Reviewerly/exHarmony huggingface dataset:
Image quality assessment for intelligent emergency application based on deep neural network

Video surveillance is widely applied in modern intelligent systems, such as access control, pedestrian re-identification. However, with the rapid development of urbanization, urban traffic situation becomes more and more complex. It is a big challenge for video surveillance to cope with such massive data. In addition, existing emergency systems are far from modern requirement. So in this paper, we propose an image quality based framework to improve the performance of video surveillance and design a new urban intelligent emergency system. Specifically, we first analyze emergency evacuation of social group security incident for data acquisition including surveillance video and labels. Then, incorporating image quality assessment and convolution neural network, the dataset can be classified into several parts, and each part demonstrates a particular situation. Afterward, we introduce entropy theory to study the application of urban intelligence emergency. The results show that the research method proposed in this paper effectively obtains the evacuation parameters of evacuation personnel in the evacuation of sudden social group events, and improves the timeliness of information transmission in the evacuation process. The results show that the research method of this paper significantly improves the pertinence, effectiveness and perfection of the emergency plan in the application of urban emergency system.
Deep Adversarial Learning for

Adversarial learning is a game-theoretic learning paradigm, which has achieved huge successes in the field of Computer Vision recently. Adversarial learning is also a general framework that enables a variety of learning models, including the popular Generative Adversarial Networks (GANs). Due to the discrete nature of language, designing adversarial learning models is still challenging for NLP problems. In this tutorial, we provide a gentle introduction to the foundation of deep adversarial learning, as well as some practical problem formulations and solutions in NLP. We describe recent advances in deep adversarial learning for NLP, with a special focus on generation, adversarial examples & rules, and dialogue. We provide an overview of the research area, categorize different types of adversarial learning models, and discuss pros and cons, aiming at providing some practical perspectives on the future of adversarial learning for solving real-world NLP problems.
Model-Predictive Policy Learning with Uncertainty Regularization for Driving in Dense Traffic

Learning a policy using only observational data is challenging because the distribution of states it induces at execution time may differ from the distribution observed during training. We propose to train a policy by unrolling a learned model of the environment dynamics over multiple time steps while explicitly penalizing two costs: the original cost the policy seeks to optimize, and an uncertainty cost which represents its divergence from the states it is trained on. We measure this second cost by using the uncertainty of the dynamics model about its own predictions, using recent ideas from uncertainty estimation for deep networks. We evaluate our approach using a large-scale observational dataset of driving behavior recorded from traffic cameras, and show that we are able to learn effective driving policies from purely observational data, with no environment interaction.
Joint Workshop on Bibliometric-enhanced Information Retrieval and Natural Language Processing for Digital Libraries (BIRNDL 2019)

The deluge of scholarly publication poses a challenge for scholars find relevant research and policy makers to seek in-depth information and understand research impact. Information retrieval (IR), natural language processing (NLP) and bibliometrics could enhance scholarly search, retrieval and user experience, but their use in digital libraries is not widespread. To address this gap, we propose the 4th Joint Workshop on BIRNDL and the 5th CL-SciSumm Shared Task. We seek to foster collaboration among researchers in NLP, IR and Digital Libraries (DL), and to stimulate the development of new methods in NLP, IR, recommendation systems and scientometrics toward improved scholarly document understanding, analysis, and retrieval at scale.
Data Science and Development Team Remote Communication: the use of the Machine Learning Canvas

In this article we present the use of the Machine Learning Canvas as a tool to help communication and project planning between a team of data scientists working in Brazil and a development team distributed in the US. The product being developed is an appliance to be deployed on telecommunication networks and has anomaly detection capabilities.
Universal phase reconstruction approach of self-calibrating phase-shifting interferometry

Self-calibrating phase-shifting interferometry (PSI) reconstructs the phase map from three-frame or more phase-shifting interferograms without the need for knowing accurate phase steps. The existing phase reconstruction methods for self-calibrating PSI still have many constraints in terms of the required number of interferograms, special usage preconditions, etc. In this Letter, a universal, accurate, and efficient phase reconstruction method for self-calibrating PSI is proposed. In this approach, we search the solution space of phase shifts to obtain the modulation amplitude of interferograms with the minimum coefficient of variation (CV). Then the phase is reconstructed through the searched phase shifts. Numerical and experimental studies demonstrate that this method can realize highly accurate phase reconstruction consistently, compared to the currently popular methods in their own usable ranges. We anticipate that this Letter may provide a universal and powerful solution for the phase reconstruction of self-calibrating PSI.
Learning from weakly dependent data under Dobrushin's condition

