nils-herrmann/clean_scirepeval huggingface dataset:
Deep Visual Attention Prediction

In this paper, we aim to predict human eye fixation with view-free scenes based on an end-to-end deep learning architecture. Although convolutional neural networks (CNNs) have made substantial improvement on human attention prediction, it is still needed to improve the CNN-based attention models by efficiently leveraging multi-scale features. Our visual attention network is proposed to capture hierarchical saliency information from deep, coarse layers with global saliency information to shallow, fine layers with local saliency response. Our model is based on a skip-layer network structure, which predicts human attention from multiple convolutional layers with various reception fields. Final saliency prediction is achieved via the cooperation of those global and local predictions. Our model is learned in a deep supervision manner, where supervision is directly fed into multi-level layers, instead of previous approaches of providing supervision only at the output layer and propagating this supervision back to earlier layers. Our model thus incorporates multi-level saliency predictions within a single network, which significantly decreases the redundancy of previous approaches of learning multiple network streams with different input scales. Extensive experimental analysis on various challenging benchmark data sets demonstrate our method yields the state-of-the-art performance with competitive inference time.11Our source code is available at https://github.com/wenguanwang/deepattention.
Gaussian LDA for Topic Models with Word Embeddings

Continuous space word embeddings learned from large, unstructured corpora have been shown to be effective at capturing semantic regularities in language. In this paper we replace LDA’s parameterization of “topics” as categorical distributions over opaque word types with multivariate Gaussian distributions on the embedding space. This encourages the model to group words that are a priori known to be semantically related into topics. To perform inference, we introduce a fast collapsed Gibbs sampling algorithm based on Cholesky decompositions of covariance matrices of the posterior predictive distributions. We further derive a scalable algorithm that draws samples from stale posterior predictive distributions and corrects them with a Metropolis–Hastings step. Using vectors learned from a domain-general corpus (English Wikipedia), we report results on two document collections (20-newsgroups and NIPS). Qualitatively, Gaussian LDA infers different (but still very sensible) topics relative to standard LDA. Quantitatively, our technique outperforms existing models at dealing with OOV words in held-out documents.
Information-Flow and Data-Flow Analysis of while-Programs

Until recently, information-flow analysis has been used primarily to verify that information transmission between program variables cannot violate security requirements. Here, the notion of information flow is explored as an aid to program development and validation.
Information-flow relations are presented for while-programs, which identify those program statements whose execution may cause information to be transmitted from or to particular input, internal, or output values. It is shown with examples how these flow relations can be helpful in writing, testing, and updating programs; they also usefully extend the class of errors which can be detected automatically in the “static analysis” of a program.
Image feature extraction of crop disease

This article described the feature extraction methods of crop disease based on computer image processing technology in detail. Based on color, texture and shape feature extraction method in three aspects features and their respective problems were introduced start from the perspective of lesion leaves. Application research of image feature extraction in the filed of crop disease was reviewed in recent years. The results were analyzed that about feature extraction methods, and then the application of image feature extraction techniques in the future detection of crop diseases in the field of intelligent was prospected.
A Study of Pilot-Aided Channel Estimation in MIMO-GFDM Systems

Generalized frequency division multiplexing (GFDM) is a promising candidate waveform for next generation wireless communications systems. Unlike conventional orthogonal frequency division multiplexing (OFDM) based systems, it is a non-orthogonal waveform subject to inter-carrier and intersymbol interference. In multiple-input multiple-output (MIMO) systems, the additional inter-antenna interference also takes place. The presence of such three-dimensional interference challenges the receiver design. This paper addresses the MIMOGFDM channel estimation problem with the aid of known reference signals also referred as pilots. Specifically, the received signal is expressed as the joint effect of the pilot part, unknown data part and noise part. On top of this formulation, least squares (LS) and linear minimum mean square error (LMMSE) estimators are presented, while their performance is evaluated for various pilot arrangements.
Stochastic Resource Provisioning for Containerized Multi-Tier Web Services in Clouds

Under today's bursty web traffic, the fine-grained per-container control promises more efficient resource provisioning for web services and better resource utilization in cloud datacenters. In this paper, we present  Two-stage Stochastic Programming Resource A llocator (2SPRA). It optimizes resource provisioning for containerized n-tier web services in accordance with fluctuations of incoming workload to accommodate predefined SLOs on response latency. In particular, 2SPRA is capable of minimizing resource over-provisioning by addressing dynamics of web traffic as workload uncertainty in a native stochastic optimization model. Using special-purpose OpenOpt optimization framework, we fully implement 2SPRA in Python and evaluate it against three other existing allocation schemes, in a Docker-based CoreOS Linux VMs on Amazon EC2. We generate workloads based on four real-world web traces of various traffic variations: AOL, WorldCup98, ClarkNet, and NASA. Our experimental results demonstrate that 2SPRA achieves the minimum resource over-provisioning outperforming other schemes. In particular, 2SPRA allocates only 6.16 percent more than application's actual demand on average and at most 7.75 percent in the worst case. It achieves 3x further reduction in total resources provisioned compared to other schemes delivering overall cost-savings of 53.6 percent on average and up to 66.8 percent. Furthermore, 2SPRA demonstrates consistency in its provisioning decisions and robust responsiveness against workload fluctuations.
Self-Evaluation of Decision-Making: A General Bayesian Framework for Metacognitive Computation

