nils-herrmann/clean_scirepeval huggingface dataset:
A Level-Set Approach to 3D Reconstruction from Range Data

This paper presents a method that uses the level sets of volumes to reconstruct the shapes of 3D objects from range data. The strategy is to formulate 3D reconstruction as a statistical problem: find that surface which is mostly likely, given the data and some prior knowledge about the application domain. The resulting optimization problem is solved by an incremental process of deformation. We represent a deformable surface as the level set of a discretely sampled scalar function of three dimensions, i.e., a volume. Such level-set models have been shown to mimic conventional deformable surface models by encoding surface movements as changes in the greyscale values of the volume. The result is a voxel-based modeling technology that offers several advantages over conventional parametric models, including flexible topology, no need for reparameterization, concise descriptions of differential structure, and a natural scale space for hierarchical representations. This paper builds on previous work in both 3D reconstruction and level-set modeling. It presents a fundamental result in surface estimation from range data: an analytical characterization of the surface that maximizes the posterior probability. It also presents a novel computational technique for level-set modeling, called the sparse-field algorithm, which combines the advantages of a level-set approach with the computational efficiency and accuracy of a parametric representation. The sparse-field algorithm is more efficient than other approaches, and because it assigns the level set to a specific set of grid points, it positions the level-set model more accurately than the grid itself. These properties, computational efficiency and subcell accuracy, are essential when trying to reconstruct the shapes of 3D objects. Results are shown for the reconstruction objects from sets of noisy and overlapping range maps.
A survey of affective brain computer interfaces : principles , state-ofthe-art , and challenges

A survey of affective brain computer interfaces: principles, state-of-the-art, and challenges Christian Mühl, Brendan Allison, Anton Nijholt & Guillaume Chanel a Inria Bordeaux Sud-Ouest, Talence, France b ASPEN Lab, Electrical and Computer Engineering Department, Old Dominion University, Norfolk, VA, USA c Department of Cognitive Science, University of California at San Diego, La Jolla, CA, USA d Faculty EEMCS, Human Media Interaction, University of Twente, Enschede, The Netherlands e Swiss Center for Affective Sciences – University of Geneva, Campus Biotech, Genève, Switzerland Published online: 14 May 2014.
Light load efficiency improvement for multi-channel PFC

With the fast growing information technologies, AC-DC front-end power supply design is facing extremely tough challenges due to the continuously increasing power density and efficiency requirement. For the PFC stage, how to reduce the boost inductor and the EMI filter size without compromising the efficiency is the key to meet the challenges. Lots of techniques has been studied and proposed during the last several years. Among these techniques, multi-channel interleaving is a quite promising one. By staggering the channels at uniform intervals, multichannel interleaved PFC can reduce the EMI filter size significantly due to the ripple cancellation effect. In addition, the interleaving multi-channel configuration makes it possible to implement the phase-shedding to improve the PFC light load efficiency. Today, the power supply industry is spending tremendous effort on improving the PFC light efficiency due to the economic reasons and environmental concerns. By decreasing the number of active channels according to the load, the PFC light load efficiency can be optimized. In this paper, the phase-shedding control is proposed to improve the PFC light load efficiency and the issues on the EMI filter design are investigated. To improve the light load efficiency without compromising the EMI filter size, the asymmetrical phase angle control strategy is proposed, and is verified and demonstrated by the experimental results.
What Makes Interruptions Disruptive?: A Process-Model Account of the Effects of the Problem State Bottleneck on Task Interruption and Resumption

In this paper we present a computational cognitive model of task interruption and resumption, focusing on the effects of the problem state bottleneck. Previous studies have shown that the disruptiveness of interruptions is for an important part determined by three factors: interruption duration, interrupting-task complexity, and moment of interruption. However, an integrated theory of these effects is still missing. Based on previous research into multitasking, we propose a first step towards such a theory in the form of a process model that attributes these effects to problem state requirements of both the interrupted and the interrupting task. Subsequently, we tested two predictions of this model in two experiments. The experiments confirmed that problem state requirements are an important predictor for the disruptiveness of interruptions. This suggests that interfaces should be designed to a) interrupt users at low-problem state moments and b) maintain the problem state for the user when interrupted.
User's hand effects on EMC internal GSM/DCS mobile phone antenna

The user's hand effects on the EMC GSM/DCS antenna embedded in the mobile phone have been presented. Although the EMC antenna is easy to be integrated into a mobile phone with nearby electronic components or conducting elements, the performance of the antenna is still greatly affected when a user's hand holds the mobile phone. Even when the phone was hand-held at the bottom, large efficiency drops have been observed, especially for the GSM operation
A vehicle for research: using street sweepers to explore the landscape of environmental community action

