nils-herrmann/clean_scirepeval huggingface dataset:
Adaptive neighborhood selection for real-time surface normal estimation from organized point cloud data using integral images

In this paper we present two real-time methods for estimating surface normals from organized point cloud data. The proposed algorithms use integral images to perform highly efficient border- and depth-dependent smoothing and covariance estimation. We show that this approach makes it possible to obtain robust surface normals from large point clouds at high frame rates and therefore, can be used in real-time computer vision algorithms that make use of Kinect-like data.
Legal Compliance by Design (LCbD) and through Design (LCtD): Preliminary Survey

The purpose of this paper is twofold: (i) carrying out a preliminary survey of the literature and research projects on Compliance by Design (CbD); and (ii) clarifying the double process of (a) extending business managing techniques to other regulatory fields, and (b) converging trends in legal theory, legal technology and Artificial Intelligence. The paper highlights the connections and differences we found across different domains and proposals. We distinguish three different policydriven types of CbD: (i) business, (ii) regulatory, (iii) and legal. The recent deployment of ethical views, and the implementation of general principles of privacy and data protection lead to the conclusion that, in order to appropriately define legal compliance, Compliance through Design (CtD) should be differentiated from CbD.
Comparison of Internet addicts and non-addicts in Taiwanese high school

This study investigated the difference between Internet addicts and non-addicts in Taiwanese high schools, and focused specifically on their Internet usage patterns, and gratification and communication pleasures. A total of 1708 valid data samples of high school adolescents were collected. Among this sample, 236 subjects (13.8%) were identified as addicts using the eight-item Internet addiction Diagnostic Questionnaire designed by Young [Internet addiction survey [Online]. ]. The analytical results revealed that Internet addicts spent almost twice as many hours on line on average than the non-addicts. Notably, surfing with a social/entertainment motivation and gratification was positively correlated with Internet addiction. Furthermore, Internet addicts obtained markedly higher overall PIUST scores and scored higher than non-addicts on four subscales (tolerance; compulsive use and withdrawal; related problems, including family, school, health, and other problems; interpersonal and financial problems). While Internet addicts perceived the Internet to have significantly more negative influences on daily routines, school performance, teacher and parental relation than non-addicts, both Internet addicts and non-addicts viewed Internet use as enhancing peer relations. Moreover, students with personalities characterized by dependence, shyness, depression and low self-esteem had a high tendency to become addicted.
CitySpectrum: a non-negative tensor factorization approach

People flow at a citywide level is in a mixed state with several basic patterns (e.g. commuting, working, commercial), and it is therefore difficult to extract useful information from such a mixture of patterns directly. In this paper, we proposed a novel tensor factorization approach to modeling city dynamics in a basic life pattern space (CitySpectral Space). To obtain the CitySpectrum, we utilized Non-negative Tensor Factorization (NTF) to decompose a people flow tensor into basic life pattern tensors, described by three bases i.e. the intensity variation among different regions, the time-of-day and the sample days. We apply our approach to a big mobile phone GPS log dataset (containing 1.6 million users) to model the fluctuation in people flow before and after the Great East Japan Earthquake from a CitySpectral perspective. In addition, our framework is extensible to a variety of auxiliary spatial-temporal data. We parametrize a people flow with a spatial distribution of the Points of Interest (POIs) to quantitatively analyze the relationship between human mobility and POI distribution. Based on the parametric people flow, we propose a spectral approach for a site-selection recommendation and people flow simulation in another similar area using POI distribution.
Typology of Distributed Ledger based Business Models

The potential of distributed ledger technology and its application in various industries is a controversially debated topic. Advocates of the technology emphasize the economic benefits of decentralization and transparency, leading to cost reductions as well as the alleviation of several of today`s economic and technological problems. In contrast, critics assert that the potential of distributed ledgers might be overhyped, possibly leading to the next tech bubble. This paper contributes to the discussion by developing a typology of business models that are based on distributed ledger technology. In particular, this paper is a first step towards a more differentiated discussion on the potential of distributed ledges, by taking the underlying business models into consideration. Despite a characterization of the types, a discussion about special features of distributed ledger based business models is provided in the context of contemporary business model literature and the associated role of IT. It is proposed that future research must evaluate each business model isolated to achieve a comprehensive assessment of the potential of distributed ledgers. This paper can be interpreted as starting point for more fruitful discussions and the repeal of the partially diametrical opposed opinions towards the potentials of the technology.
The Airport Gate Assignment Problem: Mathematical Model and a Tabu Search Algorithm

