nils-herrmann/clean_scirepeval huggingface dataset:
Correcting Forecasts with Multifactor Neural Attention

Automatic forecasting of time series data is a challenging problem in many industries. Current forecast models adopted by businesses do not provide adequate means for including data representing external factors that may have a significant impact on the time series, such as weather, national events, local events, social media trends, promotions, etc. This paper introduces a novel neural network attention mechanism that naturally incorporates data from multiple external sources without the feature engineering needed to get other techniques to work. We demonstrate empirically that the proposed model achieves superior performance for predicting the demand of 20 commodities across 107 stores of one of America’s largest retailers when compared to other baseline models, including neural networks, linear models, certain kernel methods, Bayesian regression, and decision trees. Our method ultimately accounts for a 23.9% relative improvement as a result of the incorporation of external data sources, and provides an unprecedented level of descriptive ability for a neural network forecasting model.
An intelligent discussion-bot for answering student queries in threaded discussions

This paper describes a discussion-bot that provides answers to students' discussion board questions in an unobtrusive and human-like way. Using information retrieval and natural language processing techniques, the discussion-bot identifies the questioner's interest, mines suitable answers from an annotated corpus of 1236 archived threaded discussions and 279 course documents and chooses an appropriate response. A novel modeling approach was designed for the analysis of archived threaded discussions to facilitate answer extraction. We compare a self-out and an all-in evaluation of the mined answers. The results show that the discussion-bot can begin to meet students' learning requests. We discuss directions that might be taken to increase the effectiveness of the question matching and answer extraction algorithms. The research takes place in the context of an undergraduate computer science course.
Survey on Classification Algorithms for Data Mining:(Comparison and Evaluation)

Data mining concept is growing fast in popularity, it is a technology that involving methods at the intersection of (Artificial intelligent, Machine learning, Statistics and database system), the main goal of data mining process is to extract information from a large data into form which could be understandable for further use. Some algorithms of data mining are used to give solutions to classification problems in database. In this paper a comparison among three classification’s algorithms will be studied, these are (KNearest Neighbor classifier, Decision tree and Bayesian network) algorithms. The paper will demonstrate the strength and accuracy of each algorithm for classification in term of performance efficiency and time complexity required. For model validation purpose, twenty-four-month data analysis is conducted on a mock-up basis.
From Data Fusion to Knowledge Fusion

The task of data fusion is to identify the true values of data items (e.g., the true date of birth for Tom Cruise) among multiple observed values drawn from different sources (e.g., Web sites) of varying (and unknown) reliability. A recent survey [20] has provided a detailed comparison of various fusion methods on Deep Web data. In this paper, we study the applicability and limitations of different fusion techniques on a more challenging problem: knowledge fusion. Knowledge fusion identifies true subject-predicateobject triples extracted by multiple information extractors from multiple information sources. These extractors perform the tasks of entity linkage and schema alignment, thus introducing an additional source of noise that is quite different from that traditionally considered in the data fusion literature, which only focuses on factual errors in the original sources. We adapt state-of-the-art data fusion techniques and apply them to a knowledge base with 1.6B unique knowledge triples extracted by 12 extractors from over 1B Web pages, which is three orders of magnitude larger than the data sets used in previous data fusion papers. We show great promise of the data fusion approaches in solving the knowledge fusion problem, and suggest interesting research directions through a detailed error analysis of the methods.
Predictive translation memory: a mixed-initiative system for human language translation

The standard approach to computer-aided language translation is post-editing: a machine generates a single translation that a human translator corrects. Recent studies have shown this simple technique to be surprisingly effective, yet it underutilizes the complementary strengths of precision-oriented humans and recall-oriented machines. We present Predictive Translation Memory, an interactive, mixed-initiative system for human language translation. Translators build translations incrementally by considering machine suggestions that update according to the user's current partial translation. In a large-scale study, we find that professional translators are slightly slower in the interactive mode yet produce slightly higher quality translations despite significant prior experience with the baseline post-editing condition. Our analysis identifies significant predictors of time and quality, and also characterizes interactive aid usage. Subjects entered over 99% of characters via interactive aids, a significantly higher fraction than that shown in previous work.
A novel algorithm for calculating the QR decomposition of a polynomial matrix

