nils-herrmann/clean_scirepeval huggingface dataset:
Functional anatomy of the papilla Vateri

Problems with intubation of the ampulla Vateri during diagnostic and therapeutic endoscopic maneuvers are a well-known feature. The ampulla Vateri was analyzed three-dimensionally to determine whether these difficulties have a structural background.  Thirty-five human greater duodenal papillae were examined by light and scanning electron microscopy as well as immunohistochemically.  Histologically, highly vascularized finger-like mucosal folds project far into the lumen of the ampulla Vateri. The excretory ducts of seromucous glands containing many lysozyme-secreting Paneth cells open close to the base of the mucosal folds. Scanning electron microscopy revealed large mucosal folds inside the ampulla that continued into the pancreatic and bile duct, comparable to valves arranged in a row.  Mucosal folds form pocket-like valves in the lumen of the ampulla Vateri. They allow a unidirectional flow of secretions into the duodenum and prevent reflux from the duodenum into the ampulla Vateri. Subepithelial mucous gland secretions functionally clean the valvular crypts and protect the epithelium. The arrangement of pocket-like mucosal folds may explain endoscopic difficulties experienced when attempting to penetrate the papilla of Vater during endoscopic retrograde cholangiopancreaticographic procedures.
Feature selection for content-based, time-varying musical emotion regression

In developing automated systems to recognize the emotional content of music, we are faced with a problem spanning two disparate domains: the space of human emotions and the acoustic signal of music. To address this problem, we must develop models for both data collected from humans describing their perceptions of musical mood and quantitative features derived from the audio signal. In previous work, we have presented a collaborative game, MoodSwings, which records dynamic (per-second) mood ratings from multiple players within the two-dimensional Arousal-Valence representation of emotion. Using this data, we present a system linking models of acoustic features and human data to provide estimates of the emotional content of music according to the arousal-valence space. Furthermore, in keeping with the dynamic nature of musical mood we demonstrate the potential of this approach to track the emotional changes in a song over time. We investigate the utility of a range of acoustic features based on psychoacoustic and music-theoretic representations of the audio for this application. Finally, a simplified version of our system is re-incorporated into MoodSwings as a simulated partner for single-players, providing a potential platform for furthering perceptual studies and modeling of musical mood.
Gamification - A Structured Analysis

The trend Gamification occurred several years ago in the context of digital teaching and training aka e-learning. Looking closer at the development of game-based learning and gamification in computer science, it has to be admitted, that game-based learning seems to have ended in an impasse point where the instructionally smooth integration of learning and gaming has not really been realizable, yet -- at least not without enormous effort by the learning and gaming content author. The situation in gamification in digital teaching and training systems comes in a similar shape. A structured analysis of what gamification is can help to develop a systematic approach, which then can support a structured and methodologically smooth design of gamification for digital teaching and training.
A Synchronization Method for Single-Phase Grid-Tied Inverters

The controllers of single-phase grid-tied inverters require improvements to enable distribution generation systems to meet the grid codes/standards with respect to power quality and the fault ride through capability. In that case, the response of the selected synchronization technique is crucial for the performance of the entire grid-tied inverter. In this paper, a new synchronization method with good dynamics and high accuracy under a highly distorted voltage is proposed. This method uses a Multiharmonic Decoupling Cell (MHDC), which thus can cancel out the oscillations on the synchronization signals due to the harmonic voltage distortion while maintaining the dynamic response of the synchronization. Therefore, the accurate and dynamic response of the proposed MHDC-PLL can be beneficial for the performance of the whole single-phase grid-tied inverter.
Constructing Bayesian Network by Integrating FMEA with FTA

Bayesian Network has an advantage in dealing with uncertainty. But It is difficult to construct a scientific and rational Bayesian Network model in practice application. In order to solve this problem, a novel method for constructing Bayesian Network by integrating Failure Mode and Effect Analysis (FMEA) with Fault Tree Analysis (FTA) was proposed. Firstly, the structure matrix representations of FMEA, FTA and Bayesian Network were shown and a structure matrix integration algorithm was explained. Then, an approach for constructing Bayesian Network by obtaining information on node, structure and parameter from FMEA and FTA based on structure matrix was put forward. Finally, in order to verify the feasibility of the method, an illustrative example was given. This method can simplify the modeling process and improve the modeling efficiency for constructing Bayesian Network and promote the application of Bayesian Network in the system reliability and safety analysis.
Gamification : Toward a Definition

