nils-herrmann/clean_scirepeval huggingface dataset:
Impact of Perceived usefulness , Perceived ease of use and Perceived Enjoyment on Intention to shop online

This paper explores the relationship between the three beliefs about online shopping ie. perceived usefulness, perceived ease of use and perceived enjoyment and intention to shop online. A sample of 150 respondents was selected using a purposive sampling method whereby the respondents have to be Internet users to be included in the survey. A structured, self-administered questionnaire was used to elicit responses from these respondents. The findings indicate that perceived ease of use (β = 0.70, p<0.01) and perceived enjoyment (β = 0.32, p<0.05) were positively related to intention to shop online whereas perceived usefulness was not significantly related to intention to shop online. Furthermore, perceived ease of use (β = 0.78, p<0.01) was found to be a significant predictor of perceived usefulness. This goes to show that ease of use and enjoyment are the 2 main drivers of intention to shop online. Implications of the findings for developers are discussed further.
Multimodal Network Embedding via Attention based Multi-view Variational Autoencoder

Learning the embedding for social media data has attracted extensive research interests as well as boomed a lot of applications, such as classification and link prediction. In this paper, we examine the scenario of a multimodal network with nodes containing multimodal contents and connected by heterogeneous relationships, such as social images containing multimodal contents (e.g., visual content and text description), and linked with various forms (e.g., in the same album or with the same tag). However, given the multimodal network, simply learning the embedding from the network structure or a subset of content results in sub-optimal representation. In this paper, we propose a novel deep embedding method, i.e., Attention-based Multi-view Variational Auto-Encoder (AMVAE), to incorporate both the link information and the multimodal contents for more effective and efficient embedding. Specifically, we adopt LSTM with attention model to learn the correlation between different data modalities, such as the correlation between visual regions and the specific words, to obtain the semantic embedding of the multimodal contents. Then, the link information and the semantic embedding are considered as two correlated views. A multi-view correlation learning based Variational Auto-Encoder (VAE) is proposed to learn the representation of each node, in which the embedding of link information and multimodal contents are integrated and mutually reinforced. Experiments on three real-world datasets demonstrate the superiority of the proposed model in two applications, i.e., multi-label classification and link prediction.
Visual languages and computing survey: Data flow visual programming languages

DATA FLOW IS A POPULAR COMPUTATIONAL MODEL for visual programming languages. Data flow provides a view of computation which shows the data flowing from one filter function to another, being transformed as it goes. In addition, the data flow model easily accomodates the insertion of viewing monitors at various points to show the data to the user. Consequently, many recent visual programming languages are based on the data flow model. This paper describes many of the data flow visual programming languages. The languages are grouped according to their application domain. For each language, pertinent aspects of its appearance, and the particular design alternatives it uses, are discussed. Next, some strengths of data flow visual programming languages are mentioned. Finally, unsolved problems in the design of such languages are discussed.
Effect of Abdominal Exercises versus Abdominal Supporting Belt on Post-Partum Abdominal Efficiency and Rectus Separation

This study was conducted to determine the effect of abdominal exercises versus abdominal supporting belt on abdominal efficiency and inter-recti separation following vaginal delivery.30 primiparous post-natal women participated in this study. Their age ranged from (25 35) years and their BMI < 30 Kg/m. Participants were assigned randomly into 2groups, participants of group (A) used abdominal belt from the 2 day following delivery, till the end of puerperium (6 weeks), while participants of group (B) engaged into abdominal exercises program from the 2 day following delivery for 6 weeks. The results of the present study revealed that although there was no statistical difference in waist circumference between both groups, participation in abdominal exercise program produced a pronounced reduction in waist/hip ratio, and inter-recti separation and also caused significant increase in abdominal muscles strength (peak torque, maximum repetition total work and average power) higher than the use of abdominal belt. Abdominal exercise, Abdominal supporting belt, Postnatal abdominal weakness, Rectus Diastases.
Scrum and Agile Methods in Software Engineering Courses

Over the years, a number of agile frameworks, such as Extreme Programming and Scrum, have evolved and matured. The underlying philosophy of Scrum recognizes that the customers often change their mind about the product they want and that the development challenges are unpredictable by their nature. Consequently, Scrum embraces the fact that the problem being solved cannot be fully understood or described from the start. Instead, Scrum focuses on maximizing the ability of the development team to quickly deliver in response to emerging requirements.
Systems modeling and simulation applications for critical care medicine

