Miracle-dz/UniFAR-FTUs huggingface dataset:
From NL Preference Expressions to Comparative Preference Statements: A Preliminary Study in Eliciting Preferences for Customised Decision Support

Intelligent 'services' are increasingly used on e-commerce platforms to provide assistance to customers. Numerous preference elicitation methods developed in the literature are now employed for this purpose. However, it is commonly known that there is a real bottleneck in preference handling as concerns the elicitation of preferences because it does not cater to the wide range of preference representation languages available. Thus, as a first step in developing a decision-support tool using an AI based on such languages, this paper describes a preliminary study conducted to address this issue. We propose a method of eliciting real-time user preferences expressed in natural language (NL) which can be formally represented using comparative preference statements complying with different semantics, and provide a proof of concept to demonstrate its feasibility. Since we develop NL resources to detect preference semantics, we also make a comparative study with existing resources to underline the peculiarities of our model.
Interaction of fluids with deformable solids

In this paper, we present a method for simulating the interaction of fluids with deformable solids. The method is designed for the use in interactive systems such as virtual surgery simulators where the real‐time interplay of liquids and surrounding tissue is important. In computer graphics, a variety of techniques have been proposed to model liquids and deformable objects at interactive rates. As important as the plausible animation of these substances is the fast and stable modeling of their interaction. The method we describe in this paper models the exchange of momentum between Lagrangian particle‐based fluid models and solids represented by polygonal meshes. To model the solid‐fluid interaction we use virtual boundary particles. They are placed on the surface of the solid objects according to Gaussian quadrature rules allowing the computation of smooth interaction potentials that yield stable simulations. We demonstrate our approach in an interactive simulation environment for fluids and deformable solids. Copyright © 2004 John Wiley & Sons, Ltd.
Polynomial Convergence of Primal-Dual Algorithms for Semidefinite Programming Based on the Monteiro and Zhang Family of Directions

This paper establishes the polynomial convergence of the class of primal-dual feasible interior-point algorithms for semidefinite programming (SDP) based on the Monteiro and Zhang family of search directions. In contrast to Monteiro and Zhang's work [Math. Programming, 81 (1998), pp. 281--299], here no condition is imposed on the scaling matrix that determines the search direction. We show that the polynomial iteration-complexity bounds of two well-known algorithms for linear programming, namely the short-step path-following algorithm of Kojima, Mizuno, and Yoshise [Math. Programming, 44 (1989), pp. 1--26] and Monteiro and Adler [Math. Programming, 44 (1989), pp. 27--41 and pp. 43--66] and the predictor-corrector algorithm of Mizuno, Todd, and Ye [Math. Oper. Res., 18 (1993), pp. 945--981] carry over into the context of SDP. Since the Monteiro and Zhang family of directions includes the Alizadeh, Haeberly, and Overton direction, we establish for the first time the polynomial convergence of algorithms based on this search direction.
Deriving a recipe similarity measure for recommending healthful meals

A recipe recommender system may stimulate healthful and varied eating, when the presented recipes fit the lifestyle of the user. As consumers face the barrier to change their eating and cooking behavior, we aim for a strategy to provide more healthful variations to routine recipes. In this paper, a similarity measure for recipes is derived by taking a user-centered approach. Such a measure can be used to recommend healthier alternatives to commonly selected meals, which are perceived to be similar. Recipes presented using this strategy may fit the demand for health and variation within the boundaries of a busy lifestyle. Having derived and evaluated a recipe similarity measure, we explore its use through an at-home trial.
Term Contributed Boundary Tagging by Conditional Random Fields for SIGHAN 2010 Chinese Word Segmentation Bakeoff

This paper presents a Chinese word segmentation system submitted to the closed training evaluations of CIPS-SIGHAN-2010 bakeoff. The system uses a conditional random field model with one simple feature called term contributed boundaries (TCB) in addition to the “BI” character-based tagging approach. TCB can be extracted from unlabeled corpora automatically, and segmentation variations of different domains are expected to be reflected implicitly. The experiment result shows that TCB does improve “BI” tagging domain-independently about 1% of the F1 measure score.
Recognizing Inference in Texts with Markov Logic Networks

Recognizing inference in texts (RITE) attracts growing attention of natural language processing (NLP) researchers in recent years. In this article, we propose a novel approach to recognize inference with probabilistic logical reasoning. Our approach is built on Markov logic networks (MLNs) framework, which is a probabilistic extension of first-order logic. We design specific semantic rules based on the surface, syntactic, and semantic representations of texts, and map these rules to logical representations. We also extract information from some knowledge bases as common sense logic rules. Then we utilize MLNs framework to make predictions with combining statistical and logical reasoning. Experiment results shows that our system can achieve better performance than state-of-the-art RITE systems.
A Path Algorithm for the Fused Lasso Signal Approximator