Researchers are developing mobile sensing platforms to facilitate public awareness of environmental conditions. However, turning such awareness into practical community action and political change requires more than just collecting and presenting data. To inform research on mobile environmental sensing, we conducted design fieldwork with government, private, and public interest stakeholders. In parallel, we built an environmental air quality sensing system and deployed it on street sweeping vehicles in a major U.S. city; this served as a research vehicle"by grounding our interviews and affording us status as environmental action researchers. In this paper, we present a qualitative analysis of the landscape of environmental action, focusing on insights that will help researchers frame meaningful technological interventions.
In defense of color-based model-free tracking

In this paper, we address the problem of model-free online object tracking based on color representations. According to the findings of recent benchmark evaluations, such trackers often tend to drift towards regions which exhibit a similar appearance compared to the object of interest. To overcome this limitation, we propose an efficient discriminative object model which allows us to identify potentially distracting regions in advance. Furthermore, we exploit this knowledge to adapt the object representation beforehand so that distractors are suppressed and the risk of drifting is significantly reduced. We evaluate our approach on recent online tracking benchmark datasets demonstrating state-of-the-art results. In particular, our approach performs favorably both in terms of accuracy and robustness compared to recent tracking algorithms. Moreover, the proposed approach allows for an efficient implementation to enable online object tracking in real-time.
Graph-Based Semi-Supervised Learning

While labeled data is expensive to prepare, ever increasing amounts of unlabeled data is becoming widely available. In order to adapt to this phenomenon, several semi-supervised learning (SSL) algorithms, which learn from labeled as well as unlabeled data, have been developed. In a separate line of work, researchers have started to realize that graphs provide a natural way to represent data in a variety of domains. Graph-based SSL algorithms, which bring together these two lines of work, have been shown to outperform the state-of-the-art in many applications in speech processing, computer vision, natural language processing, and other areas of Artificial Intelligence. Recognizing this promising and emerging area of research, this synthesis lecture focuses on graphbased SSL algorithms (e.g., label propagation methods). Our hope is that after reading this book, the reader will walk away with the following: (1) an in-depth knowledge of the current stateof-the-art in graph-based SSL algorithms, and the ability to implement them; (2) the ability to decide on the suitability of graph-based SSL methods for a problem; and (3) familiarity with different applications where graph-based SSL methods have been successfully applied.
Factors Influencing Consumption Experience of Mobile Commerce: a Study from Experiential View

The purpose of this study is to interpret consumers’ emotion in their consumption experience in the context of mobile commerce from an experiential view. The study seeks to address concerns about the experiential aspects of mobile commerce regardless of the consumption type. For the purpose, the authors aims to propose a stimulus-organism-response (S-O-R) based model that incorporates both utilitarian and hedonic factors of consumers.  A survey study was conducted to collect data from 293 mobile phone users. The questionnaire was administered in study classrooms, a library, or via e-mail. The measurement model and structural model were examined using LISREL 8.7.  The results of this research implied that emotion played a significant role in the mobile consumption experience; hedonic factors had a positive effect on the consumption experience, while utilitarian factors had a negative effect on the consumption experience of consumers. The empirical findings also indicated that media richness was as important as subjective norms, and more important than convenience and self-efficacy.  Few m-commerce studies have focused directly on the experiential aspects of consumption, including the hedonic experience and positive emotions among mobile device users. Applying the stimulus-organism-response (S-O-R) framework from the perspective of the experiential view, the current research model is developed to examine several utilitarian and hedonic factors in the context of the consumption experience, and indicates a comparison between the information processing (utilitarian) view and the experiential (hedonic) view of consumer behavior. It illustrates the relationships among six variables (i.e. convenience, media richness, subjective norms, self-efficacy, emotion, and consumption experience) in a mobile commerce context.
Cuckoo Search: A Brief Literature Review

Cuckoo search (CS) was introduced in 2009, and it has attracted great attention due to its promising efficiency in solving many optimization problems and real-world applications. In the last few years, many papers have been published regarding cuckoo search, and the relevant literature has expanded significantly. This chapter summarizes briefly the majority of the literature about cuckoo search in peer-reviewed journals and conferences found so far. These references can be systematically classified into appropriate categories, which can be used as a basis for further research.  A brief literature review, in: Cuckoo Search and Firefly Algorithm: Theory and Applications, Studies in Computational Intelligence, vol. 516, pp. 49-62 (2014).
Internet Traffic Engineering by Optimizing OSPF Weights

