nils-herrmann/clean_scirepeval huggingface dataset:
Scalable sentiment classification for Big Data analysis using Naïve Bayes Classifier

A typical method to obtain valuable information is to extract the sentiment or opinion from a message. Machine learning technologies are widely used in sentiment classification because of their ability to “learn” from the training dataset to predict or support decision making with relatively high accuracy. However, when the dataset is large, some algorithms might not scale up well. In this paper, we aim to evaluate the scalability of Naïve Bayes classifier (NBC) in large datasets. Instead of using a standard library (e.g., Mahout), we implemented NBC to achieve fine-grain control of the analysis procedure. A Big Data analyzing system is also design for this study. The result is encouraging in that the accuracy of NBC is improved and approaches 82% when the dataset size increases. We have demonstrated that NBC is able to scale up to analyze the sentiment of millions movie reviews with increasing throughput.
Convolutional Networks for Spherical Signals

The success of convolutional networks in learning problems involving planar signals such as images is due to their ability to exploit the translation symmetry of the data distribution through weight sharing. Many areas of science and egineering deal with signals with other symmetries, such as rotation invariant data on the sphere. Examples include climate and weather science, astrophysics, and chemistry. In this paper we present spherical convolutional networks. These networks use convolutions on the sphere and rotation group, which results in rotational weight sharing and rotation equivariance. Using a synthetic spherical MNIST dataset, we show that spherical convolutional networks are very effective at dealing with rotationally invariant classification problems.
Broad Context Language Modeling as Reading Comprehension

Progress in text understanding has been driven by large datasets that test particular capabilities, like recent datasets for reading comprehension (Hermann et al., 2015). We focus here on the LAMBADA dataset (Paperno et al., 2016), a word prediction task requiring broader context than the immediate sentence. We view LAMBADA as a reading comprehension problem and apply comprehension models based on neural networks. Though these models are constrained to choose a word from the context, they improve the state of the art on LAMBADA from 7.3% to 49%. We analyze 100 instances, finding that neural network readers perform well in cases that involve selecting a name from the context based on dialogue or discourse cues but struggle when coreference resolution or external knowledge is needed.
Cloud versus in-house cluster: Evaluating Amazon cluster compute instances for running MPI applications

The emergence of cloud services brings new possibilities for constructing and using HPC platforms. However, while cloud services provide the flexibility and convenience of customized, pay-as-you-go parallel computing, multiple previous studies in the past three years have indicated that cloud-based clusters need a significant performance boost to become a competitive choice, especially for tightly coupled parallel applications.
 In this work, we examine the feasibility of running HPC applications in clouds. This study distinguishes itself from existing investigations in several ways: 1) We carry out a comprehensive examination of issues relevant to the HPC community, including performance, cost, user experience, and range of user activities. 2) We compare an Amazon EC2-based platform built upon its newly available HPC-oriented virtual machines with typical local cluster and supercomputer options, using benchmarks and applications with scale and problem size unprecedented in previous cloud HPC studies. 3) We perform detailed performance and scalability analysis to locate the chief limiting factors of the state-of-the-art cloud based clusters. 4) We present a case study on the impact of per-application parallel I/O system configuration uniquely enabled by cloud services. Our results reveal that though the scalability of EC2-based virtual clusters still lags behind traditional HPC alternatives, they are rapidly gaining in overall performance and cost-effectiveness, making them feasible candidates for performing tightly coupled scientific computing. In addition, our detailed benchmarking and profiling discloses and analyzes several problems regarding the performance and performance stability on EC2.
Bidirectional LSTM Networks for Improved Phoneme Classification and Recognition

In this paper, we carry out two experiments on the TIMIT speech corpus with bidirectional and unidirectional Long Short Term Memory (LSTM) networks. In the first experiment (framewise phoneme classification) we find that bidirectional LSTM outperforms both unidirectional LSTM and conventional Recurrent Neural Networks (RNNs). In the second (phoneme recognition) we find that a hybrid BLSTM-HMM system improves on an equivalent traditional HMM system, as well as unidirectional LSTM-HMM.
Assessing insider threats to information security using technical, behavioural and organisational measures

