nils-herrmann/clean_scirepeval huggingface dataset:
NetworkCloudSim: Modelling Parallel Applications in Cloud Simulations

As interest in adopting Cloud computing for various applications is rapidly growing, it is important to understand how these applications and systems will perform when deployed on Clouds. Due to the scale and complexity of shared resources, it is often hard to analyze the performance of new scheduling and provisioning algorithms on actual Cloud test beds. Therefore, simulation tools are becoming more and more important in the evaluation of the Cloud computing model. Simulation tools allow researchers to rapidly evaluate the efficiency, performance and reliability of their new algorithms on a large heterogeneous Cloud infrastructure. However, current solutions lack either advanced application models such as message passing applications and workflows or scalable network model of data center. To fill this gap, we have extended a popular Cloud simulator (CloudSim) with a scalable network and generalized application model, which allows more accurate evaluation of scheduling and resource provisioning policies to optimize the performance of a Cloud infrastructure.
A multidimensional trust formation model in B-to-C e-commerce: a conceptual framework and content analyses of academia/practitioner perspectives

What dimensions can be identified in the trust formation processes in Business-to-Consumer (B-to-C) electronic commerce (e-commerce)? How do these differ in importance between academia and practitioners? The purpose of this research is to build a model of multidimensional trust formation for online exchanges in B-to-C electronic commerce. Further, to study the relative importance of the dimensions between two expert groups (academics and practitioners), two semantic network and content analyses are conducted: one for academia’s perspectives and another for practitioners’ perspectives of trust in B-to-C electronic commerce. The results show that the two perspectives are divergent in some ways and complementary in other ways. We believe that the two need to be combined to represent meaningful trust-building mechanisms in websites. D 2004 Elsevier B.V. All rights reserved.
3D Face Reconstruction Using a Single or Multiple Views

We present a 3D face reconstruction system that takes as input either one single view or several different views. Given a facial image, we first classify the facial pose into one of five predefined poses, then detect two anchor points that are then used to detect a set of predefined facial landmarks. Based on these initial steps, for a single view we apply a warping process using a generic 3D face model to build a 3D face. For multiple views, we apply sparse bundle adjustment to reconstruct 3D landmarks which are used to deform the generic 3D face model. Experimental results on the Color FERET and CMU multi-PIE databases confirm our framework is effective in creating realistic 3D face models that can be used in many computer vision applications, such as 3D face recognition at a distance.
MRAP: a novel MapReduce-based framework to support HPC analytics applications with access patterns

Due to the explosive growth in the size of scientific data sets, data-intensive computing is an emerging trend in computational science. Many application scientists are looking to integrate data-intensive computing into computational-intensive High Performance Computing facilities, particularly for data analytics. We have observed several scientific applications which must migrate their data from an HPC storage system to a data-intensive one. There is a gap between the data semantics of HPC storage and data-intensive system, hence, once migrated, the data must be further refined and reorganized. This reorganization requires at least two complete scans through the data set and then at least one MapReduce program to prepare the data before analyzing it. Running multiple MapReduce phases causes significant overhead for the application, in the form of excessive I/O operations. For every MapReduce application that must be run in order to complete the desired data analysis, a distributed read and write operation on the file system must be performed. Our contribution is to extend Map-Reduce to eliminate the multiple scans and also reduce the number of pre-processing MapReduce programs. We have added additional expressiveness to the MapReduce language to allow users to specify the logical semantics of their data such that 1) the data can be analyzed without running multiple data pre-processing MapReduce programs, and 2) the data can be simultaneously reorganized as it is migrated to the data-intensive file system. Using our augmented MapReduce system, MapReduce with Access Patterns (MRAP), we have demonstrated up to 33% throughput improvement in one real application, and up to 70% in an I/O kernel of another application.
Performance evaluation of Castalia Wireless Sensor Network simulator

Wireless Sensor Networks are by nature deployed over an undetermined geographical area with uncountable number of nodes, which makes them best studied through simulation. Due to special characteristics of these networks a number of simulation tools have been introduced by institutes and individuals each with different mechanisms and approaches. Hence, selecting a proper simulator based on the network under investigation is of vital role for the researchers. This paper provides a performance analysis of one of the renowned simulators, namely Castalia, through various scenarios and multiple cases. The study introduces some performance metrics which reflect different aspects of the tool and shows that Castalia is sensitive to large number of nodes in terms of execution time.
Longitudinal Face Modeling via Temporal Deep Restricted Boltzmann Machines