Open Shortest Path First (OSPF) is the most commonly used intra-domain internet routing protocol. Traffic flow is routed along shortest paths, sptitting flow at nodes where several outgoing tinks are on shortest paths to the destination. The weights of the tinks, and thereby the shortest path routes, can be changed by the network operator. The weights could be set proportional to their physical distances, but often the main goal is to avoid congestion, i.e. overloading of links, and the standard heuristic rec. ommended by Cisco is to make the weight of a link inversely proportional to its capacity. Our starting point was a proposed AT&T WorldNet backbone with demands projected from previous measurements. The desire was to optimize the weight setting based on the projected demands. We showed that optimiz@ the weight settings for a given set of demands is NP-hard, so we resorted to a local search heuristic. Surprisingly it turned out that for the proposed AT&T WorldNet backbone, we found weight settiis that performed within a few percent from that of the optimal general routing where the flow for each demand is optimalty distributed over all paths between source and destination. This contrasts the common belief that OSPF routing leads to congestion and it shows that for the network and demand matrix studied we cannot get a substantially better load balancing by switching to the proposed more flexible Multi-protocol Label Switching (MPLS) technologies. Our techniques were atso tested on synthetic internetworks, based on a model of Zegura et al. (INFOCOM’96), for which we dld not always get quite as close to the optimal general routing. However, we compared witIs standard heuristics, such as weights inversely proportional to the capac.. ity or proportioml to the physical distances, and found that, for the same network and capacities, we could support a 50 Yo-1 10% increase in the demands. Our assumed demand matrix can also be seen as modeling service level agreements (SLAS) with customers, with demands representing guarantees of throughput for virtnal leased lines.  OSPF, MPLS, traffic engineering, local search, hashing ta. bles, dynamic shortest paths, mntti-cosnmodity network flows.
Rateless Codes and Big Downloads

This paper presents a new algorithm for downloading big files from multiple sources in peer-to-peer networks. The algorithm is compelling with the simplicity of its implementation and the novel properties it offers. It ensures low hand-shaking cost between peers who intend to download a file (or parts of a file) from each other. Furthermore, it achieves maximal file availability, meaning that any two peers with partial knowledge of a given file will almost always be able to fully benefit from each other’s knowledge– i.e., overlapping knowledge will rarely occur. Our algorithm is made possible by the recent introduction of linear-time rateless erasure codes.
The influence of extro/introversion on the intention to pay for social networking sites

Although social networking sites (SNSs) have attracted increased attention and members in recent years, there has been little research on it: particularly on how a users’ extroversion or introversion can affect their intention to pay for these services and what other factors might influence them. We therefore proposed and tested a model that measured the users’ value and satisfaction perspectives by examining the influence of these factors in an empirical survey of 288 SNS members. At the same time, the differences due to their psychological state were explored. The causal model was validated using PLSGraph 3.0; six out of eight study hypotheses were supported. The results indicated that perceived value significantly influenced the intention to pay SNS subscription fees while satisfaction did not. Moreover, extroverts thought more highly of the social value of the SNS, while introverts placed more importance on its emotional and price value. The implications of these findings are discussed.
Across Disciplinary and Organizational Boundaries

Scientific and engineering research increasingly involves multidisciplinary collaboration, sometimes across multiple organizations. Technological advances have made such cross-boundary projects possible, yet they can carry high coordination costs. This study investigated scientific collaboration across disciplinary and university boundaries to understand the need for coordination in these collaborations and how different levels of coordination predicted success. We conducted a study of 62 scientific collaborations supported by a program of the US National Science Foundation in 1998 and 1999. Projects with principal investigators (PIs) in more disciplines reported as many positive outcomes as did projects involving fewer disciplines. By contrast, multi-university, rather than multidisciplinary, projects were problematic. Projects with PIs from more universities were significantly less well coordinated and reported fewer positive outcomes than projects with PIs from fewer universities. Coordination mechanisms that brought distant researchers together physically slightly reduced the negative impact of collaborations involving multiple universities. We discuss implications for theory, practice, and policy.
A Singularly Valuable Decomposition : The SVD of a Matrix

Every teacher of linear algebra should be familiar with the matrix singular value decomposition (or SVD). It has interesting and attractive algebraic properties, and conveys important geometrical and theoretical insights about linear transformations. The close connection between the SVD and the well known theory of diagonalization for symmetric matrices makes the topic immediately accessible to linear algebra teachers, and indeed, a natural extension of what these teachers already know. At the same time, the SVD has fundamental importance in several different applications of linear algebra. Strang was aware of these facts when he introduced the SVD in his now classical text [22, page 142], observing
Low-Rank Modeling and Its Applications in Image Analysis

