nils-herrmann/clean_scirepeval huggingface dataset:
Content Delivery Networks: Status and Trends

T he Web's growth has transformed communications and business services such that speed, accuracy, and availability of network delivered content has become absolutely critical — both on their own terms and in terms of measuring Web performance. Proxy servers partially address the need for rapid content delivery by providing multiple clients with a shared cache location. In this context, if a requested object exists in a cache (and the cached version has not expired), clients get a cached copy, which typically reduces delivery time. Web caching has three primary benefits. First, it reduces bandwidth consumption, network congestion , and network traffic because it stores the frequently requested content closer to clients. Second , it reduces external latency (the time required to transfer objects from the origin server to proxy servers) because it delivers cached objects from the proxy servers. Finally, caching improves reliability because clients can obtain a cached copy even if the remote server is unavailable. Using a shared proxy cache, however, has two significant drawbacks: If the proxy is not properly updated, a client might receive stale data, and, as the number of clients grows, origin servers typically become bottlenecks. When numerous users access a Web site simultaneously — such as when " flash crowds " 1 flooded popular news sites with requests in the wake of the September 2001 terrorist attack in the US — serious caching problems result and sites typically become unavailable. Researchers have widely considered content delivery networks to be an effective solution to reducing these disadvantages. 2,3 CDNs act as trusted overlay networks that offer high-performance delivery of common Web objects, static data, and rich multimedia content by distributing content load among servers that are close to the clients. 4,5 CDN benefits include reduced origin server load, reduced latency for end users, and increased throughput. CDNs can also improve Web scalabil-ity and disperse flash-crowd events. Here we offer an overview of the CDN architecture and popular CDN service providers. CDNs first emerged in 1998 6 to address the fact that the Web was not designed to handle large content transmissions over long distances. 7,8 According to Network World Fusion (www.nwfusion.com), about 2,500 companies now use CDNs. In the US alone, for example, Storigen Systems (www.storigen.com) estimates that the CDN market generated US$905 million in 2000 and will reach US$12 billion by 2007. Proxies and CDNs essentially address two different issues. ISPs use proxies …
Attitudes Toward Transgender Men and Women: Development and Validation of a New Measure

A series of three studies were conducted to generate, develop, and validate the Attitudes toward Transgender Men and Women (ATTMW) scale. In Study 1, 120 American adults responded to an open-ended questionnaire probing various dimensions of their perceptions of transgender individuals and identity. Qualitative thematic analysis generated 200 items based on their responses. In Study 2, 238 American adults completed a questionnaire consisting of the generated items. Exploratory factor analysis (EFA) revealed two non-identical 12-item subscales (ATTM and ATTW) of the full 24-item scale. In Study 3, 150 undergraduate students completed a survey containing the ATTMW and a number of validity-testing variables. Confirmatory factor analysis (CFA) verified the single-factor structures of the ATTM and ATTW subscales, and the convergent, discriminant, predictive, and concurrent validities of the ATTMW were also established. Together, our results demonstrate that the ATTMW is a reliable and valid measure of attitudes toward transgender individuals.
Subspace Clustering via Learning an Adaptive Low-Rank Graph

By using a sparse representation or low-rank representation of data, the graph-based subspace clustering has recently attracted considerable attention in computer vision, given its capability and efficiency in clustering data. However, the graph weights built using the representation coefficients are not the exact ones as the traditional definition is in a deterministic way. The two steps of representation and clustering are conducted in an independent manner, thus an overall optimal result cannot be guaranteed. Furthermore, it is unclear how the clustering performance will be affected by using this graph. For example, the graph parameters, i.e., the weights on edges, have to be artificially pre-specified while it is very difficult to choose the optimum. To this end, in this paper, a novel subspace clustering via learning an adaptive low-rank graph affinity matrix is proposed, where the affinity matrix and the representation coefficients are learned in a unified framework. As such, the pre-computed graph regularizer is effectively obviated and better performance can be achieved. Experimental results on several famous databases demonstrate that the proposed method performs better against the state-of-the-art approaches, in clustering.
Understanding Consumers ' Acceptance of Online Purchasing

This paper examines previous Technology Acceptance Model (TAM)-related studies in order to provide an expanded model that explains consumers’ acceptance of online purchasing. Our model provides extensions to the original TAM by including constructs such as social influence and voluntariness; it also examines the impact of external variables including trust, privacy, risk, and e-loyalty. We surveyed consumers in the United States and Australia. Our findings suggest that our expanded model serves as a very good predictor of consumers’ online purchasing behaviors. The linear regression model shows a respectable amount of variance explained for Behavioral Intention (R 2 = .627). Suggestions are provided for the practitioner and ideas are presented for future research.
Algorithmic Trading of Cryptocurrency Based on Twitter Sentiment Analysis

