nils-herrmann/clean_scirepeval huggingface dataset:
Towards cloud based big data analytics for smart future cities

A large amount of land-use, environment, socio-economic, energy and transport data is generated in cities. An integrated perspective of managing and analysing such big data can answer a number of science, policy, planning, governance and business questions and support decision making in enabling a smarter environment. This paper presents a theoretical and experimental perspective on the smart cities focused big data management and analysis by proposing a cloud-based analytics service. A prototype has been designed and developed to demonstrate the effectiveness of the analytics service for big data analysis. The prototype has been implemented using Hadoop and Spark and the results are compared. The service analyses the Bristol Open data by identifying correlations between selected urban environment indicators. Experiments are performed using Hadoop and Spark and results are presented in this paper. The data pertaining to quality of life mainly crime and safety & economy and employment was analysed from the data catalogue to measure the indicators spread over years to assess positive and negative trends.
Effects of Interactivity on Website Involvement and Purchase Intention

Zhenhui Jiang National University of Singapore jiang@comp.nus.edu.sg Jason Chan National University of Singapore jason.chan@stern.nyu.edu Bernard C.Y. Tan National University of Singapore btan@comp.nus.edu.sg Wei Siong Chua National University of Singapore livealoud@yahoo.com.sg This study aims to understand how website interactivity (active control and reciprocal communication) can impact purchase intention through website involvement and how the impacts are moderated by the type of products featured on websites. In a laboratory experiment, 186 participants were asked to perform purchasing tasks of non-fictional books or greeting cards on websites of varying levels of interactivity. Results indicate that websites with a high level of active control lead to cognitive involvement and, in some instances, affective involvement. Websites with reciprocal communication lead to affective involvement for functional products but not expressive products. Responses from the participants also reveal that an increase in website involvement leads to higher purchase intention. Implications for research and practice are discussed.
Posterolateral transfibular approach to tibial plateau fractures: technique, results, and rationale.

We describe a posterolateral transfibular neck approach to the proximal tibia. This approach was developed as an alternative to the anterolateral approach to the tibial plateau for the treatment of two fracture subtypes: depressed and split depressed fractures in which the comminution and depression are located in the posterior half of the lateral tibial condyle. These fractures have proved particularly difficult to reduce and adequately internally fix through an anterior or anterolateral approach. The approach described in this article exposes the posterolateral aspect of the tibial plateau between the posterior margin of the iliotibial band and the posterior cruciate ligament. The approach allows lateral buttressing of the lateral tibial plateau and may be combined with a simultaneous posteromedial and/or anteromedial approach to the tibial plateau. Critically, the proximal tibial soft tissue envelope and its blood supply are preserved. To date, we have used this approach either alone or in combination with a posteromedial approach for the successful reduction of tibial plateau fractures in eight patients. No complications related to this approach were documented, including no symptoms related to the common peroneal nerve, and all fractures and fibular neck osteotomies healed uneventfully.
Copilot - a Coprocessor-based Kernel Runtime Integrity Monitor

Copilot is a coprocessor-based kernel integrity monitor for commodity systems. Copilot is designed to detect malicious modifications to a host’s kernel and has correctly detected the presence of 12 real-world rootkits, each within 30 seconds of their installation with less than a 1% penalty to the host’s performance. Copilot requires no modifications to the protected host’s software and can be expected to operate correctly even when the host kernel is thoroughly compromised – an advantage over traditional monitors designed to run on the host itself.
Efficient dynamic provable possession of remote data via balanced update trees

The emergence and availability of remote storage providers prompted work in the security community that allows a client to verify integrity and availability of the data she outsourced to an untrusted remove storage server at a relatively low cost. Most recent solutions to this problem allow the client to read and update (insert, modify, or delete) stored data blocks while trying to lower the overhead associated with verifying data integrity. In this work we develop a novel and efficient scheme, computation and communication overhead of which is orders of magnitude lower than those of other state-of-the-art schemes. Our solution has a number of new features such as a natural support for operations on ranges of blocks, and revision control. The performance guarantees that we achieve stem from a novel data structure, termed balanced update tree, and removing the need to verify update operations.
The architecture of cognitive control in the human prefrontal cortex.