Statistical learning theory has largely focused on learning and generalization given independent and identically distributed (i.i.d.) samples. Motivated by applications involving time-series data, there has been a growing literature on learning and generalization in settings where data is sampled from an ergodic process. This work has also developed complexity measures, which appropriately extend the notion of Rademacher complexity to bound the generalization error and learning rates of hypothesis classes in this setting. Rather than time-series data, our work is motivated by settings where data is sampled on a network or a spatial domain, and thus do not fit well within the framework of prior work. We provide learning and generalization bounds for data that are complexly dependent, yet their distribution satisfies the standard Dobrushin's condition. Indeed, we show that the standard complexity measures of Gaussian and Rademacher complexities and VC dimension are sufficient measures of complexity for the purposes of bounding the generalization error and learning rates of hypothesis classes in our setting. Moreover, our generalization bounds only degrade by constant factors compared to their i.i.d. analogs, and our learnability bounds degrade by log factors in the size of the training set.
The impact of cryptocurrencies market development on banks’ deposits variability in the GCC region

Purpose With the continuing development of the financial technology revolution, a better understanding of bank deposits variability has become necessary for bank management and policymakers, especially central banks. This is because the novel innovations of cryptocurrencies operate beyond the realm of the banking system, which may impact the performance of banks and their deposits variability. This study aims to investigate the long- and short-run effects of cryptocurrencies’ market capitalization development on the banks’ deposit variability in the Gulf Cooperation Council (GCC) region. Design/methodology/approach In this study, the Johansen–Juselius (1990) cointegration test with vector error correction model was applied to examine the long-run relationships, while the Engle and Granger (1987) and the Granger (1969) causality tests were used to detect causal relationships in the short term. Findings The findings of Johansen–Juselius cointegration test indicate that the banks’ deposits variability in all six states of the Gulf region share negative long-run equilibrium association with the development of global cryptocurrencies market capitalization, but with different statistically significant levels. For the short-run analysis, the study found that the development of cryptocurrencies market capitalization has significant unidirectional causal effects on bank deposits variabilities in only four states, namely, UAE, Qatar, Kuwait and Bahrain. The findings of the study therefore suggest that to eradicate the effects of cryptocurrencies industry and its threats to the banking industry, banks in GCC region are encouraged to either consider cryptocurrencies as an alternative investment asset for their portfolio investment diversification strategies or adopt the blockchain technology in their operation system to facilitate their customers with low transaction cost, high level of security and ease of use and real-time settlement. Research limitations/implications The empirical findings of the study will provide valuable input for policymakers, especially central banks and bank managements, to evaluate the current situation and the threats of the cryptocurrencies market growth and its effect on the banking industry’s performance, future survival and their deposits variability for better regulation and policy planning and investment strategies. Originality/value This is a pioneering study that empirically explores the phenomenon of bank deposits variability as a consequence of expansion in cryptocurrencies market capitalization, where the findings proved evidence of a drastic decline in banks’ deposits size due to the substantial growth in cryptocurrencies market capitalization.
Eyes Localization Algorithm Based on Prior MTCNN Face Detection

Aiming at the inaccuracy of the regression position of MTCNN human eyes when the image resolution is low, a method for eyes localization algorithm which is based on prior MTCNN face detection is proposed in this paper. Firstly, the eyebrow area is segmented according to the face area and pupil position obtained by MTCNN network. Then, the gray gradient integral projection in horizontal direction and the gray integral projection in vertical direction of eyebrow area are calculated. Finally, The MTCNN key points positioning results and integral projection results are combined to position the human pupil position accurately. The experimental results show that the proposed algorithm has a detection accuracy of 95.02%, and has good robustness for eye image detection with different quality.
A role for dependent types in Haskell

Modern Haskell supports zero-cost coercions, a mechanism where types that share the same run-time representation may be freely converted between. To make sure such conversions are safe and desirable, this feature relies on a mechanism of roles to prohibit invalid coercions. In this work, we show how to incorporate roles into dependent types systems and prove, using the Coq proof assistant, that the resulting system is sound. We have designed this work as a foundation for the addition of dependent types to the Glasgow Haskell Compiler, but we also expect that it will be of use to designers of other dependently-typed languages who might want to adopt Haskell’s safe coercions feature.
Digital orthodoxy of human data collection

