nils-herrmann/clean_scirepeval huggingface dataset:
Packaging: past, present and future

In the past, microsystems packaging played two roles: 1) It provided I/O connections to and from devices, referred to as IC or wafer level packaging, and 2) It interconnected both active and passive components on system level boards, referred to as systems packaging. Both were accomplished by interconnections or multilayer wiring at the package or board level. More recently, the IC devices have begun to integrate not only more and more transistors but also active and passive components on an individual chip, leading the community to believe that someday there may be a single-chip system, referred to as SOC or system-on-chip. This can be called horizontal or 2D integration of IC blocks toward systems. The community began to realize, however, that such an approach presents fundamental, engineering and investment limits and computing and integration limits for wireless and wired communication systems over the long run. This led to 3-D packaging approaches, often referred to as SIP or system-in-package. The SIP, while providing major opportunities in both miniaturization and integration for advanced and portable electronic products, is limited by the CMOS process just like the SOC. Some existing and emerging applications, however, include sensors, memory modules and embedded processors with DRAMs. More recent 3-D solutions, which incorporate stacked package approaches, offer solutions towards faster time-to-market and business impediments that have plagued MCM deployment for the past decade. There is a new concept which is called SOP or system-on-package. With SOP, Wit package, not the board, is the system. As such, SOP is beginning to address the shortcomings of both SOC and SIP, as well as traditional packaging which is bulky, costly, and lower in performance and reliability than ICs, in two ways: It uses CMOS-based Si for what it is good for, namely, for transistor integration, and the package, for what it is good for, namely, RF, optical and digital integration by means of IC-package-system co-design. The SOP package, therefore, overcomes both the computing limitations and integration limitations of SOC, SIP, MCM and traditional system packaging. It does this by having global wiring as well as RF, digital and optical component integration in the package, not in the chip. SOP, therefore, includes both active and passive components including embedded digital, RF and
A taxonomy to guide research on the application of data mining to fraud detection in financial statement audits

Received 1 June 2013 Received in revised form 26 December 2013  May 2014 Available online 5 July 2014 This paper explores the application of data mining techniques to fraud detection in the audit offinancial statements andproposes a taxonomy to support and guide future research. Currently, the application of data mining to auditing is at an early stage of development and researchers take a scatter-shot approach, investigating patterns in financial statement disclosures, text in annual reports and MD&As, and the nature of journal entries without appropriate guidance being drawn from lessons in known fraud patterns. To develop structure to research in datamining, we create a taxonomy that combines research on patterns of observed fraud schemeswith an appreciation of areas that benefit fromproductive application of data mining. We encapsulate traditional views of data mining that operates primarily on quantitative data, such as financial statement and journal entry data. In addition, we draw on other forms of data mining, notably text and email mining.
Tensor Rank Preserving Discriminant Analysis for Facial Recognition

Facial recognition, one of the basic topics in computer vision and pattern recognition, has received substantial attention in recent years. However, for those traditional facial recognition algorithms, the facial images are reshaped to a long vector, thereby losing part of the original spatial constraints of each pixel. In this paper, a new tensor-based feature extraction algorithm termed tensor rank preserving discriminant analysis (TRPDA) for facial image recognition is proposed; the proposed method involves two stages: in the first stage, the low-dimensional tensor subspace of the original input tensor samples was obtained; in the second stage, discriminative locality alignment was utilized to obtain the ultimate vector feature representation for subsequent facial recognition. On the one hand, the proposed TRPDA algorithm fully utilizes the natural structure of the input samples, and it applies an optimization criterion that can directly handle the tensor spectral analysis problem, thereby decreasing the computation cost compared those traditional tensor-based feature selection algorithms. On the other hand, the proposed TRPDA algorithm extracts feature by finding a tensor subspace that preserves most of the rank order information of the intra-class input samples. Experiments on the three facial databases are performed here to determine the effectiveness of the proposed TRPDA algorithm.
Non-Data-Aided Feedforward Carrier Frequency Offset Estimators for QAM Constellations: A Nonlinear Least-Squares Approach

This paper performs a comprehensive performance analysis of a family of non-data-aided feedforward carrier frequency offset estimators for QAM signals transmitted through AWGN channels in the presence of unknown timing error. The proposed carrier frequency offset estimators are asymptotically (large sample) nonlinear least-squares estimators obtained by exploiting the fourthorder conjugate cyclostationary statistics of the received signal and exhibit fast convergence rates (asymptotic variances on the order of O(N−3), where N stands for the number of samples). The exact asymptotic performance of these estimators is established and analyzed as a function of the received signal sampling frequency, signal-to-noise ratio, timing delay, and number of symbols. It is shown that in the presence of intersymbol interference effects, the performance of the frequency offset estimators can be improved significantly by oversampling (or fractionally sampling) the received signal. Finally, simulation results are presented to corroborate the theoretical performance analysis, and comparisons with the modified Cramér-Rao bound illustrate the superior performance of the proposed nonlinear least-squares carrier frequency offset estimators.
The his and hers of prosocial behavior: an examination of the social psychology of gender.