People are often aware of their mistakes, and report levels of confidence in their choices that correlate with objective performance. These metacognitive assessments of decision quality are important for the guidance of behavior, particularly when external feedback is absent or sporadic. However, a computational framework that accounts for both confidence and error detection is lacking. In addition, accounts of dissociations between performance and metacognition have often relied on ad hoc assumptions, precluding a unified account of intact and impaired self-evaluation. Here we present a general Bayesian framework in which self-evaluation is cast as a "second-order" inference on a coupled but distinct decision system, computationally equivalent to inferring the performance of another actor. Second-order computation may ensue whenever there is a separation between internal states supporting decisions and confidence estimates over space and/or time. We contrast second-order computation against simpler first-order models in which the same internal state supports both decisions and confidence estimates. Through simulations we show that second-order computation provides a unified account of different types of self-evaluation often considered in separate literatures, such as confidence and error detection, and generates novel predictions about the contribution of one's own actions to metacognitive judgments. In addition, the model provides insight into why subjects' metacognition may sometimes be better or worse than task performance. We suggest that second-order computation may underpin self-evaluative judgments across a range of domains.
Lost in knowledge translation: time for a map?

There is confusion and misunderstanding about the concepts of knowledge translation, knowledge transfer, knowledge exchange, research utilization, implementation, diffusion, and dissemination. We review the terms and definitions used to describe the concept of moving knowledge into action. We also offer a conceptual framework for thinking about the process and integrate the roles of knowledge creation and knowledge application. The implications of knowledge translation for continuing education in the health professions include the need to base continuing education on the best available knowledge, the use of educational and other transfer strategies that are known to be effective, and the value of learning about planned-action theories to be better able to understand and influence change in practice settings.
An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling

For most deep learning practitioners, sequence modeling is synonymous with recurrent networks. Yet recent results indicate that convolutional architectures can outperform recurrent networks on tasks such as audio synthesis and machine translation. Given a new sequence modeling task or dataset, which architecture should one use? We conduct a systematic evaluation of generic convolutional and recurrent architectures for sequence modeling. The models are evaluated across a broad range of standard tasks that are commonly used to benchmark recurrent networks. Our results indicate that a simple convolutional architecture outperforms canonical recurrent networks such as LSTMs across a diverse range of tasks and datasets, while demonstrating longer effective memory. We conclude that the common association between sequence modeling and recurrent networks should be reconsidered, and convolutional networks should be regarded as a natural starting point for sequence modeling tasks. To assist related work, we have made code available at http://github.com/locuslab/TCN.
Active Long Term Memory Networks

Continual Learning in artificial neural networks suffers from interference and forgetting when different tasks are learned sequentially. This paper introduces the Active Long Term Memory Networks (A-LTM), a model of sequential multitask deep learning that is able to maintain previously learned association between sensory input and behavioral output while acquiring knew knowledge. A-LTM exploits the non-convex nature of deep neural networks and actively maintains knowledge of previously learned, inactive tasks using a distillation loss . Distortions of the learned input-output map are penalized but hidden layers are free to transverse towards new local optima that are more favorable for the multi-task objective. We re-frame the McClelland’s seminal Hippocampal theory [2] with respect to Catastrophic Inference (CI) behavior exhibited by modern deep architectures trained with back-propagation and inhomogeneous sampling of latent factors across epochs. We present empirical results of non-trivial CI during continual learning in Deep Linear Networks trained on the same task, in Convolutional Neural Networks when the task shifts from predicting semantic to graphical factors and during domain adaptation from simple to complex environments. We present results of the A-LTM model’s ability to maintain viewpoint recognition learned in the highly controlled iLab-20M  dataset with 10 object categories and 88 camera viewpoints, while adapting to the unstructured domain of Imagenet  with 1,000 object categories.
BorderSense: Border patrol through advanced wireless sensor networks

