Miracle-dz/UniFAR-FTUs huggingface dataset:
Peek-inthe-Pic : Architectural Scene Navigation from a Single Picture Using Line Drawing Cues

The ability to navigate through the scene captured in a photograph generates much more excitement than merely looking at one. Through our system, SMARTPhoto, we accomplish navigation by utilizing work done in reconstruction of 3D geometry from 2D line drawings. We regard objects traced from a photograph as line drawings, formulate the reconstruction problem as an optimization problem and extend current work done in reconstruction of orthographic line drawings to build navigable models from perspective images. We also provide a basic sketching tool to progressively complete invisible parts of the reconstructed buildings. We use texture synthesis to interactively fill holes created in the original image when reconstructed buildings are removed from it. Minimal manual work, ease of use and interactivity are the salient features of our approach.
Double- and Multiple-Scattering Effects in Translucent Materials

Some materials, such as coffee, milk, or marble, have a soft translucent aspect because of subsurface scattering. Light enters them and gets scattered several times before leaving in a different place. A full representation of subsurface-scattering effects in illumination simulation is computationally expensive. The main difficulty comes from multiple scattering events. The high number of events increases the results' uncertainty, requiring more computation time. However, a strong correlation exists between the surface effects of multiple scattering and the effects after just two scattering events. This knowledge can help accelerate multiple-scattering effects. In particular, researchers have exploited this knowledge to provide a model and implementation for fast computation of double-scattering events using a precomputed density function stored compactly.
Developing a vocabulary learning system with near-synonyms and similar-form words

Various vocabulary learning systems have been developed to assist students of English as a Foreign Language (EFL) in acquiring the knowledge of vocabulary. By using the electronic dictionary, people only acquire vocabularies on a superficial. Most corpus-based system focuses on lexical explanation, such as giving synonyms and antonyms. Only a few studies have attempted to use lexical structures. However, these systems do not take into consideration students of EFL who emphasized English words recognition, such as Chinese students. Therefore, the near-synonyms and similar-form (NSSF) words are difficult for them to learn. In this study, we propose a system to assist students of EFL in learning the NSSF words. Results of a pilot study showed that students regarded this NSSF word learning system to be of great assistance in learning English vocabulary. This encourages us to promote the NSSF words learning system to students of EFL in other countries.
Information-centric delay-tolerant mobile ad-hoc networks

Information-centric networks have recently been drawing increasing attention in academia as well as in industry. Information and content retrieval is a critical service for mobile ad-hoc networks. It relies on other resources and tools, such as internal storage, content searching and sharing, delay-tolerant delivery, etc. Previous studies have shown that social networking can assist delay-tolerant routing design in many respects. In this paper, we specifically address content retrieval in delay-tolerant mobile ad-hoc networks. We propose a social-tie based content retrieval scheme to support the delay-tolerant MANET. The social hierarchy is structured using balanced connectivity criteria and a K-mean clustering algorithm. The proposed scheme has been evaluated and validated on a real social network dataset.
Collaborative temporal order modeling

Past consumption of items affect current choices and influence the perceived quality. The order in which items are consumed can affect the score that a user might give to them. In this work we present two simple models that take advantage of the temporal order of choices and ratings by the user in order to improve the quality of the recommendation. Our model exploits the collaborative effects in the data while also taking into account the order in which items are seen by the users. Experiments show that our approach outperforms standard Matrix Factorization models.
Towards Composition as a Service - A Quality of Service Driven Approach

Software as a Service (SaaS) and the possibility to compose Web services provisioned over the Internet are important assets for a service-oriented architecture (SOA). However, the complexity and time for developing and provisioning a composite service is very high and it is generally an error-prone task. In this paper we address these issues by describing a semi-automated "Composition as a Service'' (CaaS) approach combined with a domain-specific language called VCL (Vienna Composition Language). The proposed approach facilitates rapid development and provisioning of composite services by specifying what to compose in a constraint-hierarchy based way using VCL. Invoking the composition service triggers the composition process and upon success the newly composed service is immediately deployed and available. This solution requires no client-side composition infrastructure because it is transparently encapsulated in the CaaS infrastructure.
Polynomial Time Reasoning in a Description Logic with Existential Restrictions, GCI Axioms, and - What Else?

