Reviewerly/exHarmony huggingface dataset:
Deep Learning for Real-Time Crime Forecasting and Its Ternarization

Real-time crime forecasting is important. However, accurate prediction of when and where the next crime will happen is difficult. No known physical model provides a reasonable approximation to such a complex system. Historical crime data are sparse in both space and time and the signal of interests is weak. In this work, the authors first present a proper representation of crime data. The authors then adapt the spatial temporal residual network on the well represented data to predict the distribution of crime in Los Angeles at the scale of hours in neighborhood-sized parcels. These experiments as well as comparisons with several existing approaches to prediction demonstrate the superiority of the proposed model in terms of accuracy. Finally, the authors present a ternarization technique to address the resource consumption issue for its deployment in real world. This work is an extension of our short conference proceeding paper [Wang, B., Zhang, D., Zhang, D. H., et al., Deep learning for real time Crime forecasting, 2017, arXiv: 1707.03340].
Gravity-Inspired Graph Autoencoders for Directed Link Prediction

Graph autoencoders (AE) and variational autoencoders (VAE) recently emerged as powerful node embedding methods. In particular, graph AE and VAE were successfully leveraged to tackle the challenging link prediction problem, aiming at figuring out whether some pairs of nodes from a graph are connected by unobserved edges. However, these models focus on undirected graphs and therefore ignore the potential direction of the link, which is limiting for numerous real-life applications. In this paper, we extend the graph AE and VAE frameworks to address link prediction in directed graphs. We present a new gravity-inspired decoder scheme that can effectively reconstruct directed graphs from a node embedding. We empirically evaluate our method on three different directed link prediction tasks, for which standard graph AE and VAE perform poorly. We achieve competitive results on three real-world graphs, outperforming several popular baselines.
A Survey on Challenges and Progresses in Blockchain Technologies: A Performance and Security Perspective

Blockchain naturally fits multiple industry sectors due its characteristics of decentralization, enhanced security, tamper-proof, improved traceability and transparency. However, there is a significant concern of blockchain’s performance, since blockchain trades off its performance for a completely distributed feature, which enhances its security. In this paper, we investigate the state-of-the-art progress of blockchain, mainly from a performance and security perspective. We extracted 42 primary papers from major scientific databases and 34 online technical articles. The objective is to understand the current research trends, challenges and future directions. We briefly introduce the key technologies of blockchain including distributed ledger, cryptography, consensus, smart contracts and benchmarks. We next summarize the performance and security concerns raised in the investigation. We discuss the architectural choices, performance metrics, database management enhancements, and hybrid blockchains, and try to identify the effort that the state-of-the-art has made to balance between the performance and security. We also make experiments on Ethereum and survey other popular blockchain platforms on the scalability feature of blockchain. We later discuss the potential applications and present the lessons learned. Finally, we attempt to identify the open issues and possible research directions.
Situational Fusion of Visual Representation for Visual Navigation

A complex visual navigation task puts an agent in different situations which call for a diverse range of visual perception abilities. For example, to "go to the nearest chair'', the agent might need to identify a chair in a living room using semantics, follow along a hallway using vanishing point cues, and avoid obstacles using depth. Therefore, utilizing the appropriate visual perception abilities based on a situational understanding of the visual environment can empower these navigation models in unseen visual environments. We propose to train an agent to fuse a large set of visual representations that correspond to diverse visual perception abilities. To fully utilize each representation, we develop an action-level representation fusion scheme, which predicts an action candidate from each representation and adaptively consolidate these action candidates into the final action. Furthermore, we employ a data-driven inter-task affinity regularization to reduce redundancies and improve generalization. Our approach leads to a significantly improved performance in novel environments over ImageNet-pretrained baseline and other fusion methods.
Physical Adversarial Textures That Fool Visual Object Tracking

We present a method for creating inconspicuous-looking textures that, when displayed as posters in the physical world, cause visual object tracking systems to become confused. As a target being visually tracked moves in front of such a poster, its adversarial texture makes the tracker lock onto it, thus allowing the target to evade. This adversarial attack evaluates several optimization strategies for fooling seldom-targeted regression models: non-targeted, targeted, and a newly-coined family of guided adversarial losses. Also, while we use the Expectation Over Transformation (EOT) algorithm to generate physical adversaries that fool tracking models when imaged under diverse conditions, we compare the impacts of different scene variables to find practical attack setups with high resulting adversarial strength and convergence speed. We further showcase that textures optimized using simulated scenes can confuse real-world tracking systems for cameras and robots.
Optimum Tilt Angle and Orientation of Photovoltaic Thermal System for Application in Greater Toronto Area, Canada

