Reviewerly/exHarmony huggingface dataset:
Video Saliency Detection via Sparsity-Based Reconstruction and Propagation

Video saliency detection aims to continuously discover the motion-related salient objects from the video sequences. Since it needs to consider the spatial and temporal constraints jointly, video saliency detection is more challenging than image saliency detection. In this paper, we propose a new method to detect the salient objects in video based on sparse reconstruction and propagation. With the assistance of novel static and motion priors, a single-frame saliency model is first designed to represent the spatial saliency in each individual frame via the sparsity-based reconstruction. Then, through a progressive sparsity-based propagation, the sequential correspondence in the temporal space is captured to produce the inter-frame saliency map. Finally, these two maps are incorporated into a global optimization model to achieve spatio-temporal smoothness and global consistency of the salient object in the whole video. The experiments on three large-scale video saliency datasets demonstrate that the proposed method outperforms the state-of-the-art algorithms both qualitatively and quantitatively.
When blockchain meets Internet of Things: Characteristics, challenges, and business opportunities

Researches in Blockchain Technology (BCT) and Internet of Things (IoT) are experiencing exponential growth in recent years. They are being witnessed as mainstream technologies that have the potential of revolutionizing our society. However, these two contain technologically unrelated in philosophies. This article has a special interest in exploring how BCT and IoT can benefit in innovating business models. The purpose of this work is to understand the state of the art how BCT can be utilized within IoT context to create secure decentralized architecture. IoT is not secure by design, making its applications vulnerable especially to security and privacy threats. This hinders the proliferation of IoT applications in a large scale. With BCT capabilities, BCT can be used as integral part that can solve many shortcomings of IoT applications. Thus, existing businesses can be improved, and new business models can be brought into reality. Eliminating the need of third parties to guarantee trust of business transactions is a significant value towards business innovation in many areas, such as digital supply chain, vehicular systems, surveillance systems, and the creation of new business models, e.g. sharing economy, local energy trading, smart city, to name but a few. In an effort to understand the state of the art, this article (1) highlights intrinsic characteristics of IoT, (2) describes BCT modes and their characteristics to be considered for IoT integration, (3) presents a comprehensive survey on business opportunities based on the usage of BCT and IoT from a wide variety of literatures, (4) analyse strengths and weaknesses, and (5) discusses research possibilities that can be a solution to the problems. This paper will serve as a good starting and reference point to new researchers or those who are interested in adopting these technologies in businesses.
Natural and Realistic Single Image Super-Resolution With Explicit Natural Manifold Discrimination

Recently, many convolutional neural networks for single image super-resolution (SISR) have been proposed, which focus on reconstructing the high-resolution images in terms of objective distortion measures. However, the networks trained with objective loss functions generally fail to reconstruct the realistic fine textures and details that are essential for better perceptual quality. Recovering the realistic details remains a challenging problem, and only a few works have been proposed which aim at increasing the perceptual quality by generating enhanced textures. However, the generated fake details often make undesirable artifacts and the overall image looks somewhat unnatural. Therefore, in this paper, we present a new approach to reconstructing realistic super-resolved images with high perceptual quality, while maintaining the naturalness of the result. In particular, we focus on the domain prior properties of SISR problem. Specifically, we define the naturalness prior in the low-level domain and constrain the output image in the natural manifold, which eventually generates more natural and realistic images. Our results show better naturalness compared to the recent super-resolution algorithms including perception-oriented ones.
Secure Data Aggregation of Lightweight E-Healthcare IoT Devices With Fair Incentives

With rapid development of e-healthcare systems, patients that are equipped with resource-limited e-healthcare devices (Internet of Things) generate huge amount of health data for health management. These health data possess significant medical value when aggregated from these distributed devices. However, efficient health data aggregation poses several security and privacy issues such as confidentiality disclosure and differential attacks, as well as patients may be reluctant to contribute their health data for aggregation. In this paper, we propose a privacy-preserving heath data aggregation scheme that securely collects health data from multiple sources and guarantee fair incentives for contributing patients. Specifically, we employ signature techniques to keep fair incentives for patients. Meanwhile, we add noises into the health data for differential privacy. Furthermore, we combine Boneh-Goh-Nissim cryptosystem and Shamir's secret sharing to keep data obliviousness security and fault tolerance. Security and privacy discussions show that our scheme can resist differential attacks, tolerate healthcare centers failures, and keep fair incentives for patients. Performance evaluations demonstrate cost-efficient computation, communication and storage overhead.
When does technology anthropomorphism help alleviate customer dissatisfaction after a service failure? – The moderating role of consumer technology self-efficacy and interdependent self-construal