In the area of Description Logic (DL) based knowledge representation, research on reasoning w.r. t. general terminologies has mainly focused on very expressive DLs. Recently, though, it was shown for the DL ℇL, providing only the constructors conjunction and existential restriction, that the subsumption problem w.r. t. cyclic terminologies can be decided in polynomial time, a surprisingly low upper bound. In this paper, we show that even admitting general concept inclusion (GCI) axioms and role hierarchies in ℇL terminologies preserves the polynomial time upper bound for subsumption. We also show that subsumption becomes co-NP hard when adding one of the constructors number restriction, disjunction, and 'allsome', an operator used in the DL K-REP. implication of the first result is that reasoning over the widely used medical terminology SNOMED is possible in polynomial time.
Concatenation of a discrete memoryless channel and a quantizer

The concatenation of an arbitrary discrete memoryless channel with binary input followed by a quantizer is considered. For a restricted quantizer alphabet size, it is shown that the maximum of the mutual information between the channel input and the quantizer output can be found by dynamic programming. Numerical examples are given to illustrate the results. This problem is shown to be an example of concave programming.
Computing null divergence velocity field using smoothed particle hydrodynamics

This paper presents a new algorithm for computing a null divergence velocity field using smoothed particle hydrodynamics (SPH) a purely SPH solution of the Helmholtz-Hodge decomposition. This step is used for the simulation of incompressible flows. Also, a new version of the Laplacian for SPH is proposed; some advantages and disadvantages on different gradient approximation and Laplacian approximation formulas used in SPH are discussed.
Where To Go Next?: Exploiting Behavioral User Models in Smart Environments

There is a growing interest in using the Internet of Things (IoT) to create smart environments, which hold the promise to provide personalized experience based on the trail of user interactions with smart devices. We experiment with behavioral user models based on interactions with smart devices in a museum, and investigate the personalized recommendation of what to see after visiting an initial set of Point of Interests (POIs), a key problem in personalizing museum visits or tour guides. We have logged users' onsite physical information interactions of visits in a museum. Moreover, to have a better understanding of users' information interaction behaviors and their preferences, we have collected and studied query logs of a search engine of the same collection, and we have found similarities between users' online digital and onsite physical information interaction behaviors. We exploit user modeling based on users' different information interaction behaviors and experiment with a novel approach to a critical one-shot POI recommendation using deep neural multilayer perceptron based on explicitly given users' contextual information, and set-based extracted features using users' physical information interaction behaviors and similar users' digital information interaction behaviors. Experimental results indicates that our proposed behavioral user modeling, using both physical and digital user information interaction behaviors, improves the onsite POI recommendation baselines' performances in all common Information Retrieval evaluation metrics. Our proposed approach provides an effective way to achieve a high precision at rank 1 in onsite critical one-shot POI recommendation problem.
Semi-Supervised Semantic Role Labeling via Structural Alignment

Large-scale annotated corpora are a prerequisite to developing high-performance semantic role labeling systems. Unfortunately, such corpora are expensive to produce, limited in size, and may not be representative. Our work aims to reduce the annotation effort involved in creating resources for semantic role labeling via semi-supervised learning. The key idea of our approach is to find novel instances for classifier training based on their similarity to manually labeled seed instances. The underlying assumption is that sentences that are similar in their lexical material and syntactic structure are likely to share a frame semantic analysis. We formalize the detection of similar sentences and the projection of role annotations as a graph alignment problem, which we solve exactly using integer linear programming. Experimental results on semantic role labeling show that the automatic annotations produced by our method improve performance over using hand-labeled instances alone.
Early acute kidney injury in military casualties

BACKGROUND While acute kidney injury (AKI) has been well studied in a variety of patient settings, there is a paucity of data in patients injured in the course of the recent wars in Iraq and Afghanistan. We sought to establish the rate of early AKI in this population and to define risk factors for its development. METHODS We combined the results of two studies performed at combat support hospitals in Afghanistan. Only US service members who required care in the intensive care unit were included for analysis. Data on age, race, sex, Injury Severity Score (ISS), first available lactate, and requirement for massive transfusion were collected. Univariate analyses were performed to identify factors associated with the subsequent development of early AKI. Multivariable Cox regression was used to adjust for potential confounders. RESULTS The two observational cohorts yielded 134 subjects for analysis. The studies had broadly similar populations but differed in terms of age and need for massive transfusion. The rate of early AKI in the combined cohort was 34.3%, with the majority (80.5%) occurring within the first two hospital days. Patients with AKI had higher unadjusted mortality rates than those without AKI (21.7% vs. 2.3%, p < 0.001). After adjustment, ISS (hazard ratio, 1.02; 95% confidence interval, 1.00–1.03; p = 0.046) and initial lactate (hazard ratio, 1.16; 95% confidence interval, 1.03–1.31; p = 0.015) were independently associated with the development of AKI. CONCLUSION AKI is common in combat casualties enrolled in two prospective intensive care unit studies, occurring in 34.3%, and is associated with crude mortality. ISS and initial lactate are independently associated with the subsequent development of early AKI. LEVEL OF EVIDENCE Prognostic and epidemiologic study, level III.
An Empirical Study of Software Aging Manifestations in Android