Critical care delivery is a complex, expensive, error prone, medical specialty and remains the focal point of major improvement efforts in healthcare delivery. Various modeling and simulation techniques offer unique opportunities to better understand the interactions between clinical physiology and care delivery. The novel insights gained from the systems perspective can then be used to develop and test new treatment strategies and make critical care delivery more efficient and effective. However, modeling and simulation applications in critical care remain underutilized. This article provides an overview of major computer-based simulation techniques as applied to critical care medicine. We provide three application examples of different simulation techniques, including a) pathophysiological model of acute lung injury, b) process modeling of critical care delivery, and c) an agent-based model to study interaction between pathophysiology and healthcare delivery. Finally, we identify certain challenges to, and opportunities for, future research in the area.
GL4D: A GPU-based Architecture for Interactive 4D Visualization

This paper describes GL4D, an interactive system for visualizing 2-manifolds and 3-manifolds embedded in four Euclidean dimensions and illuminated by 4D light sources. It is a tetrahedron-based rendering pipeline that projects geometry into volume images, an exact parallel to the conventional triangle-based rendering pipeline for 3D graphics. Novel features include GPU-based algorithms for real-time 4D occlusion handling and transparency compositing; we thus enable a previously impossible level of quality and interactivity for exploring lit 4D objects. The 4D tetrahedrons are stored in GPU memory as vertex buffer objects, and the vertex shader is used to perform per-vertex 4D modelview transformations and 4D-to-3D projection. The geometry shader extension is utilized to slice the projected tetrahedrons and rasterize the slices into individual 2D layers of voxel fragments. Finally, the fragment shader performs per-voxel operations such as lighting and alpha blending with previously computed layers. We account for 4D voxel occlusion along the 4D-to-3D projection ray by supporting a multi-pass back-to-front fragment composition along the projection ray; to accomplish this, we exploit a new adaptation of the dual depth peeling technique to produce correct volume image data and to simultaneously render the resulting volume data using 3D transfer functions into the final 2D image. Previous CPU implementations of the rendering of 4D-embedded 3-manifolds could not perform either the 4D depth-buffered projection or manipulation of the volume-rendered image in real-time; in particular, the dual depth peeling algorithm is a novel GPU-based solution to the real-time 4D depth-buffering problem. GL4D is implemented as an integrated OpenGL-style API library, so that the underlying shader operations are as transparent as possible to the user.
End-to-end encrypted traffic classification with one-dimensional convolution neural networks

Traffic classification plays an important and basic role in network management and cyberspace security. With the widespread use of encryption techniques in network applications, encrypted traffic has recently become a great challenge for the traditional traffic classification methods. In this paper we proposed an end-to-end encrypted traffic classification method with one-dimensional convolution neural networks. This method integrates feature extraction, feature selection and classifier into a unified end-to-end framework, intending to automatically learning nonlinear relationship between raw input and expected output. To the best of our knowledge, it is the first time to apply an end-to-end method to the encrypted traffic classification domain. The method is validated with the public ISCX VPN-nonVPN traffic dataset. Among all of the four experiments, with the best traffic representation and the fine-tuned model, 11 of 12 evaluation metrics of the experiment results outperform the state-of-the-art method, which indicates the effectiveness of the proposed method.
Avoidance of overlearning characterises the spacing effect

The spacing of a fixed amount of study time across multiple sessions usually increases subsequent test performance*a finding known as the spacing effect. In the spacing experiment reported here, subjects completed multiple learning trials, and each included a study phase and a test. Once a subject achieved a perfect test, the remaining learning trials within that session comprised what is known as overlearning. The number of these overlearning trials was reduced when learning trials were spaced across multiple sessions rather than massed in a single session. In addition, the degree to which spacing reduced overlearning predicted the size of the spacing effect, which is consistent with the possibility that spacing increases subsequent recall by reducing the occurrence of overlearning. By this account, overlearning is an inefficient use of study time, and the efficacy of spacing depends at least partly on the degree to which it reduces the occurrence of overlearning.
MELISA. An ontology-based agent for information retrieval in medicine

This paper describes MELISA MEdical Literature Search Agent – a prototype of an ontology-based information retrieval agent. We have designed a modular system that can be easily adapted to another medical lit erature sources or other professional domains. The major issues are the design of an architecture with three levels of abstraction, the use of separated ontologies and query models, and the definition of some aggregation operators to combine results from different queries.
The Application of Trend Following Strategies in Stock Market Trading

