nils-herrmann/clean_scirepeval huggingface dataset:
Towards an Ontology Design Pattern Quality Model

The use of semantic technologies and Semantic Web ontologies in particular have enabled many recent developments in information integration, search engines, and reasoning over formalised knowledge. Ontology Design Patterns have been proposed to be useful in simplifying the development of Semantic Web ontologies by codifying and reusing modelling best practices. This thesis investigates the quality of Ontology Design Patterns. The main contribution of the thesis is a theoretically grounded and partially empirically evaluated quality model for such patterns including a set of quality characteristics, indicators, measurement methods and recommendations. The quality model is based on established theory on information system quality, conceptual model quality, and ontology evaluation. It has been tested in a case study setting and in two experiments. The main findings of this thesis are that the quality of Ontology Design Patterns can be identified, formalised and measured, and furthermore, that these qualities interact in such a way that ontology engineers using patterns need to make tradeoffs regarding which qualities they wish to prioritise. The developed model may aid them in making these choices. This work has been supported by Jönköping University. Department of Computer and Information Science Linköping University SE-581 83 Linköping, Sweden
Inductorless DC-AC Cascaded H-Bridge Multilevel Boost Inverter for Electric/Hybrid Electric Vehicle Applications

This paper presents an inductorless cascaded H- bridge multilevel boost inverter for EV and HEV applications. Currently available power inverter systems for HEVs use a DC- DC boost converter to boost the battery voltage for a traditional 3-phase inverter. The present HEV traction drive inverters have low power density, are expensive, and have low efficiency because they need a bulky inductor. An inductorless cascaded H-bridge multilevel boost inverter for EV and HEV applications is proposed in this paper. Traditionally, each H-bridge needs a DC power supply. The proposed inductorless cascaded H-bridge multilevel boost inverter uses a standard 3-leg inverter (one leg for each phase) and an H-bridge in series with each inverter leg which uses a capacitor as the DC power source. Fundamental switching scheme is used to do modulation control and to produce a 5-level phase voltage. Experiments show that the proposed inductorless DC-AC cascaded H-bridge multilevel boost inverter can output a boosted AC voltage.
A Spatial-Temporal Probabilistic Matrix Factorization Model for Point-of-Interest Recommendation

With the rapid development of Location-based Social Network (LBSN) services, a large number of Point-of-Interests (POIs) have been available, which consequently raises a great demand of building personalized POI recommender systems. A personalized POI recommender system can significantly help users to find their preferred POIs and assist POI owners to attract more customers. However, due to the complexity of users’ checkin decision making process that is influenced by many different factors such as POI distance and region’s prosperity, and the dynamics of user’s preference, POI recommender systems usually suffer from many challenges. Although different latent factor based methods (e.g., probabilistic matrix factorization) have been proposed, most of them do not successfully incorporate both geographical influence and temporal effect together into latent factor models. To this end, in this paper, we propose a new Spatial-Temporal Probabilistic Matrix Factorization (STPMF) model that models a user’s preference for POI as the combination of his geographical preference and other general interest in POI. Furthermore, in addition to static general interest of user, we capture the temporal dynamics of user’s interest as well by modeling checkin data in a unique way. To evaluate the proposed STPMF model, we conduct extensive experiments with many state-of-the-art baseline methods and evaluation metrics on two real-world data sets. The experimental results clearly demonstrate the effectiveness of our proposed STPMF model.
A Survey of Statistical Model Checking

Interactive, distributed, and embedded systems often behave stochastically, for example, when inputs, message delays, or failures conform to a probability distribution. However, reasoning analytically about the behavior of complex stochastic systems is generally infeasible. While simulations of systems are commonly used in engineering practice, they have not traditionally been used to reason about formal specifications. Statistical model checking (SMC) addresses this weakness by using a simulation-based approach to reason about precise properties specified in a stochastic temporal logic. A specification for a communication system may state that within some time bound, the probability that the number of messages in a queue will be greater than 5 must be less than 0.01. Using SMC, executions of a stochastic system are first sampled, after which statistical techniques are applied to determine whether such a property holds. While the output of sample-based methods are not always correct, statistical inference can quantify the confidence in the result produced. In effect, SMC provides a more widely applicable and scalable alternative to analysis of properties of stochastic systems using numerical and symbolic methods. SMC techniques have been successfully applied to analyze systems with large state spaces in areas such as computer networking, security, and systems biology. In this article, we survey SMC algorithms, techniques, and tools, while emphasizing current limitations and tradeoffs between precision and scalability.
Hierarchical Focus+Context Heterogeneous Network Visualization