This paper proposes a working definition of the term gamification as the use of game design elements in non-game contexts. This definition is related to similar concepts such as serious games, serious gaming, playful interaction, and game-based technologies.  Gamification as a term originated in the digital media industry. The first documented uses dates back to 2008, but gamification only entered widespread adoption in the second half of 2010, when several industry players and conferences popularized it. It is also—still—a heavily contested term; even its entry into Wikipedia has been contested. Within the video game and digital media industry, discontent with some interpretations have already led designers to coin different terms for their own practice (e.g., gameful design) to distance themselves from recent negative connotations [13]. Until now, there has been hardly any academic attempt at a definition of gamification. Current uses of the word seem to fluctuate between two major ideas. The first is the increasing societal adoption and institutionalization of video games and the influence games and game elements have in shaping our everyday life and interactions. Game designer Jesse Schell summarized this as the trend towards a Gamepocalypse, " when Copyright is held by the author/owner(s).
JavaScript Errors in the Wild: An Empirical Study

Client-side JavaScript is being widely used in popular web applications to improve functionality, increase responsiveness, and decrease load times. However, it is challenging to build reliable applications using JavaScript. This paper presents an empirical characterization of the error messages printed by JavaScript code in web applications, and attempts to understand their root causes. We find that JavaScript errors occur in production web applications, and that the errors fall into a small number of categories. We further find that both non-deterministic and deterministic errors occur in the applications, and that the speed of testing plays an important role in exposing errors. Finally, we study the correlations among the static and dynamic properties of the application and the frequency of errors in it in order to understand the root causes of the errors.
Mobile Data Offloading: How Much Can WiFi Deliver?

This paper presents a quantitative study on the performance of 3G mobile data offloading through WiFi networks. We recruited 97 iPhone users from metropolitan areas and collected statistics on their WiFi connectivity during a two-and-a-halfweek period in February 2010. Our trace-driven simulation using the acquired whole-day traces indicates that WiFi already offloads about 65% of the total mobile data traffic and saves 55% of battery power without using any delayed transmission. If data transfers can be delayed with some deadline until users enter a WiFi zone, substantial gains can be achieved only when the deadline is fairly larger than tens of minutes. With 100-s delays, the achievable gain is less than only 2%-3%, whereas with 1 h or longer deadlines, traffic and energy saving gains increase beyond 29% and 20%, respectively. These results are in contrast to the substantial gain (20%-33%) reported by the existing work even for 100-s delayed transmission using traces taken from transit buses or war-driving. In addition, a distribution model-based simulator and a theoretical framework that enable analytical studies of the average performance of offloading are proposed. These tools are useful for network providers to obtain a rough estimate on the average performance of offloading for a given WiFi deployment condition.
Model Order Reduction for Linear and Nonlinear Systems: A System-Theoretic Perspective

In the past decades, Model Order Reduction (MOR) has demonstrated its robustness and wide applicability for simulating large-scale mathematical models in engineering and the sciences. Recently, MOR has been intensively further developed for increasingly complex dynamical systems. Wide applications of MOR have been found not only in simulation, but also in optimization and control. In this survey paper, we review some popular MOR methods for linear and nonlinear large-scale dynamical systems, mainly used in electrical and control engineering, in computational electromagnetics, as well as in microand nanoelectro-mechanical systems (NEMS/MEMS) design. This complements recent surveys on generating reduced-order models for parameter-dependent problems [37, 53, 169] which we do not consider here. Besides reviewing existing methods and the computational techniques needed to implement them, open issues are discussed, and some new results are proposed.
Policy search for learning robot control using sparse data

In many complex robot applications, such as grasping and manipulation, it is difficult to program desired task solutions beforehand, as robots are within an uncertain and dynamic environment. In such cases, learning tasks from experience can be a useful alternative. To obtain a sound learning and generalization performance, machine learning, especially, reinforcement learning, usually requires sufficient data. However, in cases where only little data is available for learning, due to system constraints and practical issues, reinforcement learning can act suboptimally. In this paper, we investigate how model-based reinforcement learning, in particular the probabilistic inference for learning control method (Pilco), can be tailored to cope with the case of sparse data to speed up learning. The basic idea is to include further prior knowledge into the learning process. As Pilco is built on the probabilistic Gaussian processes framework, additional system knowledge can be incorporated by defining appropriate prior distributions, e.g. a linear mean Gaussian prior. The resulting Pilco formulation remains in closed form and analytically tractable. The proposed approach is evaluated in simulation as well as on a physical robot, the Festo Robotino XT. For the robot evaluation, we employ the approach for learning an object pick-up task. The results show that by including prior knowledge, policy learning can be sped up in presence of sparse data.
A distribution-free procedure for the statistical analysis of bioequivalence studies.

