nils-herrmann/clean_scirepeval huggingface dataset:
Restoration of f loodplain topography by sand-splay complex formation in response to intentional levee breaches , Lower Cosumnes River , California

Restoration of sustainable geomorphic processes that create floodplain topography through development of sand-splay complexes at intentional breaches is one method to promote variability in physical structure needed for habitat restoration. The topography of splay complexes provides a range of floodplain elevations that creates local variability in (i) inundation duration and frequency and depth to ground water that influence riparian vegetation establishment; and (ii) flow depth and velocity that create refuge for fish. Two intentional levee breaches along the lowland Cosumnes River, Central Valley, CA, were evaluated during water years 1999 and 2000 in order to document changes in morphology and relief associated with deposition of sandsplay complexes. During the study period, annual peak-flow recurrence intervals ranged from 1 to 3 years, and water flowed through the breaches for a minimum of 55 days during water year 1999 and 53 days during water year 2000. At the two study sites, rapid vertical accretion and scour occurred within the first several years after intentionally breaching the levee at the Accidental Forest floodplain (constructed in 1995) and at the Corps Breach floodplain (constructed in 1997). Splay complexes are organized into a variety of landforms, including lateral levees and lobes separated by new floodplain channels. Maximum deposition measured on the splay surface is 0.36 m/year, while maximum scour in channels is 0.27 m/year. Juxtaposition of floodplain splay deposition and adjacent channel scour creates relief ranging from 1.6 to 0.25 m that decreases with distance from the breach and that becomes more pronounced over time as higher magnitude floods scour channels in the old floodplain sediment and deposit new sand and silt onto the surface of the splay. The ratio of splay complex height to depth of formative flow is estimated as 0.4. Progradation of main and secondary splay channels takes place by down-floodplain sand transport (25 m/ year maximum). Large wood recruited onto the floodplain through the breach promotes local scour and deposition that enhances topographic variability. At one of the study sites, initial grading of a low setback berm prior to opening the breach forced a change in floodplain flow direction and the geometry of the splay complex. Additionally, progradation of the complex is arrested by an excavated pond that creates a sediment trap. We present a conceptual model that describes the importance of floods in constructing and modifying sand-splay complexes that create floodplain topography. The potential habitat variability created as floodplain topography evolves is the linkage between physical and ecological processes that are critical for restoration. D 2002 Elsevier Science B.V. All rights reserved.
A Semantic Big Data Platform for Integrating Heterogeneous Wearable Data in Healthcare

Advances supported by emerging wearable technologies in healthcare promise patients a provision of high quality of care. Wearable computing systems represent one of the most thrust areas used to transform traditional healthcare systems into active systems able to continuously monitor and control the patients’ health in order to manage their care at an early stage. However, their proliferation creates challenges related to data management and integration. The diversity and variety of wearable data related to healthcare, their huge volume and their distribution make data processing and analytics more difficult. In this paper, we propose a generic semantic big data architecture based on the “Knowledge as a Service” approach to cope with heterogeneity and scalability challenges. Our main contribution focuses on enriching the NIST Big Data model with semantics in order to smartly understand the collected data, and generate more accurate and valuable information by correlating scattered medical data stemming from multiple wearable devices or/and from other distributed data sources. We have implemented and evaluated a Wearable KaaS platform to smartly manage heterogeneous data coming from wearable devices in order to assist the physicians in supervising the patient health evolution and keep the patient up-to-date about his/her status.
Metabolic pathways promoting cancer cell survival and growth

Activation of oncogenes and loss of tumour suppressors promote metabolic reprogramming in cancer, resulting in enhanced nutrient uptake to supply energetic and biosynthetic pathways. However, nutrient limitations within solid tumours may require that malignant cells exhibit metabolic flexibility to sustain growth and survival. Here, we highlight these adaptive mechanisms and also discuss emerging approaches to probe tumour metabolism in vivo and their potential to expand the metabolic repertoire of malignant cells even further.
Image Style Transfer Using Convolutional Neural Networks

Rendering the semantic content of an image in different styles is a difficult image processing task. Arguably, a major limiting factor for previous approaches has been the lack of image representations that explicitly represent semantic information and, thus, allow to separate image content from style. Here we use image representations derived from Convolutional Neural Networks optimised for object recognition, which make high level image information explicit. We introduce A Neural Algorithm of Artistic Style that can separate and recombine the image content and style of natural images. The algorithm allows us to produce new images of high perceptual quality that combine the content of an arbitrary photograph with the appearance of numerous wellknown artworks. Our results provide new insights into the deep image representations learned by Convolutional Neural Networks and demonstrate their potential for high level image synthesis and manipulation.
Mobile applications in tourism: the future of the tourism industry?