Given the increasing presence of humanoid service robots at airports, hotels and restaurants, the current study investigates how consumers’ interdependent self-construal and technology self-efficacy jointly influence their reactions to service machines with humanlike features in a service failure context. The results demonstrate that consumers show varying levels of dissatisfaction with a service failure caused by an anthropomorphic (vs. non-anthropomorphic) self-service machine depending on their levels of interdependent self-construal (high vs. low) and technology self-efficacy (high vs. low). The underlying mechanism is self-blame. The theoretical contributions to the existing service technology research and the emerging anthropomorphism literature are discussed. This research also provides practical guidelines to industry practitioners for more efficient usage of service robots in delivering customer service.
Ensemble spatiotemporal forecasting of solar irradiation using variational Bayesian convolutional gate recurrent unit network

Solar irradiation prediction is of vital important to improve solar energy utilization. In recent years, many researches on solar irradiation prediction have been arisen. However, most forecasting model are based only on time series without considering the temporal and spatial variations of the solar energy, which hinders the progress of solar irradiation prediction. In this paper, we embed solar energy and meteorological data from multiple sites into a spatial grid and focus on the spatiotemporal solar irradiation prediction problem. An ensemble spatiotemporal deep learning model is proposed for solving the problem. The proposed model contains a convolutional operator in both the input-to-state and state-to-state transitions of the Gate Recurrent Unit, which makes it particularly suitable for spatiotemporal forecasting problems. Moreover, variational inference is employed in this deep learning model in order to quantify the uncertainty of the prediction. A real-world test case with a spatial region is used to illustrate the full potential of the proposed model. Four state-of-the-art deep learning models are considered for comparison. The experimental results demonstrate that the proposed model significantly outperforms other models in term of three widely used evaluation criteria. Furthermore, the uncertainty estimation is given and it demonstrates that the proposed model is able to provide an effective uncertainty estimation for the prediction.
Lower complexity bounds of first-order methods for convex-concave bilinear saddle-point problems

On solving a convex-concave bilinear saddle-point problem (SPP), there have been many works studying the complexity results of first-order methods. These results are all about upper complexity bounds, which can determine at most how many iterations would guarantee a solution of desired accuracy. In this paper, we pursue the opposite direction by deriving lower complexity bounds of first-order methods on large-scale SPPs. Our results apply to the methods whose iterates are in the linear span of past first-order information, as well as more general methods that produce their iterates in an arbitrary manner based on first-order information. We first work on the affinely constrained smooth convex optimization that is a special case of SPP. Different from gradient method on unconstrained problems, we show that first-order methods on affinely constrained problems generally cannot be accelerated from the known convergence rate O(1 / t) to $$O(1/t^2)$$ , and in addition, O(1 / t) is optimal for convex problems. Moreover, we prove that for strongly convex problems, $$O(1/t^2)$$ is the best possible convergence rate, while it is known that gradient methods can have linear convergence on unconstrained problems. Then we extend these results to general SPPs. It turns out that our lower complexity bounds match with several established upper complexity bounds in the literature, and thus they are tight and indicate the optimality of several existing first-order methods.
The Effect of Avatar Appearance on Social Presence in an Augmented Reality Remote Collaboration

This paper investigates the effect of avatar appearance on Social Presence and users' perception in an Augmented Reality (AR) telep-resence system. Despite the development of various commercial 3D telepresence systems, there has been little evaluation and discussions about the appearance of the collaborator's avatars. We conducted two user studies comparing the effect of avatar appearances with three levels of body part visibility (head & hands, upper body, and whole body) and two different character styles (realistic and cartoon-like) on Social Presence while performing two different remote collaboration tasks. We found that a realistic whole body avatar was perceived as being the best for remote collaboration, but an upper body or cartoon style could be considered as a substitute depending on the collaboration context. We discuss these results and suggest guidelines for designing future avatar-mediated AR remote collaboration systems.
Improving Attacks on Round-Reduced Speck32/64 Using Deep Learning