The prefrontal cortex (PFC) subserves cognitive control: the ability to coordinate thoughts or actions in relation with internal goals. Its functional architecture, however, remains poorly understood. Using brain imaging in humans, we showed that the lateral PFC is organized as a cascade of executive processes from premotor to anterior PFC regions that control behavior according to stimuli, the present perceptual context, and the temporal episode in which stimuli occur, respectively. The results support an unified modular model of cognitive control that describes the overall functional organization of the human lateral PFC and has basic methodological and theoretical implications.
Alias Analysis of Executable Code

Recent years have seen increasing interest in systems that reason about and manipulate executable code. Such systems can generally benefit from information about aliasing. Unfortunately, most existing alias analyses are formulated in terms of high-level language features, and are unable to cope with features, such as pointer arithmetic, that pervade executable programs. This paper describes a simple algorithm that can be used to obtain aliasing information for executabie code. In order to be practical, the algorithm is carefut to keep its memory requirements low, sacrificing precision where necessary to achieve this goal. Experimental results indicate that it is nevertheless able to provide a reasonable amount of information about memory references across a variety of benchmark programs.
A scalable neural chip with synaptic electronics using CMOS integrated memristors.

The design and simulation of a scalable neural chip with synaptic electronics using nanoscale memristors fully integrated with complementary metal-oxide-semiconductor (CMOS) is presented. The circuit consists of integrate-and-fire neurons and synapses with spike-timing dependent plasticity (STDP). The synaptic conductance values can be stored in memristors with eight levels, and the topology of connections between neurons is reconfigurable. The circuit has been designed using a 90 nm CMOS process with via connections to on-chip post-processed memristor arrays. The design has about 16 million CMOS transistors and 73 728 integrated memristors. We provide circuit level simulations of the entire chip performing neuronal and synaptic computations that result in biologically realistic functional behavior.
FHEW: Bootstrapping Homomorphic Encryption in Less Than a Second

The main bottleneck affecting the efficiency of all known fully homomorphic encryption (FHE) schemes is Gentry’s bootstrapping procedure, which is required to refresh noisy ciphertexts and keep computing on encrypted data. Bootstrapping in the latest implementation of FHE, the HElib library of Halevi and Shoup (Crypto 2014), requires about six minutes. We present a new method to homomorphically compute simple bit operations, and refresh (bootstrap) the resulting output, which runs on a personal computer in just about half a second. We present a detailed technical analysis of the scheme (based on the worst-case hardness of standard lattice problems) and report on the performance of our prototype implementation.
A system for query-specific document summarization

There has been a great amount of work on query-independent summarization of documents. However, due to the success of Web search engines query-specific document summarization (query result snippets) has become an important problem, which has received little attention. We present a method to create query-specific summaries by identifying the most query-relevant fragments and combining them using the semantic associations within the document. In particular, we first add structure to the documents in the preprocessing stage and convert them to document graphs. Then, the best summaries are computed by calculating the top spanning trees on the document graphs. We present and experimentally evaluate efficient algorithms that support computing summaries in interactive time. Furthermore, the quality of our summarization method is compared to current approaches using a user survey.
Medial and Lateral Entorhinal Cortex Differentially Excite Deep versus Superficial CA1 Pyramidal Neurons.

Although hippocampal CA1 pyramidal neurons (PNs) were thought to comprise a uniform population, recent evidence supports two distinct sublayers along the radial axis, with deep neurons more likely to form place cells than superficial neurons. CA1 PNs also differ along the transverse axis with regard to direct inputs from entorhinal cortex (EC), with medial EC (MEC) providing spatial information to PNs toward CA2 (proximal CA1) and lateral EC (LEC) providing non-spatial information to PNs toward subiculum (distal CA1). We demonstrate that the two inputs differentially activate the radial sublayers and that this difference reverses along the transverse axis, with MEC preferentially targeting deep PNs in proximal CA1 and LEC preferentially exciting superficial PNs in distal CA1. This differential excitation reflects differences in dendritic spine numbers. Our results reveal a heterogeneity in EC-CA1 connectivity that may help explain differential roles of CA1 PNs in spatial and non-spatial learning and memory.
A Lightweight Simulator for Autonomous Driving Motion Planning Development

A good simulation environment will facilitate motion planning algorithm development for urban autonomous driving. The first requirement of such a simulator is to be able to replicate a complex urban environment, including road network, curb, general objects, etc. The second requirement is to simulate a realistic host vehicle, which includes perception, control and vehicle dynamics, to recreate imperfect inputs and non-accurate execution of the planner. The third requirement is to model traffic participants (other on-road vehicles) for microscopic traffic simulation. Intelligent-agent-based techniques are used to allow the traffic participants to interact with the environment and each other. In this paper, we present an open-source lightweight simulation environment, FastSim, which is designed to meet the three requirements above. Traffic simulator; Intelligent agent; Motion Planning; Autonomous driving
Online implicit agent modelling