In bioequivalence assessment, the consumer risk of erroneously accepting bioequivalence is of primary concern. In order to control the consumer risk, the decision problem is formulated with bioinequivalence as hypothesis and bioequivalence as alternative. In the parametric approach, a split into two one-sided test problems and application of two-sample t-tests have been suggested. Rejection of both hypotheses at nominal alpha-level is equivalent to the inclusion of the classical (shortest) (1-2 alpha) 100%-confidence interval in the bioequivalence range. This paper demonstrates that the rejection of the two one-sided hypotheses at nominal alpha-level by means of nonparametric Mann-Whitney-Wilcoxon tests is equivalent to the inclusion of the corresponding distribution-free (1-2 alpha) 100%-confidence interval in the bioequivalence range. This distribution-free (nonparametric) approach needs weaker model assumptions and hence presents an alternative to the parametric approach.
Distinguishing and contrasting two strategies for design science research

25 June 2013 Revised: 11 October 2013  25 November 2013  This paper distinguishes and contrasts two design science research strategies in information systems. In the first strategy, a researcher constructs or builds an IT meta-artefact as a general solution concept to address a class of problem. In the second strategy, a researcher attempts to solve a client’s specific problem by building a concrete IT artefact in that specific context and distils from that experience prescriptive knowledge to be packaged into a general solution concept to address a class of problem. The two strategies are contrasted along 16 dimensions representing the context, outcomes, process and resource requirements. European Journal of Information Systems (2015) 24(1), 107–115. doi:10.1057/ejis.2013.35; published online 7 January 2014
The Evolving Tree: a new kind of self-organizing neural network

This paper presents a new neural network system called the Evolving Tree. This network resembles the Self-Organizing map, but deviates from it in several aspects, which are desirable in many analysis tasks. First of all the Evolving Tree grows automatically, so the user does not have to decide the network’s size before training. Secondly the network has a hierarchical structure, which makes network training and use computationally very efficient. Test results with both synthetic and actual data show that the Evolving Tree works quite well.
Detecting malicious tweets in trending topics using clustering and classification

Detection of spam Twitter social networks is one of the significant research areas to discover unauthorized user accounts. A number of research works have been carried out to solve these issues but most of the existing techniques had not focused on various features and doesn't group similar user trending topics which become their major limitation. Trending topics collects the current Internet trends and topics of argument of each and every user. In order to overcome the problem of feature extraction,this work initially extracts many features such as user profile features, user activity features, location based features and text and content features. Then the extracted text features use Jenson-Shannon Divergence (JSD) measure to characterize each labeled tweet using natural language models. Different features are extracted from collected trending topics data in twitter. After features are extracted, clusters are formed to group similar trending topics of tweet user profile. Fuzzy K-means (FKM) algorithm primarily cluster the similar user profiles with same trending topics of tweet and centers are determined to similar user profiles with same trending topics of tweet from fuzzy membership function. Moreover, Extreme learning machine (ELM) algorithm is applied to analyze the growing characteristics of spam with similar topics in twitter from clustering result and acquire necessary knowledge in the detection of spam. The results are evaluated with F-measure, True Positive Rate (TPR), False Positive Rate (FPR) and Classification Accuracy with improved detection results.
Targeting angiogenesis in hepatocellular carcinoma: focus on VEGF and bevacizumab.