This paper has four main contributions. First, we calculate the predicted difference distribution of Speck32/64 with one specific input difference under the Markov assumption completely for up to eight rounds and verify that this yields a globally fairly good model of the difference distribution of Speck32/64. Secondly, we show that contrary to conventional wisdom, machine learning can produce very powerful cryptographic distinguishers: for instance, in a simple low-data, chosen plaintext attack on nine rounds of Speck, we present distinguishers based on deep residual neural networks that achieve a mean key rank roughly five times lower than an analogous classical distinguisher using the full difference distribution table. Thirdly, we develop a highly selective key search policy based on a variant of Bayesian optimization which, together with our neural distinguishers, can be used to reduce the remaining security of 11-round Speck32/64 to roughly 38 bits. This is a significant improvement over previous literature. Lastly, we show that our neural distinguishers successfully use features of the ciphertext pair distribution that are invisible to all purely differential distinguishers even given unlimited data. While our attack is based on a known input difference taken from the literature, we also show that neural networks can be used to rapidly (within a matter of minutes on our machine) find good input differences without using prior human cryptanalysis. Supplementary code and data for this paper is available at https://github.com/agohr/deep_speck .
A Novel Kullback–Leibler Divergence Minimization-Based Adaptive Student's t-Filter

In this paper, in order to improve the Student's t-matching accuracy, a novel Kullback-Leibler divergence (KLD) minimization-based matching method is firstly proposed by minimizing the upper bound of the KLD between the true Student's t-density and the approximate Student's t-density. To improve the Student's t-modelling accuracy, a novel KLD minimization-based adaptive method is then proposed to estimate the scale matrices of Student's t-distributions, in which the modified evidence lower bound is maximized. A novel KLD minimization-based adaptive Student's t-filter is derived via combining the proposed Student's t-matching technique and the adaptive method. A manoeuvring target tracking example is provided to demonstrate the effectiveness and potential of the proposed filter.
Disengagement, engagement and digital skills in technology-enhanced learning

Abstract With the digitalisation of education increasing, the relationship between student engagement in Technology-enhanced Learning (TEL) and digital skills has remained largely unexplored. There is a strong consensus that engagement is necessary for students to succeed in school. We hypothesised that students reporting high and low levels of general engagement display differences in terms of their engagement in TEL, and that students’ digital skills correlate with their engagement in and disengagement in TEL, which in turn is related to their learning outcomes. We used statistical tests to explore the relationship between the students’ ( N = 410) general engagement and engagement in TEL, and investigated how digital skills were related to engagement and disengagement in TEL. We found significant correlations between students’ digital skills and engagement in TEL, showing that the possession of high levels of digital skill is related to engagement in TEL. Interestingly, digital skills were not related to disengagement. This suggests that students reporting both high and low levels of digital skills disengage to some extent when learning with technologies. We also identified variables reflecting both engagement and disengagement in TEL that predict student performance as measured via final grades, implying that in order to understand and support students who learn with technologies, a broader understanding of the factors influencing engagement and disengagement is key.
Capsule Networks – A survey

Modern day computer vision tasks requires efficient solution to problems such as image recognition, natural language processing, object detection, object segmentation and language translation. Symbolic Artificial Intelligence with its hard coding rules is incapable of solving these complex problems resulting in the introduction of Deep Learning (DL) models such as Recurrent Neural Networks and Convolutional Neural Networks (CNN). However, CNNs require lots of training data and are incapable of recognizing pose and deformation of objects leading to the introduction of Capsule Networks. Capsule Networks are the new sensation in Deep Learning. They have lived to this expectation as their performance in relation to the above problems has been better than Convolutional Neural Networks. Even with this promise in performance, lack of architectural knowledge and inner workings of Capsules serves as a hindrance for researchers to take full advantage of this breakthrough. In this paper, we provide a comprehensive review of the state of the art architectures, tools and methodologies in existing implementations of capsule networks. We highlight the successes, failures and opportunities for further research to serve as a motivation to researchers and industry players to exploit the full potential of this new field. The main contribution of this survey article is that it explains and summarizes significant current state of the art Capsule Network architectures and implementations.
Privacy-Preserving and Pairing-Free Multirecipient Certificateless Encryption With Keyword Search for Cloud-Assisted IIoT

