Reviewerly/exHarmony huggingface dataset:
Tunnel Pedestrian Detection Based on Super Resolution and Convolutional Neural Network

Tunnel pedestrian detection is of great significance to traffic safety. However, because the camera used to collect road images in the tunnel video surveillance system is often far away from the ground, the size of the pedestrian target is small. What’s more, in the special monitoring environment of the tunnel, the video image is blurred, the noise is too much, and the resolution is low, which makes the pedestrian target detection difficult. Aiming at the above problems, this paper proposes a new target detection network which cascades super-resolution and target detection networks. Firstly, for the problem that the convolutional neural network is difficult to extract features of low-resolution image, super-resolution is performed before the target detection of the image according to that the super-resolution network can increase the image resolution and enrich the image information. Then, according to the characteristics of the pedestrian target detection task and the relatively fixed size and aspect ratio of pedestrian target in the video image of tunnel, the original RPN network is improved, and a candidate box which is more suitable for the pedestrian target detection task is designed. The experimental results show that the proposed method achieves better detection results in the tunnel pedestrian target detection problem.
Improving Search Time Performance for Locating Out-of-View Objects in Augmented Reality

Locating virtual objects (e.g., holograms) in head-mounted Augmented Reality (AR) can be an exhausting and frustrating task. This is mostly due to the limited field of view of current AR devices, which amplify the problem of objects receding from view. In previous work, EyeSee360 was developed to address this problem by visualizing the locations of multiple out-of-view objects. However, on small field of view devices such as the Hololens, EyeSee360 adds a lot of visual clutter that may negatively affect user performance. In this work, we compare three variants of EyeSee360 with different levels of information (assistance) to evaluate in how far they add visual clutter and thereby negatively affect search time performance. Our results show that variants of EyeSee360 with less assistance result into faster search times.
Attention Model for Articulatory Features Detection

Articulatory distinctive features, as well as phonetic transcription, play important role in speech-related tasks: computer-assisted pronunciation training, text-to-speech conversion (TTS), studying speech production mechanisms, speech recognition for low-resourced languages. End-to-end approaches to speech-related tasks got a lot of traction in recent years. We apply Listen, Attend and Spell~(LAS)~\cite{Chan-LAS2016} architecture to phones recognition on a small small training set, like TIMIT~\cite{TIMIT-1992}. Also, we introduce a novel decoding technique that allows to train manners and places of articulation detectors end-to-end using attention models. We also explore joint phones recognition and articulatory features detection in multitask learning setting.
Intelligent Conversational Agents in Patient Self-Management: A Systematic Survey Using Multi Data Sources.

Intelligent conversation agents (ICAs) have been used for patient self-management and support in recent years. This study systematically reviewed ICA resarch and innovation in academic and industrial institutions using bibliometric and patent analysis. We reported the types of diseases and patients, and the ICAs delivery approaches for patient-self-management. We identified the gaps in the productivity and focused areas.
Content Based Video Retrieval by Using Distributed Real-Time System Based on Storm

Time processing is a challenging issue for content-based video retrieval systems, especially when the process of indexing, classifying and retrieving desired and relevant videos is from a huge database. A CBVR system called bounded coordinate of motion histogram (BCMH) has been implemented as a case study. The BCMH offline step requires a long time to complete the learning phase, and the online step falls short in addressing the real-time video processing. To overcome these drawbacks, this article presents a batch-oriented computing based on Apache Hadoop to improve the time processing for the offline step, and a real-time oriented computing based on Apache Storm topologies to achieve a real-time response for the online step. The proposed approach is tested on the HOLLYWOOD2 dataset and the obtained results demonstrate reliability and efficiency of the proposed method.
Hierarchical On-line Risk Assessment at National Level

We present a hierarchical approach to on-line risk assessment at national level taking into account cyber threats and vulnerabilities as identified at local level of essential services operators and digital services providers as well as any other institutions being important for national-level risk assessment.
Driver behavior recognition using recurrent neural network in multiple depth cameras environment

To improve the driving safety triggered by driver&#x2019;s behavior recognition in an in-car environment, we propose to use depth cameras mounted in a car to generate behavior models generated by a deep learning algorithm for a driver&#x2019;s behavior classification. The contribution of this paper is trifold: 1) The proposed multi-view driver behavior recognition system can handle the occlusion problem happened in one of the cameras; 2) Using the recurrent neural network can effectively recognize the continuous time behavior; 3) the average recognition accuracy of proposed systems can achieve 83% and 88%, respectively.
Prediction Model of Ecological Environment Carrying Capacity in the Waters of Hainan Island