Prosocial behavior consists of behaviors regarded as beneficial to others, including helping, sharing, comforting, guiding, rescuing, and defending others. Although women and men are similar in engaging in extensive prosocial behavior, they are different in their emphasis on particular classes of these behaviors. The specialty of women is prosocial behaviors that are more communal and relational, and that of men is behaviors that are more agentic and collectively oriented as well as strength intensive. These sex differences, which appear in research in various settings, match widely shared gender role beliefs. The origins of these beliefs lie in the division of labor, which reflects a biosocial interaction between male and female physical attributes and the social structure. The effects of gender roles on behavior are mediated by hormonal processes, social expectations, and individual dispositions.
Diversity-Promoting GAN: A Cross-Entropy Based Generative Adversarial Network for Diversified Text Generation

•Task: • Text Generation.  • Existing text generation methods tend to produce repeated and “boring” expressions.  • We propose a new model, called Diversity-Promoting Generative Adversarial Network (DP-GAN) for diversified text generation. • The proposed model assigns low reward for repeatedly generated text and high reward for “novel” and fluent text. • Moreover, we propose a novel language model based discriminator without the saturation problem.
Do-It-Yourself Eye Tracker: Low-Cost Pupil-Based Eye Tracker for Computer Graphics Applications

Eye tracking technologies offer sophisticated methods for capturing humans’ gaze direction but their popularity in multimedia and computer graphics systems is still low. One of the main reasons for this are the high cost of commercial eye trackers that comes to 25,000 euros. Interestingly, this price seems to stem from the costs incurred in research rather than the value of used hardware components. In this work we show that an eye tracker of a satisfactory precision can be built in the budget of 30 euros. In the paper detailed instruction on how to construct a low cost pupil-based eye tracker and utilise open source software to control its behaviour is presented. We test the accuracy of our eye tracker and reveal that its precision is comparable to commercial video-based devices. We give an example of application in which our eye tracker is used to control the depth-of-field rendering in real time virtual environment.
Recurrent Neural Network-Based Sentence Encoder with Gated Attention for Natural Language Inference

The RepEval 2017 Shared Task aims to evaluate natural language understanding models for sentence representation, in which a sentence is represented as a fixedlength vector with neural networks and the quality of the representation is tested with a natural language inference task. This paper describes our system (alpha) that is ranked among the top in the Shared Task, on both the in-domain test set (obtaining a 74.9% accuracy) and on the crossdomain test set (also attaining a 74.9% accuracy), demonstrating that the model generalizes well to the cross-domain data. Our model is equipped with intra-sentence gated-attention composition which helps achieve a better performance. In addition to submitting our model to the Shared Task, we have also tested it on the Stanford Natural Language Inference (SNLI) dataset. We obtain an accuracy of 85.5%, which is the best reported result on SNLI when cross-sentence attention is not allowed, the same condition enforced in RepEval 2017.
Detecting Plagiarism in Text Documents through Grammar-Analysis of Authors

The task of intrinsic plagiarism detection is to find plagiarized sections within text documents without using a reference corpus. In this paper, the intrinsic detection approach Plag-Inn is presented which is based on the assumption that authors use a recognizable and distinguishable grammar to construct sentences. The main idea is to analyze the grammar of text documents and to find irregularities within the syntax of sentences, regardless of the usage of concrete words. If suspicious sentences are found by computing the pq-gram distance of grammar trees and by utilizing a Gaussian normal distribution, the algorithm tries to select and combine those sentences into potentially plagiarized sections. The parameters and thresholds needed by the algorithm are optimized by using genetic algorithms. Finally, the approach is evaluated against a large test corpus consisting of English documents, showing promising results.
Accurate freeway travel time prediction with state-space neural networks under missing data

