Reviewerly/exHarmony huggingface dataset:
Transmission of photonic polarization states through 55-m water: towards air-to-sea quantum communication

Quantum communication has been rapidly developed due to its unconditional security and successfully implemented through optical fibers and free-space air in experiments. To build a complete quantum communication network involving satellites in space and submersibles in ocean, the underwater quantum channel has been investigated in both theory and experiment. However, the question of whether the polarization encoded qubit can survive through a long-distance and high-loss underwater channel, which is considered as the restricted area for satellite-borne radio waves, still remains. Here, we experimentally demonstrate the transmission of blue-green photonic polarization states through 55-m-long water. We prepare six universal quantum states at the single photon level and observe their faithful transmission in a large marine test platform. We obtain complete information of the channel by quantum process tomography. The distance demonstrated in this work reaches a region allowing potential real applications, representing a step further towards air-to-sea quantum communication.
The CORAL+ Algorithm for Unsupervised Domain Adaptation of PLDA

State-of-the-art speaker recognition systems comprise an x-vector (or i-vector) speaker embedding front-end followed by a probabilistic linear discriminant analysis (PLDA) backend. The effectiveness of these components relies on the availability of a large collection of labeled training data. In practice, it is common that the domains (e.g., language, demographic) in which the system is deployed differ from that we trained the system. To close the gap due to the domain mismatch, we propose an unsupervised PLDA adaptation algorithm to learn from a small amount of unlabeled in-domain data. The proposed method was inspired by a prior work on feature-based domain adaptation technique known as the correlation alignment (CORAL). We refer to the model-based adaptation technique proposed in this paper as CORAL+. The efficacy of the proposed technique is experimentally validated on the recent NIST 2016 and 2018 Speaker Recognition Evaluation (SRE'16, SRE'18) datasets.
A Deep Dive Into Blockchain Selfish Mining

This paper studies a fundamental problem regarding the security of blockchain on how the existence of multiple misbehaving pools influences the profitability of selfish mining. Each selfish miner maintains a private chain and makes it public opportunistically for the purpose of acquiring more rewards incommensurate to his Hashrate. We establish a novel Markov chain model to characterize all the state transitions of public and private chains. The minimum requirement of Hashrate together with the minimum delay of being profitable is derived in close-form. The former reduces to 21.48% with the symmetric selfish miners, while their competition with asymmetric Hashrate puts forward a higher requirement of the profitable threshold. The profitable delay increases with the decrease of the Hashrate of selfish miners, making the mining pools more cautious on performing selfish mining.
Video Anomaly Detection and Localization Based on an Adaptive Intra-Frame Classification Network

Video anomaly detection and localization is still a challenging task in the computer vision field. Previous methods took this task as an outlier detection problem, which computed the deviation between the test samples and the normal patterns. In this paper, an adaptive intra-frame classification network (AICN) is proposed to transform this task to a multi-class classification problem. The contributions of our method are as follows. AICN is an end-to-end network for anomaly detection and localization. By using the motion convolutional layers and the shape convolutional layers, spatial-temporal features are extracted without resizing or splitting the frames before forward propagation. AICN enhances the adaptiveness of model. By using the adaptive region pooling layer and the intra-frame classifier, AICN is adaptive to frames with different resolutions and is easier to be applied on other scenes. AICN evaluates the abnormality of frames based on the intra-frame classification results. The intra-frame classification strategy reserves more connection information of sub-regions and makes the model outperform previous methods. The proposed method is examined on four public datasets with different background complexities and resolutions: UCSD Ped1 dataset, UCSD Ped2 dataset, Avenue dataset and Subway dataset. The results are further compared with previous approaches to confirm the effectiveness and the advantage of our method.
GAIM: Graph Attention Interaction Model for Collective Activity Recognition

Unbalanced interaction relationships at personal and group levels play a pivotal role in collective activity recognition, which has not been adaptively and jointly explored by previous approaches. In this paper, we propose a graph attention interaction model (GAIM) embedded with the graph attention block (GAB) to explicitly and adaptively infer unbalanced interaction relations at personal and group levels in a unified architecture, and further to learn the spatial and temporal evolutions of the collective activity from these interactions to predict the activity labels. We first design the spatiotemporal graphs tailored to the collective activity where the concurrent person and group nodes, respectively, represent individuals' actions and the collective activity. The graphs provide both spatial structures and semantic appearance features for the collective activity. Then, GAB performs convolution-like filters on the graphs to infer unequal and two-level interaction relations in the collective activity by implementing graph convolutional networks with a shared attention mechanism. At the personal level, the GAB learns different levels of interactions for each person node from its neighbor person nodes under the guidance from the group node. At the group level, the GAB assesses various degrees of interactions to the group node contributed by person nodes. Equipped with the GRUs network, the GAIM learns the spatial and temporal evolutions of individuals' actions as well as the collective activity from the captured interactions, and finally predicts the label of the collective activity. Experiments on four publicly available datasets and ablation studies are conducted to evaluate the performance of our GAIM, and the improved performance demonstrates the effectiveness of our model.
Investigating effects of reading medium and reading purpose on behavioral engagement and textual integration in a multiple text context

