nils-herrmann/clean_scirepeval huggingface dataset:
Young attitudes towards F-commerce advertising

Facebook provides the social needs of people to interact with each other. Hence, the companies can advertise their products as long as keeping in touch with their customers on Facebook. The issue in this study is the young consumer's attitude towards advertising and the trust in e-commerce as the advertisement publisher and Facebook itself. This study used a purposive sampling method and selected 300 respondents to respond the questionnaires. Pearson Correlation measured the strength and relation between demographic factors (gender, age and education) and dependent variable. A multiple regression analysis examined the relationship between independent and dependent variables. The result showed that gender and age had a positive correlation with the attitude towards F-commerce advertising. The variable of entertainment, irritation, trust in Facebook, and trust in e-commerce on Facebook had the significant impact on attitude towards F-commerce advertising. Research limitations and further research directions are presented at the end of the paper.
Heritage Recording and 3D Modeling with Photogrammetry and 3D Scanning

The importance of landscape and heritage recording and documentation with optical remote sensing sensors is well recognized at international level. The continuous development of new sensors, data capture methodologies and multi-resolution 3D representations, contributes significantly to the digital 3D documentation, mapping, conservation and representation of landscapes and heritages and to the growth of research in this field. This article reviews the actual optical 3D measurement sensors and 3D modeling techniques, with their limitations and potentialities, requirements and specifications. Examples of 3D surveying and modeling of heritage sites and objects are also shown throughout the paper.
Consensus in the Age of Blockchains

The blockchain initially gained traction in 2008 as the technology underlying Bitcoin [105], but now has been employed in a diverse range of applications and created a global market worth over $150B as of 2017. What distinguishes blockchains from traditional distributed databases is the ability to operate in a decentralized setting without relying on a trusted third party. As such their core technical component is consensus: how to reach agreement among a group of nodes. This has been extensively studied already in the distributed systems community for closed systems, but its application to open blockchains has revitalized the field and led to a plethora of new designs. The inherent complexity of consensus protocols and their rapid and dramatic evolution makes it hard to contextualize the design landscape. We address this challenge by conducting a systematic and comprehensive study of blockchain consensus protocols. After first discussing key themes in classical consensus protocols, we describe: (i) protocols based on proof-of-work (PoW), (ii) proof-of-X (PoX) protocols that replace PoW with more energy-efficient alternatives, and (iii) hybrid protocols that are compositions or variations of classical consensus protocols. We develop a framework to evaluate their performance, security and design properties, and use it to systematize key themes in the protocol categories described above. This evaluation leads us to identify research gaps and challenges for the community to consider in future research endeavours.
Learning from small data: A pairwise approach for ordinal regression

Ordinal regression which aims to classify instances into ordinal categories has numerous applications. As a supervised learning problem, a large number of labeled data is needed to train an accurate model, in particular when the number of categories is large. Learning an effective ordinal classifier from a small dataset is a challenging task. This paper proposes a framework to transform the ordinal regression problem to a binary classification problem and then recover the ordinal information from the binary outputs. The labeled instances are paired up to train a binary classifier, and therefore, the number of training points is squared, which alleviates the lack of training points. The transformed binary classification problem is solved by a pairwise SVM method. Experimental results demonstrate that on 12 widely used benchmarks, the proposed method is effective comparing with the state-of-the-art ordinal regression methods.
Convolutional recurrent neural networks: Learning spatial dependencies for image representation

In existing convolutional neural networks (CNNs), both convolution and pooling are locally performed for image regions separately, no contextual dependencies between different image regions have been taken into consideration. Such dependencies represent useful spatial structure information in images. Whereas recurrent neural networks (RNNs) are designed for learning contextual dependencies among sequential data by using the recurrent (feedback) connections. In this work, we propose the convolutional recurrent neural network (C-RNN), which learns the spatial dependencies between image regions to enhance the discriminative power of image representation. The C-RNN is trained in an end-to-end manner from raw pixel images. CNN layers are firstly processed to generate middle level features. RNN layer is then learned to encode spatial dependencies. The C-RNN can learn better image representation, especially for images with obvious spatial contextual dependencies. Our method achieves competitive performance on ILSVRC 2012, SUN 397, and MIT indoor.
A Partial Least Squares Regression-Based Fusion Model for Predicting the Trend in Drowsiness