Aggregation is a scalable strategy for dealing with large network data. Existing network visualizations have allowed nodes to be aggregated based on node attributes or network topology, each of which has its own advantages. However, very few previous systems have the capability to enjoy the best of both worlds. This paper presents OnionGraph, an integrated framework for exploratory visual analysis of large heterogeneous networks. OnionGraph allows nodes to be aggregated based on either node attributes, topology, or a mixture of both. Subsets of nodes can be flexibly split and merged under the hierarchical focus+context interaction model, supporting sophisticated analysis of the network data. Node aggregations that contain subsets of nodes are displayed with multiple concentric circles, or the onion metaphor, indicating how many levels of abstraction they contain. We have evaluated the OnionGraph tool in two real-world cases. Performance experiments demonstrate that on a commodity desktop, OnionGraph can scale to million-node networks while preserving the interactivity for analysis.
Software-defined networking (SDN): a survey

With the advent of cloud computing, many new networking concepts have been introduced to simplify network management and bring innovation through network programmability. The emergence of the software-defined networking (SDN) paradigm is one of these adopted concepts in the cloud model so as to eliminate the network infrastructure maintenance processes and guarantee easy management. In this fashion, SDN offers real-time performance and responds to high availability requirements. However, this new emerging paradigm has been facing many technological hurdles; some of them are inherent, while others are inherited from existing adopted technologies. In this paper, our purpose is to shed light on SDN related issues and give insight into the challenges facing the future of this revolutionary network model, from both protocol and architecture perspectives. Additionally, we aim to present different existing solutions and mitigation techniques that address SDN scalability, elasticity, dependability, reliability, high availability, resiliency, security, and performance concerns.
Anger, disgust, and presumption of harm as reactions to taboo-breaking behaviors.

Three experiments investigated the relationship between the presumption of harm in harmfree violations of creatural norms (taboos) and the moral emotions of anger and disgust. In Experiment 1, participants made a presumption of harm to others from taboo violations, even in conditions described as harmless and not involving other people; this presumption was predicted by anger and not disgust. Experiment 2 manipulated taboo violation and included a cognitive load task to clarify the post hoc nature of presumption of harm. Experiment 3 was similar but more accurately measured presumed harm. In Experiments 2 and 3, only without load was symbolic harm presumed, indicating its post hoc function to justify moral anger, which was not affected by load. In general, manipulations of harmfulness to others predicted moral anger better than moral disgust, whereas manipulations of taboo predicted disgust better. The presumption of harm was found on measures of symbolic rather than actual harm when a choice existed. These studies clarify understanding of the relationship between emotions and their justification when people consider victimless, offensive acts.
Black hole: A new heuristic optimization approach for data clustering

0020-0255/$ see front matter 2012 Elsevier Inc  Islamic Azad University, Khoy Branch, E-mail addresses: hatamlou@iaukhoy.ac.ir, hatam Nature has always been a source of inspiration. Over the last few decades, it has stimulated many successful algorithms and computational tools for dealing with complex and optimization problems. This paper proposes a new heuristic algorithm that is inspired by the black hole phenomenon. Similar to other population-based algorithms, the black hole algorithm (BH) starts with an initial population of candidate solutions to an optimization problem and an objective function that is calculated for them. At each iteration of the black hole algorithm, the best candidate is selected to be the black hole, which then starts pulling other candidates around it, called stars. If a star gets too close to the black hole, it will be swallowed by the black hole and is gone forever. In such a case, a new star (candidate solution) is randomly generated and placed in the search space and starts a new search. To evaluate the performance of the black hole algorithm, it is applied to solve the clustering problem, which is a NP-hard problem. The experimental results show that the proposed black hole algorithm outperforms other traditional heuristic algorithms for several benchmark datasets.
Mining churning behaviors and developing retention strategies based on a partial least squares (PLS) model

In a very competitive mobile telecommunication business environment, marketing managers need a business intelligence model that allows them to maintain an optimal (at least a near optimal) level of churners very effectively and efficiently while minimizing the costs throughout their marketing programs. As a first step toward optimal churn management program for marketing managers, this paper focuses on building an accurate and concise predictive model for the purpose of churn prediction utilizing a Partial Least Square (PLS)-based methodology on highly correlated data sets among variables. A preliminary experiment demonstrates that the presented model provides more accurate performance than traditional prediction models and identifies key variables to better understand churning behaviors. Further, a set of simple churn marketing programs--device management, overage management, and complaint management strategies—is presented and discussed.
Experimental Verification and Finite Element Analysis of Short-Circuit Electromagnetic Force for Dry-Type Transformer

