nils-herrmann/clean_scirepeval huggingface dataset:
A recurrent neural network based MPC for a hybrid neuroprosthesis system

Control input sequence in a hybrid neuroprosthesis that combines functional electrical stimulation (FES) and an electric motor can be optimized by a model based optimization method, like model predictive control (MPC). However, because the human muscle model is highly nonlinear, time-varying, and contains unmeasurable state variables, it is often difficult to identify the model. Therefore, a three-layer recurrence neural network (RNN) is developed in this paper, in which backpropagation through time (BPTT) is used as training technique and the internal states are used to represent the unmeasurable states. This structure shows the potential to approximate the model of the hybrid neuroprosthesis system. After the NN model is obtained, an adaptive model predictive control is used to simulate regulation and tracking tasks to test the performance of the NN training and the MPC method.
Appraisal of homogeneous techniques in Distributed Data Mining: classifier approach

In recent years, Distributed Data Mining (DDM) has evolved in large space aiming at minimizing computation cost and memory overhead in processing huge geographically distributed data. There are two approaches of DDM -- homogeneous and heterogeneous classifier approach. This paper presents implementation of four homogeneous classifier techniques for DDM with different Electronic Health Records (EHRs) homogeneous datasets namely diabetes, hepatitis, hypothyroid and further analyzing results based on metric evaluation.
Users Profiling Using Clickstream Data Analysis and Classification

The nature of today's online communication and the emergence of online social networks have introduced a great challenge related to the identification and the verification of users in web environments. The assumption that every user interact uniquely with a web site provides a baseline for studying user identification based on historical records of user's interactions with specific web site. In this paper, we proposed an approach for using clickstream data to identify users based on their navigational behavior. The study investigates using server-side clickstream data collected from previous users' interaction to create a behavioral profile per user. User's profile can be used to identify possible future interactions, which can be associated with distinguishing authenticated user from malicious user. To accomplish this identification, the ability to gather a historical record for user's navigation in a specific web site, is investigated. The goal of the collected data is to use it for training a model that is able to identify if a future interaction can be associated with a certain user.
State-of-the-Art Predictive Maintenance Techniques*

This paper discusses the limitations of time-based equipment maintenance methods and the advantages of predictive or online maintenance techniques in identifying the onset of equipment failure. The three major predictive maintenance techniques, defined in terms of their source of data, are described as follows: 1) the existing sensor-based technique; 2) the test-sensor-based technique (including wireless sensors); and 3) the test-signal-based technique (including the loop current step response method, the time-domain reflectrometry test, and the inductance-capacitance-resistance test). Examples of detecting blockages in pressure sensing lines using existing sensor-based techniques and of verifying calibration using existing-sensor direct current output are given. Three Department of Energy (DOE)-sponsored projects, whose aim is to develop online and wireless hardware and software systems for performing predictive maintenance on critical equipment in nuclear power plants, DOE research reactors, and general industrial applications, are described.
Deep Embedding for Spatial Role Labeling

This paper introduces the visually informed embedding of word (VIEW), a continuous vector representation for a word extracted from a deep neural model trained using the Microsoft COCO data set to forecast the spatial arrangements between visual objects, given a textual description. The model is composed of a deep multilayer perceptron (MLP) stacked on the top of a Long Short Term Memory (LSTM) network, the latter being preceded by an embedding layer. The VIEW is applied to transferring multimodal background knowledge to Spatial Role Labeling (SpRL) algorithms, which recognize spatial relations between objects mentioned in the text. This work also contributes with a new method to select complementary features and a fine-tuning method for MLP that improves the F1 measure in classifying the words into spatial roles. The VIEW is evaluated with the Task 3 of SemEval-2013 benchmark data set, SpaceEval.
Pattern-Based Quadrangulation for N-Sided Patches

We propose an algorithm to quadrangulate an N-sided patch (2 ≤ N ≤ 6) with prescribed numbers of edge subdivisions at its boundary. Our algorithm is guaranteed to succeed for arbitrary valid input, which is proved using a canonical simplification of the input and a small set of topological patterns that are sufficient for supporting all possible cases. Our algorithm produces solutions with minimal number of irregular vertices by default, but it also allows the user to choose other feasible solutions by solving a set of small integer linear programs. We demonstrate the effectiveness of our algorithm by integrating it into a sketch-based quad remeshing system. A reference C++ implementation of our algorithm is provided as a supplementary material.
A Methodology for Outlier Detection in Audit Logs for Financial Transactions