This paper proposes a new technique of modeling driver drowsiness with multiple eyelid movement features based on an information fusion technique - partial least squares regression (PLSR), with which to cope with the problem of strong collinear relations among eyelid movement features and, thus, predicting the tendency of the drowsiness. With a set of electro- oculogram signals measured in an experiment conducted in Sweden, 14 typical eyelid movement features are first extracted. Then, statistical analyses from 20 subjects indicate that the eyelid movement parameters can characterize a driver's degree of drowsiness. The intrinsic quantitative relationships between eyelid movement features and driver drowsiness degree are modeled by PLSR analysis. The developed model provides a framework for integrating multiple sleepiness features together and defining the contribution of each feature to the decision and prediction result. The predictive precision and robustness of the model thus established are validated, which show that it provides a novel way of fusing multifeatures together for enhancing our capability of detecting and predicting the state of drowsiness.
Unsupervised Calibration for Multi-beam Lasers

Keeping pace with technological progress, robot sensors are generating increasing amounts of data; a particular recent trend has been a shift from single-beam LIDAR sensors to multi-beam versions. Whereas single-beam sensors can often be calibrated without great difficulty, deriving an accurate calibration for lasers with many simultaneous beams has been a tedious and significantly harder challenge. In addition, existing calibration methods require specific and known environmental features. Instead, we propose a fully unsupervised approach to multibeam laser calibration. We attempt to recover optimal parameters for each beam’s orientation and distance-response functio n as well as a fully probabilistic generative model for each beam’s remittance response to surfaces of varying reflectivity. Our method allows simultaneous calibration of tens or hundreds of beams, each with its own parameters. In addition, we recover the sensor’s extrinsic pose relative to the robot’s coordinate frame. Crucially, our approach requires no specific calibration target, instead relying only on the weak assumption that points in space tend to lie on contiguous surfaces. Specifically, we define an energy function on point clouds that penalizes points far away from surfaces defined by points from other beams. Then, by aggregating points acquired across a series of poses, we take derivatives of the energy function across pairs of beams with respect to individual parameters. Using an iterative optimization method we arrive at a globally consistent calibration with very low error. Demonstrating our algorithm with a 64-beam LIDAR unit on a moving vehicle equipped with an IMU, we show that we can precisely solve for the LIDAR’s extrinsic pose and derive accurate 192-parameter orientation and distance calibrations even from grossly inaccurate initializations and without any calibration target or environment measurements. We also show significant improvements to the resulting remittance environment maps resulting from these calibrated parameters as well as the learned Bayesian model for each beam’s remittance response.
Artistic Rendering of Portraits

This chapter presents an overview of the latest artistic portrait rendering techniques. Artistic rendering or portraits can be viewed as an image generating process controlled by two factors. The first one is the face fidelity, namely, a rendered portrait image should preserve a certain amount of the original face’s information. The second factor is the artistic style, for example, sketch, painting, etc. In the literature, different portrait rendering algorithms either adopt different models and data structures to represent the facial information, or use different graphical elements and compositional methods to simulate various styles. These two factors essentially reveal the two principles in portraiture, namely the pursuit of likeness and aesthetic.
Evaluation of object-oriented design patterns in game development

The use of object-oriented design patterns in game development is being evaluated in this paper. Games’ quick evolution, demands great flexibility, code reusability and low maintenance costs. Games usually differentiate between versions, in respect of changes of the same type (additional animation, terrains etc). Consequently, the application of design patterns in them can be beneficial regarding maintainability. In order to investigate the benefits of using design patterns, a qualitative and a quantitative evaluation of open source projects is being performed. For the quantitative evaluation, the projects are being analyzed by reverse engineering techniques and software metrics are calculated.
A 5.4-Gbit/s Adaptive Continuous-Time Linear Equalizer Using Asynchronous Undersampling Histograms

We demonstrate a new type of adaptive continuous-time linear equalizer (CTLE) based on asynchronous undersampling histograms. Our CTLE automatically selects the optimal equalizing filter coefficient among several predetermined values by searching for the coefficient that produces the largest peak value in histograms obtained with asynchronous undersampling. This scheme is simple and robust and does not require clock synchronization for its operation. A prototype chip realized in 0.13-μm CMOS technology successfully achieves equalization for 5.4-Gbit/s 231 - 1 pseudorandom bit sequence data through 40-, 80-, and 120-cm PCB traces and 3-m DisplayPort cable. In addition, we present the results of statistical analysis with which we verify the reliability of our scheme for various sample sizes. The results of this analysis are confirmed with experimental data.
Datacenter Traffic Control: Understanding Techniques and Tradeoffs

