nils-herrmann/clean_scirepeval huggingface dataset:
Efficient RANSAC for Point-Cloud Shape Detection

In this work we present an automatic algorithm to detect basic shapes in unorganized point clouds. The algorithm decomposes the point cloud into a concise, hybrid structure of inherent shapes and a set of remaining points. Each detected shape serves as a proxy for a set of corresponding points. Our method is based on random sampling and detects planes, spheres, cylinders, cones and tori. For models with surfaces composed of these basic shapes only, e.g. CAD models, we automatically obtain a representation solely consisting of shape proxies. We demonstrate that the algorithm is robust even in the presence of many outliers and a high degree of noise. The proposed method scales well with respect to the size of the input point cloud and the number and size of the shapes within the data. Even point sets with several millions of samples are robustly decomposed within less than a minute. Moreover the algorithm is conceptually simple and easy to implement. Application areas include measurement of physical parameters, scan registration, surface compression, hybrid rendering, shape classification, meshing, simplification, approximation and reverse engineering.
The utility of low frequency heart rate variability as an index of sympathetic cardiac tone: a review with emphasis on a reanalysis of previous studies.

This article evaluates the suitability of low frequency (LF) heart rate variability (HRV) as an index of sympathetic cardiac control and the LF/high frequency (HF) ratio as an index of autonomic balance. It includes a comprehensive literature review and a reanalysis of some previous studies on autonomic cardiovascular regulation. The following sources of evidence are addressed: effects of manipulations affecting sympathetic and vagal activity on HRV, predictions of group differences in cardiac autonomic regulation from HRV, relationships between HRV and other cardiac parameters, and the theoretical and mathematical bases of the concept of autonomic balance. Available data challenge the interpretation of the LF and LF/HF ratio as indices of sympathetic cardiac control and autonomic balance, respectively, and suggest that the HRV power spectrum, including its LF component, is mainly determined by the parasympathetic system.
A ray tracing method for predicting path loss and delay spread in microcellular environments

The ability to predict path l~ss and delay spread is crucial for determining coverage and for planning interference reduction strategies in wireless radio system design. This paper presents a promising theoretical method tci accurately predict these channel characteristics in micro cells.; The method uses modified geometrical optics to evaluate average path loss and delay spread. Quantitative building data, such as lo~ation, height, and electrical properties are employed to determine_ the individual multipath component amplitudes and delays. Preliminary verification of the technique against measured data has 'been conducted. The results illustrate that accurate path loss predi.ction is possible, with predicted values being within 5 dB of the measured values. As a result of this study, a computer program is being developed to automate the prediction process. The technical issues required for automated propagation prediction are presented in !his paper. The ray optics model, computer ray tracing techniques, and building data requirements are also described. Comparisons between simulations and measurements are provided here.
Volumetric Object Recognition Using 3-D CNNs on Depth Data

Recognizing 3-D objects has a wide range of application areas from autonomous robots to self-driving vehicles. The popularity of low-cost RGB-D sensors has enabled a rapid progress in 3-D object recognition in the recent years. Most of the existing studies use depth data as an additional channel to the RGB channels. Instead of this approach, we propose two volumetric representations to reveal rich 3-D structural information hidden in depth images. We present a 3-D convolutional neural network (CNN)-based object recognition approach, which utilizes these volumetric representations and single and multi-rotational depth images. The 3-D CNN architecture trained to recognize single depth images produces competitive results with the state-of-the-art methods on two publicly available datasets. However, recognition accuracy increases further when the multiple rotations of objects are brought together. Our multirotational 3-D CNN combines information from multiple views of objects to provide rotational invariance and improves the accuracy significantly comparing with the single-rotational approach. The results show that utilizing multiple views of objects can be highly informative for the 3-D CNN-based object recognition.
A survey on player tracking in soccer videos

There is a growth of demand for automatically analyzing soccer matches and tactics. Since players are the focus of attentions in soccer matches, player tracking is a fundamental element in most soccer video analysis. The aim of player tracking is to extract the trajectories of players, and its input is provided through some preprocessing steps including playfield detection, player detection, player labeling, occlusion handling and player appearance modeling. Soccer player tracking is a complex and challenging task due to difficulties such as blur, illumination change and heavy occlusions. This paper presents the stateof-the-art in preprocessing and processing methods for soccer player tracking. We categorize different approaches, analyze their strengths and weaknesses, review evaluation criteria and conclude future re-
13C-NMR-Based Metabolomic Profiling of Typical Asian Soy Sauces