We present a study conducted to obtain optimum tilt angle and orientation of a solar panel for the collection of maximum solar irradiation. The optimum tilt angle and orientation were determined using isotropic and anisotropic diffuse sky radiation models (isotropic and anisotropic models). The four isotropic models giving varying optimum tilt angles in the range of 37 to 44°. On the other hand, results of the four anisotropic models were more consistent, with optimum tilt angles ranging between 46–47°. Both types of models indicated that the collector tilt should be changed four times a year to receive more solar radiation. The results also indicate that the solar panel should be installed with orientation west or east of due south with a flatter tilt angle. A 15° change in orientation west or east of due south results in less than 1% reduction of the total solar radiation received. For a given optimum tilt angle, the effect of photovoltaic/thermal (PV/T) orientation west or east of due south on the outlet temperature was determined using a one-dimensional steady state heat transfer model. It was found that there is less than 1.5% decrease in outlet temperature for a PV/T panel oriented up to 15° east or west of due south from March to December. This result indicates that existing roofs with orientations angles up to 15° east or west of due south can be retrofitted with a PV/T system without changing the roof shape.
Code-Switched Language Models Using Neural Based Synthetic Data from Parallel Sentences

Training code-switched language models is difficult due to lack of data and complexity in the grammatical structure.Linguistic constraint theories have been used for decades to generate artificial code-switching sentences to cope with this issue.However, this require external word alignments or constituency parsers that create erroneous results on distant languages.We propose a sequence-to-sequence model using a copy mechanism to generate code-switching data by leveraging parallel monolingual translations from a limited source of code-switching data.The model learns how to combine words from parallel sentences and identifies when to switch one language to the other.Moreover, it captures code-switching constraints by attending and aligning the words in inputs, without requiring any external knowledge.Based on experimental results, the language model trained with the generated sentences achieves state-of-theart performance and improves end-to-end automatic speech recognition.
A Deep Cybersickness Predictor Based on Brain Signal Analysis for Virtual Reality Contents

What if we could interpret the cognitive state of a user while experiencing a virtual reality (VR) and estimate the cognitive state from a visual stimulus? In this paper, we address the above question by developing an electroencephalography (EEG) driven VR cybersickness prediction model. The EEG data has been widely utilized to learn the cognitive representation of brain activity. In the first stage, to fully exploit the advantages of the EEG data, it is transformed into the multi-channel spectrogram which enables to account for the correlation of spectral and temporal coefficient. Then, a convolutional neural network (CNN) is applied to encode the cognitive representation of the EEG spectrogram. In the second stage, we train a cybersickness prediction model on the VR video sequence by designing a Recurrent Neural Network (RNN). Here, the encoded cognitive representation is transferred to the model to train the visual and cognitive features for cybersickness prediction. Through the proposed framework, it is possible to predict the cybersickness level that reflects brain activity automatically. We use 8-channels EEG data to record brain activity while more than 200 subjects experience 44 different VR contents. After rigorous training, we demonstrate that the proposed framework reliably estimates cognitive states without the EEG data. Furthermore, it achieves state-of-the-art performance comparing to existing VR cybersickness prediction models.
Efficient Violence Detection Using 3D Convolutional Neural Networks

Automatically analyzing violent content in surveillance videos is of profound significance on many applications, ranging from Internet video filtration to public security protection. In this paper, we propose a deep learning model based on 3D convolutional neural networks, without using hand-crafted features or RNN architectures exclusively for encoding temporal information. The improved internal designs adopt compact but effective bottleneck units for learning motion patterns and leverage the DenseNet architecture to promote feature reusing and channel interaction, which is proved to be more capable of capturing spatiotemporal features and requires relatively fewer parameters. The performance of the proposed model is validated on three standard datasets in terms of recognition accuracy compared to other advanced approaches. Meanwhile, supplementary experiments are carried out to evaluate its effectiveness and efficiency. The final results demonstrate the advantages of the proposed model over the state-of-the-art methods in both recognition accuracy and computational efficiency.
CapsuleVOS: Semi-Supervised Video Object Segmentation Using Capsule Routing

