nils-herrmann/clean_scirepeval huggingface dataset:
The endocannabinoid system links gut microbiota to adipogenesis

Obesity is characterised by altered gut microbiota, low-grade inflammation and increased endocannabinoid (eCB) system tone; however, a clear connection between gut microbiota and eCB signalling has yet to be confirmed. Here, we report that gut microbiota modulate the intestinal eCB system tone, which in turn regulates gut permeability and plasma lipopolysaccharide (LPS) levels. The impact of the increased plasma LPS levels and eCB system tone found in obesity on adipose tissue metabolism (e.g. differentiation and lipogenesis) remains unknown. By interfering with the eCB system using CB(1) agonist and antagonist in lean and obese mouse models, we found that the eCB system controls gut permeability and adipogenesis. We also show that LPS acts as a master switch to control adipose tissue metabolism both in vivo and ex vivo by blocking cannabinoid-driven adipogenesis. These data indicate that gut microbiota determine adipose tissue physiology through LPS-eCB system regulatory loops and may have critical functions in adipose tissue plasticity during obesity.
Potentials of 3D-Web-Applications in E-Commerce - Study about the Impact of 3D-Product-Presentations

The goal of this work is to compare different versions of three-dimensional product-presentations with two-dimensional ones. Basically the usability of these technologies will be compared and other user related factors will be integrated into the test as well. These factors were determined via a literature research. In order to achieve a generalizable conclusion about 3D-web-applications in e-commerce sample products from miscellaneous product categories are chosen for the study. This paper starts with the summary of the literature research about the factors for the study. It continues by shortly introducing research methods and strategies and explaining why certain methods are selected for this kind of study. The conception of the study is described in detail in the following paragraph. With the help of the generalized results of the study, recommendations for the usage of 3D-product-presentations in practical e-commerce environments are given.
Design of metamaterial based structure for the radar cross section reduction of a microstrip antenna

In this paper, a novel metamaterial absorber working in the C band frequency range has been proposed to reduce the in-band Radar Cross Section (RCS) of a typical planar antenna. The absorber is first designed in the shape of a hexagonal ring structure having dipoles at the corresponding arms of the rings. The various geometrical parameters of the proposed metamaterial structure have first been optimized using the numerical simulator, and the structure is fabricated and tested. In the second step, the metamaterial absorber is loaded on a microstrip patch antenna working in the same frequency band as that of the metamaterial absorber to reduce the in-band Radar Cross Section (RCS) of the antenna. The prototype is simulated, fabricated and tested. The simulated results show the 99% absorption of the absorber at 6.35 GHz which is in accordance with the measured data. A close agreement between the simulated and the measured results shows that the proposed absorber can be used for the RCS reduction of the planar antenna in order to improve its in-band stealth performance.
EMF Exposure Study Concerning mmWave Phased Array in Mobile Devices for 5G Communication

The electromagnetic field (EMF) exposure to millimeter-wave (mmWave) phased arrays in mobile devices for 5G communication is analyzed in this letter. Unlike the current cellular band, the EMF exposure in the mmWave band (10-200 GHz) is evaluated by the free-space power density instead of the specific absorption rate. However, current regulations have not been well defined for the mobile device application. In this letter, we present the power density property of phased arrays in mobile devices at 15 and 28 GHz. Uniform linear patch arrays are used, and different array configurations are compared. Suggestions for the power density evaluation are also provided.
Cytoscape Web: an interactive web-based network browser

Cytoscape Web is a web-based network visualization tool-modeled after Cytoscape-which is open source, interactive, customizable and easily integrated into web sites. Multiple file exchange formats can be used to load data into Cytoscape Web, including GraphML, XGMML and SIF.



Cytoscape Web is implemented in Flex/ActionScript with a JavaScript API and is freely available at http://cytoscapeweb.cytoscape.org/.
A Game Theoretical Model for Adversarial Learning

It is now widely accepted that in many situations where classifiers are deployed, adversaries deliberately manipulate data in order to reduce the classifier’s accuracy. The most prominent example is email spam, where spammers routinely modify emails to get past classifier-based spam filters. In this paper we model the interaction between the adversary and the data miner as a two-person sequential noncooperative Stackelberg game and analyze the outcomes when there is a natural leader and a follower. We then proceed to model the interaction (both discrete and continuous) as an optimization problem and note that even solving linear Stackelberg game is NP-Hard. Finally we use a real spam email data set and evaluate the performance of local search algorithm under different strategy spaces.
Artificial neural networks for non-stationary time series