In this paper, we consider an Airport Gate Assignment Problem that dynamically assigns airport gates to scheduled ights based on passengers' daily origin and destination ow data. The objective of the problem is to minimize the overall connection times that passengers walk to catch their connection ights. We formulate this problem as a mixed 0-1 quadratic integer programming problem and then reformulate it as a mixed 0-1 integer problem with a linear objective function and constraints. We design a simple tabu search meta-heuristic to solve the problem. The algorithm exploits the special properties of di erent types of neighborhood moves, and create highly e ective candidate list strategies. We also address issues of tabu short term memory, dynamic tabu tenure, aspiration rule, and various intensi cation and diversi cation strategies. Preliminary computational experiments are conducted and the results are presented and analyzed.
Wireless Wearable Magnetometer-Based Sensor for Sleep Quality Monitoring

Obstructive sleep apnoea is one of the most common sleep disorders, which is characterized by the repetitive cessations of respiratory flow during sleep. This paper describes an alternative method to detect sleep parameters by measuring a variation in the earth’s magnetic field. A magnetometer sensor, laid onto the body, detects millimeter night-time breathing movements by measuring the change in the magnetic vectors. A soft, non-invasive wearable sensor is designed to be printed on a miniature printed circuit board, packaged in a minimal invasive board, which includes a wireless Bluetooth low-energy (BLE) module and a low-power microcontroller. A very low power consumption is achieved by applying a smart processing algorithm, which is also described. The algorithm delivers respiration rate, apnoea time, and time in movement. A comparison with a well-known air-flow sensor is also discussed.
Organizing Programs Without Classes

All organizational functions carried out by classes can be accomplished in a simple and natural way by object inheritance in classless languages, with no need for special mechanisms. A single model—dividing types into prototypes and traits—supports sharing of behavior and extending or replacing representations. A natural extension, dynamic object inheritance, can model behavioral modes. Object inheritance can also be used to provide structured name spaces for well-known objects. Classless languages can even express “class-based” encapsulation. These stylized uses of object inheritance become instantly recognizable idioms, and extend the repertory of organizing principles to cover a wider range of programs.
A review of argumentation for the Social Semantic Web

Argumentation represents the study of views and opinions that humans express with the goal of reaching a conclusion through logical reasoning. Since the 1950’s, several models have been proposed to capture the essence of informal argumentation in different settings. With the emergence of the Web, and then the Semantic Web, this modeling shifted towards ontologies, while from the development perspective, we witnessed an important increase in Web 2.0 human-centered collaborative deliberation tools. Through a review of more than 150 scholarly papers, this article provides a comprehensive and comparative overview of approaches to modeling argumentation for the Social Semantic Web. We start from theoretical foundational models and investigate how they have influenced Social Web tools. We also look into Semantic Web argumentation models. Finally we end with Social Web tools for argumentation, including online applications combining Web 2.0 and Semantic Web technologies, following the path to a global World Wide Argument Web.
On the momentum term in gradient descent learning algorithms

A momentum term is usually included in the simulations of connectionist learning algorithms. Although it is well known that such a term greatly improves the speed of learning, there have been few rigorous studies of its mechanisms. In this paper, I show that in the limit of continuous time, the momentum parameter is analogous to the mass of Newtonian particles that move through a viscous medium in a conservative force field. The behavior of the system near a local minimum is equivalent to a set of coupled and damped harmonic oscillators. The momentum term improves the speed of convergence by bringing some eigen components of the system closer to critical damping. Similar results can be obtained for the discrete time case used in computer simulations. In particular, I derive the bounds for convergence on learning-rate and momentum parameters, and demonstrate that the momentum term can increase the range of learning rate over which the system converges. The optimal condition for convergence is also analyzed.
Faster Algorithms for the Shortest Path Problem

Efficient implementations of Dijkstra's shortest path algorithm are investigated. A new data structure, called the <italic>radix heap</italic>, is proposed for use in this algorithm. On a network with <italic>n</italic> vertices, <italic>m</italic> edges, and nonnegative integer arc costs bounded by <italic>C</italic>, a one-level form of radix heap gives a time bound for Dijkstra's algorithm of <italic>O</italic>(<italic>m</italic> + <italic>n</italic> log <italic>C</italic>). A two-level form of radix heap gives a bound of <italic>O</italic>(<italic>m</italic> + <italic>n</italic> log <italic>C</italic>/log log <italic>C</italic>). A combination of a radix heap and a previously known data structure called a <italic>Fibonacci heap</italic> gives a bound of <italic>O</italic>(<italic>m</italic> + <italic>n</italic>a @@@@log <italic>C</italic>). The best previously known bounds are <italic>O</italic>(<italic>m</italic> + <italic>n</italic> log <italic>n</italic>) using Fibonacci heaps alone and <italic>O</italic>(<italic>m</italic> log log <italic>C</italic>) using the priority queue structure of Van Emde Boas et al. .
Drought Spatial Object Prediction Approach using Artificial Neural Network