TheUKgovernment tookabruising in theheadlines (Sep 2008) after aHomeOffice contractor lost a USB stick containing unencrypted data on all 84,000 prisoners in England and Wales. As a result, the Home Office terminated the £1.5million contract with themanagement consultancy firm. The world woke up to the largest attempted bank fraud ever when the UK’s National HiTech Crime Unit foiled the world’s largest potential bank robbery in March 2005. With the help of the security supervisor, thieves masquerading as cleaning staff installed hardware keystroke loggers on computers within the London branch of a Japanese bank, to steal £220m. It is indeed sobering to imagine that any organisation could fall victim to such events and the damage an insider can do. The consulting firm lost the contract worth £1.5 million due to a small mistake by an employee. The London branch of the Japanese Bank would have lost £220 million had not the crime been foiled. Insider threat is a reality. Insiders commit fraud or steal sensitive information when motivated by money or revenge. Well-meaning employees can compromise the security of an organisation with their overzealousness in getting their job done. Every organisation has a varied mix of employees, consultants, management, partners and complex infrastructure and that makes handling insider threats a daunting challenge. With insider attacks, organisations face potential damage through loss of revenue, loss of reputation, loss of intellectual property or even loss of human life. The insider threat problem is more elusive and perplexing than any other threat. Assessing the insider threat is the first step to determine the likelihood of any insider attack. Technical solutions do not suffice since insider threats are fundamentally a people issue. Therefore, a three-pronged approach technological, behavioural and organisational assessment is essential in facilitating the prediction of insider threats and pre-empt any insider attack thus improving the organization’s security, survivability, and resiliency in light of insider threats. a 2010 Elsevier Ltd. All rights reserved.
Haptic object recognition using passive joints and haptic key features

This paper presents a novel approach for haptic object recognition with an anthropomorphic robot hand. Firstly, passive degrees of freedom are introduced to the tactile sensor system of the robot hand. This allows the planar tactile sensor patches to optimally adjust themselves to the object's surface and to acquire additional sensor information for shape reconstruction. Secondly, this paper presents an approach to classify an object directly from the haptic sensor data acquired by a palpation sequence with the robot hand - without building a 3d-model of the object. Therefore, a finite set of essential finger positions and tactile contact patterns are identified which can be used to describe a single palpation step. A palpation sequence can then be merged into a simple statistical description of the object and finally be classified. The proposed approach for haptic object recognition and the new tactile sensor system are evaluated with an anthropomorphic robot hand.
Partitioned logistic regression for spam filtering

Naive Bayes and logistic regression perform well in different regimes. While the former is a very simple generative model which is efficient to train and performs well empirically in many applications,the latter is a discriminative model which often achieves better accuracy and can be shown to outperform naive Bayes asymptotically. In this paper, we propose a novel hybrid model, partitioned logistic regression, which has several advantages over both naive Bayes and logistic regression. This model separates the original feature space into several disjoint feature groups. Individual models on these groups of features are learned using logistic regression and their predictions are combined using the naive Bayes principle to produce a robust final estimation. We show that our model is better both theoretically and empirically. In addition, when applying it in a practical application, email spam filtering, it improves the normalized AUC score at 10% false-positive rate by 28.8% and 23.6% compared to naive Bayes and logistic regression, when using the exact same training examples.
Minimal Intelligence Agents for Bargaining Behaviors in Market Based Environments

This report describes simple mechanisms that allow autonomous software agents to en gage in bargaining behaviors in market based environments Groups of agents with such mechanisms could be used in applications including market based control internet com merce and economic modelling After an introductory discussion of the rationale for this work and a brief overview of key concepts from economics work in market based control is reviewed to highlight the need for bargaining agents Following this the early experimental economics work of Smith and the recent results of Gode and Sunder are de scribed Gode and Sunder s work using zero intelligence zi traders that act randomly within a structured market appears to imply that convergence to the theoretical equilib rium price is determined more by market structure than by the intelligence of the traders in that market if this is true developing mechanisms for bargaining agents is of very limited relevance However it is demonstrated here that the average transaction prices of zi traders can vary signi cantly from the theoretical equilibrium level when supply and demand are asymmetric and that the degree of di erence from equilibrium is predictable from a pri ori statistical analysis In this sense it is shown here that Gode and Sunder s results are artefacts of their experimental regime Following this zero intelligence plus zip traders are introduced like zi traders these simple agents make stochastic bids Unlike zi traders they employ an elementary form of machine learning Groups of zip traders interacting in experimental markets similar to those used by Smith and Gode and Sunder are demonstrated and it is shown that the performance of zip traders is signi cantly closer to the human data than is the performance of Gode and Sunder s zi traders This document reports on work done during February to September while the author held a Visiting Academic post at Hewlett Packard Laboratories Bristol Filton Road Bristol BS QZ U K
Predicting and Understanding Urban Perception with Convolutional Neural Networks