The traditional view of agent modelling is to infer the explicit parameters of another agent’s strategy (i.e., their probability of taking each action in each situation). Unfortunately, in complex domains with high dimensional strategy spaces, modelling every parameter often requires a prohibitive number of observations. Furthermore, given a model of such a strategy, computing a response strategy that is robust to modelling error may be impractical to compute online. Instead, we propose an implicit modelling framework where agents aim to estimate the utility of a fixed portfolio of pre-computed strategies. Using the domain of heads-up limit Texas hold’em poker, this work describes an end-to-end approach for building an implicit modelling agent. We compute robust response strategies, show how to select strategies for the portfolio, and apply existing variance reduction and online learning techniques to dynamically adapt the agent’s strategy to its opponent. We validate the approach by showing that our implicit modelling agent would have won the heads-up limit opponent exploitation event in the 2011 Annual Computer Poker Competition.
Learning a Discriminative Null Space for Person Re-identification

Most existing person re-identification (re-id) methods focus on learning the optimal distance metrics across camera views. Typically a person's appearance is represented using features of thousands of dimensions, whilst only hundreds of training samples are available due to the difficulties in collecting matched training images. With the number of training samples much smaller than the feature dimension, the existing methods thus face the classic small sample size (SSS) problem and have to resort to dimensionality reduction techniques and/or matrix regularisation, which lead to loss of discriminative power. In this work, we propose to overcome the SSS problem in re-id distance metric learning by matching people in a discriminative null space of the training data. In this null space, images of the same person are collapsed into a single point thus minimising the within-class scatter to the extreme and maximising the relative between-class separation simultaneously. Importantly, it has a fixed dimension, a closed-form solution and is very efficient to compute. Extensive experiments carried out on five person re-identification benchmarks including VIPeR, PRID2011, CUHK01, CUHK03 and Market1501 show that such a simple approach beats the state-of-the-art alternatives, often by a big margin.
Towards Big Topic Modeling

To solve the big topic modeling problem, we need to reduce both time and space complexities of batch latent Dirichlet allocation (LDA) algorithms. Although parallel LDA algorithms on the multi-processor architecture have low time and space complexities, their communication costs among processors often scale linearly with the vocabulary size and the number of topics, leading to a serious scalability problem. To reduce the communication complexity among processors for a better scalability, we propose a novel communication-efficient parallel topic modeling architecture based on power law, which consumes orders of magnitude less communication time when the number of topics is large. We combine the proposed communication-efficient parallel architecture with the online belief propagation (OBP) algorithm referred to as POBP for big topic modeling tasks. Extensive empirical results confirm that POBP has the following advantages to solve the big topic modeling problem: 1) high accuracy, 2) communication-efficient, 3) fast speed, and 4) constant memory usage when compared with recent state-of-the-art parallel LDA algorithms on the multi-processor architecture. Index Terms —Big topic modeling, latent Dirichlet allocation, communication complexity, multi-processor architecture, online belief propagation, power law.
Convolutional recurrent neural networks for electrocardiogram classification

We propose two deep neural network architectures for classification of arbitrary-length electrocardiogram (ECG) recordings and evaluate them on the atrial fibrillation (AF) classification data set provided by the PhysioNet/CinC Challenge 2017. The first architecture is a deep convolutional neural network (CNN) with averaging-based feature aggregation across time. The second architecture combines convolutional layers for feature extraction with long-short term memory (LSTM) layers for temporal aggregation of features. As a key ingredient of our training procedure we introduce a simple data augmentation scheme for ECG data and demonstrate its effectiveness in the AF classification task at hand. The second architecture was found to outperform the first one, obtaining an Fi score of 82.1% on the hidden challenge testing set.
Finite Element Modelling of Circumferential Magnetic Flux Leakage Inspection in Pipeline

The axial magnetic flux leakage(MFL) inspection tools cannot reliably detect or size axially aligned cracks, such as SCC, longitudinal corrosion, long seam defects, and axially oriented mechanical damage. To focus on this problem, the circumferential MFL inspection tool is introduced. The finite element (FE) model is established by adopting ANSYS software to simulate magnetostatics. The results show that the amount of flux that is diverted out of the pipe depends on the geometry of the defect, the primary variables that affect the flux leakage are the ones that define the volume of the defect. The defect location can significantly affect flux leakage, the magnetic field magnitude arising due to the presence of the defect is immersed in the high field close to the permanent magnets. These results demonstrate the feasibility of detecting narrow axial defects and the practicality of developing a circumferential MFL tool.
eXpose: A Character-Level Convolutional Neural Network with Embeddings For Detecting Malicious URLs, File Paths and Registry Keys