The unified theory of acceptance and use of technology (UTAUT) was extended with psychological constructs namely perceived playfulness (PP), mobile innovativeness in information technology (MIIT) and perceived expressiveness (PEX) to examine on the consumers’ intention to adopt mobile applications (m-apps) as another mean in purchasing tourism related product and services via their mobile devices. The study also included gender as moderating variable.  Partial Least Square-Structural Equation Modeling (PLSSEM) approach was adopted to test the research framework using 474 valid respondents. The results revealed that only performance expectancy (PE) and PEX are nonsignificant with the intention to adopt. In addition, gender moderates the effect between social influence (SI) and intention.  The study has several useful implications for researchers, m-apps developers, travel related organizations and even government agencies when rendering m-apps services and disseminating information to their potential consumers. The study contributes to the growing literature on m-apps in tourism from a developing country’s perspective.
End-to-end Structure-Aware Convolutional Networks for Knowledge Base Completion

Knowledge graph embedding has been an active research topic for knowledge base completion, with progressive improvement from the initial TransE, TransH, DistMult et al to the current state-of-the-art ConvE. ConvE uses 2D convolution over embeddings and multiple layers of nonlinear features to model knowledge graphs. The model can be efficiently trained and scalable to large knowledge graphs. However, there is no structure enforcement in the embedding space of ConvE. The recent graph convolutional network (GCN) provides another way of learning graph node embedding by successfully utilizing graph connectivity structure. In this work, we propose a novel end-to-end StructureAware Convolutional Network (SACN) that takes the benefit of GCN and ConvE together. SACN consists of an encoder of a weighted graph convolutional network (WGCN), and a decoder of a convolutional network called Conv-TransE. WGCN utilizes knowledge graph node structure, node attributes and edge relation types. It has learnable weights that adapt the amount of information from neighbors used in local aggregation, leading to more accurate embeddings of graph nodes. Node attributes in the graph are represented as additional nodes in the WGCN. The decoder Conv-TransE enables the state-of-the-art ConvE to be translational between entities and relations while keeps the same link prediction performance as ConvE. We demonstrate the effectiveness of the proposed SACN on standard FB15k-237 and WN18RR datasets, and it gives about 10% relative improvement over the state-of-theart ConvE in terms of HITS@1, HITS@3 and HITS@10.
Lessons learned from the fate of AstraZeneca's drug pipeline: a five-dimensional framework

Maintaining research and development (R&D) productivity at a sustainable level is one of the main challenges currently facing the pharmaceutical industry. In this article, we discuss the results of a comprehensive longitudinal review of AstraZeneca's small-molecule drug projects from 2005 to 2010. The analysis allowed us to establish a framework based on the five most important technical determinants of project success and pipeline quality, which we describe as the five 'R's: the right target, the right patient, the right tissue, the right safety and the right commercial potential. A sixth factor — the right culture — is also crucial in encouraging effective decision-making based on these technical determinants. AstraZeneca is currently applying this framework to guide its R&D teams, and although it is too early to demonstrate whether this has improved the company's R&D productivity, we present our data and analysis here in the hope that it may assist the industry overall in addressing this key challenge.
Image Synthesis in Multi-Contrast MRI with Conditional Generative Adversarial Networks

Acquiring images of the same anatomy with multiple different contrasts increases the diversity of diagnostic information available in an MR exam. Yet, scan time limitations may prohibit acquisition of certain contrasts, and some contrasts may be corrupted by noise and artifacts. In such cases, the ability to synthesize unacquired or corrupted contrasts can improve diagnostic utility. For multi-contrast synthesis, current methods learn a nonlinear intensity transformation between the source and target images, either via nonlinear regression or deterministic neural networks. These methods can in turn suffer from the loss of structural details in synthesized images. Here, we propose a new approach for multi-contrast MRI synthesis based on conditional generative adversarial networks. The proposed approach preserves intermediate-to-high frequency details via an adversarial loss; and it offers enhanced synthesis performance via pixel-wise and perceptual losses for registered multi-contrast images and a cycle-consistency loss for unregistered images. Information from neighboring cross-sections are utilized to further improve synthesis quality. Demonstrations on T1- and T2- weighted images from healthy subjects and patients clearly indicate the superior performance of the proposed approach compared to previous state-of-the-art methods. Our synthesis approach can help improve quality and versatility of multicontrast MRI exams without the need for prolonged or repeated examinations.
Frequency-Reconfigurable Bow-Tie Antenna for Bluetooth, WiMAX, and WLAN Applications