The study addressed to what extent behavioral engagement and textual integration may differ when undergraduate readers work with identical printed versus digital texts in preparation for an exam versus for pleasure. We expected that working with printed texts would lead to greater engagement and better integration than working with digital texts, but that reading purpose would moderate this effect of reading medium because those reading in preparation for an exam would display greater engagement and better integration regardless of reading medium. Results showed interaction effects of reading medium with reading purpose on the behavioral engagement indicators of reading time and the length of the post-reading written products. For reading time, the interaction involved that students used longer time when reading digital and mixed texts for an exam, compared to reading for pleasure, whereas there were no difference between exam and pleasure oriented reading when reading printed texts. For the length of the written responses, students produced more text when reading printed texts for an exam than when reading printed texts for pleasure, whereas there were no differences in text production between reading for an exam and reading for pleasure when reading digital or mixed texts. Finally, there was an indirect effect of reading purpose on textual integration via text production when students read printed texts: students who read printed texts in preparation for an exam produced longer written responses compared to those who read for pleasure and, in turn, gained a more integrated understanding of the issue in question. These results are discussed in terms of the implications they offer and the avenues they suggest for future research.
Evaluation of modified adaptive k-means segmentation algorithm

Segmentation is the act of partitioning an image into different regions by creating boundaries between regions. k-means image segmentation is the simplest prevalent approach. However, the segmentation quality is contingent on the initial parameters (the cluster centers and their number). In this paper, a convolution-based modified adaptive k-means (MAKM) approach is proposed and evaluated using images collected from different sources (MATLAB, Berkeley image database, VOC2012, BGH, MIAS, and MRI). The evaluation shows that the proposed algorithm is superior to k-means++, fuzzy c-means, histogram-based k-means, and subtractive k-means algorithms in terms of image segmentation quality (Q-value), computational cost, and RMSE. The proposed algorithm was also compared to state-of-the-art learning-based methods in terms of IoU and MIoU; it achieved a higher MIoU value.
PALM: An Incremental Construction of Hyperplanes for Data Stream Regression

Data stream has been the underlying challenge in the age of big data because it calls for real-time data processing with the absence of a retraining process and/or an iterative learning approach. In the realm of the fuzzy system community, data stream is handled by algorithmic development of self-adaptive neuro-fuzzy systems (SANFS) characterized by the single-pass learning mode and the open structure property that enables effective handling of fast and rapidly changing natures of data streams. The underlying bottleneck of SANFSs lies in its design principle, which involves a high number of free parameters (rule premise and rule consequent) to be adapted in the training process. This figure can even double in the case of the type-2 fuzzy system. In this paper, a novel SANFS, namely parsimonious learning machine (PALM), is proposed. PALM features utilization of a new type of fuzzy rule based on the concept of hyperplane clustering, which significantly reduces the number of network parameters because it has no rule premise parameters. PALM is proposed in both type-1 and type-2 fuzzy systems where all of which characterize a fully dynamic rule-based system. That is, it is capable of automatically generating, merging, and tuning the hyperplane-based fuzzy rule in the single-pass manner. Moreover, an extension of PALM, namely recurrent PALM, is proposed and adopts the concept of teacher-forcing mechanism in the deep learning literature. The efficacy of PALM has been evaluated through numerical study with six real-world and synthetic data streams from public database and our own real-world project of autonomous vehicles. The proposed model showcases significant improvements in terms of computational complexity and number of required parameters against several renowned SANFSs, while attaining comparable and often better predictive accuracy.
DAC-SDC Low Power Object Detection Challenge for UAV Applications

The 55th Design Automation Conference (DAC) held its first System Design Contest (SDC) in 2018. SDC'18 features a lower power object detection challenge (LPODC) on designing and implementing novel algorithms based object detection in images taken from unmanned aerial vehicles (UAV). The dataset includes 95 categories and 150k images, and the hardware platforms include Nvidia's TX2 and Xilinx's PYNQ Z1. DAC-SDC'18 attracted more than 110 entries from 12 countries. This paper presents in detail the dataset and evaluation procedure. It further discusses the methods developed by some of the entries as well as representative results. The paper concludes with directions for future improvements.
A Blockchain-based Data Sharing Scheme in The Supply Chain by IIoT