PAST research has shown that real-time Twitter data can be used to predict market movement of securities and other financial instruments . The goal of this paper is to prove whether Twitter data relating to cryptocurrencies can be utilized to develop advantageous crypto coin trading strategies. By way of supervised machine learning techniques, our team will outline several machine learning pipelines with the objective of identifying cryptocurrency market movement. The prominent alternative currency examined in this paper is Bitcoin (BTC). Our approach to cleaning data and applying supervised learning algorithms such as logistic regression, Naive Bayes, and support vector machines leads to a final hour-to-hour and day-to-day prediction accuracy exceeding 90%. In order to achieve this result, rigorous error analysis is employed in order to ensure that accurate inputs are utilized at each step of the model. This analysis yields a 25% accuracy increase on average.
Clustering Convolutional Kernels to Compress Deep Neural Networks

In this paper, we propose a novel method to compress CNNs by reconstructing the network from a small set of spatial convolution kernels. Starting from a pre-trained model, we extract representative 2D kernel centroids using k-means clustering. Each centroid replaces the corresponding kernels of the same cluster, and we use indexed representations instead of saving whole kernels. Kernels in the same cluster share their weights, and we fine-tune the model while keeping the compressed state. Furthermore, we also suggest an efficient way of removing redundant calculations in the compressed convolutional layers. We experimentally show that our technique works well without harming the accuracy of widely-used CNNs. Also, our ResNet-18 even outperforms its uncompressed counterpart at ILSVRC2012 classification task with over 10x compression ratio.
Joint entity recognition and relation extraction as a multi-head selection problem

State-of-the-art models for joint entity recognition and relation extraction strongly rely on external natural language processing (NLP) tools such as POS (part-of-speech) taggers and dependency parsers. Thus, the performance of such joint models depends on the quality of the features obtained from these NLP tools. However, these features are not always accurate for various languages and contexts. In this paper, we propose a joint neural model which performs entity recognition and relation extraction simultaneously, without the need of any manually extracted features or the use of any external tool. Specifically, we model the entity recognition task using a CRF (Conditional Random Fields) layer and the relation extraction task as a multi-head selection problem (i.e., potentially identify multiple relations for each entity). We present an extensive experimental setup, to demonstrate the effectiveness of our method using datasets from various contexts (i.e., news, biomedical, real estate) and languages (i.e., English, Dutch). Our model outperforms the previous neural models that use automatically extracted features, while it performs within a reasonable margin of feature-based neural models, or even beats them.
Skip Connections as Effective Symmetry-Breaking

Skip connections made the training of very deep neural networks possible and have become an indispendable component in a variety of neural architectures. A satisfactory explanation for their success remains elusive. Here, we present an explanation for the benefits of skip connections in training very deep neural networks. We argue that skip connections help break symmetries inherent in the loss landscapes of deep networks, leading to drastically simplified landscapes. In particular, skip connections between adjacent layers in a multilayer network break the permutation symmetry of nodes in a given layer, and the recently proposed DenseNet architecture, where each layer projects skip connections to every layer above it, also breaks the rescaling symmetry of connectivity matrices between different layers. This hypothesis is supported by evidence from a toy model with binary weights and from experiments with fully-connected networks suggesting (i) that skip connections do not necessarily improve training unless they help break symmetries and (ii) that alternative ways of breaking the symmetries also lead to significant performance improvements in training deep networks, hence there is nothing special about skip connections in this respect. We find, however, that skip connections confer additional benefits over and above symmetry-breaking, such as the ability to deal effectively with the vanishing gradients problem.
Patterns of Engagement in Connectivist MOOCs

Connectivist massive open online courses (cMOOCs) represent an important new pedagogical approach ideally suited to the network age. However, little is known about how the learning experience afforded by cMOOCs is suited to learners with different skills, motivations, and dispositions. In this study, semi-structured interviews were conducted with 29 participants on the Change11 cMOOC. These accounts were analyzed to determine patterns of engagement and factors affecting engagement in the course. Three distinct types of engagement were recognized – active participation, passive participation, and lurking. In addition, a number of key factors that mediated engagement were identified including confidence, prior experience, and motivation. This study adds to the overall understanding of learning in cMOOCs and provides additional empirical data to a nascent research field. The findings provide an insight into how the learning experience afforded by cMOOCs suits the diverse range of learners that may coexist within a cMOOC. These insights can be used by designers of future cMOOCs to tailor the learning experience to suit the diverse range of learners that may choose to learn in this way.
Mobile payment in mobile e-commerce