The notion that more data are more informative has helped lay the foundation for a new era in medical diagnostics. But this “kitchen sink” approach can be misguided. A classic example is a middle-aged executive without any symptoms who undergoes a yearly health check that sets off a cascade of additional testing, sometimes invasive and not without risk but often without any benefit except for the creation of revenue. That example precedes today's data collection technologies of whole genome sequencing (WGS), wearable biosensors, high-resolution imaging, gut microbiome metagenomics, and more. We have already learned a great deal from this deep phenotyping in the research setting, but there is a big gap from introducing this to the clinic under the rubric of wellness and disease prevention. Digital medicine: empowering both patients and cliniciansWhen physicians and health-care professionals think of the term digital medicine a first reaction might be that this represents an oxymoron. Medicine involves human touch and anything digital has traditionally been conceived as its antithesis. This sentiment is unsurprising given reactions to the big foray of computers in medicine—electronic medical records—considered by some to have diminished the relationship between doctors and their patients. Full-Text PDF The rise of the virtualistFrom time immemorial, an invariable feature of doctor–patient interaction has been that it takes place in person. But the status quo is changing. A large portion of patient care might eventually be delivered via telemedicine by virtualists, physicians who treat patients they may never meet. Full-Text PDF
Interaction Techniques for Cinematic Virtual Reality

For watching omnidirectional movies via HMD, turning the head is the most relevant and natural input technique to choose the visible part of the movie. However, there is a need for additional interactivity in cinematic virtual reality (CVR), e.g. for navigating the movie, for nonlinear story lines or for communication with other viewers watching a movie together. The input device should not disturb the viewing experience and the viewer should not be primarily aware of it. We present a design space based on numerous methods in literature and our own experiences. As a result of the design space we describe interaction techniques which meet the challenges of cinematic virtual reality. For doing this, various dimensions of the design space will be combined. The most promising method, eye-based head gestures, is described in more detail and was implemented for CVR. The conducted user study will be analyzed in future work.
Towards Real-Time Image Enhancement GANs

Video stream compression, using lossy algorithms, is performed to reduce the bandwidth required for transmission. To improve the video quality, either for human view or for automatic video analysis, videos are post-processed to eliminate the introduced compression artifacts. Generative Adversarial Network have been shown to obtain extremely high quality results in image enhancement tasks; however, to obtain top quality results high capacity large generators are usually employed, resulting in high computational costs and processing time. In this paper we present an architecture that can be used to reduce the cost of generators, paving a way towards real-time frame enhancement with GANs. With the proposed approach, enhanced images appear natural and pleasant to the eye. Locally high frequency patterns often differ from the raw uncompressed images. A possible application is to improve video conferencing, or live streaming. In these cases there is no original uncompressed video stream available. Therefore, we report results using popular no-reference metrics showing high naturalness and quality even for efficient networks.
Intelligent Learning Environments: Design, Usage and Analytics for Future Schools

This chapter reviews the field broadly referred to as Intelligent Learning Environments (ILE), capturing the state-of-the-art in both Intelligent Tutoring Systems (ITS) and Artificial Intelligence in Education (AIED). After a brief historical account, we report design architectures and implementation approaches exemplified by a recent example. We then shift our attention to classroom implementation and blended learning strategies that take into account the challenges of using ILE in the classroom. We present Learning Analytics tools as a way to support teachers addressing these challenges, to increase their awareness and ultimately to support students directly. We conclude with a summary of efficacy studies and open issues while advocating that these systems should not be seen as displacing teachers but augmenting the human aspects of teaching.
Sentiment Analysis of Weibo Comments Based on Deep Neural Network

With the rapid development of network information technology, social media has become a new information dissemination platform, and has the characteristics of rapid dissemination and wide audience. This paper proposes a micro-blog commentary sentiment analysis method based on deep neural network. By constructing multi-level pooling layer and convolution layer, the network complexity is increased, and then the data features can be effectively extracted, the sentiment analysis accuracy rate can be improved, and the network can be avoided. Locally optimal. Firstly, the microblog commentary is preprocessed, then the word2vec algorithm is used for text representation, and the feature matrix is input into the convolutional neural network to extract the key features in the text. Finally, the softmax logistic regression network is used to classify the features and obtain the sentiment analysis results. Through experimental verification, the accuracy of this method is significantly improved compared with the traditional machine learning method.
Turkish Sign Language Recognition Using Kinect Skeleton and Convolutional Neural Network