A supply chain is a system that includes multiple entities such as suppliers, manufacturers, carriers, retailers, and customers. How to encourage the willingness to cooperate and share information between these entities has always been one of the major challenges in the field of supply chain management. The Industrial Internet of Things (IIoT) can help entities get real-time data in the supply chain to share the key information and reduce costs. However, the supply chain not only needs to know how to gather the data but also ensures that the data processed is not leaked. Since Blockchain, as the main technology of Bitcoin, has the advantages of traceability and anti-counterfeiting, blockchain is the right tool to tackle the date sharing problem in supply chain. In this paper, we connect IIoT devices to the blockchain by combining the monitoring and recording of IIoT devices and storing real-time data in the network by smart contract. In addition, a blockchain-based supply chain structure is proposed. This structure provides collaboration solutions between different entities in the supply chain. In this structure, a fine-grained data sharing scheme for supply chain is proposed. By setting access policies to the smart contract, only the companies that satisfy the attributes of access policies can execute the smart contract and view the details of the transaction. The proposed scheme ensures reliability of data in the supply chain, and the privacy protection of the chain.
RDGAN: Retinex Decomposition Based Adversarial Learning for Low-Light Enhancement

Pictures taken under the low-light condition often suffer from low contrast and loss of image details, thus an approach that can effectively improve low-light images is demanded. Traditional Retinex-based methods assume that the reflectance components of low-light images keep unchanged, which neglect the color distortion and lost details. In this paper, we propose an end-to-end learning-based framework that first decomposes the low-light image and then learns to fuse the decomposed results to obtain the high quality enhanced result. Our framework can be divided into a RDNet (Retinex Decomposition Network) for decomposition and a FENet (Fusion Enhancement Network) for fusion. Specific multi-term losses are respectively designed for the two networks. We also present a new RDGAN (Retinex Decomposition based Generative Adversarial Network) loss, which is computed on the decomposed reflectance components of the enhanced and the reference images. Experiments demonstrate that our approach is good at color and detail restoration, which outperforms other state-of-the-art methods.
Hardware Implementation of Sigmoid Activation Functions using FPGA

The methods of approximation of the sigmoid function are developed and modified, using piecewise linear approximation and approximation by a second order polynomial. The estimation of the accuracy of approximation by these methods of sigmoid function and its derivative is performed. The considered methods are implemented on FPGA using VHDL. The comparison of the required hardware resources and performance is given.
Multi-Task Self-Supervised Object Detection via Recycling of Bounding Box Annotations

In spite of recent enormous success of deep convolutional networks in object detection, they require a large amount of bounding box annotations, which are often time-consuming and error-prone to obtain. To make better use of given limited labels, we propose a novel object detection approach that takes advantage of both multi-task learning (MTL) and self-supervised learning (SSL). We propose a set of auxiliary tasks that help improve the accuracy of object detection. They create their own labels by recycling the bounding box labels (i.e. annotations of the main task) in an SSL manner, and are jointly trained with the object detection model in an MTL way. Our approach is integrable with any region proposal based detection models. We empirically validate that our approach effectively improves detection performance on various architectures and datasets. We test two state-of-the-art region proposal object detectors, including Faster R-CNN and R-FCN, with three CNN backbones of ResNet-101, Inception-ResNet-v2, and MobileNet on two benchmark datasets of PASCAL VOC and COCO.
Blockchain‐based trusted data sharing among trusted stakeholders in IoT

Summary Sharing trusted data among trusted stakeholders is very important to large‐scale Internet of Things (IoT) applications. However, the entities and organizations involved in IoT naturally lack trusted relationships, which poses significant challenges to the above vision. Specifically, the first challenge is to ensure that the data in the physical world can be objectively and truly injected into the information world of IoT. The second is to ensure the credibility of the entities' identities in IoT. The third is to ensure the authenticity of data, the credibility of identity, and the reliable transmission of data when a third trusted party is unable to provide the expected trusted services. In view of the above challenges, this paper proposes a secure and lightweight triple‐trusting architecture (SLTA), which fully uses a blockchain‐related supporting technology. The architecture includes an oracle‐based data collection mechanism, which ensures that the data collected from edge devices of IoT cannot be modified, and the distributed identity management mechanism, which enhances personal privacy, security, and control of digital identities. Furthermore, a series of innovative designs for applying the blockchain to special large‐scale cooperation scenario in IoT are proposed, which is also a part of the key mechanisms of the SLTA. The innovative design includes a new software‐defined blockchain structure model and a lightweight Byzantine fault‐tolerant algorithm that provides credible support for decentralized data collection, identity management, and data transfer, as well as low‐overhead sequential storage mechanism.
The Effect of Hand Size and Interaction Modality on the Virtual Hand Illusion