Accuracy and robustness with respect to missing or corrupt input data are two key characteristics for any travel time prediction model that is to be applied in a real-time environment (e.g. for display on variable message signs on freeways). This article proposes a freeway travel time prediction framework that exhibits both qualities. The framework exploits a recurrent neural network topology, the so-called statespace neural network (SSNN), with preprocessing strategies based on imputation. Although the SSNN model is a neural network, its design (in terms of inputand model selection) is not ‘‘black box’’ nor location-specific. Instead, it is based on the lay-out of the freeway stretch of interest. In this sense, the SSNN model combines the generality of neural network approaches, with traffic related (‘‘white-box’’) design. Robustness to missing data is tackled by means of simple imputation (data replacement) schemes, such as exponential forecasts and spatial interpolation. Although there are clear theoretical shortcomings to ‘‘simple’’ imputation schemes to remedy input failure, our results indicate that their use is justified in this particular application. The SSNN model appears to be robust to the ‘‘damage’’ done by these imputation schemes. This is true for both incidental (random) and structural input failure. We demonstrate that the SSNN travel time prediction framework yields good accurate and robust travel time predictions on both synthetic and real data.  E-mail address: h.vanlint@citg.tudelft.nl (J.W.C. van Lint). 348 J.W.C. van Lint et al. / Transportation Research Part C 13 (2005) 347–369
Mobile Wireless Sensor Network: Architecture and Enabling Technologies for Ubiquitous Computing

A mobile wireless sensor network owes its name to the presence of mobile sink or sensor nodes within the network. The advantages of mobile WSN over static WSN are better energy efficiency, improved coverage, enhanced target tracking and superior channel capacity. In this paper we present and discuss hierarchical multi-tiered architecture for mobile wireless sensor network. This architecture is proposed for the future pervasive computing age. We also elaborate on the impact of mobility on different performance metrics in mobile WSN. A study of some of the possible application scenarios for pervasive computing involving mobile WSN is also presented. These application scenarios will be discussed in their implementation context. While discussing the possible applications, we also study related technologies that appear promising to be integrated with mobile WSN in the ubiquitous computing. With an enormous growth in number of cellular subscribers, we therefore place the mobile phone as the key element in future ubiquitous wireless networks. With the powerful computing, communicating and storage capacities of these mobile devices, the network performance can benefit from the architecture in terms of scalability, energy efficiency and packet delay, etc.
Research on attention memory networks as a model for learning natural language inference

Natural Language Inference (NLI) is a fundamentally important task in natural language processing that has many applications. It is concerned with classifying the logical relation between two sentences. In this paper, we propose attention memory networks (AMNs) to recognize entailment and contradiction between two sentences. In our model, an attention memory neural network (AMNN) has a variable sized encoding memory and supports semantic compositionality. AMNN captures sentence level semantics and reasons relation between the sentence pairs; then we use a Sparsemax layer over the output of the generated matching vectors (sentences) for classification. Our experiments on the Stanford Natural Language Inference (SNLI) Corpus show that our model outperforms the state of the art, achieving an accuracy of 87.4% on the test data.
Application of statistical process control in healthcare improvement: systematic review.

To systematically review the literature regarding how statistical process control--with control charts as a core tool--has been applied to healthcare quality improvement, and to examine the benefits, limitations, barriers and facilitating factors related to such application.



Original articles found in relevant databases, including Web of Science and Medline, covering the period 1966 to June 2004.



From 311 articles, 57 empirical studies, published between 1990 and 2004, met the inclusion criteria.



A standardised data abstraction form was used for extracting data relevant to the review questions, and the data were analysed thematically.



Statistical process control was applied in a wide range of settings and specialties, at diverse levels of organisation and directly by patients, using 97 different variables. The review revealed 12 categories of benefits, 6 categories of limitations, 10 categories of barriers, and 23 factors that facilitate its application and all are fully referenced in this report. Statistical process control helped different actors manage change and improve healthcare processes. It also enabled patients with, for example asthma or diabetes mellitus, to manage their own health, and thus has therapeutic qualities. Its power hinges on correct and smart application, which is not necessarily a trivial task. This review catalogs 11 approaches to such smart application, including risk adjustment and data stratification.



Statistical process control is a versatile tool which can help diverse stakeholders to manage change in healthcare and improve patients' health.
A Virtual Reality Dance Training System Using Motion Capture Technology

In this paper, a new dance training system based on the motion capture and virtual reality (VR) technologies is proposed. Our system is inspired by the traditional way to learn new movements-imitating the teacher's movements and listening to the teacher's feedback. A prototype of our proposed system is implemented, in which a student can imitate the motion demonstrated by a virtual teacher projected on the wall screen. Meanwhile, the student's motions will be captured and analyzed by the system based on which feedback is given back to them. The result of user studies showed that our system can successfully guide students to improve their skills. The subjects agreed that the system is interesting and can motivate them to learn.
Photon counting polarimetry measurement towards non-invasive biomedical glucose monitoring