A frequency-reconfigurable bow-tie antenna for Bluetooth, WiMAX, and WLAN applications is proposed. The bow-tie radiator is printed on two sides of the substrate and is fed by a microstripline continued by a pair of parallel strips. By embedding p-i-n diodes over the bow-tie arms, the effective electrical length of the antenna can be changed, leading to an electrically tunable operating band. The simple biasing circuit used in this design eliminates the need for extra bias lines, and thus avoids distortion of the radiation patterns. Measured results are in good agreement with simulations, which shows that the proposed antenna can be tuned to operate in either 2.2-2.53, 2.97-3.71, or 4.51-6 GHz band with similar radiation patterns.
Measuring the Structural and Conceptual Similarity of Folktales using Plot Graphs

This paper presents an approach to organizing folktales based on a data structure called a plot graph, which captures the narrative flow of events in a folktale. The similarity between two folktales can be computed as the structural similarity between their corresponding plot graphs. This is performed using the well-known Needleman-Wunsch algorithm. To test the efficacy of this approach, experiments are carried out using a small collection of 24 folktales grouped into 5 categories based on the Aarne-Thompson index. The best result is obtained by combining the proposed structural-based similarity measure with a more conventional bag of words vector space model, where 19 out of the 24 folktales (79.16%) yield higher average similarity with folktales within their respective categories as opposed to across categories.
A Survey of Traffic Data Visualization

Data-driven intelligent transportation systems utilize data resources generated within intelligent systems to improve the performance of transportation systems and provide convenient and reliable services. Traffic data refer to datasets generated and collected on moving vehicles and objects. Data visualization is an efficient means to represent distributions and structures of datasets and reveal hidden patterns in the data. This paper introduces the basic concept and pipeline of traffic data visualization, provides an overview of related data processing techniques, and summarizes existing methods for depicting the temporal, spatial, numerical, and categorical properties of traffic data.
An Unsupervised Segmentation Method for Retinal Vessel Using Combined Filters

Image segmentation of retinal blood vessels is an important procedure for the prediction and diagnosis of cardiovascular related diseases, such as hypertension and diabetes, which are known to affect the retinal blood vessels appearance. This work develops an unsupervised segmentation procedure for the segmentation of retinal vessels images using a combined matched filter, Frangi filter and Gabor Wavelet Filter. After the vessel enhancement, two segmentation methods are tested. The first method uses an approach based on deformable models and the second uses fuzzy C-means for the image segmentation. The procedure is evaluated using two public image databases, Drive and Stare. The results are compared to other state-of-the-art methods described in the literature.
A Novel Serial / Parallel Pole Climbing / Manipulating Robot : Design , Kinematic Analysis and Workspace Optimization with Genetic Algorithm

This paper introduces an optimized multi-task novel 4 DOF pole climbing/manipulating robot for construction works. The robot can travel along poles with bends, branches and step changes in cross section. It is also able to perform manipulation, repair, testing and maintenance tasks after reaching the target point on the pole. A hybrid serial/parallel mechanism, providing 2 translations and 2 rotations, have been designed as the main part of the mechanism. Optimization of this robot contains workspace optimization of the proposed mechanism and decreasing the total time of reaching the target point, has been established with genetic algorithm method.
Sign language interpreter using a smart glove

Sign language is the communication medium for the deaf and the mute people. It uses hand gestures along with the facial expressions and the body language to convey the intended message. This paper proposes a novel approach of interpreting the sign language using the portable smart glove. LED-LDR pair on each finger senses the signing gesture and couples the analog voltage to the microcontroller. The microcontroller MSP430G2553 converts these analog voltage values to digital samples and the ASCII code of the letter gestured is wirelessly transmitted using the ZigBee. Upon reception, the letter corresponding to the received ASCII code is displayed on the computer and the corresponding audio is played.
Case-based reasoning is a methodology not a technology

This paper asks whether case-based reasoning is an artificial intelligence (AI) technology like rule-based reasoning, neural networks or genetic algorithms or whether it is better described as a methodology for problem solving, that may use any appropriate technology. By describing four applications of case-based reasoning (CBR), that variously use: nearest neighbour, induction, fuzzy logic and SQL, the author shows that CBR is a methodology and not a technology. The implications of this are discussed. q 1999 Elsevier Science B.V. All rights reserved.
APPLYING MACHINE LEARNING METHODS FOR TIME SERIES FORECASTING