Zhao, M. and Hao, S., 2019. Prediction model of ecological environment carrying capacity in the waters of Hainan Island. In: Guido-Aldana, P.A. and Mulahasan, S. (eds.), Advances in Water Resources and Exploration. Journal of Coastal Research, Special Issue No. 93, pp. 436–442. Coconut Creek (Florida), ISSN 0749-0208.In order to improve the optimal management and bearing control ability of Hainan boundary island waters ecological environment, it is necessary to optimize and predict the ecological environment carrying capacity of Hainan boundary island waters, and put forward the optimization prediction method of Hainan boundary island waters ecological environment carrying capacity based on association rule bearing control and fuzzy adaptive clustering. The statistical sequence distribution model of ecological environment carrying capacity of Hainan boundary island waters is constructed. Big data statistical information modeling of Hainan boundary island waters ecological environment carrying capacity is carried out by using big data mining method. The characteristics of association rules of Hainan boundary island waters ecological environment carrying capacity are extracted, and the automatic clustering treatment of Hainan boundary island waters ecological environment carrying capacity big data is carried out by using fuzzy clustering method. The optimal iterative model for predicting the ecological environment carrying capacity of Hainan boundary island waters is established, and the optimal prediction of ecological environment carrying capacity of Hainan boundary island waters is realized by combining the adaptive optimization algorithm. The simulation results show that the method has good adaptability and high prediction accuracy, and improves the adaptive bearing control and management ability of the ecological environment of Hainan boundary island waters.
True Random Number Generator (TRNG) Utilizing FM Radio Signals for Mobile and Embedded Devices in Multi-Access Edge Computing

As transmissions of data between mobile and embedded devices in multi-access edge computing (MEC) increase, data must be protected, ensuring confidentiality and integrity. These issues are usually solved with cryptographic algorithms systems, which utilize a random number generator to create seeds and keys randomly. Their role in cryptography is so important that they need to be generated securely. In this paper, a true random number generator (TRNG) utilizing FM radio signals as a source is proposed. The proposed method can generate random numbers with high entropy, increased by at least 118% and up to 431% compared to existing generators.
Blockchain or Distributed Ledger Technology What Is in It for the Healthcare Industry?

Distributed ledger technology has seen its debut into communities of practice in healthcare where the reliance on knowledge sharing between participants postulates the foundations of secure and distributed knowledge, especially in some sensitive context, such as patient information. This knowledge is essential for the practice of care from patient contact to research, pharmaceutical supply chain, medication adherence and management of the plethora of bedside data into a collection of knowledge about the patient, essential to quality care. We introduce different schools of thought and implementation contexts of the distributed ledger technology or Blockchain. We provide an overview of Blockchain and Distributed Ledger Technology, focused on the Healthcare industry, as an initial assessment of the validity of an application of Distributed Ledger Technology in a specific knowledge management model to solve problems related to knowledge sharing in medical knowledge management systems. The paper summarizes some instances of most likely and unlikely uses of Blockchain in the healthcare setting. The paper also introduces a few use cases where some short-term benefits from such implementation.
Rapid Prototyping for Social Science Research

Given social science researchers’ increasing interest in materiality and embodied practices, there is considerable potential for productive methodological engagement with design-based research practices. In this chapter, we explore the potential of conducting social science research ‘through design’ (Lupton, 2018:2, emphasis added). We reflect on a series of workshops investigating the societal implications and impact of digital technologies on the mediation of touch communication. The workshops used the design-based research method of rapid prototyping as a quick and approximate way to engage with ideas of remote digital touch communication that draw the body, touch and materiality into focus. Our emphasis in this chapter is on illustrating the kinds of insights that can be gained through this method, rather than an exhaustive analysis of touch.The chapter first introduces our rationale for using prototyping and our methodology. The second section illustrates the potentials of rapid prototyping as a socially orientated methodological strategy that enabled us, as qualitative researchers, to attend to the body as a meaning making resource, and to materialize ideas, sensory knowing, and wider discourses of personal remote digital touch communication. Drawing on selected episodes from the workshops, we show how this method helped participants to describe, explore or discover aspects of touch communication or stimulate dialogue. This enabled us to ask methodology and content focused questions including: What could we learn about touch through touch during the process of making? How did participants use touch as a form of telling and imagining? What were the social meanings and tensions that emerged around touch and the digital? How were experiential categories employed and made meaningful by participants in the process of making? In the final section of the chapter we reflect on the strengths and limitations of rapid prototyping for social science research, and suggest future developments for its use within social sciences.
Attention-Guided Region Proposal Network for Pedestrian Detection