Modeling the face aging process is a challenging task due to large and non-linear variations present in different stages of face development. This paper presents a deep model approach for face age progression that can efficiently capture the non-linear aging process and automatically synthesize a series of age-progressed faces in various age ranges. In this approach, we first decompose the long-term age progress into a sequence of short-term changes and model it as a face sequence. The Temporal Deep Restricted Boltzmann Machines based age progression model together with the prototype faces are then constructed to learn the aging transformation between faces in the sequence. In addition, to enhance the wrinkles of faces in the later age ranges, the wrinkle models are further constructed using Restricted Boltzmann Machines to capture their variations in different facial regions. The geometry constraints are also taken into account in the last step for more consistent age-progressed results. The proposed approach is evaluated using various face aging databases, i.e. FGNET, Cross-Age Celebrity Dataset (CACD) and MORPH, and our collected large-scale aging database named AginG Faces in the Wild (AGFW). In addition, when ground-truth age is not available for input image, our proposed system is able to automatically estimate the age of the input face before aging process is employed.
Generation of nodding, head tilting and eye gazing for human-robot dialogue interaction

Head motion occurs naturally and in synchrony with speech during human dialogue communication, and may carry paralinguistic information, such as intentions, attitudes and emotions. Therefore, natural-looking head motion by a robot is important for smooth human-robot interaction. Based on rules inferred from analyses of the relationship between head motion and dialogue acts, this paper proposes a model for generating head tilting and nodding, and evaluates the model using three types of humanoid robot (a very human-like android, "Geminoid F", a typical humanoid robot with less facial degrees of freedom, "Robovie R2", and a robot with a 3-axis rotatable neck and movable lips, "Telenoid R2"). Analysis of subjective scores shows that the proposed model including head tilting and nodding can generate head motion with increased naturalness compared to nodding only or directly mapping people's original motions without gaze information. We also find that an upwards motion of a robot's face can be used by robots which do not have a mouth in order to provide the appearance that utterance is taking place. Finally, we conduct an experiment in which participants act as visitors to an information desk attended by robots. As a consequence, we verify that our generation model performs equally to directly mapping people's original motions with gaze information in terms of perceived naturalness.
Deployment of UAV-mounted access points according to spatial user locations in two-tier cellular networks

We envision small cells mounted on unmanned aerial vehicles, to complement existing macrocell infrastructure. We demonstrate through numerical analysis that clustering algorithms can be used to position the airborne access points and select users to offload from the macrocells. We compare the performance of these deployments against equivalent simulated picocell deployments. We demonstrate that due to their ability to position themselves around exact user locations while maintaining a direct line-of-sight link the airborne access points provide a significantly improved received signal strength than the static picocell alternatives. We also find that the airborne access points provide superior service quality even in the presence of user and access point positioning errors.
Pay-per-action Model for Online Advertising

The online advertising industry is currently based on two dominant business models: the pay-per-impression model and the pay-per-click model. With the growth of sponsored search during the last few years, there has been a move toward the pay-per-click model as it decreases the risk to small advertisers. An alternative model, discussed but not widely used in the advertising industry, is pay-per-conversion, or more generally, pay-per-action. In this paper, we discuss mechanisms for the pay-per-action model and various challenges involved in designing such mechanisms.
Real-Time Credit-Card Fraud Detection using Artificial Neural Network Tuned by Simulated Annealing Algorithm

Now-a-days, Internet has become an important part of human’s life, a person can shop, invest, and perform all the banking task online. Almost, all the organizations have their own website, where customer can perform all the task like shopping, they only have to provide their credit card details. Online banking and e-commerce organizations have been experiencing the increase in credit card transaction and other modes of on-line transaction. Due to this credit card fraud becomes a very popular issue for credit card industry, it causes many financial losses for customer and also for the organization. Many techniques like Decision Tree, Neural Networks, Genetic Algorithm based on modern techniques like Artificial Intelligence, Machine Learning, and Fuzzy Logic have been already developed for credit card fraud detection. In this paper, an evolutionary Simulated Annealing algorithm is used to train the Neural Networks for Credit Card fraud detection in real-time scenario. This paper shows how this technique can be used for credit card fraud detection and present all the detailed experimental results found when using this technique on real world financial data (data are taken from UCI repository) to show the effectiveness of this technique. The algorithm used in this paper are likely beneficial for the organizations and for individual users in terms of cost and time efficiency. Still there are many cases which are misclassified i.e. A genuine customer is classified as fraud customer or vise-versa.
The Stanford Hydra CMP