The Lasso is a very well-known penalized regression model, which adds an L1 penalty with parameter λ1 on the coefficients to the squared error loss function. The Fused Lasso extends this model by also putting an L1 penalty with parameter λ2 on the difference of neighboring coefficients, assuming there is a natural ordering. In this article, we develop a path algorithm for solving the Fused Lasso Signal Approximator that computes the solutions for all values of λ1 and λ2. We also present an approximate algorithm that has considerable speed advantages for a moderate trade-off in accuracy. In the Online Supplement for this article, we provide proofs and further details for the methods developed in the article.
A two-tier index architecture for fast processing large RDF data over distributed memory

We propose an efficient method for fast processing large RDF data over distributed memory. Our approach adopts a two-tier index architecture on each computation node: (1) a light-weight primary index, to keep loading times low, and (2) a dynamic, multi-level secondary index, calculated as a by-product of query execution, to decrease or remove inter-machine data movement for subsequent queries that contain the same graph patterns. Experimental results on a commodity cluster show that we can load large RDF data very quickly in memory while remaining within an interactive range for query processing with the secondary index.
Authenticated Key-Insulated Public Key Encryption and Timed-Release Cryptography

In this paper we consider two security notions related to Identity Based Encryption: Key-insulated public key encryption, introduced by Dodis, Katz, Xu and Yung; and Timed-Release Public Key cryptography, introduced independently by May and Rivest, Shamir and Wagner. We first formalize the notion of secure timed-release cryptography, and show that, despite several differences in its formulation, it is equivalent to strongly key-insulated public key encryption (with optimal threshold). Next, we introduce the concept of an authenticated timed-release cryptosystem, briefly consider generic constructions, and then give a construction based on a single primitive which is efficient and provably secure. Our construction also gives a strongly key-insulated authenticated encryption scheme.
Enhancing collaborative human-robot interaction through physiological-signal based communication

—In order to develop a friendly and safe interaction between humans and robots, it is essential for the robot to evaluate users’ affective states and respond accordingly. This paper investigates the use of physiological signals to estimate human affective states during a Human-Robot Interaction (HRI) task. We focus on characterizing physiological responses and understanding how affective states evolve in a collaborative human-robot task. We propose to both design a model that maps physiological signals to affective states in real time and design a methodology for the robot to exhibit an appropriate behavior during the task in response to estimated changes in affective states.
A Rare Case of Adductor Longus Muscle Rupture

An adductor longus muscle rupture is a rare injury. This case report describes a 32-year-old patient with an adductor longus rupture. The trauma mechanism was a hyperabduction movement during a soccer game. Nonoperative treatment was initiated. After a follow-up of 4 years, the patient was without pain but a small swelling was still visible. This report describes the anatomy, pathophysiology, and evidence-based treatment of adductor longus rupture.
A behavioral biometrics based authentication method for MOOC's that is robust against imitation attempts

Ensuring authorship in online taken exams is a major challenge for e-learning in general and MOOC's in particular. In this paper, we introduce and evaluate a method to verify student identities using stylometry. We present a carefully composed feature set and use it with a K-Nearest Neighbor algorithm. We demonstrate that our method can effectively authenticate authors and is robust against imitation attacks.
HLA-B27 detection – comparison of genetic sequence-based method and flow cytometry assay

Objectives The presence of human leukocyte antigen B27 (HLA-B27) is strongly associated with ankylosing spondylitis. HLA-B27 testing is routinely applied in the diagnosis of this disease. The aim of the present study was to compare two methods of HLA-B27 detection – a genetic sequence-based method and a flow cytometry assay. Material and methods Peripheral blood was obtained from 300 individuals with suspected spondyloarthropathy. Expression of HLA-B27 on the T cell surface was analysed by flow cytometry assay using GS145.2 monoclonal antibody specific for HLA-B27. DNA was isolated from the whole blood. Genes coding for HLA-B27, -B40 and -B47:01 were detected by polymerase chain reaction using the MW02/MW09 primer pair. Then, positive samples were sequenced in order to discriminate allelic variations of the HLA-B27 gene. Results of sequencing were analysed using Chromas LITE 2.1. 1 software, BLAST software and the IMGT/HLA database. Ambiguous samples were additionally analysed by polymerase chain reaction using E91 and E136 primers amplifying a 135-bp fragment of the human HLA-B27 gene. Results Among 300 samples, 76 were HLA-B27-positive on the basis of flow cytometry analysis. Genetic sequence analysis confirmed positivity of 73 from among 76 samples. Two hundred twenty six samples were HLA-B27-negative, whereas the result of one sample analysis was ambiguous. Fifty-three samples were identified as allelic variation 27:05, 19 samples as allelic variation 27:02, and one sample as allelic variation 27:07. Conclusions This study shows that the genetic sequence-based method and the flow cytometry assay give consistent results in 99% of cases. The performed genetic analysis proves that the majority of HLA-B27-positive samples belong to the 27:05 allelic variation, which is strongly associated with high risk of ankylosing spondylitis.
Stochastic Model Updating Using Perturbation Methods Combined With Neural Network Estimations