A novel algorithm for calculating the QR decomposition (QRD) of polynomial matrix is proposed. The algorithm operates by applying a series of polynomial Givens rotations to transform a polynomial matrix into an upper-triangular polynomial matrix and, therefore, amounts to a generalisation of the conventional Givens method for formulating the QRD of a scalar matrix. A simple example is given to demonstrate the algorithm, but also illustrates two clear advantages of this algorithm when compared to an existing method for formulating the decomposition. Firstly, it does not demonstrate the same unstable behaviour that is sometimes observed with the existing algorithm and secondly, it typically requires less iterations to converge. The potential application of the decomposition is highlighted in terms of broadband multi-input multi-output (MIMO) channel equalisation.
Introducing the Enron Corpus

A large set of email messages, the Enron corpus, was made public during the legal investigation concerning the Enron corporation. This dataset, along with a thorough explanation of its origin, is available at http://www-2.cs.cmu.edu/~enron/. This paper provides a brief introduction and analysis of the dataset. The raw Enron corpus contains 619,446 messages belonging to 158 users. We cleaned the corpus before this analysis by removing certain folders from each user, such as “discussion_threads”. These folders were present for most users, and did not appear to be used directly by the users, but rather were computer generated. Many, such as “all_documents”, also contained large numbers of duplicate emails, which were already present in the users’ other folders. Our goal in this paper is to analyze the suitability of this corpus for exploring how to classify messages as organized by a human, so these folders would have likely been misleading.
Most likely heteroscedastic Gaussian process regression

This paper presents a novel Gaussian process (GP) approach to regression with input-dependent noise rates. We follow Goldberg et al.'s approach and model the noise variance using a second GP in addition to the GP governing the noise-free output value. In contrast to Goldberg et al., however, we do not use a Markov chain Monte Carlo method to approximate the posterior noise variance but a most likely noise approach. The resulting model is easy to implement and can directly be used in combination with various existing extensions of the standard GPs such as sparse approximations. Extensive experiments on both synthetic and real-world data, including a challenging perception problem in robotics, show the effectiveness of most likely heteroscedastic GP regression.
A statistical analysis of disclosed storage security breaches

Many storage security breaches have recently been reported in the mass media as the direct result of new breach disclosure state laws across the United States (unfortunately, not internationally). In this paper, we provide an empirical analysis of disclosed storage security breaches for the period of 2005-2006. By processing raw data from the best available sources, we seek to understand the what, who, how, where, and when questions about storage security breaches so that others can build upon this evidence when developing best practices for preventing and mitigating storage breaches. While some policy formulation has already started in reaction to media reports (many without empirical analysis), this work provides initial empirical analysis upon which future empirical analysis and future policy decisions can be based.
HANDEXOS: Towards an exoskeleton device for the rehabilitation of the hand

This paper introduces a novel exoskeleton device (HANDEXOS) for the rehabilitation of the hand for post-stroke patients. The nature of the impaired hand can be summarized in a limited extension, abduction and adduction leaving the fingers in a flexed position, so the exoskeleton goal is to train a safe extension motion from the typical closed position of the impaired hand.
Fog Data: Enhancing Telehealth Big Data Through Fog Computing

The size of multi-modal, heterogeneous data collected through various sensors is growing exponentially. It demands intelligent data reduction, data mining and analytics at edge devices. Data compression can reduce the network bandwidth and transmission power consumed by edge devices. This paper propose s, validates and evaluates Fog Data, a service-oriented architecture for Fog computing. The center piece of the proposed architecture is a low power embedded computer that carries out data mining and data analytics on raw data collected from various wearable sensors used for telehealth applications. The embedded computer collects the sensed data as time series, analyzes it, and finds similar patterns present. Patterns are stored, and unique patterns are transmited. Also, the embedded computer extracts clinically relevant information that is sent to the cloud. A working prototype of the proposed architecture was built and used to carry out case studies on telehealth big data applications. Specifically, our case studies used the data from the sensors worn by patients with either speech motor disorders or cardiovascular problems. We implemented and evaluated both generic and application specific data mining techniques to show orders of magnitude data reduction and hence transmission power savings. Quantitative evaluations were conducted for comparing various da ta mining techniques and standard data compression techniques. The obtained results showed substantial improvement in system efficiency using the Fog Data architecture. CCS Concepts •Networks ➝ Cloud computing •Social and professional topics ➝ Remote medicine Hardware ➝ Signal processing systems Hardware➝Sensors and actuators •Applied computing ➝ Health care information systems
Doubly Robust Off-policy Value Evaluation for Reinforcement Learning