Cities' visual appearance plays a central role in shaping human perception and response to the surrounding urban environment. For example, the visual qualities of urban spaces affect the psychological states of their inhabitants and can induce negative social outcomes. Hence, it becomes critically important to understand people's perceptions and evaluations of urban spaces. Previous works have demonstrated that algorithms can be used to predict high level attributes of urban scenes (e.g. safety, attractiveness, uniqueness), accurately emulating human perception. In this paper we propose a novel approach for predicting the perceived safety of a scene from Google Street View Images. Opposite to previous works, we formulate the problem of learning to predict high level judgments as a ranking task and we employ a Convolutional Neural Network (CNN), significantly improving the accuracy of predictions over previous methods. Interestingly, the proposed CNN architecture relies on a novel pooling layer, which permits to automatically discover the most important areas of the images for predicting the concept of perceived safety. An extensive experimental evaluation, conducted on the publicly available Place Pulse dataset, demonstrates the advantages of the proposed approach over state-of-the-art methods.
Correlation-based Intrinsic Evaluation of Word Vector Representations

We introduce QVEC-CCA—an intrinsic evaluation metric for word vector representations based on correlations of learned vectors with features extracted from linguistic resources. We show that QVECCCA scores are an effective proxy for a range of extrinsic semantic and syntactic tasks. We also show that the proposed evaluation obtains higher and more consistent correlations with downstream tasks, compared to existing approaches to intrinsic evaluation of word vectors that are based on word similarity.
A General Model for Authenticated Data Structures

Query answers from on-line databases can easily be corrupted by hackers or malicious database publishers. Thus it is important to provide mechanisms which allow clients to trust the results from on-line queries. Authentic publication allows untrusted publishers to answer securely queries from clients on behalf of trusted off-line data owners. Publishers validate answers using hard-to-forge verification objects VOs), which clients can check efficiently. This approach provides greater scalability, by making it easy to add more publishers, and better security, since on-line publishers do not need to be trusted. To make authentic publication attractive, it is important for the VOs to be small, efficient to compute, and efficient to verify. This has lead researchers to develop independently several different schemes for efficient VO computation based on specific data structures. Our goal is to develop a unifying framework for these disparate results, leading to a generalized security result. In this paper we characterize a broad class of data structures which we call Search DAGs, and we develop a generalized algorithm for the construction of VOs for Search DAGs. We prove that the VOs thus constructed are secure, and that they are efficient to compute and verify. We demonstrate how this approach easily captures existing work on simple structures such as binary trees, multi-dimensional range trees, tries, and skip lists. Once these are shown to be Search DAGs, the requisite security and efficiency results immediately follow from our general theorems. Going further, we also use Search DAGs to produce and prove the security of authenticated versions of two complex data models for efficient multi-dimensional range searches. This allows efficient VOs to be computed (size O(log N + T)) for typical one- and two-dimensional range queries, where the query answer is of size T and the database is of size N. We also show I/O-efficient schemes to construct the VOs. For a system with disk blocks of size B, we answer one-dimensional and three-sided range queries and compute the VOs with O(logB N + T/B) I/O operations using linear size data structures.
The Evolution of Android Malware and Android Analysis Techniques

With the integration of mobile devices into daily life, smartphones are privy to increasing amounts of sensitive information. Sophisticated mobile malware, particularly Android malware, acquire or utilize such data without user consent. It is therefore essential to devise effective techniques to analyze and detect these threats. This article presents a comprehensive survey on leading Android malware analysis and detection techniques, and their effectiveness against evolving malware. This article categorizes systems by methodology and date to evaluate progression and weaknesses. This article also discusses evaluations of industry solutions, malware statistics, and malware evasion techniques and concludes by supporting future research paths.
Offline Handwriting Recognition with Multidimensional Recurrent Neural Networks

Offline handwriting recognition—the automatic transcription of images of handwritten text—is a challenging task that combines computer vision with sequence learning. In most systems the two elements are handled separately, with sophisticated preprocessing techniques used to extract the image features and sequential models such as HMMs used to provide the transcriptions. By combining two recent innovations in neural networks—multidimensional recurrent neural networks and connectionist temporal classification—this paper introduces a globally trained offline handwriting recogniser that takes raw pixel data as input. Unlike competing systems, it does not require any alphabet specific preprocessing, and can therefore be used unchanged for any language. Evidence of its generality and power is provided by data from a recent international Arabic recognition competition, where it outperformed all entries (91.4% accuracy compared to 87.2% for the competition winner) despite the fact that neither author understands a word of Arabic.
Measuring Belief in Conspiracy Theories: The Generic Conspiracist Beliefs Scale