Datacenters provide cost-effective and flexible access to scalable compute and storage resources necessary for today’s cloud computing needs. A typical datacenter is made up of thousands of servers connected with a large network and usually managed by one operator. To provide quality access to the variety of applications and services hosted on datacenters and maximize performance, it deems necessary to use datacenter networks effectively and efficiently. Datacenter traffic is often a mix of several classes with different priorities and requirements. This includes user-generated interactive traffic, traffic with deadlines, and long-running traffic. To this end, custom transport protocols and traffic management techniques have been developed to improve datacenter network performance. In this tutorial paper, we review the general architecture of datacenter networks, various topologies proposed for them, their traffic properties, general traffic control challenges in datacenters and general traffic control objectives. The purpose of this paper is to bring out the important characteristics of traffic control in datacenters and not to survey all existing solutions (as it is virtually impossible due to massive body of existing research). We hope to provide readers with a wide range of options and factors while considering a variety of traffic control mechanisms. We discuss various characteristics of datacenter traffic control, including management schemes, transmission control, traffic shaping, prioritization, load balancing, multipathing, and traffic scheduling. Next, we point to several open challenges as well as new and interesting networking paradigms. At the end of this paper, we briefly review inter-datacenter networks that connect geographically dispersed datacenters, which have been receiving increasing attention recently and pose interesting and novel research problems. To measure the performance of datacenter networks, different performance metrics have been used, such as flow completion times, deadline miss rate, throughput, and fairness. Depending on the application and user requirements, some metrics may need more attention. While investigating different traffic control techniques, we point out the tradeoffs involved in terms of costs, complexity, and performance. We find that a combination of different traffic control techniques may be necessary at particular entities and layers in the network to improve the variety of performance metrics. We also find that despite significant research efforts, there are still open problems that demand further attention from the research community.
Fundamental noise limit of piezoelectric accelerometer

Since significant progress is achieved in the development of piezoelectric (PE) accelerometers for small signal applications nowadays (for example, piezoelectric seismic vibration sensors), the question about the fundamental noise limit of these sensors becomes vital. The noise of the PE transducer is the fundamental noise limit of the PE accelerometer and should be taken into account if the noise of the electronics is small enough. The two noise sources of PE transducer, the mechanical-thermal noise of the damped mechanical harmonic oscillator and the electrical-thermal noise of the PE element's material, are analyzed in this paper. The equation of the total fundamental noise limit of the PE accelerometer is presented. This equation can be used for the calculation of the fundamental noise limit of PE accelerometers if their parameters are known or can be obtained by measurement.
A Comprehensive Survey of Recent Trends in Cloud Robotics Architectures and Applications

Cloud robotics has recently emerged as a collaborative technology between cloud computing and service robotics enabled through progress in wireless networking, large scale storage and communication technologies, and the ubiquitous presence of Internet resources over recent years. Cloud computing empowers robots by offering them faster and more powerful computational capabilities through massively parallel computation and higher data storage facilities. It also offers access to open-source, big datasets and software, cooperative learning capabilities through knowledge sharing, and human knowledge through crowdsourcing. The recent progress in cloud robotics has led to active research in this area spanning from the development of cloud robotics architectures to its varied applications in different domains. In this survey paper, we review the recent works in the area of cloud robotics technologies as well as its applications. We draw insights about the current trends in cloud robotics and discuss the challenges and limitations in the current literature, open research questions and future research directions.
The impacts of brand trust, customer satisfaction, and brand loyalty on word-of-mouth

The study mainly investigates the relationships among brand trust, customer satisfaction, brand loyalty, and word-of-mouth. Nowadays, the automotive industry is facing the competitive environment. Whether the industry can enhance brand trust, increase customer satisfaction, and then improve brand loyalty and word-of-mouth, which is the key issue of this study. Toyota was selected as the object of this study. 375 questionnaires were provided and 258 valid replies were received. This study uses the structural equation model to empirically explore the relationships among brand trust, customer satisfaction, brand loyalty, and word-of-mouth. The results indicate the best model is causal chain, that is, brand trust must affect brand loyalty through customer satisfaction initially, and then impact word-of-mouth through brand loyalty.
SDN & NFV in 5G: Advancements and challenges