The present work concentrates on stochastic model updating for finite element models using uncertain data from measurements. One model updating technique, based on the perturbation method, has been proposed by Khodaparast et al [6] [7]. The method gives accurate results, but the numerical effort is not acceptable, especially for real-scale structures. The current approach will reduce the numerical effort effectively by two main improvements. First, the monte carlo simulation will be replaced by the more efficient latin hypercube simulation and second, the finite element solver runs are replaced by neural network approximations. Using these methods, the same accuracy can be obtained by reducing the numerical effort. A numerical benchmark has been performed by a three degree-of-freedom system using uncertain natural frequencies while three stiffness parameters with their stochastic parameters, mean value and covariance, are identified.
Exploring Playful Experiences in Social Network Games

Social network games are popular pastime for millions of players on Facebook. Despite their popularity, qualitative research on experiences in these games has been scarce. In our study, 110 informants played 23 games on Facebook and reported their experiences using the Playful Experiences (PLEX) framework. We analyzed 110 reports containing 330 PLEX descriptions and present findings from three perspectives. First, we provide an overall analysis on playful experiences in social network games. Then we focus on genrespecific experiences in casual puzzle, casual simulation, and mid-core strategy games. Lastly, we provide examples of interesting outlier experiences. Based on our study, Competition, Completion and Challenge are the most common playful experiences in these games. The genre-specific analysis revealed both similarities and differences between the genres, while the outlier experiences provide new perspectives on social network games. Through the PLEX framework, this research helps to understand the playful experiences in social network games.
Lack of Evidence for Schmallenberg Virus Infection in Highly Exposed Persons, Germany, 2012

Schmallenberg virus, a novel orthobunyavirus, is spreading among ruminants, especially sheep, throughout Europe. To determine the risk for human infection, we conducted a survey among shepherds to assess possible exposure and symptoms. We also performed serologic and molecular assays. No evidence of transmission to humans was detected.
A simple approach for point-based object capturing and rendering

Point-based object representations are a powerful alternative to traditional polygonal object representations. Capturing 3D geometry is a mission-critical content acquisition technique in application domains such as virtual reality, CAD/CAM, and physical asset management. We describe SPOC, a simple, point-based object capturing system we've developed to automatically capture and process 3D geometry for point-based image rendering. We also identify the problems involved and describe the technical solutions we've implemented. Finally, we address several algorithmic issues in capturing point models using a simple digital camera and turntable setup, and in processing and rendering point clouds.
Social Embeddedness of Dynamic Capabilities: The Case of Product Development of Small Finnish Software Supplier Companies

The purpose of this paper is to analyze how institutional factors (DiMaggio and Powell 1983) and strategic industry factors Porter (1979, 1980) affect the product development routines of small software supplier firms. The organizational routines are analyzed using the ‘dynamic capability’ perspective. Dynamic capabilities, when used effectively, have been found to be sources of competitive advantage in highly dynamic markets. Despite of some idiosyncratic details these capabilities have commonalties, i.e. best practices between firms (Teece, Pisano, Shuen 1997, Eisenhard and Martin 2000). The context of the study is software subcontracting in the Finnish electronics industry where the customer organization is a large firm and the supplier companies are rather small in size. Currently, the software supplier firms produce highly customized software in customer projects. Their main goal in the subcontracting relationship is, however, to develop towards more independent production of software modules or software products.
Sequential Data Cleaning: A Statistical Approach

Errors are prevalent in data sequences, such as GPS trajectories or sensor readings. Existing methods on cleaning sequential data employ a constraint on value changing speeds and perform constraint-based repairing. While such speed constraints are effective in identifying large spike errors, the small errors that do not significantly deviate from the truth and indeed satisfy the speed constraints can hardly be identified and repaired. To handle such small errors, in this paper, we propose a statistical based cleaning method. Rather than declaring a broad constraint of max/min speeds, we model the probability distribution of speed changes. The repairing problem is thus to maximize the likelihood of the sequence w.r. t. the probability of speed changes. We formalize the likelihood-based cleaning problem, show its NP-hardness, devise exact algorithms, and propose several approximate/heuristic methods to trade off effectiveness for efficiency. Experiments on real data sets (in various applications) demonstrate the superiority of our proposal.
The Efficacy of Influenza Vaccine for Healthy Children: A Meta-Analysis Evaluating Potential Sources of Variation in Efficacy Estimates Including Study Quality