The short-circuit force induces critical mechanical stress on a transformer. This paper deals with experimental verification and finite element analysis (FEA) for short-circuit force prediction of a 50 kVA dry-type transformer. We modeled high voltage (HV) winding into 20 sections and low voltage (LV) winding into 22 sections as similar as those windings of a model transformer. With this modeling technique, we could calculate electromagnetic forces acting on each section of the windings of a dry-type transformer under short-circuit condition. The magnetic vector potentials, magnetic flux densities, and electromagnetic forces due to short-circuit current are solved by FEA. The electromagnetic forces consisting of radial and axial directions depend both on short-circuit current and leakage flux density. These results were used as input source of sequential finite element method (FEM) to predict the resultant mechanical forces considering the structural characteristics such as stress distributions or deformations of windings, accurately. The obtained resultant mechanical forces in HV winding are compared with those of the experimental ones.
An Omnidirectional Vision System for Soccer Robots

This paper describes a complete and efficient vision system d eveloped for the robotic soccer team of the University of Aveiro, CAMB ADA (Cooperative Autonomous Mobile roBots with Advanced Distributed Ar chitecture). The system consists on a firewire camera mounted vertically on th e top of the robots. A hyperbolic mirror placed above the camera reflects the 360 d egrees of the field around the robot. The omnidirectional system is used to find t he ball, the goals, detect the presence of obstacles and the white lines, used by our localization algorithm. In this paper we present a set of algorithms to extract efficiently the color information of the acquired images and, in a second phase, ex tract the information of all objects of interest. Our vision system architect ure uses a distributed paradigm where the main tasks, namely image acquisition, co lor extraction, object detection and image visualization, are separated in se veral processes that can run at the same time. We developed an efficient color extracti on algorithm based on lookup tables and a radial model for object detection. Our participation in the last national robotic contest, ROBOTICA 2007, where we have obtained the first place in the Medium Size League of robotic soccer, shows the e ffectiveness of our algorithms. Moreover, our experiments show that the sys tem is fast and accurate having a maximum processing time independently of the r obot position and the number of objects found in the field.
GPU-based Real-time Discrete Euclidean Distance Transforms with Precise Error Bounds

We present a discrete distance transform in style of the vector propagation algorithm by Danielsson. Like other vector propagation algorithms, the proposed method is close to exact, i.e., the error can be strictly bounded from above and is significantly smaller than one pixel. Our contribution is that the algorithm runs entirely on consumer class graphics hardware, thereby achieving a throughput of up to 96 Mpixels/s. This allows the proposed method to be used in a wide range of applications that rely both on high speed and high quality.
Data fusion of radar and image measurements for multi-object tracking via Kalman filtering

Data fusion is an important issue for object tracking in autonomous systems such as robotics and surveillance. In this paper, we present a multiple-object tracking system whose design is based on multiple Kalman filters dealing with observations from two different kinds of physical sensors. Hardware integration which combines a cheap radar module and a CCD camera has been developed and data fusion method has been proposed to process measurements from those modules for multi-object tracking. Due to the limited resolution of bearing angle measurements of the cheap radar module, CCD measurements are used to compensate for the low angle resolution. Conversely, the radar module provides radial distance information which cannot be measured easily by the CCD camera. The proposed data fusion enables the tracker to efficiently utilize the radial measurements of objects from the cheap radar module and 2D location measurements of objects in image space of the CCD camera. To achieve the multi-object tracking we combine the proposed data fusion method with the integrated probability data association (IPDA) technique underlying the multiple-Kalman filter framework. The proposed complementary system based on the radar and CCD camera is experimentally evaluated through a multi-person tracking scenario. The experimental results demonstrate that the implemented system with fused observations considerably enhances tracking performance over a single sensor system.
Sustainable Fashion Supply Chain : Lessons from H &

Sustainability is significantly important for fashion business due to consumers’ increasing awareness of environment. When a fashion company aims to promote sustainability, the main linkage is to develop a sustainable supply chain. This paper contributes to current knowledge of sustainable supply chain in the textile and clothing industry. We first depict the structure of sustainable fashion supply chain including eco-material preparation, sustainable manufacturing, green distribution, green retailing, and ethical consumers based on the extant literature. We study the case of the Swedish fast fashion company, H&M, which has constructed its sustainable supply chain in developing eco-materials, providing safety training, monitoring sustainable manufacturing, reducing carbon emission in distribution, and promoting eco-fashion. Moreover, based on the secondary data and analysis, we learn the lessons of H&M’s sustainable fashion supply chain from the country perspective: (1) the H&M’s sourcing managers may be more likely to select suppliers in the countries with lower degrees of human wellbeing; (2) the H&M’s supply chain manager may set a higher level of inventory in a country with a higher human wellbeing; and (3) the H&M CEO may consider the degrees of human wellbeing and economic wellbeing, instead of environmental wellbeing when launching the online shopping channel in a specific country.
Dynamic Map Building and Localization : New Theoretical Foundations