Database audit logs contain the information about database operations which are helpful to verify accuracy, lawfulness and to report risks. In financial systems, the audit logs should be monitored on continuous basis in order to detect and take action against any reasonably abnormal behavior. Outlier detection is a very important concept in the data mining which is useful in data analysis. Nowadays, a direct mapping can be found between the data outliers and the real world anomalies, and hence the outlier detection techniques can be applied to detect the abnormal activities in financial transactions. The purpose of the present study is to automate the process of abnormal activity detection and report generation. In the proposed framework, we will detect outliers based on rules and then we will apply Bayesian classification technique for abnormal activity risk classification. We will also generate user transaction profiles by obtaining user behavior according to historic transactions based on categorical or numerical attributes. Later we will monitor new transactions and will compare it against the corresponding user profile, in order to determine if the transaction is unusual. This framework will work on audit logs from a financial system, and thus will be useful for financial system auditors performing data analysis tasks.
Fault tolerant three-phase AC motor drive topologies: a comparison of features, cost, and limitations

This paper compares the many fault tolerant three-phase ac motor drive topologies that have been proposed to provide output capacity for the inverter faults of switch short or open-circuits, phase-leg short-circuits, and single-phase open-circuits. Also included is a review of the respective control methods for fault tolerant inverters including two-phase and unipolar control methods. The output voltage and current space in terms of dq components is identified for each topology and fault. These quantities are then used to normalize the power capacity of each system during a fault to a standard inverter during normal operation. A silicon overrating cost factor is adopted as a metric to compare the relative switching device costs of the topologies compared to a standard three-phase inverter.
Mental health of hospital consultants: the effects of stress and satisfaction at work

Burnout and psychiatric morbidity among gastroenterologists, surgeons, radiologists, and oncologists in the UK have been estimated by means of a questionnaire-based survey. The relationship between consultants' mental health and their job stress and satisfaction, as well as their job and demographic characteristics, were also examined.



Psychiatric morbidity was estimated using the 12-item General Health Questionnaire. The three components of burnout-emotional exhaustion, depersonalization, and low personal accomplishment-were assessed using the Maslach Burnout Inventory. Job stress and satisfaction were measured using study-specific questions.



Of 1133 consultants, 882 (78%) returned questionnaires. The estimated prevalence of psychiatric morbidity was 27%, with no significant differences between the four specialist groups. Radiologists reported the highest level of burnout in terms of low personal accomplishment. Job satisfaction significantly protected consultants' mental health against job stress. Three sources of stress were associated with both burnout and psychiatric morbidity; feeling overloaded, and its effect on home life; feeling poorly managed and resourced; and dealing with patients' suffering. Burnout was also associated with low satisfaction in three domains: relationships with patients, relatives and staff; professional status/esteem; intellectual stimulation. In addition, being aged 55 years or less and being single were independent risk factors for burnout. Burnout was also more prevalent among consultants who felt insufficiently trained in communication and management skills.



Consultants' mental health is likely to be protected against the high demands of medical practice by maintaining or enhancing job satisfaction, and by providing training in communication and management skills.
High dimensional polynomial interpolation on sparse grids

We study polynomial interpolation on a d-dimensional cube, where d is large. We suggest to use the least solution at sparse grids with the extrema of the Chebyshev polynomials. The polynomial exactness of this method is almost optimal. Our error bounds show that the method is universal, i.e., almost optimal for many different function spaces. We report on numerical experiments for d = 10 using up to 652 065 interpolation points.
Real-time video summarization on mobile

This paper proposes a novel approach for real-time video summarization on mobile using Dictionary Learning, Global Camera Motion analysis and Colorfulness. A dictionary is represented as a distinct set of events that are described as spatio-temporal features. Uniqueness measure is predicted based on the correlation scores of the dictionary elements whereas the quality measure is estimated using Global Camera Motion analysis and Colorfulness. Our proposed technique combines the uniqueness measure and the quality measure to predict the interestingness. Experiments indicate that our method outperforms state-of-the-arts in terms of computation speed while retaining the similar subjective quality.
Exploring the Impact of Gambling Advertising: An Interview Study of Problem Gamblers

This study qualitatively explored the impact of gambling advertising on problem gambling by interviewing twenty-five people with current or past gambling problems. Interviews were relatively long and involved the participants’ viewing numerous examples of gambling advertising. A quarter of the participants reported that gambling advertising had no impact on their problems, slightly over half of them reported that advertising had a marginal impact, and one fifth reported a tangible impact. However, none considered advertising to be a main cause of their gambling problems. The negative self-perceived impact was primarily that advertising triggered impulses to gamble. Advertising thus increased already high involvement in gambling and/or made it harder to stick to a decision to gamble less or not at all.
Forecasting time series of inhomogeneous Poisson processes with application to call center workforce management