As a research hotspot and difficulty in the field of computer vision, pedestrian detection has been widely used in intelligent driving and traffic monitoring. The popular detection method at present uses region proposal network (RPN) to generate candidate regions, and then classifies the regions. But the RPN produces many erroneous candidate areas, causing region proposals for false positives to increase. This letter uses improved residual attention network to capture the visual attention map of images, then normalized to get the attention score map. The attention score map is used to guide the RPN network to generate more precise candidate regions containing potential target objects. The region proposals, confidence scores, and features generated by the RPN are used to train a cascaded boosted forest classifier to obtain the final results. The experimental results show that our proposed approach achieves highly competitive results on the Caltech and ETH datasets.
To Comprehend the New: On Measuring the Freshness of a Document

Detecting the novelty or freshness of an entire document is essential in this age of data duplication and semanticlevel redundancy all across the web. Current techniques for the problem mostly root on handcrafted similarity and divergence based measures to classify a document as novel or nonnovel. However, document-level novelty detection is relatively less explored in literature if compared to its sentence-level counterpart. In this work, we present a deep neural architecture to automatically predict the amount of new information contained in a document in the form of a novelty score. Along with, we offer a dataset of more than 7500 documents, annotated at the sentence-level to facilitate further research. Our approach which learns the notion of novelty and redundancy only from the data achieves significant performance improvement over the existing methods and adopted baselines (@ 17% error reduction). Also, our approach complies with the Two-Stage theory of human recall essential to comprehend new information.
Comparing Data Augmentation Strategies for Deep Image Classification

Currently deep learning requires large volumes of training data to fit accurate models. In practice,however, there is often insufficient training data available and augmentation is used to expand the dataset.Historically, only simple forms of augmentation, such as cropping and horizontal flips, were used. Morecomplex augmentation methods have recently been developed, but it is still unclear which techniques aremost effective, and at what stage of the learning process they should be introduced. This paper investigatesdata augmentation strategies for image classification, including the effectiveness of different forms ofaugmentation, dependency on the number of training examples, and when augmentation should be introducedduring training. The most accurate results in all experiments are achieved using random erasing due to itsability to simulate occlusion. As expected, reducing the number of training examples significantly increasesthe importance of augmentation, but surprisingly the improvements in generalization from augmentationdo not appear to be only as a result of augmentation preventing overfitting. Results also indicate a learningcurriculum that injects augmentation after the initial learning phase has passed is more effective than thestandard practice of using augmentation throughout, and that injection too late also reduces accuracy. We findthat careful augmentation can improve accuracy by +2.83% to 95.85% using a ResNet model on CIFAR-10with more dramatic improvements seen when there are fewer training examples. Source code is available athttps://git.io/fjPPy
AdaptiveMutate: a technique for privacy preservation

Mobile apps are known to be rich sources for gathering privacy-sensitive information about smartphone users. Despite the presence of encryption, passive network adversaries who have access to the network infrastructure can eavesdrop on the traffic and therefore fingerprint a user’s app by means of packet-level traffic analysis. Since it is difficult to prevent the adversaries from accessing the network, providing secrecy in hostile environments becomes a serious concern. In this study, we propose AdaptiveMutate, a privacy-leak thwarting technique to defend against the statistical traffic analysis of apps. First, we present a method for the identification of mobile apps using traffic analysis. Further, we propose a confusion system in which we obfuscate packet lengths, and/or inter-arrival time information leaked by the mobile traffic to make it hard for intruders to differentiate between the altered app traffic and the actual one using statistical analysis. Our aim is to shape one class of app traffic to obscure its features with the minimum overhead. Our system strives to dynamically maximize its efficiency by matching each app with the corresponding most dissimilar app. Also, AdaptiveMutate has an adaptive capability that allows it to choose the most suitable feature to mutate, depending on the type of apps analyzed and the classifier used, if known. We evaluate the efficiency of our model by conducting a comprehensive simulation analysis that mutates different apps to each other using AdaptiveMutate. We conclude that our algorithm is most efficient when we mutate a feature of one app to its most dissimilar one in another app. When applying the identification technique, we achieve a classification accuracy of 91.1%. Then, using our obfuscation technique, we are able to reduce this accuracy to 7%. Also, we test our algorithm against a recently published approach for mobile apps classification and we are able to reduce its accuracy from 94.8% to 17.9%. Additionally, we analyze the tradeoff between the shaping cost and traffic privacy protection, specifically, the associated overhead and the feasibility for real-time implementation.
A Hybrid Multi-atrous and Multi-scale Network for Liver Lesion Detection

