nils-herrmann/clean_scirepeval huggingface dataset:
Reflective and Refractive Objects for Mixed Reality

In this paper, we present a novel rendering method which integrates reflective or refractive objects into a differential instant radiosity (DIR) framework usable for mixed-reality (MR) applications. This kind of objects are very special from the light interaction point of view, as they reflect and refract incident rays. Therefore they may cause high-frequency lighting effects known as caustics. Using instant-radiosity (IR) methods to approximate these high-frequency lighting effects would require a large amount of virtual point lights (VPLs) and is therefore not desirable due to real-time constraints. Instead, our approach combines differential instant radiosity with three other methods. One method handles more accurate reflections compared to simple cubemaps by using impostors. Another method is able to calculate two refractions in real-time, and the third method uses small quads to create caustic effects. Our proposed method replaces parts in light paths that belong to reflective or refractive objects using these three methods and thus tightly integrates into DIR. In contrast to previous methods which introduce reflective or refractive objects into MR scenarios, our method produces caustics that also emit additional indirect light. The method runs at real-time frame rates, and the results show that reflective and refractive objects with caustics improve the overall impression for MR scenarios.
Model-based clustering for social networks

Network models are widely used to represent relations between interacting units or actors. Network data often exhibit transitivity, meaning that two actors that have ties to a third actor are more likely to be tied than actors that do not, homophily by attributes of the actors or dyads, and clustering. Interest often focuses on finding clusters of actors or ties, and the number of groups in the data is typically unknown. We propose a new model, the latent position cluster model , under which the probability of a tie between two actors depends on the distance between them in an unobserved Euclidean ‘social space’, and the actors’ locations in the latent social space arise from a mixture of distributions, each corresponding to a cluster. We propose two estimation methods: a two-stage maximum likelihood method and a fully Bayesian method that uses Markov chain Monte Carlo sampling. The former is quicker and simpler, but the latter performs better. We also propose a Bayesian way of determining the number of clusters that are present by using approximate conditional Bayes factors. Our model represents transitivity, homophily by attributes and clustering simultaneously and does not require the number of clusters to be known. The model makes it easy to simulate realistic networks with clustering, which are potentially useful as inputs to models of more complex systems of which the network is part, such as epidemic models of infectious disease. We apply the model to two networks of social relations. A free software package in the R statistical language, latentnet, is available to analyse data by using the model.
Methods of suicide: international suicide patterns derived from the WHO mortality database.

Accurate information about preferred suicide methods is important for devising strategies and programmes for suicide prevention. Our knowledge of the methods used and their variation across countries and world regions is still limited. The aim of this study was to provide the first comprehensive overview of international patterns of suicide methods.



Data encoded according to the International Classification of Diseases (10th revision) were derived from the WHO mortality database. The classification was used to differentiate suicide methods. Correspondence analysis was used to identify typical patterns of suicide methods in different countries by providing a summary of cross-tabulated data.



Poisoning by pesticide was common in many Asian countries and in Latin America; poisoning by drugs was common in both Nordic countries and the United Kingdom. Hanging was the preferred method of suicide in eastern Europe, as was firearm suicide in the United States and jumping from a high place in cities and urban societies such as Hong Kong Special Administrative Region, China. Correspondence analysis demonstrated a polarization between pesticide suicide and firearm suicide at the expense of traditional methods, such as hanging and jumping from a high place, which lay in between.



This analysis showed that pesticide suicide and firearm suicide replaced traditional methods in many countries. The observed suicide pattern depended upon the availability of the methods used, in particular the availability of technical means. The present evidence indicates that restricting access to the means of suicide is more urgent and more technically feasible than ever.
An efficient k-means clustering algorithm

In this paper, we present a novel algorithm for performing k-means clustering. It organizes all the patterns in a k-d tree structure such that one can find all the patterns which are closest to a given prototype efficiently. The main intuition behind our approach is as follows. All the prototypes are potential candidates for the closest prototype at the root level. However, for the children of the root node, we may be able to prune the candidate set by using simple geometrical constraints. This approach can be applied recursively until the size of the candidate set is one for each node. Our experimental results demonstrate that our scheme can improve the computational speed of the direct k-means algorithm by an order to two orders of magnitude in the total number of distance calculations and the overall time of computation.
A Bayesian Approach to Unsupervised One-Shot Learning of Object Categories