For years security machine learning research has promised to obviate the need for signature based detection by automatically learning to detect indicators of attack. Unfortunately, this vision hasn’t come to fruition: in fact, developing and maintaining today’s security machine learning systems can require engineering resources that are comparable to that of signature-based detection systems, due in part to the need to develop and continuously tune the “features” these machine learning systems look at as attacks evolve. Deep learning, a subfield of machine learning, promises to change this by operating on raw input signals and automating the process of feature design and extraction. In this paper we propose the eXpose neural network, which uses a deep learning approach we have developed to take generic, raw short character strings as input (a common case for security inputs, which include artifacts like potentially malicious URLs, file paths, named pipes, named mutexes, and registry keys), and learns to simultaneously extract features and classify using character-level embeddings and convolutional neural network. In addition to completely automating the feature design and extraction process, eXpose outperforms manual feature extraction based baselines on all of the intrusion detection problems we tested it on, yielding a 5%10% detection rate gain at 0.1% false positive rate compared to these baselines.
The Latent Relation Mapping Engine: Algorithm and Experiments

Many AI researchers and cognitive scientists have argued that analogy is the core of cognition. The most influential work on computational modeling of analogy-making is Structure Mapping Theory (SMT) and its implementation in the Structure Mapping Engine (SME). A limitation of SME is the requirement for complex hand-coded representations. We introduce the Latent Relation Mapping Engine (LRME), which combines ideas from SME and Latent Relational Analysis (LRA) in order to remove the requirement for handcoded representations. LRME builds analogical mappings between lists of words, using a large corpus of raw text to automatically discover the semantic relations among the words. We evaluate LRME on a set of twenty analogical mapping problems, ten based on scientific analogies and ten based on common metaphors. LRME achieves human-level performance on the twenty problems. We compare LRME with a variety of alternative approaches and find that they are not able to reach the same level of performance.
What drives a successful e-Learning? An empirical investigation of the critical factors influencing learner satisfaction

E-learning is emerging as the new paradigm of modern education. Worldwide, the e-learning market has a growth rate of 35.6%, but failures exist. Little is known about why many users stop their online learning after their initial experience. Previous research done under different task environments has suggested a variety of factors affecting user satisfaction with e-Learning. This study developed an integrated model with six dimensions: learners, instructors, courses, technology, design, and environment. A survey was conducted to investigate the critical factors affecting learners’ satisfaction in e-Learning. The results revealed that learner computer anxiety, instructor attitude toward e-Learning, e-Learning course flexibility, e-Learning course quality, perceived usefulness, perceived ease of use, and diversity in assessments are the critical factors affecting learners’ perceived satisfaction. The results show institutions how to improve learner satisfaction and further strengthen their e-Learning implementation.
Matrix inversion on CPU-GPU platforms with applications in control theory

In this paper we tackle the inversion of large-scale dense matrices via conventional matrix factorizations (LU, Cholesky, LDL ) and the Gauss-Jordan method on hybrid platforms consisting of a multi-core CPU and a many-core graphics processor (GPU). Specifically, we introduce the different matrix inversion algorithms using a unified framework based on the notation from the FLAME project; we develop hybrid implementations for those matrix operations underlying the algorithms, alternative to those in existing libraries for singleGPU systems; and we perform an extensive experimental study on a platform equipped with state-of-the-art general-purpose architectures from Intel and a “Fermi” GPU from NVIDIA that exposes the efficiency of the different inversion approaches. Our study and experimental results show the simplicity and performance advantage of the GJE-based inversion methods, and the difficulties associated with the symmetric indefinite case.
Automatic Mood Classification Using TF*IDF Based on Lyrics

This paper presents the outcomes of research into using lingual parts of music in an automatic mood classification system. Using a collection of lyrics and corresponding user-tagged moods, we build classifiers that classify lyrics of songs into moods. By comparing the performance of different mood frameworks (or dimensions), we examine to what extent the linguistic part of music reveals adequate information for assigning a mood category and which aspects of mood can be classified best. Our results show that word oriented metrics provide a valuable source of information for automatic mood classification of music, based on lyrics only. Metrics such as term frequencies and tf*idf values are used to measure relevance of words to the different mood classes. These metrics are incorporated in a machine learning classifier setup. Different partitions of the mood plane are investigated and we show that there is no large difference in mood prediction based on the mood division. Predictions on the valence, tension and combinations of aspects lead to similar performance.