The concept of object identification and modeling has fueled a lengthy scientific effort to convert remotely sensed images into geographic phenomena. The objective of this article was to develop a new concept for characterizing and identifying drought spatial objects from satellite images for improved drought prediction and mitigation using a back propagation artificial neural network (ANN). To characterize drought as a spatial object, 11 attributes from multi-sensors and resolutions (such as Standardized Deviation of Normalized Difference Vegetation Index [SDNDVI], Digital Elevation Model [DEM], Soil Water Holding Capacity, Ecological Regions, Land Cover, Standard Precipitation Index [SPI], and oceanic indices were used. After characterizing and identifying drought spatial objects, the experimental focus was on predicting drought in a oneto four-month time lag using a back propagation ANN. Using this approach, the drought was predicted for one to four months lead time with correlation coefficient (r) accuracies of 0.70–0.95. The output of this new concept could help in integrating the available information from multi-sensors and resolutions for a drought mitigation application at different levels of decision making. Future research may focus on experimenting with the approach in wider coverage areas, such as at regional or continental levels, and quantifying the uncertainty level of the approach for its practical use in drought adaptation planning and mitigation applications.
Caching based socially-aware D2D communications in wireless content delivery networks: a hypergraph framework

The emergence of the D2D communications paradigm has transformed the way in which cellular networks are operated. Previous work in D2D communications, however, mainly focused on interference management and capacity maximization of both D2D links and cellular links. Recent literature has observed that the QoE can be greatly enhanced by caching contents in mobile devices, with a carefully designed caching strategy. In this article, we propose a novel hypergraph framework that designs the caching based D2D communication scheme by taking social ties among users and common interests into consideration. We first present the considered features of the D2D transmission scheme, social characteristics consideration, and interest similarity impact. The key concepts of hypergraph and related techniques, such as hypergraph coloring and multidimensional matching, are explained. Then some design issues with simulation results in the proposed framework are discussed in detail, which show the potential of the proposed approach. By jointly considering social ties, common interests, and the D2D transmission scheme, we believe the proposed framework explores new opportunities and future directions in caching based socially-aware D2D communications.
An ontology for clinical questions about the contents of patient notes

Many studies have been completed on question classification in the open domain, however only limited work focuses on the medical domain. As well, to the best of our knowledge, most of these medical question classifications were designed for literature based question and answering systems. This paper focuses on a new direction, which is to design a novel question processing and classification model for answering clinical questions applied to electronic patient notes.



There are four main steps in the work. Firstly, a relatively large set of clinical questions was collected from staff in an Intensive Care Unit. Then, a clinical question taxonomy was designed for question and answering purposes. Subsequently an annotation guideline was created and used to annotate the question set. Finally, a multilayer classification model was built to classify the clinical questions.



Through the initial classification experiments, we realized that the general features cannot contribute to high performance of a minimum classifier (a small data set with multiple classes). Thus, an automatic knowledge discovery and knowledge reuse process was designed to boost the performance by extracting and expanding the specific features of the questions. In the evaluation, the results show around 90% accuracy can be achieved in the answerable subclass classification and generic question templates classification. On the other hand, the machine learning method does not perform well at identifying the category of unanswerable questions, due to the asymmetric distribution.



In this paper, a comprehensive study on clinical questions has been completed. A major outcome of this work is the multilayer classification model. It serves as a major component of a patient records based clinical question and answering system as our studies continue. As well, the question collections can be reused by the research community to improve the efficiency of their own question and answering systems.
The generation of environmental map based on a NDT grid mapping -Proposal of convergence calculation corresponding to high resolution grid-

The paper presents a new convergence calculation method of the normal distributions transform (NDT) scan matching for high resolution grid map. The proposed method employs interactive closest point (ICP) algorithm to find corresponding point, even if the input scan points are in the outside of the reference grid area. It also enlarges the convergence area by modifying the eigenvalue of normal distribution so that the evaluation value can be driven effectively by all the pairing data for improving convergence speed. In the normal NDT scan matching algorithm, the grid size especially has much effect on the capability of convergence area and speed. Thus, it is difficult to generate the detailed map with small grids. The proposed convergence approach can solve the problem by the phased adjustment of convergence area. Experimental result shows the feasibility of the detailed environmental map generated by the NDT grid mapping.
Road environment modeling using robust perspective analysis and recursive Bayesian segmentation