Most commercial virtual reality applications with self avatars provide users with a “one-size fits all” avatar. While the height of this body may be scaled to the user's height, other body proportions, such as limb length and hand size, are rarely customized to fit an individual user. Prior research has shown that mismatches between users' avatars and their actual bodies can affect size perception and feelings of body ownership. In this paper, we consider how concepts related to the virtual hand illusion, user experience, and task efficiency are influenced by variations between the size of a user's actual hand and their avatar's hand. We also consider how using a tracked controller or tracked gestures affect these concepts. We conducted a 2×3 within-subjects study (n=20), with two levels of input modality: using tracked finger motion vs. a hand-held controller (Glove vs. Controller), and three levels of hand scaling (Small, Fit, and Large). Participants completed 2 block-assembly trials for each condition (for a total of 12 trials). Time, mistakes, and a user experience survey were recorded for each trial. Participants experienced stronger feelings of ownership and realism in the Glove condition. Efficiency was higher in the Controller condition and supported by play data of more time spent, blocks grabbed, and blocks dropped in the Glove condition. We did not find enough evidence for a change in agency and the intensity of the virtual hand illusion depending on hand size. Over half of the participants indicated preferring the Glove condition over the Controller condition, mentioning fun and efficiency as factors in their choices. Preferences on hand scaling were mixed but often attributed to efficiency. Participants liked the appearance of their virtual hand more while using the Fit instead of Large hands. Several interaction effects were observed between input modality and hand scaling, for example, for smaller hands, tracked hands evoked stronger feelings of ownership compared to using a controller. Our results show that the virtual hand illusion is stronger when participants are able to control a hand directly rather than with a hand-held device, and that the virtual reality task must first be considered to determine which modality and hand size are the most applicable.
Single image dehazing using deep neural networks

The rapid growth in computer vision applications that are affected by environmental conditions challenge the limitations of existing techniques. This is driving the development of new deep learning based vision techniques that are robust to environmental noise and interference. We propose a novel deep CNN model, which is trained from unmatched images for the purpose of image dehazing. This solution is enabled by the concept of the Siamese network architecture. Using object performance measures of image PSNR and SSIM we are able to demonstrate a quantitative and qualitative improvement in the network dehazing performance. This superior performance is achieved with significantly smaller training datasets than existing methods.
ReconViguRation: Reconfiguring Physical Keyboards in Virtual Reality

Physical keyboards are common peripherals for personal computers and are efficient standard text entry devices. Recent research has investigated how physical keyboards can be used in immersive head-mounted display-based Virtual Reality (VR). So far, the physical layout of keyboards has typically been transplanted into VR for replicating typing experiences in a standard desktop environment. In this paper, we explore how to fully leverage the immersiveness of VR to change the input and output characteristics of physical keyboard interaction within a VR environment. This allows individual physical keys to be reconfigured to the same or different actions and visual output to be distributed in various ways across the VR representation of the keyboard. We explore a set of input and output mappings for reconfiguring the virtual presentation of physical keyboards and probe the resulting design space by specifically designing, implementing and evaluating nine VR-relevant applications: emojis, languages and special characters, application shortcuts, virtual text processing macros, a window manager, a photo browser, a whack-a-mole game, secure password entry and a virtual touch bar. We investigate the feasibility of the applications in a user study with 20 participants and find that, among other things, they are usable in VR. We discuss the limitations and possibilities of remapping the input and output characteristics of physical keyboards in VR based on empirical findings and analysis and suggest future research directions in this area.
GlyphGAN: Style-consistent font generation based on generative adversarial networks

In this paper, we propose GlyphGAN: style-consistent font generation based on generative adversarial networks (GANs). GANs are a framework for learning a generative model using a system of two neural networks competing with each other. One network generates synthetic images from random input vectors, and the other discriminates between synthetic and real images. The motivation of this study is to create new fonts using the GAN framework while maintaining style consistency over all characters. In GlyphGAN, the input vector for the generator network consists of two vectors: character class vector and style vector. The former is a one-hot vector and is associated with the character class of each sample image during training. The latter is a uniform random vector without supervised information. In this way, GlyphGAN can generate an infinite variety of fonts with the character and style independently controlled. Experimental results showed that fonts generated by GlyphGAN have style consistency and diversity different from the training images without losing their legibility.