Learning visual models of object categories notoriously requires thousands of training examples; this is due to the diversity and richness of object appearance which requires models containing hundreds of parameters. We present a method for learning object categories from just a few images ( ). It is based on incorporating “generic” knowledge which may be obtained from previously learnt models of unrelated categories. We operate in a variational Bayesian framework: object categories are represented by probabilistic models, and “prior” knowledge is represented as a probability density function on the parameters of these models. The “posterior” model for an object category is obtained by updating the prior in the light of one or more observations. Our ideas are demonstrated on four diverse categories (human faces, airplanes, motorcycles, spotted cats). Initially three categories are learnt from hundreds of training examples, and a “prior” is estimated from these. Then the model of the fourth category is learnt from 1 to 5 training examples, and is used for detecting new exemplars a set of test images.
Uncovering the overlapping community structure of complex networks in nature and society

Many complex systems in nature and society can be described in terms of networks capturing the intricate web of connections among the units they are made of. A key question is how to interpret the global organization of such networks as the coexistence of their structural subunits (communities) associated with more highly interconnected parts. Identifying these a priori unknown building blocks (such as functionally related proteins, industrial sectors and groups of people) is crucial to the understanding of the structural and functional properties of networks. The existing deterministic methods used for large networks find separated communities, whereas most of the actual networks are made of highly overlapping cohesive groups of nodes. Here we introduce an approach to analysing the main statistical features of the interwoven sets of overlapping communities that makes a step towards uncovering the modular structure of complex systems. After defining a set of new characteristic quantities for the statistics of communities, we apply an efficient technique for exploring overlapping communities on a large scale. We find that overlaps are significant, and the distributions we introduce reveal universal features of networks. Our studies of collaboration, word-association and protein interaction graphs show that the web of communities has non-trivial correlations and specific scaling properties.
Collection flow

Computing optical flow between any pair of Internet face photos is challenging for most current state of the art flow estimation methods due to differences in illumination, pose, and geometry. We show that flow estimation can be dramatically improved by leveraging a large photo collection of the same (or similar) object. In particular, consider the case of photos of a celebrity from Google Image Search. Any two such photos may have different facial expression, lighting and face orientation. The key idea is that instead of computing flow directly between the input pair (I, J), we compute versions of the images (I', J') in which facial expressions and pose are normalized while lighting is preserved. This is achieved by iteratively projecting each photo onto an appearance subspace formed from the full photo collection. The desired flow is obtained through concatenation of flows (I → I') o (J' → J). Our approach can be used with any two-frame optical flow algorithm, and significantly boosts the performance of the algorithm by providing invariance to lighting and shape changes.
Design of an IGBT-based LCL-Resonant Inverter for High-Frequency Induction Heating

A power electronic inverter is developed for a high-frequency induction heating application. The application requires up to 160kW of power at a frequency of 1OOkHz. This power-frequency product represents a significant challenge for today's power semiconductor technology. Voltage source and current source inverters both using ZCS or ZVS are analyzed and compared. To attain the level of performance required, an LCL loadresonant topology is selected to enable ZVS close to the zero current crossing of the load. This mode of softswitching is suitable to greatly reduce the IGBT losses. Inverter control is achieved via a Phase Locked Loop (PLL). This paper presents the circuit design, modeling and control considerations.
Machine learning classifiers and fMRI: A tutorial overview

Interpreting brain image experiments requires analysis of complex, multivariate data. In recent years, one analysis approach that has grown in popularity is the use of machine learning algorithms to train classifiers to decode stimuli, mental states, behaviours and other variables of interest from fMRI data and thereby show the data contain information about them. In this tutorial overview we review some of the key choices faced in using this approach as well as how to derive statistically significant results, illustrating each point from a case study. Furthermore, we show how, in addition to answering the question of 'is there information about a variable of interest' (pattern discrimination), classifiers can be used to tackle other classes of question, namely 'where is the information' (pattern localization) and 'how is that information encoded' (pattern characterization).
Types for Units-of-Measure: Theory and Practice

Units-of-measure are to science what types are to programming. In science and engineering, dimensional and unit consistency provides a first check on the correctness of an equation or formula, just as in programming the passing of a program by the type-checker eliminates one possible reason for failure. Units-of-measure errors can have catastrophic consequences, the most famous of which was the loss in September 1999 of NASA’s Mars Climate Orbiter probe, caused by a confusion between newtons (the SI unit of force) and lbf (poundforce, a unit of force sometimes used in the US). The report into the disaster made many recommendations [14]. Notably absent, though, was any suggestion that programming languages might assist in the prevention of such errors, either through static analysis tools, or through type-checking. Over the years, many people have suggested ways of extending programming languages with support for static checking of units-of-measure. It’s even possible to abuse the rich type systems of existing languages such as C++ and Haskell to achieve it, but at some cost in usability [18, 6]. More recently, Sun’s design for its Fortress programming language has included type system support for dimensions and units . In this short course, we’ll look at the design supported by the F# programming language, the internals of its type system, and the theoretical basis for units safety. The tutorial splits into three parts:
Modeling and Control of MM-UAV: Mobile Manipulating Unmanned Aerial Vehicle