Doctor of Philosophy Trinity Term 1995 This thesis examines the theoretical and computational problems associated with map building and localization for autonomous vehicles. In particular, components of a system are described for performing terrain-aided navigation in real time for high speed vehicles or aircraft. Such a system would be able to dynamically construct a map of distinctive naturally-occurring environmental features while simultaneously using those features as landmarks to estimate the position of the vehicle. In order to develop such a system, a variety of challenges are addressed.  A new approach for nonlinear filtering is described that is not only easier to implement, but substantially more accurate than the conventional methods. 2. A new approach is developed for avoiding problems associated with correlations among the position estimates of mapped features. Such correlations prevent the application of standard real time filtering methods and constitu te the key challenge in the area of large scale map build ing. A byproduct of this development is a new general-purpose filtering and data fusion technique. 3. A new data structure is developed for storing the map so that sensor observations can be associated with candidate features in the map in real time. This data structure is shown to be capable of supporting real time performance for maps having many thousands of features. 4. A new combinatorial result is derived that facilitates the decision process for determining which mapped feature is most likely to have produced a given sensor observation. Applications of the above results to other more general engineering problems are also d iscussed.
Role of video games in improving health-related outcomes: a systematic review.

Video games represent a multibillion-dollar industry in the U.S. Although video gaming has been associated with many negative health consequences, it also may be useful for therapeutic purposes. The goal of this study was to determine whether video games may be useful in improving health outcomes.



Literature searches were performed in February 2010 in six databases: the Center on Media and Child Health Database of Research, MEDLINE, CINAHL, PsycINFO, EMBASE, and the Cochrane Central Register of Controlled Trials. Reference lists were hand-searched to identify additional studies. Only RCTs that tested the effect of video games on a positive, clinically relevant health consequence were included. Study selection criteria were strictly defined and applied by two researchers working independently. Study background information (e.g., location, funding source); sample data (e.g., number of study participants, demographics); intervention and control details; outcomes data; and quality measures were abstracted independently by two researchers.



Of 1452 articles retrieved using the current search strategy, 38 met all criteria for inclusion. Eligible studies used video games to provide physical therapy, psychological therapy, improved disease self-management, health education, distraction from discomfort, increased physical activity, and skills training for clinicians. Among the 38 studies, a total of 195 health outcomes were examined. Video games improved 69% of psychological therapy outcomes, 59% of physical therapy outcomes, 50% of physical activity outcomes, 46% of clinician skills outcomes, 42% of health education outcomes, 42% of pain distraction outcomes, and 37% of disease self-management outcomes. Study quality was generally poor; for example, two thirds (66%) of studies had follow-up periods of <12 weeks, and only 11% of studies blinded researchers.



There is potential promise for video games to improve health outcomes, particularly in the areas of psychological therapy and physical therapy. RCTs with appropriate rigor will help build evidence in this emerging area.
Image Data Sharing for Biomedical Research—Meeting HIPAA Requirements for De-identification

Data sharing is increasingly recognized as critical to cross-disciplinary research and to assuring scientific validity. Despite National Institutes of Health and National Science Foundation policies encouraging data sharing by grantees, little data sharing of clinical data has in fact occurred. A principal reason often given is the potential of inadvertent violation of the Health Insurance Portability and Accountability Act privacy regulations. While regulations specify the components of private health information that should be protected, there are no commonly accepted methods to de-identify clinical data objects such as images. This leads institutions to take conservative risk-averse positions on data sharing. In imaging trials, where images are coded according to the Digital Imaging and Communications in Medicine (DICOM) standard, the complexity of the data objects and the flexibility of the DICOM standard have made it especially difficult to meet privacy protection objectives. The recent release of DICOM Supplement 142 on image de-identification has removed much of this impediment. This article describes the development of an open-source software suite that implements DICOM Supplement 142 as part of the National Biomedical Imaging Archive (NBIA). It also describes the lessons learned by the authors as NBIA has acquired more than 20 image collections encompassing over 30 million images.