0272-1732/00/$10.00  2000  The Hydra chip multiprocessor (CMP) integrates four MIPS-based processors and their primary caches on a single chip together with a shared secondary cache. A standard CMP offers implementation and performance advantages compared to wide-issue superscalar designs. However, it must be programmed with a more complicated parallel programming model to obtain maximum performance. To simplify parallel programming, the Hydra CMP supports thread-level speculation and memory renaming, a paradigm that allows performance similar to a uniprocessor of comparable die area on integer programs. This article motivates the design of a CMP, describes the architecture of the Hydra design with a focus on its speculative thread support, and describes our prototype implementation.
A Framework for Classifying and Comparing Architecture Description Languages

Software architectures shift developers’ focus from lines-of-code to coarser-grained architectural elements and their interconnection structure. Architecture description languages (ADLs) have been proposed as modeling notations to support architecture-based development. There is, however, little consensus in the research community on what is an ADL, what aspects of an architecture should be modeled in an ADL, and which ADL is best suited for a particular problem. Furthermore, the distinction is rarely made between ADLs on one hand and formal specification, module interconnection, simulation, and programming languages on the other. This paper attempts to provide an answer to these questions. It motivates and presents a definition and a classification framework for ADLs. The utility of the definition is demonstrated by using it to differentiate ADLs from other modeling notations. The framework is used to classify and compare several existing ADLs.1
Semantic Process Mining Tools: Core Building Blocks

Process mining aims at discovering new knowledge based on information hidden in event logs. Two important enablers for such analysis are powerful process mining techniques and the omnipresence of event logs in today's information systems. Most information systems supporting (structured) business processes (e.g. ERP, CRM, and workflow systems) record events in some form (e.g. transaction logs, audit trails, and database tables). Process mining techniques use event logs for all kinds of analysis, e.g., auditing, performance analysis, process discovery, etc. Although current process mining techniques/tools are quite mature, the analysis they support is somewhat limited because it is purely based on labels in logs. This means that these techniques cannot benefit from the actual semantics behind these labels which could cater for more accurate and robust analysis techniques. Existing analysis techniques are purely syntax oriented, i.e., much time is spent on filtering, translating, interpreting, and modifying event logs given a particular question. This paper presents the core building blocks necessary to enable semantic process mining techniques/tools. Although the approach is highly generic, we focus on a particular process mining technique and show how this technique can be extended and implemented in the ProM framework tool.
A latency and fault-tolerance optimizer for online parallel query plans

We address the problem of making online, parallel query plans fault-tolerant: i.e., provide intra-query fault-tolerance without blocking. We develop an approach that not only achieves this goal but does so through the use of different fault-tolerance techniques at different operators within a query plan. Enabling each operator to use a different fault-tolerance strategy leads to a space of fault-tolerance plans amenable to cost-based optimization. We develop FTOpt, a cost-based fault-tolerance optimizer that automatically selects the best strategy for each operator in a query plan in a manner that minimizes the expected processing time with failures for the entire query. We implement our approach in a prototype parallel query-processing engine. Our experiments demonstrate that (1) there is no single best fault-tolerance strategy for all query plans, (2) often hybrid strategies that mix-and-match recovery techniques outperform any uniform strategy, and (3) our optimizer correctly identifies winning fault-tolerance configurations.
Factors Affecting the Successful Introduction of Mobile Payment Systems

A prerequisite to carry out transactions using a mobile phone is an effective mobile payment system. However, no standardised, widely adopted mobile payment system has yet emerged, and this is believed to be one of the factors that inhibits widespread use of mobile commerce. This paper reports on a research project in which the factors are examined that affect the introduction success of mobile payment systems. We start from the venture point that a lot can be learned from research on internet paying systems, payment systems that have been introduced to faciliate payments made over the internet. First we transferred factors affecting the introduction of internet payment systems to a mobile setting. We then contrasted this list with the views of 13 executives we interviewed in Sweden and the Netherlands. We found that while many factors are at play at the same time, a subset of these stood out at the early stages of the lifecycle of mobile payment systems. In the area of consumer acceptance, these are their cost and their ease of use relative to other payment methods, and the perceived risk. In the area of merchant acceptance, transaction fees compared to debit and credit card systems are important, as is, to a significant extent, the ease of use for the merchant. Finally, both customer and merchant acceptance are highly interdependent as each influences the other, especially during the early stages. Hans van der Heijden
Learning Temporal Dynamics for Video Super-Resolution: A Deep Learning Approach