Hepatocellular carcinoma (HCC) is one of the most common malignancies worldwide and one of the few malignancies with an increasing incidence in the USA. While the relationship between HCC and its inciting risk factors (e.g., hepatitis B, hepatitis C and alcohol liver disease) is well defined, driving genetic alterations are still yet to be identified. Clinically, HCC tends to be hypervascular and, for that reason, transarterial chemoembolization has proven to be effective in managing many patients with localized disease. More recently, angiogenesis has been targeted effectively with pharmacologic strategies, including monoclonal antibodies against VEGF and the VEGF receptor, as well as small-molecule kinase inhibitors of the VEGF receptor. Targeting angiogenesis with these approaches has been validated in several different solid tumors since the initial approval of bevacizumab for advanced colon cancer in 2004. In HCC, only sorafenib has been shown to extend survival in patients with advanced HCC and has opened the door for other anti-angiogenic strategies. Here, we will review the data supporting the targeting of the VEGF axis in HCC and the preclinical and early clinical development of bevacizumab.
A Secure Erasure Code-Based Cloud Storage System with Secure Data Forwarding

A cloud storage system, consisting of a collection of storage servers, provides long-term storage services over the Internet. Storing data in a third party's cloud system causes serious concern over data confidentiality. General encryption schemes protect data confidentiality, but also limit the functionality of the storage system because a few operations are supported over encrypted data. Constructing a secure storage system that supports multiple functions is challenging when the storage system is distributed and has no central authority. We propose a threshold proxy re-encryption scheme and integrate it with a decentralized erasure code such that a secure distributed storage system is formulated. The distributed storage system not only supports secure and robust data storage and retrieval, but also lets a user forward his data in the storage servers to another user without retrieving the data back. The main technical contribution is that the proxy re-encryption scheme supports encoding operations over encrypted messages as well as forwarding operations over encoded and encrypted messages. Our method fully integrates encrypting, encoding, and forwarding. We analyze and suggest suitable parameters for the number of copies of a message dispatched to storage servers and the number of storage servers queried by a key server. These parameters allow more flexible adjustment between the number of storage servers and robustness.
Trends of power semiconductor wafer level packaging

A review of recent advances in power wafer level electronic packaging is presented based on the development of power device integration. The paper covers in more detail how advances in both semiconductor content and power advanced wafer level package design and materials have co-enabled significant advances in power device capability during recent years. Extrapolating the same trends in representative areas for the remainder of the decade serves to highlight where further improvement in materials and techniques can drive continued enhancements in usability, efficiency, reliability and overall cost of power semiconductor solutions. Along with next generation wafer level power packaging development, the role of modeling is a key to assure successful package design. An overview of the power package modeling is presented. Challenges of wafer level power semiconductor packaging and modeling in both next generation design and assembly processes are presented and discussed.
An Approach to Model and Predict the Popularity of Online Contents with Explanatory Factors

In this paper, we propose a methodology to predict the popularity of online contents. More precisely, rather than trying to infer the popularity of a content itself, we infer the likelihood that a content will be popular. Our approach is rooted in survival analysis where predicting the precise lifetime of an individual is very hard and almost impossible but predicting the likelihood of one's survival longer than a threshold or another individual is possible. We position ourselves in the standpoint of an external observer who has to infer the popularity of a content only using publicly observable metrics, such as the lifetime of a thread, the number of comments, and the number of views. Our goal is to infer these observable metrics, using a set of explanatory factors, such as the number of comments and the number of links in the first hours after the content publication, which are observable by the external observer. We use a Cox proportional hazard regression model that divides the distribution function of the observable popularity metric into two components: a) one that can be explained by the given set of explanatory factors (called risk factors) and b) a baseline distribution function that integrates all the factors not taken into account. To validate our proposed approach, we use data sets from two different online discussion forums: dpreview.com, one of the largest online discussion groups providing news and discussion forums about all kinds of digital cameras, and myspace.com, one of the representative online social networking services. On these two data sets we model two different popularity metrics, the lifetime of threads and the number of comments, and show that our approach can predict the lifetime of threads from Dpreview (Myspace) by observing a thread during the first 5~6 days (24 hours, respectively) and the number of comments of Dpreview threads by observing a thread during first 2~3 days.
Preprocessing DNS Log Data for Effective Data Mining

The Domain Name Service (DNS) provides a critical function in directing Internet traffic. Defending DNS servers from bandwidth attacks is assisted by the ability to effectively mine DNS log data for statistical patterns. Processing DNS log data can be classified as a data-intensive problem, and as such presents challenges unique to this class of problem. When problems occur in capturing log data, or when the DNS server experiences an outage (scheduled or unscheduled), the normal pattern of traffic for that server becomes clouded. Simple linear interpolation of the holes in the data does not preserve features such as peaks in traffic (which can occur during an attack, making them of particular interest). We demonstrate a method for estimating values for missing portions of time sensitive DNS log data. This method would be suitable for use with a variety of datasets containing time series values where certain portions are missing.