Compared to autonomous ground vehicles, UAVs (unmanned aerial vehicles) have significant mobility advantages and the potential to operate in otherwise unreachable locations. Micro UAVs still suffer from one major drawback: they do not have the necessary payload capabilities to support high performance arms. This paper, however, investigates the key challenges in controlling a mobile manipulating UAV using a commercially available aircraft and a light-weight prototype 3arm manipulator. Because of the overall instability of rotorcraft, we use a motion capture system to build an efficient autopilot. Our results indicate that we can accurately model and control our prototype system given significant disturbances when both moving the manipulators and interacting with the ground.
Threatened egotism, narcissism, self-esteem, and direct and displaced aggression: does self-love or self-hate lead to violence?

It has been widely asserted that low self-esteem causes violence, but laboratory evidence is lacking, and some contrary observations have characterized aggressors as having favorable self-opinions. In 2 studies, both simple self-esteem and narcissism were measured, and then individual participants were given an opportunity to aggress against someone who had insulted them or praised them or against an innocent third person. Self-esteem proved irrelevant to aggression. The combination of narcissism and insult led to exceptionally high levels of aggression toward the source of the insult. Neither form of self-regard affected displaced aggression, which was low in general. These findings contradict the popular view that low self-esteem causes aggression and point instead toward threatened egotism as an important cause.
Perspectives to Predict Dropout in University Students with Machine Learning

This study analyzes the performance of four machine learning algorithms with different perspectives for defining data files, in the prediction of university student desertion. The algorithms used were: Random Forest, Neural Networks, Support Vector Machines and Logistic Regression. It was found that the Random Forest algorithm with 10 variables randomly sampled as candidates in each division, was the best for predicting dropouts and that the ideal perspective for training the algorithm is to use information on all semesters that students take within a given period of time, using a classification variable that defines the non-dropout as the graduated student. In a first validation sample, this approach correctly predicted 91% of dropouts, with a sensitivity of 87%.
Bonfire: a nomadic system for hybrid laptop-tabletop interaction

We present Bonfire, a self-contained mobile computing system that uses two laptop-mounted laser micro-projectors to project an interactive display space to either side of a laptop keyboard. Coupled with each micro-projector is a camera to enable hand gesture tracking, object recognition, and information transfer within the projected space. Thus, Bonfire is neither a pure laptop system nor a pure tabletop system, but an integration of the two into one new nomadic computing platform. This integration (1) enables observing the periphery and responding appropriately, e.g., to the casual placement of objects within its field of view, (2) enables integration between physical and digital objects via computer vision, (3) provides a horizontal surface in tandem with the usual vertical laptop display, allowing direct pointing and gestures, and (4) enlarges the input/output space to enrich existing applications. We describe Bonfire's architecture, and offer scenarios that highlight Bonfire's advantages. We also include lessons learned and insights for further development and use.
Mechanisms of facial emotion recognition in autism spectrum disorders: Insights from eye tracking and electroencephalography

While behavioural difficulties in facial emotion recognition (FER) have been observed in individuals with Autism Spectrum Disorder (ASD), behavioural studies alone are not suited to elucidate the specific nature of FER challenges in ASD. Eye tracking (ET) and electroencephalography (EEG) provide insights in to the attentional and neurological correlates of performance, and may therefore provide insight in to the mechanisms underpinning FER in ASD. Given that these processes develop over the course of the developmental trajectory, there is a need to synthesise findings in regard to the developmental stages to determine how the maturation of these systems may impact FER in ASD. We conducted a systematic review of fifty-four studies investigating ET or EEG meeting inclusion criteria. Findings indicate divergence of visual processing pathways in individuals with ASD. Altered function of the social brain in ASD impacts the processing of facial emotion across the developmental trajectory, resulting in observable differences in ET and EEG outcomes.
A portable powered ankle-foot orthosis for rehabilitation.