Trend-following (TF) strategies use fixed trading mechanism in order to take advantages from the long-term market moves without regards to the past price performance.In contrast with most prediction tools that stemmed from soft computing such as neural networks to predict a future trend, TF just rides on the current trend pattern to decide on buying or selling. While TF is widely applied in currency markets with a good track record for major currency pairs , it is doubtful that if TF can be applied in stock market. In this paper a new TF model that features both strategies of evaluating the trend by static and adaptive rules, is created from simulations and later verified on Hong Kong Hang Seng future indices. The model assesses trend profitability from the statistical features of the return distribution of the asset under consideration. The results and examples facilitate some insights on the merits of using the trend following model.
Social Media Data Analytics for Tourism - A Preliminary Study

Social media data are increasingly used as the source of research in a variety of domains. A typical example is urban analytics, which aims at solving urban problems by analyzing data from different sources including social media. The potential value of social media data in tourism studies, which is one of the key topics in urban research, however has been much less investigated. This paper seeks to understand the relationship between social media dynamics and the visiting patterns of visitors to touristic locations in real-world cases. By conducting a comparative study, we demonstrate how social media characterizes touristic locations differently from other data sources. Our study further shows that social media data can provide real-time insights of tourists’ visiting patterns in big events, thus contributing to the understanding of social media data utility in tourism studies.
Using News Articles for Real-time Cross-Lingual Event Detection and Filtering

The written medium through which we commonly learn about relevant news are news articles. Since there is an abundance of news articles that are written daily, the readers have a common problem of discovering the content of interest and still not be overwhelmed with the amount of it. In this paper we present a system called Event Registry which is able to group articles about an event across languages and extract from the articles core event information in a structured form. In this way, the amount of content that the reader has to check is significantly reduced while additionally providing the reader with a global coverage of each event. Since all event information is structured this also provides extensive and fine-grained options for information searching and filtering that are not available with current news aggregators.
Efficient parallel and distributed topological sort algorithms

Abstarct: I n thzs paper, we gave eficzent parallel and dastnbuted algomthms for the topologacal sort problem on acyclac graphs wath n vertzces. Our parallel algorathm solves the problem on a CREW PRAM zn O(log2 n) tame wath O(M(n)/ log n) processors, where M(n) denotes the number of processors needed to multaply two n x n anteger matnces ouer the znteger nng . The best known upper bound of M(n) zs O(n2376). The parallel algorathm can also solve the problem on processor arrays wath reconjigurable bus systems an O(1) tame and O(n3) processors. Our dastrzbuted algorathm solves the topologacal sort problem of an arbztrary asynchronous network wath communacataon complexaty O(n2).
Comparing Virtual and Physical Robotics Environments for Supporting Complex Systems and Computational Thinking

Both complex systems methods (such as agentbased modeling) and computational methods (such as programming) provide powerful ways for students to understand new phenomena. To understand how to effectively teach complex systems and computational content to younger students, we conducted a study in four urban middle school classrooms comparing 2-week-long curricular units—one using a physical robotics participatory simulation and one using a virtual robotics participatory simulation. We compare the two units for their effectiveness in supporting students’ complex systems thinking and computational thinking skills. We find that while both units improved student outcomes to roughly the same extent, they engendered different perspectives on the content. That is, students using the physical system were more likely to interpret situations from a bottom-up (‘‘agent’’) perspective, and students using the virtual system were more likely to employ a top-down (‘‘aggregate’’) perspective. Our outcomes suggest that the medium of students’ interactions with systems leads to differences in their learning from and about those systems. We explore the reasons for and effects of these differences, challenges in teaching this content, and student learning gains. The paper contributes operationalizable definitions of complex systems perspectives and computational perspectives and provides both a theoretical framework for and empirical evidence of a relationship between those
The impact of network parameters in cloud environment. A detailed analysis

Cloud computing is network based access for computing and data storage services. It aims to shift the location of computing infrastructure to the network, in order to reduce the costs of management and maintenance of hardware along with software resources. With Virtualization as key technology having impact on both the computation and communication performance of cloud services. We have fined that the abnormally unstable network performance can dramatically skew the results of certain network performance measurement techniques andapplications in cloud environment.Therefore, there is a need for elaborate study on the various parameters that effects cloud environment. In this paper we provide a detailanalysis of significance of various network-centric parameters in cloud computing.
Are Social Media Emancipatory or Hegemonic? Societal Effects of Mass Media Digitization in the Case of the SOPA Discourse