The conventional border patrol systems suffer from intensive human involvement. Recently, unmanned border patrol systems employ high-tech devices, such as unmanned aerial vehicles, unattended ground sensors, and surveillance towers equipped with camera sensors. However, any single technique encounters inextricable problems, such as high false alarm rate and line-of-sight-constraints. There lacks a coherent system that coordinates various technologies to improve the system accuracy. In this paper, the concept of BorderSense, a hybrid wireless sensor network architecture for border patrol systems, is introduced. BorderSense utilizes the most advanced sensor network technologies, including the wireless multimedia sensor networks and the wireless underground sensor networks. The framework to deploy and operate BorderSense is developed. Based on the framework, research challenges and open research issues are discussed.
Review of Unmanned Aircraft System ( UAS )

Unmanned Aircraft Systems (UAS) is an emerging technology with a tremendous potential to revolutionize warfare and to enable new civilian applications. It is integral part of future urban civil and military applications. It technologically matures enough to be integrated into civil society. The importance of UAS in scientific applications has been thoroughly demonstrated in recent years (DoD, 2010). Whatever missions are chosen for the UAS, their number and use will significantly increase in the future. UAS today play an increasing role in many public missions such as border surveillance, wildlife surveys, military training, weather monitoring, and local law enforcement. Challenges such as the lack of an on-board pilot to see and avoid other aircraft and the wide variation in unmanned aircraft missions and capabilities must be addressed in order to fully integrate UAS operations in the NAS in the Next Gen time frame. UAVs are better suited for dull, dirty, or dangerous missions than manned aircraft. UAS are mainly used for intelligence, surveillance and reconnaissance (ISR), border security, counter insurgency, attack and strike, target identification and designation, communications relay, electronic attack, law enforcement and security applications, environmental monitoring and agriculture, remote sensing, aerial mapping and meteorology. Although armed forces around the world continue to strongly invest in researching and developing technologies with the potential to advance the capabilities of UAS.
A new modeling methodology for continuous cuffless blood pressure measurement

Continuous blood pressure (BP) monitoring in a noninvasive and unobtrusive way can significantly improve the awareness, control and treatment rate of prevalent hypertension. Pulse transit time (PTT) has become increasingly popular in recent years for continuous BP measurement without a cuff. However, the accuracy issue of PTT-based method remains to be solved for clinical application. Some previous studies have attempted to estimate BP with only PTT by using linear regression, which is susceptible to arterial regulation and may not reflect the actual relationship between PTT and BP. Furthermore, PTT does not contain all the information of BP variation, thereby resulting in unsatisfactory accuracy. In this paper we establish a cuffless BP estimation model from a physiological perspective by utilizing PTT and photoplethysmogram (PPG) intensity ratio (PIR), an indicator we have recently proposed for evaluation of the change in arterial diameter and the low frequency variation of BP, with the consideration that PIR can track changes in mean BP (MBP) and arterial diameter change. The performance of the proposed BP model was evaluated by comparing the estimated BP with Finapres BP as reference on 10 healthy subjects. The results showed that the mean ± standard deviation (SD) of the estimation error for systolic and diastolic BP were -0.41 ± 5.15 and -0.84 ± 4.05 mmHg, and mean absolute difference (MAD) were 4.18 and 3.43 mmHg, respectively. Furthermore, the proposed modeling method was superior to one contrast PTT-based method, demonstrating the proposed model would be promising for reliable continuous cuffless BP measurement.
Urban Emotions - Geo-Semantic Emotion Extraction from Technical Sensors, Human Sensors and Crowdsourced Data

How people in the city perceive their surroundings depends on a variety of dynamic and static context factors such as road traffic, the feeling of safety, urban architecture, etc. Such subjective and context-dependent perceptions can trigger different emotions, which enable additional insights into the spatial and temporal configuration of urban structures. This paper presents the Urban Emotions concept that proposes a human-centred approach for extracting contextual emotional information from human and technical sensors. The methodology proposed in this paper consists of four steps: 1) detecting emotions using wristband sensors, 2) “ground-truthing” these measurements using a People as Sensors location-based service, 3) extracting emotion information from crowdsourced data like Twitter, and 4) correlating the measured and extracted emotions. Finally, the emotion information is mapped and fed back into urban planning for decision support and for evaluating ongoing planning processes.
Adversarial Generation of Training Examples for Vehicle License Plate Recognition

Generative Adversarial Networks (GAN) have attracted much research attention recently, leading to impressive results for natural image generation. However, to date little success was observed in using GAN generated images for improving classification tasks. Here we attempt to explore, in the context of car license plate recognition, whether it is possible to generate synthetic training data using GAN to improve recognition accuracy. With a carefully-designed pipeline, we show that the answer is affirmative. First, a large-scale image set is generated using the generator of GAN, without manual annotation. Then, these images are fed to a deep convolutional neural network (DCNN) followed by a bidirectional recurrent neural network (BRNN) with long short-term memory (LSTM), which performs the feature learning and sequence labelling. Finally, the pre-trained model is fine-tuned on real images. Our experimental results on a few data sets demonstrate the effectiveness of using GAN images: an improvement of 7.5% over a strong baseline with moderate-sized real data being available. We show that the proposed framework achieves competitive recognition accuracy on challenging test datasets. We also leverage the depthwise separate convolution to construct a lightweight convolutional RNN, which is about half size and 2× faster on CPU. Combining this framework and the proposed pipeline, we make progress in performing accurate recognition on mobile and embedded devices.
Proboscis lateralis with ipsilateral sinonasal and olfactory pathway aplasia.

