple patients to ﬁnd the beneﬁcial learning agents and action selection policies. The single patient tests show the advantages of selecting actions according to an Upper Conﬁdence Bound (UCB) policy, while the multi-patient tests analyse the beneﬁts of using additional, contextual information. Afterwards, the learning application was provided with a framework to operate in more realistic situations. We expect that this framework can be used for personalised interactions in many different healthcare domains.
Asymptotic Properties of Primal-Dual Algorithm for Distributed Stochastic Optimization over Random Networks with Imperfect Communications

This paper studies a distributed stochastic optimization problem over random networks with imperfect communications subject to a global constraint, which is the intersection of local constraint sets assigned to agents. The global cost function is the sum of local cost functions, each of which is the expectation of a random cost function. By incorporating the augmented Lagrange technique with the projection method, a stochastic approximation based distributed primal-dual algorithm is proposed to solve the problem. Each agent updates its estimate by using the local observations and the information derived from neighbors. For the constrained problem, the estimates are first shown to be bounded almost surely (a.s.), and then are proved to converge to the optimal solution set a.s. Furthermore, the asymptotic normality and efficiency of the algorithm are addressed for the unconstrained case. The results demonstrate the influence of random networks, communication noises, and gradient errors on the performance of the algorithm. Finally, numerical simulations demonstrate the theoretic results.
Does the rise of the Internet bring erosion of strong ties? Analyses of social media use and changes in core discussion networks

We derive hypotheses from popular accounts of how use of social media affects our strong ties. Several authors have suggested that social media use erodes our strong ties by increasing the volume of social interactions and decreasing their depth. Using two-wave panel data representative of the Dutch population between 15 and 45 years, we examine changes in the core discussion networks (CDNs) of 5312 respondents (with 10,896 relations). Contradicting an erosion of strong ties, we found positive effects of social media use on CDN size, both cross-sectionally and longitudinally. Social media use was positively related to talking to CDN members in our cross-sectional model. Finally, we found that the CDNs of frequent social media users were more dynamic than those of less frequent users: they are more likely to both lose old and gain new ties. This suggests that Internet use is associated with more, and more dynamic, social interaction.
Microgravity-Induced Alterations of Inflammation-Related Mechanotransduction in Endothelial Cells on Board SJ-10 Satellite

Endothelial cells (ECs) are mechanosensitive cells undergoing morphological and functional changes in space. Ground-based study has provided a body of evidences about how ECs can respond to the effect of simulated microgravity, however, these results need to be confirmed by spaceflight experiments in real microgravity. In this work, we cultured EA.hy926 ECs on board the SJ-10 Recoverable Scientific Satellite for 3 and 10 days, and analyzed the effects of space microgravity on the ECs. Space microgravity suppressed the glucose metabolism, modulated the expression of cellular adhesive molecules such as ICAM-1, VCAM-1, and CD44, and depressed the pro-angiogenesis and pro-inflammation cytokine secretion. Meanwhile, it also induced the depolymerization of actin filaments and microtubules, promoted the vimentin accumulation, restrained the collagen I and fibronectin deposition, regulated the mechanotransduction through focal adhesion kinase and Rho GTPases, and enhanced the exosome-mediated mRNA transfer. Unlike the effect of simulated microgravity, neither three-dimensional growth nor enhanced nitric oxide production was observed in our experimental settings. This work furthers the understandings in the effects and mechanisms of space microgravity on ECs, and provides useful information for future spaceflight experimental design.
Privacy Protection of IoT Based on Fully Homomorphic Encryption

With the rapid development of Internet of Things (IoT), grave questions of privacy protection are raised. This greatly impacts the large-scale applications of IoT. Fully homomorphic encryption (FHE) can provide privacy protection for IoT. But, its efficiency needs to be greatly improved. Nowadays, Gentry’s bootstrapping technique is still the only known method of obtaining a “pure” FHE scheme. And it is also the key for the low efficiency of FHE scheme due to the complexity homomorphic decryption. In this paper, the bootstrapping technique of Halevi and Shoup (EUROCRYPT 15) is improved. Firstly, by introducing a definition of “load capacity”, we optimize the parameter range for which their bootstrapping technique works. Next we generalize their ciphertext modulus from closing to a power of two to more general situations. This enables the method to be applied in a larger number of situations. Moreover, this paper also shows how to introduce SIMD homomorphic computation techniques into the new method, to improve the efficiency of recryption.
Approximating the Nash Social Welfare with Indivisible Items