We study the problem of off-policy value evaluation in reinforcement learning (RL), where one aims to estimate the value of a new policy based on data collected by a different policy. This problem is often a critical step when applying RL in real-world problems. Despite its importance, existing general methods either have uncontrolled bias or suffer high variance. In this work, we extend the doubly robust estimator for bandits to sequential decision-making problems, which gets the best of both worlds: it is guaranteed to be unbiased and can have a much lower variance than the popular importance sampling estimators. We demonstrate the estimator’s accuracy in several benchmark problems, and illustrate its use as a subroutine in safe policy improvement. We also provide theoretical results on the hardness of the problem, and show that our estimator can match the lower bound in certain scenarios.
Agile Enterprise Architecture: a Case of a Cloud Technology-Enabled Government Enterprise Transformation

Australian government enterprises have shown a significant interest in the cloud technology-enabled enterprise transformation. Australian government suggests the whole-of-a-government strategy to cloud adoption. The challenge is how best to realise this cloud adoption strategy for the cloud technology-enabled enterprise transformation? The cloud adoption strategy realisation requires concrete guidelines and a comprehensive practical framework. This paper proposes the use of an agile enterprise architecture framework to developing and implementing the adaptive cloud technology-enabled enterprise architecture in the Australian government context. The results of this paper indicate that a holistic strategic agile enterprise architecture approach seems appropriate to support the strategic whole-of-a-government approach to cloud technology-enabled government enterprise transformation.
Differentiating malware from cleanware using behavioural analysis

This paper proposes a scalable approach for distinguishing malicious files from clean files by investigating the behavioural features using logs of various API calls. We also propose, as an alternative to the traditional method of manually identifying malware files, an automated classification system using runtime features of malware files. For both projects, we use an automated tool running in a virtual environment to extract API call features from executables and apply pattern recognition algorithms and statistical methods to differentiate between files. Our experimental results, based on a dataset of 1368 malware and 456 cleanware files, provide an accuracy of over 97% in distinguishing malware from cleanware. Our techniques provide a similar accuracy for classifying malware into families. In both cases, our results outperform comparable previously published techniques.
On Multiplicative Integration with Recurrent Neural Networks

We introduce a general and simple structural design called “Multiplicative Integration” (MI) to improve recurrent neural networks (RNNs). MI changes the way in which information from difference sources flows and is integrated in the computational building block of an RNN, while introducing almost no extra parameters. The new structure can be easily embedded into many popular RNN models, including LSTMs and GRUs. We empirically analyze its learning behaviour and conduct evaluations on several tasks using different RNN models. Our experimental results demonstrate that Multiplicative Integration can provide a substantial performance boost over many of the existing RNN models.
Analysis of Classification Algorithms Applied to Hepatitis Patients

This paper mainly deals with various classification algorithms namely, Bayes. NaiveBayes, Bayes. BayesNet, Bayes. NaiveBayesUpdatable, J48, Randomforest, and Multi Layer Perceptron. It analyzes the hepatitis patients from the UC Irvine machine learning repository. The results of the classification model are accuracy and time. Finally, it concludes that the Naive Bayes performance is better than other classification techniques for hepatitis patients.
Ensemble Adversarial Training: Attacks and Defenses

Machine learning models are vulnerable to adversarial examples, inputs maliciously perturbed to mislead the model. These inputs transfer between models, thus enabling black-box attacks against deployed models. Adversarial training increases robustness to attacks by injecting adversarial examples into training data. Surprisingly, we find that although adversarially trained models exhibit strong robustness to some white-box attacks (i.e., with knowledge of the model parameters), they remain highly vulnerable to transferred adversarial examples crafted on other models. We show that the reason for this vulnerability is the model’s decision surface exhibiting sharp curvature in the vicinity of the data points, thus hindering attacks based on first-order approximations of the model’s loss, but permitting black-box attacks that use adversarial examples transferred from another model. We harness this observation in two ways: First, we propose a simple yet powerful novel attack that first applies a small random perturbation to an input, before finding the optimal perturbation under a first-order approximation. Our attack outperforms prior “single-step” attacks on models trained with or without adversarial training. Second, we propose Ensemble Adversarial Training, an extension of adversarial training that additionally augments training data with perturbed inputs transferred from a number of fixed pre-trained models. On MNIST and ImageNet, ensemble adversarial training vastly improves robustness to black-box attacks.
Wireless power transfer and applications to sensor networks

Energy constraints are widely regarded as a fundamental limitation of wireless and mobile devices. For sensor networks, a limited lifetime due to battery constraint poses a performance bottleneck and barrier for large scale deployment. Recently, wireless power transfer has emerged as a promising technology to address energy and lifetime bottlenecks in a sensor network. In this article, we give a review of the history of wireless power transfer and describe its recent developments. We show how such technologies can be applied to sensor networks and address their energy constraints.
Automated de-identification of free-text medical records

Text-based patient medical records are a vital resource in medical research. In order to preserve patient confidentiality, however, the U.S. Health Insurance Portability and Accountability Act (HIPAA) requires that protected health information (PHI) be removed from medical records before they can be disseminated. Manual de-identification of large medical record databases is prohibitively expensive, time-consuming and prone to error, necessitating automatic methods for large-scale, automated de-identification.