Mass media digitization is an unfolding phenomenon, posing novel societal opportunities and challenges that researchers are beginning to note. We build on and extend MIS research on process digitization and digital versus traditional communication media to study how and to what extent social media—one form of digital mass media—are emancipatory (i.e., permitting wide-spread participation in public discourse and surfacing of diverse perspectives) versus hegemonic (i.e., contributing to ideological control by a few). While a pressing concern to activists and scholars, systematic study of this issue has been elusive, owing partially to the complexity of the emancipation and hegemony concepts. Using a case study approach, we iteratively engaged with data on the discourse surrounding the Stop Online Piracy Act (SOPA) and source literature to identify six facets of interpretive media packages (i.e., competing social constructions of an issue) as measurable constructs pertinent to emancipation and hegemony. These facets included three structural constraints (on authorship, citation, and influence) and three content restrictions (on frames, signatures, and emotion). We investigated propositions regarding effects of social versus traditional media and lean versus rich social media on these interpretive media package facets by comparing the SOPA discourse across two lean traditional and social media (newspapers and Twitter), and two rich traditional and social media (television and YouTube). Our findings paradoxically revealed social media to be emancipatory with regard to structural constraints, but hegemonic with regard to an important content restriction (i.e., frames). Lean social media mitigated structural advantages and exacerbated content problems. These findings suggest that, as with traditional media, some inevitable evils accompany the societal benefits of social media and that mass media is having a detrimental effect on public discourse. We offer practical steps by which private and public institutions may counter this effect, theoretical implications for wider consideration of the six interpretive media package facets proposed here, and encouragement to MIS researchers to increase efforts to compare different digitized processes so that a more comprehensive theory of the effects of different forms of digitized processes can be developed.
Learn to segment single cells with deep distance estimator and deep cell detector

Single cell segmentation is critical and challenging in live cell imaging data analysis. Traditional image processing methods and tools require time-consuming and labor-intensive efforts of manually fine-tuning parameters. Slight variations of image setting may lead to poor segmentation results. Recent development of deep convolutional neural networks(CNN) provides a potentially efficient, general and robust method for segmentation. Most existing CNN-based methods treat segmentation as a pixel-wise classification problem. However, three unique problems of cell images adversely affect segmentation accuracy: lack of established training dataset, few pixels on cell boundaries, and ubiquitous blurry features. The problem becomes especially severe with densely packed cells, where a pixel-wise classification method tends to identify two neighboring cells with blurry shared boundary as one cell, leading to poor cell count accuracy and affecting subsequent analysis. Here we developed a different learning strategy that combines strengths of CNN and watershed algorithm. The method first trains a CNN to learn Euclidean distance transform of binary masks corresponding to the input images. Then another CNN is trained to detect individual cells in the Euclidean distance transform. In the third step, the watershed algorithm takes the outputs from the previous steps as inputs and performs the segmentation. We tested the combined method and various forms of the pixel-wise classification algorithm on segmenting fluorescence and transmitted light images. The new method achieves similar pixel accuracy but significant higher cell count accuracy than pixel-wise classification methods do, and the advantage is most obvious when applying on noisy images of densely packed cells.
AROMA: A Deep Multi-Task Learning Based Simple and Complex Human Activity Recognition Method Using Wearable Sensors

Human activity recognition (HAR) is a promising research issue in ubiquitous and wearable computing. However, there are some problems existing in traditional methods: 1) They treat HAR as a single label classification task, and ignore the information from other related tasks, which is helpful for the original task. 2) They need to predesign features artificially, which are heuristic and not tightly related to HAR task. To address these problems, we propose AROMA (human activity recognition using deep multi-task learning). Human activities can be divided into simple and complex activities. They are closely linked. Simple and complex activity recognitions are two related tasks in AROMA. For simple activity recognition task, AROMA utilizes a convolutional neural network (CNN) to extract deep features, which are task dependent and non-handcrafted. For complex activity recognition task, AROMA applies a long short-term memory (LSTM) network to learn the temporal context of activity data. In addition, there is a shared structure between the two tasks, and the object functions of these two tasks are optimized jointly. We evaluate AROMA on two public datasets, and the experimental results show that AROMA is able to yield a competitive performance in both simple and complex activity recognitions.
Image Blind Denoising with Generative Adversarial Network Based Noise Modeling

In this paper, we consider a typical image blind denoising problem, which is to remove unknown noise from noisy images. As we all know, discriminative learning based methods, such as DnCNN, can achieve state-of-the-art denoising results, but they are not applicable to this problem due to the lack of paired training data. To tackle the barrier, we propose a novel two-step framework. First, a Generative Adversarial Network (GAN) is trained to estimate the noise distribution over the input noisy images and to generate noise samples. Second, the noise patches sampled from the first step are utilized to construct a paired training dataset, which is used, in turn, to train a deep Convolutional Neural Network (CNN) for denoising. Extensive experiments have been done to demonstrate the superiority of our approach in image blind denoising.