The next generation of mobile networks has risen the expectations and has induced a whole new approach in the networking future. This study summarizes solutions, that meet all the existing requirements integrating Software Defined Network (SDN) and Network Function Virtualization (NFV) in mobile networking. The main contribution of this research activity is that it is a tool for literature reference for scientists and engineers, because it presents and compares the most fundamental existing solutions and the future directions. In this paper, the main characteristics of SDN and NFV are summarized as fundamental alternatives contributing to succeed the goals set by 5G. The main demands and challenges faced by these solutions and the most beneficial applications of the combination of SDN with NFV are described. Finally, the main conclusions of this research and suggestions for possible future activity in the field are described.
Proposal of millimeter-wave holographic radar with antenna switching

This paper proposes a millimeter-wave holographic radar with a simple structure for automotive applications. The simplicity can be realized by switching both transmitting antennas and receiving antennas. Also, a super resolution technique is introduced for the detection of angular positions in the proposed radar. The radar developed experimentally has accomplished an azimuthal angular resolution of less than 2 degrees and an azimuthal field of view (FoV) of more than 20 degrees simultaneously.
Guarding Against the Erosion of Competitive Advantage: A Knowledge Leakage Mitigation Model

A critical objective of knowledge-intensive organizations is to prevent erosion of their competitive knowledge base through leakage. Our review of the literature highlights the need for a more refined conceptualization of perceived leakage risk. We propose a Knowledge Leakage Mitigation (KLM) model to explain the incongruity between perceived high-risk of leakage and lack of protective actions. We argue that an organization’s perceived risk of leakage increases if competitors can benefit from leakage incidents. Further, perceived leakage risk decreases if the organization is shielded from impact due to their diversity of knowledge assets and their ability to reconfigure knowledge resources to refresh their competitive knowledge base. We describe our approach to the design of a large-scale survey instrument that has been tested and refined in two stakeholder communities: 1) knowledge managers responsible for organizational strategy, and 2) Information security management consultants.
Textually Enriched Neural Module Networks for Visual Question Answering

Problems at the intersection of language and vision, like visual question answering, have recently been gaining a lot of attention in the field of multi-modal machine learning as computer vision research moves beyond traditional recognition tasks. There has been recent success in visual question answering using deep neural network models which use the linguistic structure of the questions to dynamically instantiate network layouts. In the process of converting the question to a network layout, the question is simplified, which results in loss of information in the model. In this paper, we enrich the image information with textual data using image captions and external knowledge bases to generate more coherent answers. We achieve 57.1% overall accuracy on the test-dev open-ended questions from the visual question answering (VQA 1.0) real image dataset.
Large Language Models in Machine Translation

This paper reports on the benefits of largescale statistical language modeling in machine translation. A distributed infrastructure is proposed which we use to train on up to 2 trillion tokens, resulting in language models having up to 300 billion n-grams. It is capable of providing smoothed probabilities for fast, single-pass decoding. We introduce a new smoothing method, dubbed Stupid Backoff, that is inexpensive to train on large data sets and approaches the quality of Kneser-Ney Smoothing as the amount of training data increases.
ParaBox: Exploiting Parallelism for Virtual Network Functions in Service Chaining

Service Function Chains (SFCs) comprise a sequence of Network Functions (NFs) that are typically traversed in-order by data flows. Consequently, SFC delay grows linearly with the length of the SFC. Yet, for highly latency sensitive applications, this delay may be unacceptable---particularly when the constituent NFs are virtualized, running on commodity servers. In this paper, we investigate how SFC latency may be reduced by exploiting opportunities for parallel packet processing across NFs. We propose ParaBox, a novel hybrid packet processing architecture that, when possible, dynamically distributes packets to VNFs in parallel and merges their outputs intelligently to ensure the preservation of correct sequential processing semantics. To demonstrate the feasibility of our approach, we implement a ParaBox prototype on top of the DPDK-enabled Berkeley Extensible Software Switch. Our preliminary experiment results show that ParaBox can not only significantly reduce the service chaining latency, but also improve throughput.