We consider forecasting the latent rate profiles of a time series of inhomogeneous Poisson processes. The work is motivated by operations management of queueing systems, in particular, telephone call centers, where accurate forecasting of call arrival rates is a crucial primitive for efficient staffing of such centers. Our forecasting approach utilizes dimension reduction through a factor analysis of Poisson variables, followed by time series modeling of factor score series. Time series forecasts of factor scores are combined with factor loadings to yield forecasts of future Poisson rate profiles. Penalized Poisson regressions on factor loadings guided by time series forecasts of factor scores are used to generate dynamic within-process rate updating. Methods are also developed to obtain distributional forecasts. Our methods are illustrated using simulation and real data. The empirical results demonstrate how forecasting and dynamic updating of call arrival rates can affect the accuracy of call center staffing.
Neural Named Entity Recognition Using a Self-Attention Mechanism

We propose a novel supervised approach for text tagging and multi-label text classification based on a multi-head encoder-decoder neural network architecture. Our method predicts which subset of possible tags best matches an input text. It efficiently spends computational resources, exploiting dependencies between tags by encoding an input text into a compact representation which is then passed to multiple decoder classifier heads. We test our architecture on a Twitter hashtag prediction task, comparing it to a baseline model with multiple feedforward networks and a baseline model with multiple recurrent neural networks with GRU cells. We show that our approach achieves a significantly better performance than baselines with an equivalent number of parameters.
Machine Learning Methods for Data-Driven Turbulence Modeling

As part of a larger effort on data-driven turbulence modeling, this paper investigates machine learning models in their capability to reconstruct the functional forms of spatially distributed quantities extracted from high fidelity simulation and experimental data. Such datasets typically involve very high dimensional feature spaces with sparsely populated and noisy data. A new multiscale Gaussian process regression technique is described and is compared to ‘conventional’ Gaussian process regression and artificial neural networks. All these techniques are applied to the reconstruction of functions arising from Bayesian inference applied to turbulent channel flow and bypass transition. The efficiency, accuracy and effectiveness of each learning algorithm as well as factors that influence their output is assessed. The results highlight the potential of machine learning as an enabling tool in data-driven turbulence modeling.
DroidFusion: A Novel Multilevel Classifier Fusion Approach for Android Malware Detection

Android malware has continued to grow in volume and complexity posing significant threats to the security of mobile devices and the services they enable. This has prompted increasing interest in employing machine learning to improve Android malware detection. In this paper, we present a novel classifier fusion approach based on a multilevel architecture that enables effective combination of machine learning algorithms for improved accuracy. The framework (called DroidFusion), generates a model by training base classifiers at a lower level and then applies a set of ranking-based algorithms on their predictive accuracies at the higher level in order to derive a final classifier. The induced multilevel DroidFusion model can then be utilized as an improved accuracy predictor for Android malware detection. We present experimental results on four separate datasets to demonstrate the effectiveness of our proposed approach. Furthermore, we demonstrate that the DroidFusion method can also effectively enable the fusion of ensemble learning algorithms for improved accuracy. Finally, we show that the prediction accuracy of DroidFusion, despite only utilizing a computational approach in the higher level, can outperform stacked generalization, a well-known classifier fusion method that employs a meta-classifier approach in its higher level.
Embedding new data points for manifold learning via coordinate propagation

In recent years, a series of manifold learning algorithms have been proposed for nonlinear dimensionality reduction. Most of them can run in a batch mode for a set of given data points, but lack a mechanism to deal with new data points. Here we propose an extension approach, i.e., mapping new data points into the previously learned manifold. The core idea of our approach is to propagate the known coordinates to each of the new data points. We first formulate this task as a quadratic programming, and then develop an iterative algorithm for coordinate propagation. Tangent space projection and smooth splines are used to yield an initial coordinate for each new data point, according to their local geometrical relations. Experimental results and applications to camera direction estimation and face pose estimation illustrate the validity of our approach.
Using sonification

The idea behind sonification is that synthetic non-verbal sounds can represent numerical data and provide support for information processing activities of many different kinds. This article describes some of the ways that sonification has been used in assistive technologies, remote collaboration, engineering analyses, scientific visualisations, emergency services and aircraft cockpits. Approaches for designing sonifications are surveyed, and issues raised by the existing approaches and applications are outlined. Relations are drawn to other areas of knowledge where similar issues have also arisen, such as human-computer interaction, scientific visualisation, and computer music. At the end is a list of resources that will help you delve further into the topic.
Novel Compact Circularly Polarized Square Microstrip Antenna