It has been a strong consumer interest to choose high quality food products with clear information about their origin and composition. In the present study, a total of 22 Asian soy sauce samples have been analyzed in terms of (13)C-NMR spectroscopy. Spectral data were analyzed by multivariate statistical methods in order to find out the important metabolites causing the discrimination among typical soy sauces from different Asian regions. It was found that significantly higher concentrations of glutamate in Chinese red cooking (CR) soy sauce may be the result of the manual addition of monosodium glutamate (MSG) in the final soy sauce product. Whereas lower concentrations of amino acids, like leucine, isoleucine and valine, observed in CR indicate the different fermentation period used in production of CR soy sauce, on the other hand, the concentration of some fermentation cycle metabolites, such as acetate and sucrose, can be divided into two groups. The concentrations of these fermentation cycle metabolites were lower in CR and Singapore Kikkoman (SK), whereas much higher in Japanese shoyu (JS) and Taiwan (China) light (TL), which depict the influence of climatic conditions. Therefore, the results of our study directly indicate the influences of traditional ways of fermentation, climatic conditions and the selection of raw materials and can be helpful for consumers to choose their desired soy sauce products, as well as for researchers in further authentication studies about soy sauce.
OAuth Demystified for Mobile Application Developers

OAuth has become a highly influential protocol due to its swift and wide adoption in the industry. The initial objective of the protocol was specific: it serves the authorization needs for websites. What motivates our work is the realization that the protocol has been significantly re-purposed and re-targeted over the years: (1) all major identity providers, e.g., Facebook, Google and Microsoft, have re-purposed OAuth for user authentication; (2) developers have re-targeted OAuth to the mobile platforms, in addition to the traditional web platform. Therefore, we believe that it is necessary and timely to conduct an in-depth study to demystify OAuth for mobile application developers. Our work consists of two pillars: (1) an in-house study of the OAuth protocol documentation that aims to identify what might be ambiguous or unspecified for mobile developers; (2) a field-study of over 600 popular mobile applications that highlights how well developers fulfill the authentication and authorization goals in practice. The result is really worrisome: among the 149 applications that use OAuth, 89 of them (59.7%) were incorrectly implemented and thus vulnerable. In the paper, we pinpoint the key portions in each OAuth protocol flow that are security critical, but are confusing or unspecified for mobile application developers. We then show several representative cases to concretely explain how real implementations fell into these pitfalls. Our findings have been communicated to vendors of the vulnerable applications. Most vendors positively confirmed the issues, and some have applied fixes. We summarize lessons learned from the study, hoping to provoke further thoughts about clear guidelines for OAuth usage in mobile applications.
Nursing Theory in Holistic Nursing Practice

Concept: An abstract idea or notion.  A group of interrelated concepts described to suggest relationships among them.  A basic structure; the context in which theory is developed; the structure that permits theory to be understood.  A theory that covers a broad area of the discipline’s concerns. Metaparadigm: Concepts that identify the domain of a discipline.  Theory about theory development; theory about theory. Midrange theory: A focused theory for nursing that deals with a portion of nurses’ concerns or that is oriented to patient outcomes.  A representation of interactions between and among concepts.  A framework; a set of interrelated concepts that are testable; a way of Nursing Theory in Holistic Nursing Practice
The mutual information in random linear estimation

We consider the estimation of a signal from the knowledge of its noisy linear random Gaussian projections, a problem relevant in compressed sensing, sparse superposition codes or code division multiple access just to cite few. There has been a number of works considering the mutual information for this problem using the heuristic replica method from statistical physics. Here we put these considerations on a firm rigorous basis. First, we show, using a Guerra-type interpolation, that the replica formula yields an upper bound to the exact mutual information. Secondly, for many relevant practical cases, we present a converse lower bound via a method that uses spatial coupling, state evolution analysis and the I-MMSE theorem. This yields, in particular, a single letter formula for the mutual information and the minimal-mean-square error for random Gaussian linear estimation of all discrete bounded signals.
Integrality and Separability of Input Devices

Current input device taxonomies and other frameworks typically emphasize the mechanical structure of input devices. We suggest that selecting an appropriate input device for an interactive task requires looking beyond the physical structure of devices to the deeper perceptual structure of the task, the device, and the interrelationship between the perceptual structure of the task and the control properties of the device. We affirm that perception is key to understanding performance of multidimensional input devices on multidimensional tasks. We have therefore extended the theory of processing of percetual structure to graphical interactive tasks and to the control structure of input devices. This allows us to predict task and device combinations that lead to better performance and hypothesize that performance is improved when the perceptual structure of the task matches the control structure of the device. We conducted an experiment in which subjects performed two tasks with different perceptual structures, using two input devices with correspondingly different control structures, a three-dimensional tracker and a mouse. We analyzed both speed and accuracy, as well as the trajectories generated by subjects as they used the unconstrained three-dimensional tracker to perform each task. The result support our hypothesis and confirm the importance of matching the perceptual structure of the task and the control structure of the input device.
Visual analysis of self-organizing maps