Nowadays, cloud-assisted Industrial Internet of Things (IIoT) has become pervasive in modern enterprises, because it supplies a promising way to transform the operation mode of existing industrial facilities, to enhancing the production efficiency and lowering the manufacturing cost. In order to preserve the privacy of enterprises, sensitive industrial data needs to be encrypted prior to being uploaded to the cloud. Recently, certificateless encryption with keyword search (CLKS) was introduced to resolve the problem of encrypted data retrieval in cloud-assisted IIoT. However, the existing CLKS schemes only support a single-recipient keyword search and need to depend on the costly bilinear pairing that is disliked by the resource-constrained IIoT devices. Moreover, most of the existing CLKS schemes are vulnerable to the keyword guessing attack, and thus fail to protect the privacy of searched data. In this article, we develop a privacy-preserving and pairing-free multirecipient CLKS scheme for cloud-assisted IIoT. The proposed scheme has the following merits: 1) supporting multirecipient keyword search function; 2) requiring no costly bilinear pairing operations; and 3) providing resistance against keyword guessing attacks. The performance comparison and analysis demonstrate that it is more efficient than the existing CLKS schemes and is appropriate for the cloud-assisted IIoT.
MASTER: Multi-aspect non-local network for scene text recognition

Attention-based scene text recognizers have gained huge success, which leverages a more compact intermediate representation to learn 1d- or 2d- attention by a RNN-based encoder-decoder architecture. However, such methods suffer from attention-drift problem because high similarity among encoded features leads to attention confusion under the RNN-based local attention mechanism. Moreover, RNN-based methods have low efficiency due to poor parallelization. To overcome these problems, we propose the MASTER, a self-attention based scene text recognizer that (1) not only encodes the input-output attention but also learns self-attention which encodes feature-feature and target-target relationships inside the encoder and decoder and (2) learns a more powerful and robust intermediate representation to spatial distortion, and (3) owns a great training efficiency because of high training parallelization and a high-speed inference because of an efficient memory-cache mechanism. Extensive experiments on various benchmarks demonstrate the superior performance of our MASTER on both regular and irregular scene text. Pytorch code can be found at https://github.com/wenwenyu/MASTER-pytorch, and Tensorflow code can be found at https://github.com/jiangxiluning/MASTER-TF.
Co-Segmentation Inspired Attention Networks for Video-Based Person Re-Identification

Person re-identification (Re-ID) is an important real-world surveillance problem that entails associating a person's identity over a network of cameras. Video-based Re-ID approaches have gained significant attention recently since a video, and not just an image, is often available. In this work, we propose a novel Co-segmentation inspired video Re-ID deep architecture and formulate a Co-segmentation based Attention Module (COSAM) that activates a common set of salient features across multiple frames of a video via mutual consensus in an unsupervised manner. As opposed to most of the prior work, our approach is able to attend to person accessories along with the person. Our plug-and-play and interpretable COSAM module applied on two deep architectures (ResNet50, SE-ResNet50) outperform the state-of-the-art methods on three benchmark datasets.
Second-Order Non-Local Attention Networks for Person Re-Identification

Recent efforts have shown promising results for person re-identification by designing part-based architectures to allow a neural network to learn discriminative representations from semantically coherent parts. Some efforts use soft attention to reallocate distant outliers to their most similar parts, while others adjust part granularity to incorporate more distant positions for learning the relationships. Others seek to generalize part-based methods by introducing a dropout mechanism on consecutive regions of the feature map to enhance distant region relationships. However, only few prior efforts model the distant or non-local positions of the feature map directly for the person re-ID task. In this paper, we propose a novel attention mechanism to directly model long-range relationships via second-order feature statistics. When combined with a generalized DropBlock module, our method performs equally to or better than state-of-the-art results for mainstream person re-identification datasets, including Market1501, CUHK03, and DukeMTMC-reID.
Trusting artificial intelligence in cybersecurity is a double-edged sword

Applications of artificial intelligence (AI) for cybersecurity tasks are attracting greater attention from the private and the public sectors. Estimates indicate that the market for AI in cybersecurity will grow from US$1 billion in 2016 to a US$34.8 billion net worth by 2025. The latest national cybersecurity and defence strategies of several governments explicitly mention AI capabilities. At the same time, initiatives to define new standards and certification procedures to elicit users’ trust in AI are emerging on a global scale. However, trust in AI (both machine learning and neural networks) to deliver cybersecurity tasks is a double-edged sword: it can improve substantially cybersecurity practices, but can also facilitate new forms of attacks to the AI applications themselves, which may pose severe security threats. We argue that trust in AI for cybersecurity is unwarranted and that, to reduce security risks, some form of control to ensure the deployment of ‘reliable AI’ for cybersecurity is necessary. To this end, we offer three recommendations focusing on the design, development and deployment of AI for cybersecurity. Current national cybersecurity and defence strategies of several governments mention explicitly the use of AI. However, it will be important to develop standards and certification procedures, which involves continuous monitoring and assessment of threats. The focus should be on the reliability of AI-based systems, rather than on eliciting users’ trust in AI.