We study the problem of allocating a set of indivisible items among agents with additive valuations, with the goal of maximizing the geometric mean of the agents' valuations, i.e., the Nash social welfare. This problem is known to be NP-hard, and our main result is the first efficient constant-factor approximation algorithm for this objective. We first observe that the integrality gap of the natural fractional relaxation is exponential, so we propose a different fractional allocation which implies a tighter upper bound and, after appropriate rounding, yields a good integral allocation. An interesting contribution of this work is the fractional allocation that we use. The relaxation of our problem can be solved efficiently using the Eisenberg-Gale program, whose optimal solution can be interpreted as a market equilibrium with the dual variables playing the role of item prices. Using this market-based interpretation, we define an alternative equilibrium allocation where the amount of spending that can go into any given item is bounded, thus keeping the highly priced items under-allocated, and forcing the agents to spend on lower priced items. The resulting equilibrium prices reveal more information regarding how to assign items so as to obtain a good integral allocation.
A Posteriori Stopping Criteria for Optimized Schwarz Domain Decomposition Algorithms in Mixed Formulations

Abstract This paper develops a posteriori estimates for domain decomposition methods with optimized Robin transmission conditions on the interface between subdomains. We choose to demonstrate the methodology for mixed formulations, with a lowest-order Raviart–Thomas–Nédélec discretization, often used for heterogeneous and anisotropic porous media diffusion problems. Our estimators allow to distinguish the spatial discretization and the domain decomposition error components. We propose an adaptive domain decomposition algorithm wherein the iterations are stopped when the domain decomposition error does not affect significantly the overall error. Two main goals are thus achieved. First, a guaranteed bound on the overall error is obtained at each step of the domain decomposition algorithm. Second, important savings in terms of the number of domain decomposition iterations can be realized. Numerical experiments illustrate the efficiency of our estimates and the performance of the adaptive stopping criteria.
Regional gastrointestinal pH profile is altered in patients with type 1 diabetes and peripheral neuropathy

Gastrointestinal (GI) symptoms, such as nausea and bloating, are common in people with type 1 diabetes (T1DM). Autonomic dysfunction can lead to changes in the GI secreto‐motor function which can be associated with GI symptom development. We hypothesized that regional pH profiles in T1DM differs from health and would be associated with objective physiological/clinical markers.
Modeling Deliberative Argumentation Strategies on Wikipedia

This paper studies how the argumentation strategies of participants in deliberative discussions can be supported computationally. Our ultimate goal is to predict the best next deliberative move of each participant. In this paper, we present a model for deliberative discussions and we illustrate its operationalization. Previous models have been built manually based on a small set of discussions, resulting in a level of abstraction that is not suitable for move recommendation. In contrast, we derive our model statistically from several types of metadata that can be used for move description. Applied to six million discussions from Wikipedia talk pages, our approach results in a model with 13 categories along three dimensions: discourse acts, argumentative relations, and frames. On this basis, we automatically generate a corpus with about 200,000 turns, labeled for the 13 categories. We then operationalize the model with three supervised classifiers and provide evidence that the proposed categories can be predicted.
Plural dominance and the production of determiner-noun phrases in French

In two experiments, we examined the functional locus of plural dominance in the French spoken word production system, where singulars and plurals share the same phonological word form. The materials included singular-dominant (singular more frequent than plural) and plural-dominant nouns (plural more frequent than singular). In Experiment 1, participants were instructed to produce determiner-noun phrases in response to singular and plural depictions of objects. In contrast to the dominance-by-number interaction that is typically observed in English, Dutch and German, the French picture-naming data revealed a main effect of number, but no effect of plural dominance. When participants were instructed to produce determiner-noun phrases in a reading aloud task (Experiment 2), where number is orthographically marked, a number-by-dominance interaction emerged. Our data suggest that plural dominance is encoded at the word form level within the context of recent theories of spoken word production.
Will it Blend? Blending Weak and Strong Labeled Data in a Neural Network for Argumentation Mining

The process of obtaining high quality labeled data for natural language understanding tasks is often slow, error-prone, complicated and expensive. With the vast usage of neural networks, this issue becomes more notorious since these networks require a large amount of labeled data to produce satisfactory results. We propose a methodology to blend high quality but scarce strong labeled data with noisy but abundant weak labeled data during the training of neural networks. Experiments in the context of topic-dependent evidence detection with two forms of weak labeled data show the advantages of the blending scheme. In addition, we provide a manually annotated data set for the task of topic-dependent evidence detection. We believe that blending weak and strong labeled data is a general notion that may be applicable to many language understanding tasks, and can especially assist researchers who wish to train a network but have a small amount of high quality labeled data for their task of interest.
Focus on the mechatronics design of a new dexterous robotic hand for inside hand manipulation