In this work we propose a capsule-based approach for semi-supervised video object segmentation. Current video object segmentation methods are frame-based and often require optical flow to capture temporal consistency across frames which can be difficult to compute. To this end, we propose a video based capsule network, CapsuleVOS, which can segment several frames at once conditioned on a reference frame and segmentation mask. This conditioning is performed through a novel routing algorithm for attention-based efficient capsule selection. We address two challenging issues in video object segmentation: 1) segmentation of small objects and 2) occlusion of objects across time. The issue of segmenting small objects is addressed with a zooming module which allows the network to process small spatial regions of the video. Apart from this, the framework utilizes a novel memory module based on recurrent networks which helps in tracking objects when they move out of frame or are occluded. The network is trained end-to-end and we demonstrate its effectiveness on two benchmark video object segmentation datasets; it outperforms current offline approaches on the Youtube-VOS dataset while having a run-time that is almost twice as fast as competing methods. The code is publicly available at https://github.com/KevinDuarte/CapsuleVOS.
Secure Channel Free Certificate-Based Searchable Encryption Withstanding Outside and Inside Keyword Guessing Attacks

Searchable public key encryption (SPKE) is a useful public key cryptographic primitive that allows a user to perform keyword searches over publicly encrypted messages on an untrusted storage server while guaranteeing the privacy of the original messages as well as the search keywords. However, most of the previously proposed SPKE frameworks suffer from the security vulnerability caused by the keyword guessing attack and some other weaknesses. Inspired by the ideas of certificate-based cryptography and signcryption, we present a new SPKE framework called certificate-based searchable encryption. The new framework not only provides resistance to the existing known types of keyword guessing attacks, but also enjoys some appealing merits, such as implicit authentication, no key escrow and no secure channel. Under this new framework, we devise a concrete searchable certificate-based encryption scheme. In the random oracle model, it is proven to meet the keyword ciphertext indistinguishability, the keyword ciphertext unforgeability and the keyword trapdoor indistinguishability under the adaptive chosen-keyword attack. The comparisons indicate that it is secure and practicable.
The Adaptive Roles of Positive and Negative Emotions in Organizational Insiders’ Security-Based Precaution Taking

Protecting organizational information is a top priority for most firms. This reality, coupled with the fact that organizational insiders control much of their organizations’ valuable information, has led both researchers and practitioners to acknowledge the importance of insiders’ behavior for information security. Until recently, researchers have employed only a few theories to understand these influences, and this has generated calls for a broadened theoretical repertoire. Given this opportunity, we incorporate the previously developed framework of emotions and add the broaden-and-build theory (BBT) to understand the influence of discrete positive and negative emotions on insiders’ precaution-taking activities. Our findings demonstrate that the relationship between both positive and negative emotions and precaution taking is mediated by insiders’ (1) psychological capital (PsyCap), a higher-order, work-related construct of positive psychological resource capabilities; and (2) psychological distancing, a coping mechanism characterized by insiders’ attempts to detach themselves psychologically from a situation. By considering these factors, our model explains 32% of the variance in insiders’ precaution taking in organizations. Researchers and practitioners can use these findings to develop information-security programs that more effectively utilize emotional appeals to promote insiders’ precaution taking.
Proof-of-Reputation Based-Consortium Blockchain for Trust Resource Sharing in Internet of Vehicles

Resource sharing among vehicles can highly improve the capability and efficiency of Internet of Vehicles (IoV).However, it is challenging to establish trust and preserve privacy during the resource sharing process because of the high mobility and topological variability in IoV.Emerging blockchain technology expresses the excellent performance in handling distributed trust due to its verifiable and immutable ledger.In this paper, we first propose a consortium blockchain-based resource sharing paradigm in IoV, in which the resource sharing interactions are encapsulated as transactions and recorded by Road Side Units (RSUs).Moreover, a lightweight consensus mechanism named as Proof-of-Reputation is proposed to reduce computational power consumption and motivate vehicles involved in resource sharing.Finally a differentiated resource pricing scheme is proposed based on the dynamic match game of resource demand and supply.The reputation value is designed to indicate the trustworthy degree of vehicles, and the trust is established via the consensus procedure.We couple the resource sharing process and consensus together by utilizing the reputation value of each vehicle.The security and privacy analysis as well as simulation experiments on communication performance can verify the efficiency of the proposed blockchain system.
Imbalanced Learning in Land Cover Classification: Improving Minority Classes’ Prediction Accuracy Using the Geometric SMOTE Algorithm