A novel compact circular-polarization (CP) operation of the square microstrip antenna with four slits and a pair of truncated corners is proposed and investigated. Experimental results show that the proposed compact CP design can have an antenna-size reduction of about 36% as compared to the conventional corner-truncated square microstrip antenna at a given operating frequency. Also, the required size of the truncated corners for CP operation is much greater than that for the conventional CP design using a simple square microstrip patch, providing a relaxed manufacturing tolerance for the proposed compact CP design. Details of the experimental results are presented and discussed.
Matching Web Tables To DBpedia - A Feature Utility Study

Relational HTML tables on the Web contain data describing a multitude of entities and covering a wide range of topics. Thus, web tables are very useful for filling missing values in cross-domain knowledge bases such as DBpedia, YAGO, or the Google Knowledge Graph. Before web table data can be used to fill missing values, the tables need to be matched to the knowledge base in question. This involves three matching tasks: table-to-class matching, rowto-instance matching, and attribute-to-property matching. Various matching approaches have been proposed for each of these tasks. Unfortunately, the existing approaches are evaluated using different web table corpora. Each individual approach also only exploits a subset of the web table and knowledge base features that are potentially helpful for the matching tasks. These two shortcomings make it difficult to compare the different matching approaches and to judge the impact of each feature on the overall matching results. This paper contributes to improve the understanding of the utility of different features for web table to knowledge base matching by reimplementing different matching techniques as well as similarity score aggregation methods from literature within a single matching framework and evaluating different combinations of these techniques against a single gold standard. The gold standard consists of class-, instance-, and property correspondences between the DBpedia knowledge base and web tables from the Web Data Commons web table corpus.
Overconfidence as a cause of diagnostic error in medicine.

The great majority of medical diagnoses are made using automatic, efficient cognitive processes, and these diagnoses are correct most of the time. This analytic review concerns the exceptions: the times when these cognitive processes fail and the final diagnosis is missed or wrong. We argue that physicians in general underappreciate the likelihood that their diagnoses are wrong and that this tendency to overconfidence is related to both intrinsic and systemically reinforced factors. We present a comprehensive review of the available literature and current thinking related to these issues. The review covers the incidence and impact of diagnostic error, data on physician overconfidence as a contributing cause of errors, strategies to improve the accuracy of diagnostic decision making, and recommendations for future research.
Topological analysis of network attack vulnerability

This talk will discuss issues and methods for survivability of systems under malicious attacks. To protect from such attacks, it is necessary to take steps to prevent attacks from succeeding. At the same time, it is important to recognize that not all attacks can be averted at the outset; attacks that are successful to some degree must be recognized as unavoidable and comprehensive support for identifying and responding to attacks is required.
 In my talk, I will describe the recent research on attack graphs that represent known attack sequences attackers can use to penetrate computer networks. I will show how attack graphs can be used to compute actual sets of hardening measures that guarantee the safety of given critical resources. Attack graphs can also be used to correlate received alerts, hypothesize missing alerts, and predict future alerts, all at the same time. Thus, they offer a promising solution for administrators to monitor and predict the progress of an intrusion, and take appropriate countermeasures in a timely manner.
The Foreign Language Effect on Moral Judgment: The Role of Emotions and Norms

We investigated whether and why the use of a foreign language influences moral judgment. We studied the trolley and footbridge dilemmas, which propose an action that involves killing one individual to save five. In line with prior work, the use of a foreign language increased the endorsement of such consequentialist actions for the footbridge dilemma, but not for the trolley dilemma. But contrary to recent theorizing, this effect was not driven by an attenuation of emotions. An attenuation of emotions was found in both dilemmas, and it did not mediate the foreign language effect on moral judgment. An examination of additional scenarios revealed that foreign language influenced moral judgment when the proposed action involved a social or moral norm violation. We propose that foreign language influences moral judgment by reducing access to normative knowledge.
Analog CMOS-based resistive processing unit for deep neural network training

Recently we have shown that an architecture based on resistive processing unit (RPU) devices has potential to achieve significant acceleration in deep neural network (DNN) training compared to today's software-based DNN implementations running on CPU/GPU. However, currently available device candidates based on non-volatile memory technologies do not satisfy all the requirements to realize the RPU concept. Here, we propose an analog CMOS-based RPU design (CMOS RPU) which can store and process data locally and can be operated in a massively parallel manner. We analyze various properties of the CMOS RPU to evaluate the functionality and feasibility for acceleration of DNN training.