In the article, an additional visualization of self-organizing maps (SOM) has been investigated. The main objective of self-organizing maps is data clustering and their graphical presentation. Opportunities of SOM visualization in four systems (NeNet, SOM-Toolbox, Databionic ESOM and Viscovery SOMine) have been investigated. Each system has its additional tools for visualizing SOM. A comparative analysis has been made for two data sets: Fisher’s iris data set and the economic indices of the European Union countries. A new SOM system is also introduced and researched. The system has a specific visualization tool. It is missing in other SOM systems. It helps to see the proportion of neurons, corresponding to the data items, belonging to the different classes, and fallen in the same SOM cell.
MELD: A Multimodal Multi-Party Dataset for Emotion Recognition in Conversations

Emotion recognition in conversations is a challenging Artificial Intelligence (AI) task. It has recently gained popularity due to its potential applications in many interesting AI tasks such as empathetic dialogue generation, user behavior understanding, and so on. To the best of our knowledge, there is no multimodal multiparty conversational dataset available that contains more than two speakers in a dialogue. In this work, we propose the Multimodal EmotionLines Dataset (MELD), which we created by enhancing and extending the previously introduced EmotionLines dataset. MELD contains 13,708 utterances from 1433 dialogues of Friends TV series. MELD is superior to other conversational emotion recognition datasets SEMAINE and IEMOCAP as it consists of multiparty conversations and number of utterances in MELD is almost twice as these two datasets. Every utterance in MELD is associated with an emotion and a sentiment label. Utterances in MELD are multimodal encompassing audio and visual modalities along with the text. We have also addressed several shortcomings in EmotionLines and proposed a strong multimodal baseline. The baseline results show that both contextual and multimodal information play important role in emotion recognition in conversations.
Fast Explicit Diffusion for Accelerated Features in Nonlinear Scale Spaces

We propose a novel and fast multiscale feature detection and description approach that exploits the benefits of nonlinear scale spaces. Previous attempts to detect and describe features in nonlinear scale spaces are highly time consuming due to the computational burden of creating the nonlinear scale space. In this paper we propose to use recent numerical schemes called Fast Explicit Diffusion (FED) embedded in a pyramidal framework to dramatically speed-up feature detection in nonlinear scale spaces. In addition, we introduce a Modified-Local Difference Binary (M-LDB) descriptor that is highly efficient, exploits gradient information from the nonlinear scale space, is scale and rotation invariant and has low storage requirements. We present an extensive evaluation that shows the excellent compromise between speed and performance of our approach compared to state-of-the-art methods such as BRISK, ORB, SURF, SIFT and KAZE.
From Throw-Away Traffic to Bots: Detecting the Rise of DGA-Based Malware

Many botnet detection systems employ a blacklist of known command and control (C&C) domains to detect bots and block their traffic. Similar to signature-based virus detection, such a botnet detection approach is static because the blacklist is updated only after running an external (and often manual) process of domain discovery. As a response, botmasters have begun employing domain generation algorithms (DGAs) to dynamically produce a large number of random domain names and select a small subset for actual C&C use. That is, a C&C domain is randomly generated and used for a very short period of time, thus rendering detection approaches that rely on static domain lists ineffective. Naturally, if we know how a domain generation algorithm works, we can generate the domains ahead of time and still identify and block botnet C&C traffic. The existing solutions are largely based on reverse engineering of the bot malware executables, which is not always feasible. In this paper we present a new technique to detect randomly generated domains without reversing. Our insight is that most of the DGA-generated (random) domains that a bot queries would result in Non-Existent Domain (NXDomain) responses, and that bots from the same botnet (with the same DGA algorithm) would generate similar NXDomain traffic. Our approach uses a combination of clustering and classification algorithms. The clustering algorithm clusters domains based on the similarity in the make-ups of domain names as well as the groups of machines that queried these domains. The classification algorithm is used to assign the generated clusters to models of known DGAs. If a cluster cannot be assigned to a known model, then a new model is produced, indicating a new DGA variant or family. We implemented a prototype system and evaluated it on real-world DNS traffic obtained from large ISPs in North America. We report the discovery of twelve DGAs. Half of them are variants of known (botnet) DGAs, and the other half are brand new DGAs that have never been reported before.
A Corpus and Model Integrating Multiword Expressions and Supersenses

This paper introduces a task of identifying and semantically classifying lexical expressions in running text. We investigate the online reviews genre, adding semantic supersense annotations to a 55,000 word English corpus that was previously annotated for multiword expressions. The noun and verb supersenses apply to full lexical expressions, whether singleor multiword. We then present a sequence tagging model that jointly infers lexical expressions and their supersenses. Results show that even with our relatively small training corpus in a noisy domain, the joint task can be performed to attain 70% class labeling F1.
Perceived barriers to effective knowledge sharing in agile software teams