The automatic production of land use/land cover maps continues to be a challenging problem, with important impacts on the ability to promote sustainability and good resource management. The ability to build robust automatic classifiers and produce accurate maps can have a significant impact on the way we manage and optimize natural resources. The difficulty in achieving these results comes from many different factors, such as data quality and uncertainty. In this paper, we address the imbalanced learning problem, a common and difficult conundrum in remote sensing that affects the quality of classification results, by proposing Geometric-SMOTE, a novel oversampling method, as a tool for addressing the imbalanced learning problem in remote sensing. Geometric-SMOTE is a sophisticated oversampling algorithm which increases the quality of the instances generated in previous methods, such as the synthetic minority oversampling technique. The performance of Geometric- SMOTE, in the LUCAS (Land Use/Cover Area Frame Survey) dataset, is compared to other oversamplers using a variety of classifiers. The results show that Geometric-SMOTE significantly outperforms all the other oversamplers and improves the robustness of the classifiers. These results indicate that, when using imbalanced datasets, remote sensing researchers should consider the use of these new generation oversamplers to increase the quality of the classification results.
DeepBalance: Deep-Learning and Fuzzy Oversampling for Vulnerability Detection

Software vulnerability has long been an important but critical research issue in cybersecurity. Recently, the machine learning (ML)-based approach has attracted increasing interest in the research of software vulnerability detection. However, the detection performance of existing ML-based methods require further improvement. There are two challenges: one is code representation for ML and the other is class imbalance between vulnerable code and nonvulnerable code. To overcome these challenges, this article develops a DeepBalance system, which combines the new ideas of deep code representation learning and fuzzy-based class rebalancing. We design a deep neural network with bidirectional long short-term memory to learn invariant and discriminative code representations from labeled vulnerable and nonvulnerable code. Then, a new fuzzy oversampling method is employed to rebalance the training data by generating synthetic samples for the class of vulnerable code. To evaluate the performance of the new system, we carry out a series of experiments in a real-world ground-truth dataset that consists of the code from the projects of LibTIFF, LibPNG, and FFmpeg. The results show that the proposed new system can significantly improve the vulnerability detection performance. For example, the improvement is 15% in terms of F-measure.
Sound Active Attention Framework for Remote Sensing Image Captioning

Attention mechanism-based image captioning methods have achieved good results in the remote sensing field, but are driven by tagged sentences, which is called passive attention. However, different observers may give different levels of attention to the same image. The attention of observers during testing, then, may not be consistent with the attention during training. As a direct and natural human-machine interaction, speech is much faster than typing sentences. Sound can represent the attention of different observers. This is called active attention. Active attention can be more targeted to describe the image; for example, in disaster assessments, the situation can be obtained quickly and the corresponding disaster areas can be located related to the specific disaster. A novel sound active attention framework is proposed for more specific caption generation according to the interest of the observer. First, sound is modeled by mel-frequency cepstral coefficients (MFCCs) and the image is encoded by convolutional neural networks (CNNs). Then, to handle the continuity characteristic of sound, a sound module and an attention module are designed based on the gated recurrent units (GRUs). Finally, the sound-guided image feature processed by the attention module is imported into the output module to generate descriptive sentence. Experiments based on both fake and real sound data sets show that the proposed method can generate sentences that can capture the focus of human.
A Smartphone-Enabled Fall Detection Framework for Elderly People in Connected Home Healthcare

In recent years, connected home healthcare, which involves multiple technologies such as wearable sensors, audio and video technology, and pervasive computing, has drawn attention for its ability to improve quality of life for elderly people. One of its most important services is fall detection. Falls represent a significant threat to the health and independence of adults older than 65. However, commercial fall detection devices are expensive and charge a monthly fee for their use. A more cost-effective, adaptable, and reliable fall detection system is necessary to detect falls and send alarms to an appropriate authority. This article introduces the MEFD framework that detects falls by elderly people and allows family members and caregivers to help by immediately locating them. In the proposed framework, real-time data retrieved from an accelerometer sensor on a smartphone are processed and analyzed by an online fall detection system running on the smartphone itself. The system sends an indoor sound alert to family members through a wireless access point at home or an outdoor SMS alert to a hospital or caregiver through a mobile network base station. A hybrid deep learning model is used to detect falls. The model is trained offline using a public dataset called MobiAct. Experimental results show that the proposed framework can detect falls from real-time streaming data with high accuracy compared to state-of-the-art approaches.