Video super-resolution (SR) aims at estimating a high-resolution video sequence from a low-resolution (LR) one. Given that the deep learning has been successfully applied to the task of single image SR, which demonstrates the strong capability of neural networks for modeling spatial relation within one single image, the key challenge to conduct video SR is how to efficiently and effectively exploit the temporal dependence among consecutive LR frames other than the spatial relation. However, this remains challenging because the complex motion is difficult to model and can bring detrimental effects if not handled properly. We tackle the problem of learning temporal dynamics from two aspects. First, we propose a temporal adaptive neural network that can adaptively determine the optimal scale of temporal dependence. Inspired by the inception module in GoogLeNet , filters of various temporal scales are applied to the input LR sequence before their responses are adaptively aggregated, in order to fully exploit the temporal relation among the consecutive LR frames. Second, we decrease the complexity of motion among neighboring frames using a spatial alignment network that can be end-to-end trained with the temporal adaptive network and has the merit of increasing the robustness to complex motion and the efficiency compared with the competing image alignment methods. We provide a comprehensive evaluation of the temporal adaptation and the spatial alignment modules. We show that the temporal adaptive design considerably improves the SR quality over its plain counterparts, and the spatial alignment network is able to attain comparable SR performance with the sophisticated optical flow-based approach, but requires a much less running time. Overall, our proposed model with learned temporal dynamics is shown to achieve the state-of-the-art SR results in terms of not only spatial consistency but also the temporal coherence on public video data sets. More information can be found in http://www.ifp.illinois.edu/~dingliu2/videoSR/.
Dual-band planar quadrature hybrid with enhanced bandwidth response

This paper presents the theory, design procedure, and implementation of a dual-band planar quadrature hybrid with enhanced bandwidth. The topology of the circuit is a three-branch-line (3-BL) quadrature hybrid, which provides much larger flexibility to allocate the desired operating frequencies and necessary bandwidths than other previously published configurations. A performance comparison with other dual-band planar topologies is presented. Finally, a 3-BL quadrature hybrid for dual band (2.4 and 5 GHz) wireless local area network systems was fabricated, aimed to cover the bands corresponding to the standards IEEE802.11a/b. The measurements show a 16% and 18% bandwidth for the lower and upper frequency, respectively, satisfying and exceeding the bandwidth requirements for the above standards
Moving Vehicle Detection for Measuring Traffic Count Using OpenCV

System in this paper is designed and implemented with Visual C++ software with Intel's OpenCV video stream processing system to realize the real-time automatic vehicle detection and vehicle counting. Expressways, highways and roads are getting overcrowded due to increase in number of vehicles. Vehicle detection, tracking, classification and counting is very important for military, civilian and government applications, such as highway monitoring, traffic planning, toll collection and traffic flow. For the traffic management, vehicles detection is the critical step. Computer Vision based techniques are more suitable because these systems do not disturb traffic while installation and they are easy to modify. In this paper we present inexpensive, portable and Computer Vision based system for moving vehicle detection and counting. Image from video sequence are taken to detect moving vehicles, so that background is extracted from the images. The extracted background is used in subsequent analysis to detect and classify moving vehicles as light vehicles, heavy vehicles and motorcycle. The system is implemented using OpenCV image development kits and experimental results are demonstrated from real-time video taken from single camera. We tested this system on a laptop powered by an Intel Core Duo (1.83 GHZ) CPU and 2GB RAM. This highway traffic counting process has been developed by background subtraction, image filtering, image binary and segmentation methods are used. This system is also capable of counting moving vehicles from pre-recorded videos. 
Natural Language Inference over Interaction Space

Natural Language Inference (NLI) task requires an agent to determine the logical relationship between a natural language premise and a natural language hypothesis. We introduce Interactive Inference Network (IIN), a novel class of neural network architectures that is able to achieve high-level understanding of the sentence pair by hierarchically extracting semantic features from interaction space. We show that an interaction tensor (attention weight) contains semantic information to solve natural language inference, and a denser interaction tensor contains richer semantic information. One instance of such architecture, Densely Interactive Inference Network (DIIN), demonstrates the state-of-the-art performance on large scale NLI copora and large-scale NLI alike corpus. It’s noteworthy that DIIN achieve a greater than 20% error reduction on the challenging Multi-Genre NLI (MultiNLI; Williams et al. 2017) dataset with respect to the strongest published system.