In this paper, a system that converts Turkish signs to words using convolutional neural networks is presented. Skeleton data obtained by Microsoft's Kinect device is used in the proposed system. User located in front of Kinect sensor makes the signs of corresponding word for a limited time. Afterwards, skeleton joints are extracted. Finally, skeleton points on consecutive frames are merged, and Word classification is performed by convulational neural network. In experimental studies, evaluations are carried out with our own dataset and performance of the proposed method is compared with various classification methods. Moreover, the effect of being closer or further to the camera and movements in different speeds are also investigated. In the lights of the experimental results, it is seen that proposed convolutional neural network gives better performances than other classifiers such as Gaussian SVM, Linear SVM, weighted KNN and decision trees.
Grid Saliency for Context Explanations of Semantic Segmentation

Recently, there has been a growing interest in developing saliency methods that provide visual explanations of network predictions. Still, the usability of existing methods is limited to image classification models. To overcome this limitation, we extend the existing approaches to generate grid saliencies, which provide spatially coherent visual explanations for (pixel-level) dense prediction networks. As the proposed grid saliency allows to spatially disentangle the object and its context, we specifically explore its potential to produce context explanations for semantic segmentation networks, discovering which context most influences the class predictions inside a target object area. We investigate the effectiveness of grid saliency on a synthetic dataset with an artificially induced bias between objects and their context as well as on the real-world Cityscapes dataset using state-of-the-art segmentation networks. Our results show that grid saliency can be successfully used to provide easily interpretable context explanations and, moreover, can be employed for detecting and localizing contextual biases present in the data.
Diversified Quality Centric Service Recommendation

In the past decade, a large number and variety of services have been published on the Internet, e.g., public services, web services, cloud services, etc., offering customers a wide range of choices for fulfilling their demands. This has made it a challenge for customers to select from many mutually-substitutable competing services, especially when they are offered with differentiated quality. Thus, service recommendation that helps customers find the right services has become of paramount research and practical importance. The correlations in customers' preferences for different quality dimensions make service recommendation an even more complicated problem. It has not been properly addressed by existing approaches. This paper proposes DQCSR (diversified quality centric service recommendation), an approach that finds diversified services that are representative in different quality dimensions with respect to customer's quality preferences. The results of experiments conducted on a real-world dataset demonstrate the representativeness and diversity in the recommendation results produced by DQCSR.
Poisson-Randomized Gamma Dynamical Systems

This paper presents the Poisson-randomized gamma dynamical system (PRGDS), a model for sequentially observed count tensors that encodes a strong inductive bias toward sparsity and burstiness. The PRGDS is based on a new motif in Bayesian latent variable modeling, an alternating chain of discrete Poisson and continuous gamma latent states that is analytically convenient and computationally tractable. This motif yields closed-form complete conditionals for all variables by way of the Bessel distribution and a novel discrete distribution that we call the shifted confluent hypergeometric distribution. We draw connections to closely related models and compare the PRGDS to these models in studies of real-world count data sets of text, international events, and neural spike trains. We find that a sparse variant of the PRGDS, which allows the continuous gamma latent states to take values of exactly zero, often obtains better predictive performance than other models and is uniquely capable of inferring latent structures that are highly localized in time.
GIFT: Learning Transformation-Invariant Dense Visual Descriptors via Group CNNs

Finding local correspondences between images with different viewpoints requires local descriptors that are robust against geometric transformations. An approach for transformation invariance is to integrate out the transformations by pooling the features extracted from transformed versions of an image. However, the feature pooling may sacrifice the distinctiveness of the resulting descriptors. In this paper, we introduce a novel visual descriptor named Group Invariant Feature Transform (GIFT), which is both discriminative and robust to geometric transformations. The key idea is that the features extracted from the transformed versions of an image can be viewed as a function defined on the group of the transformations. Instead of feature pooling, we use group convolutions to exploit underlying structures of the extracted features on the group, resulting in descriptors that are both discriminative and provably invariant to the group of transformations. Extensive experiments show that GIFT outperforms state-of-the-art methods on several benchmark datasets and practically improves the performance of relative pose estimation.