The use of Artificial Neural Networks (ANN) has received increasing attention in the analysis and prediction of financial time series. Stationarity of the observed financial time series is the basic underlying assumption in the practical application of ANN on financial time series. In this paper, we will investigate whether it is feasible to relax the stationarity condition to non-stationary time series. Our result discusses the range of complexities caused by non-stationary behavior and finds that overfitting by ANN could be useful in the analysis of such non-stationary complex financial time series.
Capacitor-Less Design of Power-Rail ESD Clamp Circuit With Adjustable Holding Voltage for On-Chip ESD Protection

The RC-based power-rail ESD clamp circuit with the n-channel metal-oxide-semiconductor (NMOS) transistor drawn in the layout style of big field-effect transistor (BigFET) has been utilized to effectively enhance the ESD robustness of CMOS ICs. In this work, a new ESD-transient detection circuit without using the capacitor has been proposed and verified in a 65 nm 1.2 V CMOS process. The layout area of the new ESD-transient detection circuit can be greatly reduced by more than 54%, as compared to the traditional RC-based ESD-transient detection circuit realized with capacitor. From the experimental results, the new proposed ESD-transient detection circuit with adjustable holding voltage can achieve long enough turn-on duration under the ESD stress condition, as well as better immunity against mistrigger and transient-induced latch-on event under the fast power-on and transient noise conditions.
A Local Density Based Spatial Clustering Algorithm with Noise

Density-based clustering algorithms are attractive for the task of class identification in spatial database. However, in many cases, very different local-density clusters exist in different regions of data space, therefore, DBSCAN [Ester, M. et al., A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise. In E. Simoudis, J. Han, & U. M. Fayyad (Eds.),   using a global density parameter is not suitable. As an improvement, OPTICS [Ankerst, M. et al,(1999).  Ordering Points To Identify the Clustering Structure. In A. Delis, C. Faloutsos, & S. Ghandeharizadeh (Eds.), . Philadelphia,  creates an augmented ordering of the database representing its density-based clustering structure, but it only generates the clusters whose local-density exceeds some threshold instead of similar local-density clusters and doesn't produce a clustering of a data set explicitly. Furthermore the parameters required by almost all the well-known clustering algorithms are hard to determine but have a significant influence on the clustering result. In this paper, a new clustering algorithm LDBSCAN relying on a local-density-based notion of clusters is proposed to solve those problems and, what is more, it is very easy for us to pick the appropriate parameters and takes the advantage of the LOF [Breunig, M. M., et al.,(2000). LOF: Identifying Density-Based Local Outliers. In W. Chen, J. F. Naughton, & P. A. Bernstein (Eds.), ). Dalles, TX: ACM.] to detect the noises comparing with other density-based clustering algorithms. The proposed algorithm has potential applications in business intelligence and enterprise information systems.
Modeling Long- and Short-Term Temporal Patterns with Deep Neural Networks

Multivariate time series forecasting is an important machine learning problem across many domains, including predictions of solar plant energy output, electricity consumption, and traffic jam situation. Temporal data arise in these real-world applications often involves a mixture of long-term and short-term patterns, for which traditional approaches such as Autoregressive models and Gaussian Process may fail. In this paper, we proposed a novel deep learning framework, namely Long- and Short-term Time-series network (LSTNet), to address this open challenge. LSTNet uses the Convolution Neural Network (CNN) and the Recurrent Neural Network (RNN) to extract short-term local dependency patterns among variables and to discover long-term patterns for time series trends. Furthermore, we leverage traditional autoregressive model to tackle the scale insensitive problem of the neural network model. In our evaluation on real-world data with complex mixtures of repetitive patterns, LSTNet achieved significant performance improvements over that of several state-of-the-art baseline methods. All the data and experiment codes are available online.
Medical Image Segmentation: A Brief Survey

4 Abstract Accurate segmentation of 2-D, 3-D, and 4-D medical images to isolate 5 anatomical objects of interest for analysis is essential in almost any computer-aided 6 diagnosis system or other medical imaging applications. Various aspects of segmen7 tation features and algorithms have been extensively explored for many years in a 8 host of publications. However, the problem remains challenging, with no general and 9 unique solution, due to a large and constantly growing number of different objects of 10 interest, large variations of their properties in images, different medical imaging 11 modalities, and associated changes of signal homogeneity, variability, and noise for 12 each object. This chapter overviews most popular medical image segmentation 13 techniques and discusses their capabilities, and basic advantages and limitations. 14 The state-of-the-art techniques of the last decade are also outlined.
Personifying programming tool feedback improves novice programmers' learning