We describe an automated Perl-based de-identification software package that is generally usable on most free-text medical records, e.g., nursing notes, discharge summaries, X-ray reports, etc. The software uses lexical look-up tables, regular expressions, and simple heuristics to locate both HIPAA PHI, and an extended PHI set that includes doctors' names and years of dates. To develop the de-identification approach, we assembled a gold standard corpus of re-identified nursing notes with real PHI replaced by realistic surrogate information. This corpus consists of 2,434 nursing notes containing 334,000 words and a total of 1,779 instances of PHI taken from 163 randomly selected patient records. This gold standard corpus was used to refine the algorithm and measure its sensitivity. To test the algorithm on data not used in its development, we constructed a second test corpus of 1,836 nursing notes containing 296,400 words. The algorithm's false negative rate was evaluated using this test corpus.



Performance evaluation of the de-identification software on the development corpus yielded an overall recall of 0.967, precision value of 0.749, and fallout value of approximately 0.002. On the test corpus, a total of 90 instances of false negatives were found, or 27 per 100,000 word count, with an estimated recall of 0.943. Only one full date and one age over 89 were missed. No patient names were missed in either corpus.



We have developed a pattern-matching de-identification system based on dictionary look-ups, regular expressions, and heuristics. Evaluation based on two different sets of nursing notes collected from a U.S. hospital suggests that, in terms of recall, the software out-performs a single human de-identifier (0.81) and performs at least as well as a consensus of two human de-identifiers (0.94). The system is currently tuned to de-identify PHI in nursing notes and discharge summaries but is sufficiently generalized and can be customized to handle text files of any format. Although the accuracy of the algorithm is high, it is probably insufficient to be used to publicly disseminate medical data. The open-source de-identification software and the gold standard re-identified corpus of medical records have therefore been made available to researchers via the PhysioNet website to encourage improvements in the algorithm.
Choosing the right NoSQL database for the job: a quality attribute evaluation

For over forty years, relational databases have been the leading model for data storage, retrieval and management. However, due to increasing needs for scalability and performance, alternative systems have emerged, namely NoSQL technology. The rising interest in NoSQL technology, as well as the growth in the number of use case scenarios, over the last few years resulted in an increasing number of evaluations and comparisons among competing NoSQL technologies. While most research work mostly focuses on performance evaluation using standard benchmarks, it is important to notice that the architecture of real world systems is not only driven by performance requirements, but has to comprehensively include many other quality attribute requirements. Software quality attributes form the basis from which software engineers and architects develop software and make design decisions. Yet, there has been no quality attribute focused survey or classification of NoSQL databases where databases are compared with regards to their suitability for quality attributes common on the design of enterprise systems. To fill this gap, and aid software engineers and architects, in this article, we survey and create a concise and up-to-date comparison of NoSQL engines, identifying their most beneficial use case scenarios from the software engineer point of view and the quality attributes that each of them is most suited to.
3D Reconstruction of Background and Objects Moving on Ground Plane Viewed from a Moving Camera

We present a novel method to obtain a 3D Euclidean reconstruction of both the background and moving objects in a video sequence. We assume that, multiple objects are moving rigidly on a ground plane observed by a moving camera. The video sequence is first segmented into static background and motion blobs by a homography-based motion segmentation method. Then classical "Structure from Motion" (SfM) techniques are applied to obtain a Euclidean reconstruction of the static background. The motion blob corresponding to each moving object is treated as if there were a static object observed by a hypothetical moving camera, called a "virtual camera". This virtual camera shares the same intrinsic parameters with the real camera but moves differently due to object motion. The same SfM techniques are applied to estimate the 3D shape of each moving object and the pose of the virtual camera. We show that the unknown scale of moving objects can be approximately determined by the ground plane, which is a key contribution of this paper. Another key contribution is that we prove that the 3D motion of moving objects can be solved from the virtual camera motion with a linear constraint imposed on the object translation. In our approach, a planartranslation constraint is formulated: "the 3D instantaneous translation of moving objects must be parallel to the ground plane". Results on real-world video sequences demonstrate the effectiveness and robustness of our approach.