Low-rank modeling generally refers to a class of methods that solves problems by representing variables of interest as low-rank matrices. It has achieved great success in various fields including computer vision, data mining, signal processing, and bioinformatics. Recently, much progress has been made in theories, algorithms, and applications of low-rank modeling, such as exact low-rank matrix recovery via convex programming and matrix completion applied to collaborative filtering. These advances have brought more and more attention to this topic. In this article, we review the recent advances of low-rank modeling, the state-of-the-art algorithms, and the related applications in image analysis. We first give an overview of the concept of low-rank modeling and the challenging problems in this area. Then, we summarize the models and algorithms for low-rank matrix recovery and illustrate their advantages and limitations with numerical experiments. Next, we introduce a few applications of low-rank modeling in the context of image analysis. Finally, we conclude this article with some discussions.
What strategies are effective for formative assessment in an e-learning environment?

The web-based formative assessment developed in this research is named Formative Assessment Module of the Web-based Assessment and Test Analysis System (FAM-WATA). FAMWATA is a multiple-choice web-based formative assessment module containing six effective strategies: ‘repeat the test’, ‘correct answers are not given’, ‘query scores’, ‘ask questions’, ‘monitor answering history’, and ‘all pass and then reward’. This research explored the effectiveness of FAM-WATA, cognitive styles and e-learning, and student attitudes towards the six strategies of FAM-WATA. A total of 503 seventh-grade students in central Taiwan were valid in this research. Overall results indicated that students displayed a positive attitude towards the six strategies of FAM-WATA. In addition, results also showed that students in an e-learning environment equipped with FAM-WATA achieved better learning effectiveness, and that field independent students appeared to make better use of FAM-WATA strategies than field dependent students. This research concluded that FAM-WATA benefited student learning in an e-learning environment.
Learning a deep neural net policy for end-to-end control of autonomous vehicles

Deep neural networks are frequently used for computer vision, speech recognition and text processing. The reason is their ability to regress highly nonlinear functions. We present an end-to-end controller for steering autonomous vehicles based on a convolutional neural network (CNN). The deployed framework does not require explicit hand-engineered algorithms for lane detection, object detection or path planning. The trained neural net directly maps pixel data from a front-facing camera to steering commands and does not require any other sensors. We compare the controller performance with the steering behavior of a human driver.
Macroesthetic elements of smile design.

Clinicians' expanding use of cosmetic restorative procedures has generated greater interest in the determination of esthetic guidelines and standards. The overall esthetic impact of a smile can be divided into four specific areas: gingival esthetics, facial esthetics, microesthetics and macroesthetics. In this article, the authors focus on the principles of macroesthetics, which represents the relationships and ratios of relating multiple teeth to each other, to soft tissue and to facial characteristics.



The authors categorize macroesthetic criteria based on two reference points: the facial midline and the amount and position of tooth reveal. The facial midline is a critical reference position for determining multiple design criteria. The amount and position of tooth reveal in various views and lip configurations also provide valuable guidelines in determining esthetic tooth positions and relationships.



Esthetics is an inherently subjective discipline. By understanding and applying simple esthetic rules, tools and strategies, dentists have a basis for evaluating natural dentitions and the results of cosmetic restorative procedures. Macroesthetic components of teeth and their relationship to each other can be influenced to produce more natural and esthetically pleasing restorative care.
Gremlin: An Interactive Visualization Model for Analyzing Genomic Rearrangements

In this work we present, apply, and evaluate a novel, interactive visualization model for comparative analysis of structural variants and rearrangements in human and cancer genomes, with emphasis on data integration and uncertainty visualization. To support both global trend analysis and local feature detection, this model enables explorations continuously scaled from the high-level, complete genome perspective, down to the low-level, structural rearrangement view, while preserving global context at all times. We have implemented these techniques in Gremlin, a genomic rearrangement explorer with multi-scale, linked interactions, which we apply to four human cancer genome data sets for evaluation. Using an insight-based evaluation methodology, we compare Gremlin to Circos, the state-of-the-art in genomic rearrangement visualization, through a small user study with computational biologists working in rearrangement analysis. Results from user study evaluations demonstrate that this visualization model enables more total insights, more insights per minute, and more complex insights than the current state-of-the-art for visual analysis and exploration of genome rearrangements.