Software aging phenomenon has been widely observed in Android mobile operating system, which will make a great influence on people's life. In this paper we present an empirical study on the manifestations of aging-related bugs in Android operating system. By conducting a set of experiments through building various aging conditions in Android, we clarify the fact that the aging-related bugs injected in Android are with several manifestation patterns from two angles of view (Users' and System's viewpoints), depending on their location (Dalvik Heap or Native Heap), process priorities (cached, persistent, background, foreground and etc.). To show the new features of software aging in Android, the differences of software aging manifestations between Android and Linux are further explored in this work.
A Rare Variant at the KYNU Gene Is Associated With Kynureninase Activity and Essential Hypertension in the Han Chinese Population

Background— Genetic studies in mouse and human suggest that kynureninase activity may influence blood pressure and renal function. The gene coding kynureninase (KYNU) is also located on chromosome band 2q14-q23, where a linkage peak for essential hypertension was previously detected in the Chinese Han population. Methods and Results— After having found no association with common polymorphisms, this study aimed to assess the role of 1 rare variant of KYNU, Arg188Gln, and kynureninase activity in relation to hypertension. Thirty-three of 1124 Chinese patients with hypertension were heterozygous for Arg188Gln, whereas only 14 of 1084 normotensive controls were heterozygous for Arg188Gln (188G1n allele frequency, 0.015 versus 0.006; P=0.0075). A genotype-discordant sibling-pair study was performed in another 924 individuals from 213 families, indicating that 188G1n carriers had higher systolic blood pressure (168.29±24.67 versus 139.00±12.82 mm Hg, P<0.001) and diastolic blood pressure (105.50±14.08 versus 90.75±11.07 mm Hg, P=0.001) than did Arg188 homozygous siblings. The Arg188Gln variant was found to be rarer in 2 other ethnic groups (3 heterozygous among 880 hypertensive French whites and 0 of 90 black Africans with hypertension). The kynureninase activity in plasma was correlated with blood pressure in subjects from hypertensive families (P<0.05). The Kinetic Michaelis constants of 188Gln carriers was lower than that of Arg188 homozygous subjects (0.05±0.02 versus 0.10±0.02 mmol/L, P=0.005). Arg188Gln mutation in vitro also showed less catalytic efficiency than the wild-type KYNU enzyme (maximal reaction velocity/Kinetic Michaelis constant ratio, 0.050±0.012 versus 0.11±0.016 mL/min per mg; P=0.029). Conclusions— The results show that the rare KYNU variant Arg188Gln affects kynureninase activity and are consistent with the hypothesis that this mutation can predispose to essential hypertension.
Adjusted functional boxplots for spatio‐temporal data visualization and outlier detection

This article proposes a simulation‐based method to adjust functional boxplots for correlations when visualizing functional and spatio‐temporal data, as well as detecting outliers. We start by investigating the relationship between the spatio‐temporal dependence and the 1.5 times the 50% central region empirical outlier detection rule. Then, we propose to simulate observations without outliers on the basis of a robust estimator of the covariance function of the data. We select the constant factor in the functional boxplot to control the probability of correctly detecting no outliers. Finally, we apply the selected factor to the functional boxplot of the original data. As applications, the factor selection procedure and the adjusted functional boxplots are demonstrated on sea surface temperatures, spatio‐temporal precipitation and general circulation model (GCM) data. The outlier detection performance is also compared before and after the factor adjustment. Copyright © 2011 John Wiley & Sons, Ltd.
Strontium Ranelate and bone healing: report of two cases.

Introduction There are several factors influencing the fracture healing process, the most important being age and bone quality. Cellular and molecular alterations that can be found in elderly patients make the healing process difficult, and may lead to complications such as pseudoarthrosis or delayed non-union. In the same way, in altered bone metabolism condition such as osteoporosis, physiological phases of fracture healing are impaired. Due to the wide availability of anti-osteoporotic drugs whose primary end-points are to improve BMD and to reduce fracture risk, the secondary end-point of these drugs is now focused on their possible use on fracture healing deficiency conditions.
Generating Reproducible and Replayable Bug Reports from Android Application Crashes