Recently, vision-based advanced driver-assistance systems (ADAS) have received a new increased interest to enhance driving safety. In particular, due to its high performance–cost ratio, mono-camera systems are arising as the main focus of this field of work. In this paper we present a novel on-board road modeling and vehicle detection system, which is a part of the result of the European I-WAY project. The system relies on a robust estimation of the perspective of the scene, which adapts to the dynamics of the vehicle and generates a stabilized rectified image of the road plane. This rectified plane is used by a recursive Bayesian classifier, which classifies pixels as belonging to different classes corresponding to the elements of interest of the scenario. This stage works as an intermediate layer that isolates subsequent modules since it absorbs the inherent variability of the scene. The system has been tested on-road, in different scenarios, including varied illumination and adverse weather conditions, and the results have been proved to be remarkable even for such complex scenarios.
Explainable PCGML via Game Design Patterns

Procedural content generation via Machine Learning (PCGML) is the umbrella term for approaches that generate content for games via machine learning. One of the benefits of PCGML is that, unlike search or grammar-based PCG, it does not require hand authoring of initial content or rules. Instead, PCGML relies on existing content and black box models, which can be difficult to tune or tweak without expert knowledge. This is especially problematic when a human designer needs to understand how to manipulate their data or models to achieve desired results. We present an approach to Explainable PCGML via Design Patterns in which the design patterns act as a vocabulary and mode of interaction between user and model. We demonstrate that our technique outperforms non-explainable versions of our system in interactions with five expert designers, four of whom lack any machine learning expertise.
QRPp1-4: Characterizing Quality of Time and Topology in a Time Synchronization Network

As Internet computing gains speed, complexity, and becomes ubiquitous, the need for precise and accurate time synchronization increases. In this paper, we present a characterization of a clock synchronization network managed by Network Time Protocol (NTP), composed by thousands of nodes, including hundreds of Stratum 1 servers, based on data collected recently by a robot. NTP is the most common protocol for time synchronization in the Internet. Many aspects that define the quality of timekeeping are analyzed, as well as topological characteristics of the network. The results are compared to previous characterizations of the NTP network, showing the evolution of clock synchronization in the last fifteen years.
Reliability of Capacitors for DC-Link Applications in Power Electronic Converters—An Overview

DC-link capacitors are an important part in the majority of power electronic converters which contribute to cost, size and failure rate on a considerable scale. From capacitor users' viewpoint, this paper presents a review on the improvement of reliability of dc link in power electronic converters from two aspects: 1) reliability-oriented dc-link design solutions; 2) conditioning monitoring of dc-link capacitors during operation. Failure mechanisms, failure modes and lifetime models of capacitors suitable for the applications are also discussed as a basis to understand the physics-of-failure. This review serves to provide a clear picture of the state-of-the-art research in this area and to identify the corresponding challenges and future research directions for capacitors and their dc-link applications.
Simulating Urban Growth Using the SLEUTH Model in a Coastal Peri-Urban District in China

China’s southeast coastal areas have witnessed rapid growth in the last two decades, owing mostly to their economic and social attractions. In this paper, we chose Jimei, a coastal peri-urban district of Xiamen city on the southeast coast of China, as a study area to explore the district’s growth dynamics, to predict future sprawl during the next decade and to provide a basis for urban planning. The SLEUTH urban growth model was calibrated against historical data derived from a series of Landsat TM 5 satellite images taken between 1992 and 2007. A Lee-Sallee value of 0.48 was calculated for the district, which is a satisfactory result compared with related studies. Five coefficients of urban growth, diffusion, spread, breed, slope resistance and road gravity had values of 25, 68, 86, 24 and 23, respectively, in 2007. The growth coefficients (i.e., urban character) can capture urban growth characteristics in Jimei district. The urban DNA revealed that, over the study period, urban growth in the district was dominated both by urbanization through establishment of new urban centers, and by expansion outward from existing urban centers. In contrast to interior cities, in which expansions are dramatically shaped by actual road patterns, urban expansion in the district was likely constrained by the nearby coastline. Future urban growth patterns were predicted to 2020 assuming three different development scenarios. The first scenario simulated a continuation of historical urban growth without changing current conditions. The second scenario projected managed growth in which OPEN ACCESS Sustainability 2014, 6 3900 urban growth is limited by a layer with areas excluded from urbanization, which is the future development plan for Jimei district and Xiamen city. The third scenario depicted a growth with maximum protection in which growth was allowed to continue, similar to the second scenario, but with lower diffusion and spread coefficients applied to the growth pattern. The third scenario demonstrated that valuable land could be saved, which is the most desirable outcome for Jimei urban development. The study showed that SLEUTH can be an extremely useful tool for coastal city managers to explore the likely outcomes of their city development plans.