Proboscis lateralis is a rare craniofacial malformation characterized by absence of nasal cavity on one side with a trunk-like nasal appendage protruding from superomedial portion of the ipsilateral orbit. High-resolution computed tomography and magnetic resonance imaging are extremely useful in evaluating this congenital condition and the wide spectrum of associated anomalies occurring in the surrounding anatomical regions and brain. We present a case of proboscis lateralis in a 2-year-old girl with associated ipsilateral sinonasal aplasia, orbital cyst, absent olfactory bulb and olfactory tract. Absence of ipsilateral olfactory pathway in this rare disorder has been documented on high-resolution computed tomography and magnetic resonance imaging by us for the first time in English medical literature.
Parallelizing MCMC via Weierstrass Sampler

With the rapidly growing scales of statistical problems, subset based communicationfree parallel MCMC methods are a promising future for large scale Bayesian analysis. In this article, we propose a new Weierstrass sampler for parallel MCMC based on independent subsets. The new sampler approximates the full data posterior samples via combining the posterior draws from independent subset MCMC chains, and thus enjoys a higher computational efficiency. We show that the approximation error for the Weierstrass sampler is bounded by some tuning parameters and provide suggestions for choice of the values. Simulation study shows the Weierstrass sampler is very competitive compared to other methods for combining MCMC chains generated for subsets, including averaging and kernel smoothing.
Deep CNN Ensemble with Data Augmentation for Object Detection

We report on the methods used in our recent DeepEnsembleCoco submission to the PASCAL VOC 2012 challenge, which achieves state-of-theart performance on the object detection task. Our method is a variant of the R-CNN model proposed by Girshick et al.  with two key improvements to training and evaluation. First, our method constructs an ensemble of deep CNN models with different architectures that are complementary to each other. Second, we augment the PASCAL VOC training set with images from the Microsoft COCO dataset to significantly enlarge the amount training data. Importantly, we select a subset of the Microsoft COCO images to be consistent with the PASCAL VOC task. Results on the PASCAL VOC evaluation server show that our proposed method outperform all previous methods on the PASCAL VOC 2012 detection task at time of submission.
Noninvasive Cuffless Estimation of Blood Pressure from Pulse Arrival Time and Heart Rate with Adaptive Calibration

We study the problem of noninvasively estimating Blood Pressure (BP) without using a cuff, which is attractive for continuous monitoring of BP over Body Area Networks. It has been shown that the Pulse Arrival Time (PAT) measured as the delay between the ECG peak and a point in the finger PPG waveform can be used to estimate systolic and diastolic BP. Our aim is to evaluate the performance of such a method using the available MIMIC database, while at the same time improve the performance of existing techniques. We propose an algorithm to estimate BP from a combination of PAT and heart rate, showing improvement over PAT alone. We also show how the method achieves recalibration using an RLS adaptive algorithm. Finally, we address the use case of ECG and PPG sensors wirelessly communicating to an aggregator and study the effect of skew and jitter on BP estimation.
A renewed approach to serious games for cyber security

We are living in a world which is continually evolving and where modern conflicts have moved to the cyber domain. In its 2010 Strategic Concept, NATO affirmed its engagement to reinforce the defence and deterrence of its state members. In this light, it has been suggested that the gamification of training and education for cyber security will be beneficial. Although serious games have demonstrated pedagogic effectiveness in this field, they have only been used in a limited number of contexts, revealing some limitations. Thus, it is argued that serious games could be used in informal contexts while achieving similar pedagogic results. It is also argued that the use of such a serious game could potentially reach a larger audience than existing serious games, while complying with national cyber strategies. To this end, a framework for designing serious games which are aimed at raising an awareness of cyber security to those with little or no knowledge of the subject is presented. The framework, based upon existing frameworks and methodologies, is also accompanied with a set of cyber security skills, itself based upon content extracted from government sponsored awareness campaigns, and a method of integrating these skills into the framework. Finally, future research will be conducted to refine the framework and to improve the set of cyber security related skills in order to suit a larger range of players. A proof of concept will also be designed in order to collect empirical data and to validate the effectiveness of the framework.