The main problem of Chinese mobile e-commerce at present is its insufficient modes of payment. Therefore, the research on brand-new and high-efficiency form of mobile payment is very important for the development of Chinese mobile e-commerce. There are two forms of mobile payment in China at present, namely mobile fee account payment and bank card mobile payment. The paper brings forward a new form of mobile payment by referring to the advantages of the existing two forms of mobile payment and aiming at their disadvantages at the same time.
Cross-Graph Learning of Multi-Relational Associations

Cross-graph Relational Learning (CGRL) refers to the problem of predicting the strengths or labels of multi-relational tuples of heterogeneous object types, through the joint inference over multiple graphs which specify the internal connections among each type of objects. CGRL is an open challenge in machine learning due to the daunting number of all possible tuples to deal with when the numbers of nodes in multiple graphs are large, and because the labeled training instances are extremely sparse as typical. Existing methods such as tensor factorization or tensor-kernel machines do not work well because of the lack of convex formulation for the optimization of CGRL models, the poor scalability of the algorithms in handling combinatorial numbers of tuples, and/or the non-transductive nature of the learning methods which limits their ability to leverage unlabeled data in training. This paper proposes a novel framework which formulates CGRL as a convex optimization problem, enables transductive learning using both labeled and unlabeled tuples, and offers a scalable algorithm that guarantees the optimal solution and enjoys a linear time complexity with respect to the sizes of input graphs. In our experiments with a subset of DBLP publication records and an Enzyme multi-source dataset, the proposed method successfully scaled to the large cross-graph inference problem, and outperformed other representative approaches significantly.
DriveAHead — A Large-Scale Driver Head Pose Dataset

Head pose monitoring is an important task for driver assistance systems, since it is a key indicator for human attention and behavior. However, current head pose datasets either lack complexity or do not adequately represent the conditions that occur while driving. Therefore, we introduce DriveAHead, a novel dataset designed to develop and evaluate head pose monitoring algorithms in real driving conditions. We provide frame-by-frame head pose labels obtained from a motion-capture system, as well as annotations about occlusions of the driver's face. To the best of our knowledge, DriveAHead is the largest publicly available driver head pose dataset, and also the only one that provides 2D and 3D data aligned at the pixel level using the Kinect v2. Existing performance metrics are based on the mean error without any consideration of the bias towards one position or another. Here, we suggest a new performance metric, named Balanced Mean Angular Error, that addresses the bias towards the forward looking position existing in driving datasets. Finally, we present the Head Pose Network, a deep learning model that achieves better performance than current state-of-the-art algorithms, and we analyze its performance when using our dataset.
A Complexity Measure

This paper describes a graph-theoretic complexity measure and illustrates how it can be used to manage and control program complexity. The paper first explains how the graph theory concepts apply and gives an intuitive explanation of the graph concepts in programming terms. The control graphs of several actual FORTRAN programs are then presented to illustrate the correlation between intuitive complexity and the graph theoretic complexity. Several properties of the graph-theoretic complexity are then proved which show, for example, that complexity is independent of physical size (adding or subtracting functional statements leaves complexity unchanged) and complexity depends only on the decision structure of a program.
 The issue of using non-structured control flow is also discussed. A characterization of non-structured control graphs is given and a method of measuring the “structuredness” of a program is developed. The relationship between structure and reducibility is illustrated with several examples.
 The last section of the paper deals with a testing methodology used in conjunction with the complexity measure; a testing strategy is defined that dictates that a program can either admit of a certain minimal testing level or the program can be structurally reduced.
Unsupervised Domain Adaptation: from Simulation Engine to the RealWorld

Large-scale labeled training datasets have enabled deep neural networks to excel on a wide range of benchmark vision tasks. However, in many applications it is prohibitively expensive or timeconsuming to obtain large quantities of labeled data. To cope with limited labeled training data, many have attempted to directly apply models trained on a large-scale labeled source domain to another sparsely labeled target domain. Unfortunately, direct transfer across domains often performs poorly due to domain shift and dataset bias. Domain adaptation is the machine learning paradigm that aims to learn a model from a source domain that can perform well on a different (but related) target domain. In this paper, we summarize and compare the latest unsupervised domain adaptation methods in computer vision applications. We classify the non-deep approaches into sample re-weighting and intermediate subspace transformation categories, while the deep strategy includes discrepancy-based methods, adversarial generative models, adversarial discriminative models and reconstruction-based methods. We also discuss some potential directions.
Quantifying the Bullwhip Effect in a Simple Supply Chain : The Impact of Forecasting , Lead Times , and Information