Background: Two systematic reviews evaluating influenza vaccine efficacy in healthy children have recently been published. Although quantitative summary estimates were similar, authors’ conclusions were quite contrasting. We carried out another meta-analysis reevaluating study inclusion criteria and using metaregression techniques in addition to sensitivity and subgroups analyses to evaluate potential sources of heterogeneity of efficacy estimates, including methodologic quality of studies. Methods: Only randomized clinical studies assessing the efficacy of influenza vaccine in healthy children/adolescents (age ≤18 years) for preventing naturally occurring influenza and/or acute otitis media cases were included. Summary estimates of effect were obtained using a random effects model. The methodologic quality of each study was assessed using 3 systems: Chalmers scale, Jadad scale and Schulz components (randomization, allocation concealment and double-blinding). Results: The overall vaccination efficacy was 36% (95% confidence interval: 31–40%) against clinically diagnosed illnesses (evaluated by 19 randomized clinical studies for a total of 247,517 children); 67% (51–78%) against laboratory-confirmed cases (18 trials, n = 8574); and 51% (21–70%) against acute otitis media (11 trials, n = 11,349). Significant sources of between-study heterogeneity were participants’ age and study quality both directly correlated with the efficacy. When the analysis was performed excluding USSR studies, the overall efficacy of the vaccine in preventing clinical cases substantially increased (from 36% to 61%). Conclusions: These findings may indicate that the vaccine efficacy might be greater than the overall estimates. Although no safety and cost considerations are addressed in this analysis, the present findings support vaccination as a possible option for the prevention of influenza in healthy children and adolescents.
Intrinsic Differences in the Inner Jets of High and Low Optically Polarized Radio Quasars

A significant fraction of flat-spectrum, radio-loud quasars display most of the characteristics of relativistically beamed, high-optical polarization blazars yet are weakly polarized in the optical regime (mopt ≲ 3%). We have conducted a high-resolution polarization study with the VLBA at 22 and 43 GHz to look for differences in the parsec-scale magnetic field structures of 18 high and low optically polarized, compact radio-loud quasars (HPQs and LPRQs, respectively). We find a strong correlation between the polarization level of the unresolved parsec-scale radio core at 43 GHz and overall optical polarization of the source, which suggests a common (possibly cospatial) origin for the emission at these two wavelengths. The electric vectors of the polarized 43 GHz radio cores are roughly aligned with the inner jet direction, indicating magnetic fields perpendicular to the flow. Similar orientations are seen in the optical, which suggests that the polarized flux at both wavelengths is caused by one or more strong transverse shocks located very close to the base of the jet. In LPRQs, these shocks appear to be weak near the core and gradually increase in strength down the jet. The LPRQs in our sample tend to have less luminous radio cores than the HPQs and jet components with magnetic fields predominantly parallel to the jet. The components in HPQ jets, on the other hand, tend to have perpendicular magnetic field orientations. These differences cannot be accounted for by a simple model in which HPQs and LPRQs are the same type of object seen at different angles to the line of sight. A more likely scenario is that LPRQs represent a quiescent phase of blazar activity in which the inner jet flow does not contain strong shocks. Our high-resolution observations have shown that high rotation measures (up to 3000 rad m-2) previously seen in the nuclear regions of HPQs are present in LPRQs as well. The low-redshift quasars in our sample tend to have jet components with larger 43/22 GHz depolarization ratios than those found in the high-redshift sources. This may be caused by small-scale magnetic field fluctuations in the Faraday screens that are being smeared out in the high-redshift sources by the poorer spatial resolution of the restoring beam.
Full On-Device Stay Points Detection in Smartphones for Location-Based Mobile Applications

The tracking of frequently visited places, also known as stay points, is a critical feature in location-aware mobile applications as a way to adapt the information and services provided to smartphones users according to their moving patterns. Location based applications usually employ the GPS receiver along with Wi-Fi hot-spots and cellular cell tower mechanisms for estimating user location. Typically, fine-grained GPS location data are collected by the smartphone and transferred to dedicated servers for trajectory analysis and stay points detection. Such Mobile Cloud Computing approach has been successfully employed for extending smartphone’s battery lifetime by exchanging computation costs, assuming that on-device stay points detection is prohibitive. In this article, we propose and validate the feasibility of having an alternative event-driven mechanism for stay points detection that is executed fully on-device, and that provides higher energy savings by avoiding communication costs. Our solution is encapsulated in a sensing middleware for Android smartphones, where a stream of GPS location updates is collected in the background, supporting duty cycling schemes, and incrementally analyzed following an event-driven paradigm for stay points detection. To evaluate the performance of the proposed middleware, real world experiments were conducted under different stress levels, validating its power efficiency when compared against a Mobile Cloud Computing oriented solution.