While the literature offers several frameworks that explain barriers to knowledge sharing within software development teams, little is known about differences in how team members perceive these barriers. Based on an in-depth multi-case study of four software projects, we investigate how project managers, developers, testers, and user representatives think about barriers to effective knowledge sharing in agile development. Adapting comparative causal modeling (CCM), we constructed causal maps for each of the four roles and identified overlap and divergence in map constructs and causal linkages. The results indicate that despite certain similarities, the four roles differ in how they perceive and emphasize knowledge sharing barriers. The project managers put primary emphasis on project setting barriers, while the primary concern of developers, testers, and user representatives were project communication, project organization, and team capabilities barriers respectively. Integrating the four causal maps and the agile literature, we propose a conceptual framework with seven types of knowledge sharing barriers and 37 specific barriers. We argue that to bridge communication gaps and create shared understanding in software teams, it is critical to take the revealed concerns of different roles into account. We conclude by discussing our findings in relation to knowledge sharing in agile teams and software teams more generally.
Confer: A Conference Recommendation and Meetup Tool

One of the primary goals of academic conferences is to promote scientiï¬c exchange of advances among people who may otherwise not have the opportunity to hear from one another. We present Confer, a tool designed to help conference attendees ï¬nd interesting papers and talks, dis-cover and meet people with shared interests, and manage their time using a personalized schedule for the conference. So far, we have deployed Confer to 17 academic confer-ences including several years of CHI and CSCW. Confer is also the primary program tool for CSCW 2016. Log analysis and survey results have shown that the tool helps confer-ence attendees ï¬nd interesting papers and manage their schedule. Furthermore the recommendation data gener-ated from the tool has been used by conference organizers to help plan conference schedules as well as to organize social gatherings. Finally, the meetup feature seeks to help conference attendees reach out to other attendees with similar interests.
Workload Patterns for Quality-Driven Dynamic Cloud Service Configuration and Auto-Scaling

Cloud service providers negotiate SLAs for customer services they offer based on the reliability of performance and availability of their lower-level platform infrastructure. While availability management is more mature, performance management is less reliable. In order to support an iterative approach that supports the initial static infrastructure configuration as well as dynamic reconfiguration and auto-scaling, an accurate and efficient solution is required. We propose a prediction-based technique that combines a pattern matching approach with a traditional collaborative filtering solution to meet the accuracy and efficiency requirements. Service workload patterns abstract common infrastructure workloads from monitoring logs and act as a part of a first-stage high-perform ant configuration mechanism before more complex traditional methods are considered. This enhances current reactive rule-based scalability approaches and basic prediction techniques based on for example exponential smoothing.
Emotion-based music recommendation by affinity discovery from film music

0957-4174/$ see front matter 2008  q Part of the content of this paper has been publi International Conference on Multimedia, 2005.  Corresponding author. Tel.: +886 2 29393091x67 E-mail address: mkshan@cs.nccu.edu.tw (M.-K. Sh With the growth of digital music, the development of music recommendation is helpful for users to pick desirable music pieces from a huge repository of music. The existing music recommendation approaches are based on a user’s preference on music. However, sometimes, it might better meet users’ requirement to recommend music pieces according to emotions. In this paper, we propose a novel framework for emotion-based music recommendation. The core of the recommendation framework is the construction of the music emotion model by affinity discovery from film music, which plays an important role in conveying emotions in film. We investigate the music feature extraction and propose the Music Affinity Graph and Music Affinity Graph-Plus algorithms for the construction of music emotion model. Experimental result shows the proposed emotion-based music recommendation achieves 85% accuracy in average.
Episodic Future Thinking Reduces Reward Delay Discounting through an Enhancement of Prefrontal-Mediotemporal Interactions

Humans discount the value of future rewards over time. Here we show using functional magnetic resonance imaging (fMRI) and neural coupling analyses that episodic future thinking reduces the rate of delay discounting through a modulation of neural decision-making and episodic future thinking networks. In addition to a standard control condition, real subject-specific episodic event cues were presented during a delay discounting task. Spontaneous episodic imagery during cue processing predicted how much subjects changed their preferences toward more future-minded choice behavior. Neural valuation signals in the anterior cingulate cortex and functional coupling of this region with hippocampus and amygdala predicted the degree to which future thinking modulated individual preference functions. A second experiment replicated the behavioral effects and ruled out alternative explanations such as date-based processing and temporal focus. The present data reveal a mechanism through which neural decision-making and prospection networks can interact to generate future-minded choice behavior.