SUMMARY This paper presents a novel tendon-driven bio-inspired robotic hand design for in-hand manipulation. Many dexterous robot hands are able to produce adaptive grasping, but only a few human-sized hands worldwide are able to produce fine motions of the object in the hand. One of the challenges for the near future is to develop human-sized robot hands with human dexterity. Most of the existing hands considered in the literature suffer from dry friction which creates unwanted backlash and non-linearities. These problems limit the accurate control of the fingers and the capabilities of the hand. Such was the case with our first fully actuated dexterous robot hand: the Laboratoire de Mécanique des Solides (LMS) hand. The mechanical design of the hand relies on a tendon-based transmission system. Developing a fully actuated dexterous robot hand requires the routing of the tendons through the finger for the actuation of each joint. This paper focuses on the evolution of the tendon routing; from the LMS hand to the new RoBioSS dexterous hand. The motion transmission in the new design creates purely linear coupling relations between joints and actuators. Experimental results using the same protocol for the previous hand and the new hand illustrate the evolution in the quality of the mechanical design. With the improvements of the mechanical behavior of the robotic fingers, the hand control software could be extensively simplified. The choice of a common architecture for all fingers makes it possible to consider the hand as a collaboration of four serial robots. Moreover, with the transparency of the motor-joint transmissions, we could use robust, industrial-grade cascaded feedback loops for the axis controls. An inside-hand manipulation task concerning the manipulation of a bottle cap is presented at the end of the paper. As proof of the robustness of the hand, demonstrations of the hand's capabilities were carried out continuously over three days at SPS IPC Drives international exhibition in Nuremberg, in November 2016.
Non-linear velocity observer for vehicles with tyre–road friction estimation

ABSTRACT A non-linear observer is proposed for the estimation of the longitudinal and lateral velocities of automotive vehicles due to highly non-linear friction. To take the unknown time-varying road conditions into account, a real-time tyre–road friction estimation algorithm is provided for adaptation to changes of different road adhesion characteristics. Besides, the coupling effects of the longitudinal and lateral forces are fully exploited to design the non-linear observer based on the longitudinal and lateral acceleration measurements. The observer is computationally efficient and is based on a standard sensor configuration commonly available in modern cars. The uniform global asymptotical stability of the adaptive observer is guaranteed under a certain persistency-of-excitation condition. Moreover, a stronger local stability result can also be obtained, i.e. uniform local exponential stability. The performance of the observer is compared with that of existing approaches under different manoeuvres and road conditions.
NEWS 2018 Whitepaper

Transliteration is defined as phonetic translation of names across languages. Transliteration of Named Entities (NEs) is necessary in many applications, such as machine translation, corpus alignment, cross-language IR, information extraction and automatic lexicon acquisition. All such systems call for high-performance transliteration, which is the focus of shared task in the NEWS 2018 workshop. The objective of the shared task is to promote machine transliteration research by providing a common benchmarking platform for the community to evaluate the state-of-the-art technologies.
Construction d'ontologie pour le domaine du sourcing

Ces dernieres annees, de nombreuses entreprises s'orientent vers l'integration du developpement d'ontologies au sein de leurs processus pour mieux organiser les connaissances mises en jeu et ameliorer les performances de leur traitement automatique. Dans cet article, nous decrivons notre travail de modelisation d'ontologie dans le domaine du sourcing, dans le but de decrire le contenu semantique des offres et demandes de prestations. Nous nous focalisons sur la representation des competences et des domaines de competences, dans le but de raisonner sur ces connaissances pour ameliorer la recommandation des prestataires. Notre strategie de developpement d'ontologie repose sur (i) la reutilisation de referentiels existants pour representer les competences et domaines de competences, (ii) la construction de referentiels internes et (iii) un travail d'alignement d'ontologies.
Botnet Forensic: Issues, Challenges and Good Practices

Unethical hacking of sites, probing, click frauds, phishing, denial of services attack and many such malicious practices affects the organizational integrity and sovereignty. Such activities are direct attacks on the safety, security and confidentiality of the organization. These activities put organizational privacy at stake. Botnet forensic is utilized to strengthen the security issues by understanding the modus operandi of the attacks. The available observations can be utilized in future also to prevent a potential threat to network security. This paper enlightens the novel summary of previous survey including life cycle, classification, framework, detection, analysis and the challenges for botnet forensics.
Sensor Deployment Recommendation for 3D Fine-Grained Air Quality Monitoring Using Semi-Supervised Learning