Manually reproducing bugs is time-consuming and tedious. Software maintainers routinely try to reproduce unconfirmed issues using incomplete or no informative bug reports. Consequently, while reproducing an issue, the maintainer must augment the report with information - such as a reliable sequence of descriptive steps to reproduce the bug - to aid developers with diagnosing the issue. This process encumbers issue resolution from the time the bug is entered in the issue tracking system until it is reproduced. This paper presents Crash Droid, an approach for automating the process of reproducing a bug by translating the call stack from a crash report into expressive steps to reproduce the bug and a kernel event trace that can be replayed on-demand. Crash Droid manages trace ability links between scenarios' natural language descriptions, method call traces, and kernel event traces. We evaluated Crash Droid on several open-source Android applications infected with errors. Given call stacks from crash reports, Crash Droid was able to generate expressive steps to reproduce the bugs and automatically replay the crashes. Moreover, users were able to confirm the crashes faster with Crash Droid than manually reproducing the bugs or using a stress-testing tool.
Quality of online signature templates

This paper proposes a metric to measure the quality of an online signature template derived from a set of enrolled signature samples in terms of its distinctiveness against random signatures. Particularly, the proposed quality score is computed based on statistical analysis of histogram features that are used as part of an online signature representation. Experiments performed on three datasets consistently confirm the effectiveness of the proposed metric as an indication of false acceptance rate against random forgeries when the system is operated at a particular decision threshold. Finally, the use of the proposed quality metric to enforce a minimum signature strength policy in order to enhance security and reliability of the system against random forgeries is demonstrated.
PetaBricks: a language and compiler for algorithmic choice

It is often impossible to obtain a one-size-fits-all solution for high performance algorithms when considering different choices for data distributions, parallelism, transformations, and blocking. The best solution to these choices is often tightly coupled to different architectures, problem sizes, data, and available system resources. In some cases, completely different algorithms may provide the best performance. Current compiler and programming language techniques are able to change some of these parameters, but today there is no simple way for the programmer to express or the compiler to choose different algorithms to handle different parts of the data. Existing solutions normally can handle only coarse-grained, library level selections or hand coded cutoffs between base cases and recursive cases. We present PetaBricks, a new implicitly parallel language and compiler where having multiple implementations of multiple algorithms to solve a problem is the natural way of programming. We make algorithmic choice a first class construct of the language. Choices are provided in a way that also allows our compiler to tune at a finer granularity. The PetaBricks compiler autotunes programs by making both fine-grained as well as algorithmic choices. Choices also include different automatic parallelization techniques, data distributions, algorithmic parameters, transformations, and blocking. Additionally, we introduce novel techniques to autotune algorithms for different convergence criteria. When choosing between various direct and iterative methods, the PetaBricks compiler is able to tune a program in such a way that delivers near-optimal efficiency for any desired level of accuracy. The compiler has the flexibility of utilizing different convergence criteria for the various components within a single algorithm, providing the user with accuracy choice alongside algorithmic choice.
Somatic Symptoms among Children and Adolescents in Poland: A Confirmatory Factor Analytic Study of the Children Somatization Inventory

The aim of the present study was to examine the factor structure and psychometric properties of the short version of the Children’s Somatization Inventory (CSI-24) in Poland. The CSI-24 is a self-report questionnaire designed to assess somatic symptoms in children and adolescents. A total of 733 children and adolescents, aged 12–17 years, participated in this research. The participants for this study were recruited from urban and suburban schools of Opole province in South Western Poland. In addition to the CSI-24, all participants completed the Spence Children’s Anxiety Scale (SCAS) and the Strength and Difficulties Questionnaire (SDQ). The correlated four-factor model that included four-correlated dimensions (pain/weakness, gastrointestinal problems, cardiovascular symptoms, and pseudoneurological problems) showed a better fit compared to the single-factor model. The Cronbach’s Alpha for the CSI-24 was 0.91. Somatic symptoms correlated significantly highly with the SCAS total scores and the SDQ emotional subscale, suggesting good construct validity. Somatic symptoms had low correlation with the SDQ behavioral problems symptoms, suggesting adequate discriminant validity. The CSI-24 reliably measured somatic symptoms in children and adolescents in Poland.
Tunneling Smart Energy protocols over ZigBee

In the course of increasing the energy efficiency, simplifying processes and providing new customer services, millions of smart meters will be rolled out during the next few years. Wireless personal area network (WPAN) technologies play a major role in the deployment of such systems. The ZigBee Alliance noted the big potential lying beneath smart energy solutions and developed the Smart Energy profile, an application protocol that could take the role of the missing interoperable standard. However, electronic meters have been around for quite some time now and the industry has spent much effort on creating powerful and popular metering protocol standards such as DLMS/COSEM or IEC62056-21. A possible solution combining state of the art wireless mesh network technology and existing metering standards is to provide a mechanism to transport metering PDU over ZigBee. This paper elaborates the requirements of such an approach, proposes a design and discusses the results of a first implementation tunneling DLMS over ZigBee that can also be adapted to other domains.