Innovative technological advancements in the field of orthotics, such as portable powered orthotic systems, could create new treatment modalities to improve the functional out come of rehabilitation. In this article, we present a novel portable powered ankle-foot orthosis (PPAFO) to provide untethered assistance during gait. The PPAFO provides both plantar flexor and dorsiflexor torque assistance by way of a bidirectional pneumatic rotary actuator. The system uses a portable pneumatic power source (compressed carbon dioxide bottle) and embedded electronics to control the actuation of the foot. We collected pilot experimental data from one impaired and three nondisabled subjects to demonstrate design functionality. The impaired subject had bilateral impairment of the lower legs due to cauda equina syndrome. We found that data from nondisabled walkers demonstrated the PPAFO's capability to provide correctly timed plantar flexor and dorsiflexor assistance during gait. Reduced activation of the tibialis anterior during stance and swing was also seen during assisted nondisabled walking trials. An increase in the vertical ground reaction force during the second half of stance was present during assisted trials for the impaired subject. Data from nondisabled walkers demonstrated functionality, and data from an impaired walker demonstrated the ability to provide functional plantar flexor assistance.
An empirical study on the use of i* by non-technical stakeholders: the case of strategic dependency diagrams

Early phases of information systems engineering include the understanding of the enterprise’s context and the construction of models at different levels of decomposition, required to design the system architecture. These time-consuming activities are usually conducted by relatively large teams, composed of groups of non-technical stakeholders playing mostly an informative role (i.e. not involved in documentation and even less in modelling), led by few experienced technical consultants performing most of the documenting and modelling effort. This paper evaluates the ability of non-technical stakeholders to create strategic dependency diagrams written with the i* language in the design of the context model of a system architecture, and find out which difficulties they may encounter and what the quality of the models they build is. A case study involving non-technical stakeholders from 11 organizational areas in an Ecuadorian university held under the supervision and coordination of the two authors acting as consultants. The non-technical stakeholders identified the majority of the dependencies that should appear in the case study’s context model, although they experienced some difficulties in declaring the type of dependency, representing such dependencies graphically and applying the description guidelines provided in the training. Managers were observed to make more mistakes than other more operational roles. From the observations of these results, a set of methodological advices were compiled for their use in future, similar endeavours. It is concluded that non-technical stakeholders can take an active role in the construction of the context model. This conclusion is relevant for both researchers and practitioners involved in technology transfer actions with use of i*.
Toward a Maturity Model Development Process for Information Systems ( MMDePSI )

Maturity models are significant tools to ensure continuous improvement of systems and activities. They allow selfassessment and provide a means benchmark of these activities in relation to best practices. This article proposes the MMDePSI process for the development of new models of maturity in information systems. It begins by presenting the existing methods and approaches. Then he describes the MMDePSI process and the corresponding meta-model. Before concluding and presenting prospects of this work, it evaluates the proposed process.
Cleaning Validation in the Pharmaceutical Industry

Cleaning validation in the pharmaceutical industry has been a topic of ever-increasing interest and scrutiny in recent Food and Drug Administration (FDA) inspections. The validation of procedures used to clean the equipment employed during the various steps of a manufacturing process is a clear requirement of current Good Manufacturing Practice (cGMP). As such, FDA inspectors now expect to see a functioning cleaning validation program with appropriate documentation in place during their inspections. The requirement that equipment be clean before being used is not a new concept. The equally important requirement that it also be sanitary is many times obfuscated by the word, clean.
SecureUML: A UML-Based Modeling Language for Model-Driven Security

We present a modeling language for the model-driven develop ment of secure, distributed systems based on the Unified Modeling Language (UML). Our approach is based on role-based access control with addi tional support for specifying authorization constraints. We show how UML can b e used to specify information related to access control in the overall des ign of an application and how this information can be used to automatically genera te complete access control infrastructures. Our approach can be used to improv e productivity during the development of secure distributed systems and the qu ality of the resulting systems.
Supervoxel-Consistent Foreground Propagation in Video

A major challenge in video segmentation is that the foreground object may move quickly in the scene at the same time its appearance and shape evolves over time. While pairwise potentials used in graph-based algorithms help smooth labels between neighboring (super)pixels in space and time, they offer only a myopic view of consistency and can be misled by inter-frame optical flow errors. We propose a higher order supervoxel label consistency potential for semi-supervised foreground segmentation. Given an initial frame with manual annotation for the foreground object, our approach propagates the foreground region through time, leveraging bottom-up supervoxels to guide its estimates towards long-range coherent regions. We validate our approach on three challenging datasets and achieve state-of-the-art results.