This paper describes a strategy on learning from time series data and on using learned model for forecasting. Time series forecasting, which analyzes and predicts a variable changing over time, has received much attention due to its use for forecasting stock prices, but it can also be used for pattern recognition and data mining. Our method for learning from time series data consists of detecting patterns within the data, describing the detected patterns, clustering the patterns, and creating a model to describe the data. It uses a change-point detection method to partition a time series into segments, each of the segments is then described by an autoregressive model. Then, it partitions all the segments into clusters, each of the clusters is considered as a state for a Markov model. It then creates the transitions between states in the Markov model based on the transitions between segments as the time series progressing. Our method for using the learned model for forecasting consists of indentifying current state, forecasting trends, and adapting to changes. It uses a moving window to monitor real-time data and creates an autoregressive model for the recently observed data, which is then matched to a state of the learned Markov model. Following the transitions of the model, it forecasts future trends. It also continues to monitor real-time data and makes corrections if necessary for adapting to changes. We implemented and successfully tested the methods for an application of load balancing on a parallel computing system.
Grounded Theory and Sensitizing Concepts

Grounded theory is a qualitative research approach that uses inductive analysis as a principal technique. Yet, researchers who embrace this approach often use sensitizing concepts to guide their analysis. In this article, the author examines the relationship between sensitizing concepts and grounded theory. Furthermore, he illustrates the application of sensitizing concepts in a study of community-based antipoverty projects in Jamaica. The article contains commentary about trustworthiness techniques, the coding process, and the constant comparative method of analysis, as well as a synopsis of study findings.
An In-depth Comparison of Subgraph Isomorphism Algorithms in Graph Databases

Finding subgraph isomorphisms is an important problem in many applications which deal with data modeled as graphs. While this problem is NP-hard, in recent years, many algorithms have been proposed to solve it in a reasonable time for real datasets using different join orders, pruning rules, and auxiliary neighborhood information. However, since they have not been empirically compared one another in most research work, it is not clear whether the later work outperforms the earlier work. Another problem is that reported comparisons were often done using the original authors’ binaries which were written in different programming environments. In this paper, we address these serious problems by re-implementing five state-of-the-art subgraph isomorphism algorithms in a common code base and by comparing them using many real-world datasets and their query loads. Through our in-depth analysis of experimental results, we report surprising empirical findings.
A Survey of Mobile Computing for the Visually Impaired

The number of visually impaired or blind (VIB) people in the world is estimated at several hundred million[4]. Based on a series of interviews with the VIB and developers of assistive technology, this paper provides a survey of machine-learning based mobile applications and identifies the most relevant applications. We discuss the functionality of these apps, how they align with the needs and requirements of the VIB users, and how they can be improved with techniques such as federated learning and model compression. As a result of this study we identify promising future directions of research in mobile perception, micro-navigation, and contentsummarization.
Elucidating user behavior of mobile learning: A perspective of the extended technology acceptance model

The purpose of this paper is to propose and verify that the technology acceptance model (TAM) can be employed to explain and predict the acceptance of mobile learning (M-learning); an activity in which users access learning material with their mobile devices. The study identifies two factors that account for individual differences, i.e. perceived enjoyment (PE) and perceived mobility value (PMV), to enhance the explanatory power of the model.  An online survey was conducted to collect data. A total of 313 undergraduate and graduate students in two Taiwan universities answered the questionnaire. Most of the constructs in the model were measured using existing scales, while some measurement items were created specifically for this research. Structural equation modeling was employed to examine the fit of the data with the model by using the LISREL software.  The results of the data analysis shows that the data fit the extended TAM model well. Consumers hold positive attitudes for M-learning, viewing M-learning as an efficient tool. Specifically, the results show that individual differences have a great impact on user acceptance and that the perceived enjoyment and perceived mobility can predict user intentions of using M-learning.  There is scant research available in the literature on user acceptance of M-learning from a customer’s perspective. The present research shows that TAM can predict user acceptance of this new technology. Perceived enjoyment and perceived mobility value are antecedents of user acceptance. The model enhances our understanding of consumer motivation of using M-learning. This understanding can aid our efforts when promoting M-learning.
Attribute and simile classifiers for face verification

We present two novel methods for face verification. Our first method - “attribute” classifiers - uses binary classifiers trained to recognize the presence or absence of describable aspects of visual appearance (e.g., gender, race, and age). Our second method - “simile” classifiers - removes the manual labeling required for attribute classification and instead learns the similarity of faces, or regions of faces, to specific reference people. Neither method requires costly, often brittle, alignment between image pairs; yet, both methods produce compact visual descriptions, and work on real-world images. Furthermore, both the attribute and simile classifiers improve on the current state-of-the-art for the LFW data set, reducing the error rates compared to the current best by 23.92% and 26.34%, respectively, and 31.68% when combined. For further testing across pose, illumination, and expression, we introduce a new data set - termed PubFig - of real-world images of public figures (celebrities and politicians) acquired from the internet. This data set is both larger (60,000 images) and deeper (300 images per individual) than existing data sets of its kind. Finally, we present an evaluation of human performance.