Many novice programmers view programming tools as all-knowing, infallible authorities about what is right and wrong about code. This misconception is particularly detrimental to beginners, who may view the cold, terse, and often judgmental errors from compilers as a sign of personal failure. It is possible, however, that attributing this failure to the computer, rather than the learner, may improve learners' motivation to program. To test this hypothesis, we present Gidget, a game where the eponymous robot protagonist is cast as a fallible character that blames itself for not being able to correctly write code to complete its missions. Players learn programming by working with Gidget to debug its problematic code. In a two-condition controlled experiment, we manipulated Gidget's level of personification in: communication style, sound effects, and image. We tested our game with 116 self-described novice programmers recruited on Amazon's Mechanical Turk and found that, when given the option to quit at any time, those in the experimental condition (with a personable Gidget) completed significantly more levels in a similar amount of time. Participants in the control and experimental groups played the game for an average time of 39.4 minutes (SD=34.3) and 50.1 minutes (SD=42.6) respectively. These finding suggest that how programming tool feedback is portrayed to learners can have a significant impact on motivation to program and learning success.
Evolving Mobile Media: Uses and Conceptualizations of the Mobile Internet

Technological convergence has led to the ability to access the internet from a variety of mobile devices. Drawing on the Mobile Phone Appropriation Model (Wirth, von Pape & Karnowski, 2008), we sought to understand how people conceptualize and use the mobile internet by conducting semistructured interviews with 21 mobile internet users, half American and half German in order to explore cross-cultural differences. Findings suggest little cross-cultural difference in use and understanding of the mobile Internet. Users do not perceive the act of ‘‘going online’’ as a significant step, even if it is on a mobile device. They do, however, distinguish between different ways of consuming information online (extractive and immersive), relating them to different situations and devices.
Spoofing detection from a feature representation perspective

Spoofing detection, which discriminates the spoofed speech from the natural speech, has gained much attention recently. Low-dimensional features that are used in speaker recognition/verification are also used in spoofing detection. Unfortunately, they don't capture sufficient information required for spoofing detection. In this work, we investigate the use of high-dimensional features for spoofing detection, that maybe more sensitive to the artifacts in the spoofed speech. Six types of high-dimensional feature are employed. For each kind of feature, four different representations are extracted, i.e. the original high-dimensional feature, corresponding low-dimensional feature, the low- and the high-frequency regions of the original high-dimensional feature. Dynamic features are also calculated to assess the effectiveness of the temporal information to detect the artifacts across frames. A neural network-based classifier is adopted to handle the high-dimensional features. Experimental results on the standard ASVspoof 2015 corpus suggest that high-dimensional features and dynamic features are useful for spoofing attack detection. A fusion of them has been shown to achieve 0.0% the equal error rates for nine of ten attack types.
Steering the Beam of Medium-to-High Gain Antennas Using Near-Field Phase Transformation

A method to steer the beam of aperture-type antennas is presented in this paper. Beam steering is achieved by transforming phase of the antenna near field using a pair of totally passive metasurfaces, which are located just above and parallel to the antenna. They are rotated independently or synchronously around the antenna axis. A prototype, with a peak gain of 19.4 dBi, demonstrated experimentally that the beam of a resonant cavity antenna can be steered to any direction within a large conical region (with an apex angle of 102°), with less than 3-dB gain variation, by simply turning the two metasurfaces without moving the antenna at all. Measured gain variation within a 92° cone is only 1.9 dBi. Contrary to conventional mechanical steering methods, such as moving reflector antennas with multiaxis rotary joints, the 3-D volume occupied by this antenna system does not change during beam steering. This advantage, together with its low profile, makes it a strong contender for space-limited applications where beam steering with active devices is not desirable due to cost, nonlinear distortion, limited power handling, sensitivity to temperature variations, radio frequency losses, or associated heating. This beam steering method using near-field phase transformation can also be applied to other aperture-type antennas and arrays with medium-to-high gains.
Towards Accurate Binary Convolutional Neural Network

We introduce a novel scheme to train binary convolutional neural networks (CNNs) – CNNs with weights and activations constrained to {-1,+1} at run-time. It has been known that using binary weights and activations drastically reduce memory size and accesses, and can replace arithmetic operations with more efficient bitwise operations, leading to much faster test-time inference and lower power consumption. However, previous works on binarizing CNNs usually result in severe prediction accuracy degradation. In this paper, we address this issue with two major innovations: (1) approximating full-precision weights with the linear combination of multiple binary weight bases; (2) employing multiple binary activations to alleviate information loss. The implementation of the resulting binary CNN, denoted as ABC-Net, is shown to achieve much closer performance to its full-precision counterpart, and even reach the comparable prediction accuracy on ImageNet and forest trail datasets, given adequate binary weight bases and activations.
A hybrid dynamic time warping-deep neural network architecture for unsupervised acoustic modeling

We report on an architecture for the unsupervised discovery of talker-invariant subword embeddings. It is made out of two components: a dynamic-time warping based spoken term discovery (STD) system and a Siamese deep neural network (DNN). The STD system clusters word-sized repeated fragments in the acoustic streams while the DNN is trained to minimize the distance between time aligned frames of tokens of the same cluster, and maximize the distance between tokens of different clusters. We use additional side information regarding the average duration of phonemic units, as well as talker identity tags. For evaluation we use the datasets and metrics of the Zero Resource Speech Challenge. The model shows improvement over the baseline in subword unit modeling.
Question Answering System for Non-factoid Type Questions and Automatic Evaluation based on BE Method