Commercially available glucose measurement device for diabetes monitoring require extracting of blood and this means there will be a physical contact with human body. Demand on non-invasive measurement has invites research and development of new detection methods to measure blood glucose level. In this work, a very sensitive optical polarimetry measurement technique using ratio-metric photon counting detection has been introduced and tested for a range of known glucose concentrations that mimic the level of glucose in human blood. The setup utilizes 785nm diode laser that emits weak coherent optical signal onto glucose concentration samples in aqueous. The result shows a linear proportional of different glucose concentration and successfully detected 10260 mg/dl to 260 mg/dl glucose samples. This indicates a potential improvement method for non-invasive glucose measurement by a sensitive polarimetry based optical sensor in single photon level for biomedical applications.
DeepRain: ConvLSTM Network for Precipitation Prediction using Multichannel Radar Data

Accurate rainfall forecasting is critical because it has a great impact on people’s social and economic activities. Recent trends on various literatures shows that Deep Learning (Neural Network) is a promising methodology to tackle many challenging tasks. In this study, we introduce a brand-new data-driven precipitation prediction model called DeepRain. This model predicts the amount of rainfall from weather radar data, which is three-dimensional and four-channel data, using convolutional LSTM (ConvLSTM). ConvLSTM is a variant of LSTM (Long Short-Term Memory) containing a convolution operation inside the LSTM cell. For the experiment, we used radar reflectivity data for a twoyear period whose input is in a time series format in units of 6 min divided into 15 records. The output is the predicted rainfall information for the input data. Experimental results show that two-stacked ConvLSTM reduced RMSE by 23.0% compared to linear regression.
Phishing threat avoidance behaviour: An empirical investigation

Phishing is an online identity theft that aims to steal sensitive information such as username, password and online banking details from its victims. Phishing education needs to be considered as a means to combat this threat. This paper reports on a design and development of a mobile game prototype as an educational tool helping computer users to protect themselves against phishing attacks. The elements of a game design framework for avoiding phishing attacks were used to address the game design issues. Our mobile game design aimed to enhance the users' avoidance behaviour through motivation to protect themselves against phishing threats. A think-aloud study was conducted, along with a preand post-test, to assess the game design framework though the developed mobile game prototype. The study results showed a significant improvement of participants' phishing avoidance behaviour in their post-test assessment. Furthermore, the study findings suggest that participants' threat perception, safeguard effectiveness, self-efficacy, perceived severity and perceived susceptibility elements positively impact threat avoidance behaviour, whereas safeguard cost had a negative impact on it.
Survey of Stochastic Computing

Stochastic computing (SC) was proposed in the 1960s as a low-cost alternative to conventional binary computing. It is unique in that it represents and processes information in the form of digitized probabilities. SC employs very low-complexity arithmetic units which was a primary design concern in the past. Despite this advantage and also its inherent error tolerance, SC was seen as impractical because of very long computation times and relatively low accuracy. However, current technology trends tend to increase uncertainty in circuit behavior and imply a need to better understand, and perhaps exploit, probability in computation. This article surveys SC from a modern perspective where the small size, error resilience, and probabilistic features of SC may compete successfully with conventional methodologies in certain applications. First, we survey the literature and review the key concepts of stochastic number representation and circuit structure. We then describe the design of SC-based circuits and evaluate their advantages and disadvantages. Finally, we give examples of the potential applications of SC and discuss some practical problems that are yet to be solved.
Constrained Multi-Task Learning for Automated Essay Scoring

Supervised machine learning models for automated essay scoring (AES) usually require substantial task-specific training data in order to make accurate predictions for a particular writing task. This limitation hinders their utility, and consequently their deployment in real-world settings. In this paper, we overcome this shortcoming using a constrained multi-task pairwisepreference learning approach that enables the data from multiple tasks to be combined effectively. Furthermore, contrary to some recent research, we show that high performance AES systems can be built with little or no task-specific training data. We perform a detailed study of our approach on a publicly available dataset in scenarios where we have varying amounts of task-specific training data and in scenarios where the number of tasks increases.
Mono-vision corner SLAM for indoor navigation

We present a real-time monocular vision based range measurement method for Simultaneous Localization and Mapping (SLAM) for an Autonomous Micro Aerial Vehicle (MAV) with significantly constrained payload. Our navigation strategy assumes a GPS denied manmade environment, whose indoor architecture is represented via corner based feature points obtained through a monocular camera. We experiment on a case study mission of vision based path-finding through a conventional maze of corridors in a large building.