The psychology of conspiracy theory beliefs is not yet well understood, although research indicates that there are stable individual differences in conspiracist ideation - individuals' general tendency to engage with conspiracy theories. Researchers have created several short self-report measures of conspiracist ideation. These measures largely consist of items referring to an assortment of prominent conspiracy theories regarding specific real-world events. However, these instruments have not been psychometrically validated, and this assessment approach suffers from practical and theoretical limitations. Therefore, we present the Generic Conspiracist Beliefs (GCB) scale: a novel measure of individual differences in generic conspiracist ideation. The scale was developed and validated across four studies. In Study 1, exploratory factor analysis of a novel 75-item measure of non-event-based conspiracist beliefs identified five conspiracist facets. The 15-item GCB scale was developed to sample from each of these themes. Studies 2, 3, and 4 examined the structure and validity of the GCB, demonstrating internal reliability, content, criterion-related, convergent and discriminant validity, and good test-retest reliability. In sum, this research indicates that the GCB is a psychometrically sound and practically useful measure of conspiracist ideation, and the findings add to our theoretical understanding of conspiracist ideation as a monological belief system unpinned by a relatively small number of generic assumptions about the typicality of conspiratorial activity in the world.
A Tutorial on Planning Graph Based Reachability Heuristics

The primary revolution in automated planning in the last decade has been the very impressive scaleup in planner performance. A large part of the credit for this can be attributed squarely to the invention and deployment of powerful reachability heuristics. Most, if not all, modern reachability heuristics are based on a remarkably extensible data structure called the planning graph, which made its debut as a bit player in the success of GraphPlan, but quickly grew in prominence to occupy the center stage. Planning graphs are a cheap means to obtain informative look-ahead heuristics for search and have become ubiquitous in state of the art heuristic search planners. We present the foundations of planning graph heuristics in classical planning and explain how their flexibility lets them adapt to more expressive scenarios that consider action costs, goal utility, numeric resources, time, and uncertainty.
Learning by observation for surgical subtasks: Multilateral cutting of 3D viscoelastic and 2D Orthotropic Tissue Phantoms

Automating repetitive surgical subtasks such as suturing, cutting and debridement can reduce surgeon fatigue and procedure times and facilitate supervised tele-surgery. Programming is difficult because human tissue is deformable and highly specular. Using the da Vinci Research Kit (DVRK) robotic surgical assistant, we explore a “Learning By Observation” (LBO) approach where we identify, segment, and parameterize motion sequences and sensor conditions to build a finite state machine (FSM) for each subtask. The robot then executes the FSM repeatedly to tune parameters and if necessary update the FSM structure. We evaluate the approach on two surgical subtasks: debridement of 3D Viscoelastic Tissue Phantoms (3d-DVTP), in which small target fragments are removed from a 3D viscoelastic tissue phantom; and Pattern Cutting of 2D Orthotropic Tissue Phantoms (2d-PCOTP), a step in the standard Fundamentals of Laparoscopic Surgery training suite, in which a specified circular area must be cut from a sheet of orthotropic tissue phantom. We describe the approach and physical experiments with repeatability of 96% for 50 trials of the 3d-DVTP subtask and 70% for 20 trials of the 2d-PCOTP subtask. A video is available at: http://j.mp/Robot-Surgery-Video-Oct-2014.
How to Train Your DRAGAN

Generative Adversarial Networks have emerged as an effective technique for estimating data distributions. The basic setup consists of two deep networks playing against each other in a zero-sum game setting. However, it is not understood if the networks reach an equilibrium eventually and what dynamics makes this possible. The current GAN training procedure, which involves simultaneous gradient descent, lacks a clear game-theoretic justification in the literature. In this paper, we introduce regret minimization as a technique to reach equilibrium in games and use this to motivate the use of simultaneous GD in GANs. In addition, we present a hypothesis that mode collapse, which is a common occurrence in GAN training, happens due to the existence of spurious local equilibria in non-convex games. Motivated by these insights, we develop an algorithm called DRAGAN that is fast, simple to implement and achieves competitive performance in a stable fashion across different architectures, datasets (MNIST, CIFAR-10, and CelebA), and divergence measures with almost no hyperparameter tuning.
Doubly fed induction generator model for transient stability analysis

This paper proposes a model of the doubly fed induction generator (DFIG) suitable for transient stability studies. The main assumption adopted in the model is that the current control loops, which are much faster than the electromechanic transients under study, do not have a significant influence on the transient stability of the power system and may be considered instantaneous. The proposed DFIG model is a set of algebraic equations which are solved using an iterative procedure. A method is also proposed to calculate the DFIG initial conditions. A detailed variable-speed windmill model has been developed using the proposed DFIG model. This windmill model has been integrated in a transient stability simulation program in order to demonstrate its feasibility. Several simulations have been performed using a base case which includes a small grid, a wind farm represented by a single windmill, and different operation points. The evolution of several electric variables during the simulations is shown and discussed.