In this paper, we describe answer extraction method for non-factoid questions. We classified non-factoid type questions into three types: why type, definition type and how type. We analyzed each type of questions and developed answer extraction patterns for these types of questions. For automatic evaluation, we have developed BE based evaluation tool for answers of questions. BE method is originally proposed by Hovy et. and we applied BE method for question answering evaluation. Evaluation is done by comparison between BEs of system answer and BEs of correct answers.
Software validation using power profiles

While historically software validation focused on the functional requirements, recent approaches also encompass the validation of quality requirements; for example, system reliability, performance or usability. Application development for mobile platforms opens an additional area of qual i ty-power consumption. In PDAs or mobile phones, power consumption varies depending on the hardware resources used, making it possible to specify and validate correct or incorrect executions. Consider an application that downloads a video stream from the network and displays it on the mobile device's display. In the test scenario the viewing of the video is paused at a certain point. If the specification does not allow video prefetching, the user expects the network card activity to stop when video is paused. How can a test engineer check this expectation? Simply running a test suite or even tracing the software execution does not detect the network activity. However, the extraneous network activity can be detected by power measurements and power model application . Tools to find the power inconsistencies and to validate software from the energy point of view are needed.
Severe Equinus Deformity After Radiofrequency-Induced Calf Muscle Reduction

Radiofrequency-induced calf muscle volume reduction is a commonly used method for cosmetic shaping of the lower leg contour. Functional disabilities associated with the use of the radiofrequency (RF) technique, with this procedure targeting the normal gastrocnemius muscle, still have not been reported. However, the authors have experienced several severe ankle equinus cases after RF-induced calf muscle volume reduction. This study retrospectively reviewed 19 calves of 12 patients who showed more than 20° of fixed equinus even though they underwent physical therapy for more than 6 months. All were women with a mean age of 32 years (range, 23–41 years). Of the 12 patients, 7 were bilateral. All the patients received surgical Achilles lengthening for deformity correction. To evaluate the clinical outcome, serial ankle dorsiflexion was measured, and the American Orthopedic Foot and Ankle Society (AOFAS) score was evaluated at the latest follow-up visit. The presence of soleus muscle involvement and an ongoing lesion that might affect the postoperative results of preoperative magnetic resonance imaging (MRI) were investigated. Statistical analysis was conducted to analyze preoperative factors strongly associated with patient clinical outcomes. The mean follow-up period after surgery was 18.6 months (range, 12–28 months). At the latest follow-up visit, the mean ankle dorsiflexion was 9° (range, 0–20°), and the mean AOFAS score was 87.7 (range, 80–98). On preoperative MRI, 13 calves showed soleus muscle involvement. Seven calves had ongoing lesions. Five of the ongoing lesions were muscle edema, and the remaining two lesions were cystic mass lesions resulting from muscle necrosis. Ankle dorsiflexion and AOFAS scores at the latest follow-up evaluation were insufficient in the ongoing lesions group. Although RF-induced calf muscle reduction is believed to be a safer method than conventional procedures, careful handling is needed because of the side effects that may occur in some instances. The slow progression of fibrosis could be observed after RF-induced calf reduction. Therefore, long-term follow-up evaluation is needed after the procedure. Therapeutic case series.
Goal Oriented Requirement Engineering: A Critical Study of Techniques

The term "Goal" is increasingly being used in Requirement Engineering. Goal-Oriented requirement engineering (GORE) provides an incremental approach for elicitation, analysis, elaboration & refinement, specification and modeling of requirements. Various Goal Oriented Requirement Engineering (GORE) methods exist for these requirement engineering processes like KAOS, GBRAM etc. GORE techniques are based on certain underlying concepts and principles. This paper presents and synthesizes the underlying concepts of GORE with respect to coverage of requirement engineering activities. The advantages of GORE claimed in the literature are presented. This paper evaluates GORE techniques on the basis of concepts, process and claimed advantages.
GAVS+: An Open Platform for the Research of Algorithmic Game Solving

This paper presents a major revision of the tool GAVS. First, the number of supported games has been greatly extended and now encompasses in addition many classes important for the design and analysis of programs, e.g., it now allows to explore concurrent / probabilistic / distributed games, and games played on pushdown graphs. Second, among newly introduced utility functions, GAVS+ includes features such that the user can now process and synthesize planning (game) problems described in the established STRIPS/PDDL language by introducing a slight extension which allows to specify a second player. This allows researchers in verification to profit from the rich collection of examples coming from the AI community.