Liver lesion detection on abdominal computed tomography (CT) is a challenging topic because of its large variance. Current detection methods based on a 2D convolutional neural network (CNN) are limited by the inconsistent view of lesions. One obvious observation is that it can easily lead to a discontinuity problem since it ignores the information between CT slices. To solve this problem, we propose a novel hybrid multi-atrous and multi-scale network (HMMNet). Our network treats the liver lesion detection in a 3D setting as finding a 3D cubic bounding box of a liver lesion. In our work, a multi-atrous 3D convolutional network (MA3DNet) is designed as the backbone. It comes with different dilation rate along z-axis to tackle the various resolutions in z-axis for different CT volumes. In addition, multi-scale features are extracted in a component, called feature extractor, to cover the volume and appearance diversities of liver lesions in a transversal plane. Finally, the features from our backbone and feature extractor are combined to offer the sizing and position measures of liver lesions. These information are frequently referred in a diagnostic report. Compared with other state-of-the-art 2D and 3D convolutional detection models, our HMMNet achieves the top-notch detection performance on the public Liver Tumor Segmentation Challenge (LiTS) dataset, where the F-score are 54.8% and 34.2% on average with the intersection-over-union (IoU) of 0.5 and 0.75 respectively. We also notice that our HMMNet model can be directly applied to the public Medical Segmentation Decathlon dataset without fine-tuning. This further illustrates the generalization capability of our proposed method.
Characterizing Man-made vs. Machine-made Chatbot Dialogs

The increasing usage of machine-made artifacts in news and social media can severely exacerbate the problem of false news.While knowing the parts of news content, or embedded images therein, are machine-generated or not helps determine the veracity of news, due to the recent improvement in AI techniques, it has become more difficult to accurately distinguish machine-made artifacts from man-made ones.In this work, therefore, we attempt to better understand and characterize distinguishing features between man-made and machinemade artifacts, especially chatbot dialog texts, which tend to be short and erroneous.Some of the characteristics that we found include: machine-made texts tend to use more words per message, interjections (e.g., hey, hi), use more filler words (e.g., blah, you, and know) and appear to be less confident than man-made texts in their speech.However, we noted that privacy or entropy related features between two types of texts do not appear to be significantly different.
PAChain: Private, Authenticated and Auditable Consortium Blockchain

Blockchain provides a distributed ledger recording a globally agreed, immutable transaction history, which may not be suitable for Fintech applications that process sensitive information. This paper aims to solve three important problems for practical blockchain applications: privacy, authentication and auditability. Private transaction means that the transaction can be validated without revealing the transaction details, such as the identity of the transacting parties and the transaction amount. Auditable transaction means that the complete transaction details can be revealed by auditors, regulators or law enforcement agencies. Authenticated transaction means that only authorized parties can be involved in the transaction. In this paper, we present a private, authenticated and auditable consortium blockchain, using a number of cryptographic building blocks.
Identification of citation and cited texts for fine‐grained citation content analysis

ABSTRACT Citation content Analysis is very useful to reveal the nature of citations among scientific papers. However, most of previous studies had been done only from the side of citing papers and ignored the side of cited papers. We proposed a similarity‐based unsupervised citation text identification method and a ranking‐based supervised cited text identification method. Based on the identified citation and cited texts, we performed a fine‐grained citation analysis and found there was a difference in the cited texts between the citing papers in different disciplines.
Continuous-learning work environment: A study with developers in software development organizations

In today’s economy of knowledge, knowledge is considered as the most important element in the processes related to products and services in most organizations including software development organizations (SDOs). It is essential for SDOs to create a work environment that supports continuous learning in order to deal with the challenges of the new economy and remain competitive in the market. This study aims to perceive how the work environment in SDOs supports continuous learning in multiple ways. The specific objectives are to understand and describe the characteristics of the SDOs’ work environment which demonstrate the occurrence of continuous learning and to understand how developers apply the resources and structures available in this environment for their learning. Semi-structured interviews were used to collect data from the subjects involved in the software development process. Seven common characteristics were identified in all the participants’ organizations, which are also typical in a work environment conducive to continuous learning: continuous learning as a responsibility and a competitive advantage; emphasis on innovation and competition; an open and error tolerant environment; supporting structures and resources; reward and recognition systems; leader support and peer support. Based on the result, this paper proposes a continuous learning model in software development environments.