Driven by the increasingly serious air pollution problem, the monitoring of fine-grained air quality index (AQI) in urban areas has drawn considerable attention. In this paper, we design a novel algorithm to recommend the placement of sensors for energy- efficient AQI monitoring in urban three-dimensional (3D) space. Specifically, we first propose an entropy- based semi-supervised learning (ESSL) model to estimate the AQI distribution of unobserved locations, using the sparse historical spatial-temporal data and other features, including 3D coordinates, wind speed and weather conditions. Based on ESSL, we then design an entropy minimization ranking (EMR) algorithm to recommend the best sensor locations for AQI monitoring. Through the emulation on a fine-grained AQI dataset, the results demonstrate our scheme can provide energy- efficient solutions by using the least number of sensors to achieve higher accuracy than other existing approaches.
A retrospective survey on herpes zoster disease burden and characteristics in Beijing, China

ABSTRACT Little data are available on the overall incidence and characteristics of herpes zoster (HZ) in China, thus a community-based retrospective survey was conducted during December 2012 to March 2013 to collect HZ incident data over the past year in selected districts of Beijing, China. A total of 237 incident HZ cases were identified from 118,220 residents and majority of HZ patients (219/237, 92.4%) sought healthcare. Annual HZ incident rates were 1.90/1000 after adjusted and higher among females (2.4/1000) than males (1.7/1000). HZ rates increased with age and with a sharply rise among those aged ≥50 years particular for female. 217 cases were available for interview with 193 (88.9%) primary, 24 (11.1%) recurrent and 10 (4.6%) postherpetic neuralgia (PHN) cases respectively. No difference was shown between the primary and recurrent HZ cases in sex (P = 0.42), age (P = 0.72), times of healthcare seeking (P = 0.45), number of dermatome (P = 0.72), pain degree (P = 0.54), and hospitalization (P = 1.0). According to the survey, it can be speculated that disease burden of HZ is serious and caused at least 2.77 million cases annually in China. The recurrent HZ cases has similar characteristics to primary HZ cases. High proportion of healthcare seeking reminds that HIS records would be a feasible source for HZ surveillance to evaluate the trends and changes in China.
Do all Tournaments Admit Irrelevant Matches?

We consider tournaments played by a set of agents in order to establish a ranking among them. We introduce the notion of irrelevant match, as a match that does not influence the ultimate ranking of the involved parties. After discussing the basic properties of this notion, we seek out tournaments that have no irrelevant matches, focusing on the class of tournaments where each agent challenges each other exactly once. We prove that tournaments with a static schedule and at least 5 agents always include irrelevant matches. Conversely, dynamic schedules can be devised in ways that avoid irrelevant matches, at least for one of the involved agents.
A Bootstrapping approach for Entity Linking from Biomedical Literature

Aim: Entity linking (EL) is a task of aligning literal of a named-entity from an unstructured document to appropriate entities in a knowledge base. The main objective of EL in biomedical domain stems on the construction of efficient computational models. Methodology: The development corpus is a subset of PubMed and Medline abstracts dealing with Huntington disease and its genes. It was annotated with disease and gene relations, based on “etiology” and “clinical biomarker. ” The input corpus consists of text related to Huntington disease, gene names with their functions and all words related to neurogenetic disorders. The input corpus which is manually curated has 8998 sentences and 140,481 words. Results and Discussion: A bootstrap approach based on uniformity perception and similarity computation to link entities from unstructured biomedical texts to ontologies. A rich semantic information and structures in ontologies are influenced by the proposed approach for similarity computation and entity ranking. Conclusion: The proposed approach addresses the EL in the biomedical domain. The experiments show that our approach outperforms the existing state-of-the-art algorithms in terms of linkage accuracy.
Leveraging distributed representations and lexico-syntactic fixedness for token-level prediction of the idiomaticity of English verb-noun combinations

Verb-noun combinations (VNCs) - e.g., blow the whistle, hit the roof, and see stars - are a common type of English idiom that are ambiguous with literal usages. In this paper we propose and evaluate models for classifying VNC usages as idiomatic or literal, based on a variety of approaches to forming distributed representations. Our results show that a model based on averaging word embeddings performs on par with, or better than, a previously-proposed approach based on skip-thoughts. Idiomatic usages of VNCs are known to exhibit lexico-syntactic fixedness. We further incorporate this information into our models, demonstrating that this rich linguistic knowledge is complementary to the information carried by distributed representations.