Frank Chen • Zvi Drezner • Jennifer K. Ryan • David Simchi-Levi Decision Sciences Department, National University of Singapore, 119260 Singapore Department of MS & IS, California State University, Fullerton, California 92834 School of Industrial Engineering, Purdue University, West Lafayette, Indiana 47907 Department of IE & MS, Northwestern University, Evanston, Illinois 60208 fbachen@nus.edu.sg • drezner@exchange.fullerton.edu • jkryan@ecn.purdue.edu •
Secure Automotive On-Board Protocols: A Case of Over-the-Air Firmware Updates

The software running on electronic devices is regularly updated, these days. A vehicle consists of many such devices, but is operated in a completely different manner than consumer devices. Update operations are safety critical in the automotive domain. Thus, they demand for a very well secured process. We propose an on-board security architecture which facilitates such update processes by combining hardware and software modules. In this paper, we present a protocol to show how this security architecture is employed in order to achieve secure firmware updates for automotive control units.
The Guide to the Software Engineering Body of Knowledge

§ Mission and structure of the project. § Overview of the Stone Man version of the Guide to the SWEBOK. § Status and development process of the Guide. § Applications of the Guide in the fields of education, human resource management, professional development and licensing and certification. § Class exercise in applying the Guide to defining the competencies needed to support software life cycle process deployment. § Strategy for uptake and promotion of the Guide. § Discussion of promotion, trial usage and experimentation.
CMOS image sensors

In this article, we provide a basic introduction to CMOS image-sensor technology, design and performance limits and present recent developments and future directions in this area. We also discuss image-sensor operation and describe the most popular CMOS image-sensor architectures. We note the main non-idealities that limit CMOS image sensor performance, and specify several key performance measures. One of the most important advantages of CMOS image sensors over CCDs is the ability to integrate sensing with analog and digital processing down to the pixel level. Finally, we focus on recent developments and future research directions that are enabled by pixel-level processing, the applications of which promise to further improve CMOS image sensor performance and broaden their applicability beyond current markets.
Decentralized Execution of Smart Contracts: Agent Model Perspective and Its Implications

Smart contracts are one of the most important applications of the blockchain. Most existing smart contract systems assume that for executing contract over a network of decentralized nodes, the outcome in accordance with the majority can be trusted. However, we observe that users involved with a smart contract may strategically take actions to manipulate execution of the contract for purpose to increase their own benefits. We propose an agent model, as the underpinning mechanism for contract execution over a network of decentralized nodes and public ledger, to address this problem and discuss the possibility of preventing users from manipulating smart contract execution by applying principles of game theory and agent based analysis.
Predictive Analytics for Supply Chains : a Systematic Literature Review

Predictive analytics is a group of methods that uses statistical and other empirical techniques to predict future events, based on past occurrences. Predictive analytics can generate valuable information for the management of a supply chain company to improve decision-making. Even though the importance of the topic is clear, there is no clear overview of the use of predictive analytics in the supply chain currently. This research provides a state-ofthe-art by performing a systematic literature review. In the literature review we have found the models, methods, techniques and applications of predictive analytics in the supply chain and have determined the trends and literature gaps. The most important finding is that even though there is only a limited amount of literature available, the interest in this topic is growing gradually. We also provide future research directions for further research on this subject.
STOCK MARKET FORECASTING TECHNIQUES : A

This paper surveys recent literature in the area of Neural Network, Data Mining, Hidden Markov Model and Neuro-Fuzzy system used to predict the stock market fluctuation. Neural Networks and Neuro-Fuzzy systems are identified to be the leading machine learning techniques in stock market index prediction area. The Traditional techniques are not cover all the possible relation of the stock price fluctuations. There are new approaches to known in-depth of an analysis of stock price variations. NN and Markov Model can be used exclusively in the finance markets and forecasting of stock price. In this paper, we propose a forecasting method to provide better an accuracy rather traditional method.
Belief in Life After Death and Attitudes Toward Voluntary Euthanasia.

Research has documented associations among religious affiliation, religious practice, and attitudes toward voluntary euthanasia, yet very few studies have investigated how particular religious beliefs influence these attitudes. I use data from the General Social Survey (GSS; N = 19,967) to evaluate the association between the belief in life after death and attitudes toward voluntary euthanasia. I find that those who believe in life after death are significantly less likely than those who do not believe in life after death or those who doubt the existence of life after death to have positive attitudes toward voluntary euthanasia. These associations hold even after controlling for religious affiliation, religious attendance, views of the Bible, and sociodemographic factors. The findings indicate that to understand individuals' views about voluntary euthanasia, one must pay attention to individuals' particular religious beliefs.