Reviewerly/exHarmony huggingface dataset:
KERMIT: Generative Insertion-Based Modeling for Sequences

We present KERMIT, a simple insertion-based approach to generative modeling for sequences and sequence pairs. KERMIT models the joint distribution and its decompositions (i.e., marginals and conditionals) using a single neural network and, unlike much prior work, does not rely on a prespecified factorization of the data distribution. During training, one can feed KERMIT paired data $(x, y)$ to learn the joint distribution $p(x, y)$, and optionally mix in unpaired data $x$ or $y$ to refine the marginals $p(x)$ or $p(y)$. During inference, we have access to the conditionals $p(x \mid y)$ and $p(y \mid x)$ in both directions. We can also sample from the joint distribution or the marginals. The model supports both serial fully autoregressive decoding and parallel partially autoregressive decoding, with the latter exhibiting an empirically logarithmic runtime. We demonstrate through experiments in machine translation, representation learning, and zero-shot cloze question answering that our unified approach is capable of matching or exceeding the performance of dedicated state-of-the-art systems across a wide range of tasks without the need for problem-specific architectural adaptation.
Discretization Based Solutions for Secure Machine Learning Against Adversarial Attacks

Adversarial examples are perturbed inputs that are designed (from a deep learning network's (DLN) parameter gradients) to mislead the DLN during test time. Intuitively, constraining the dimensionality of inputs or parameters of a network reduces the 'space' in which adversarial examples exist. Guided by this intuition, we demonstrate that discretization greatly improves the robustness of DLNs against adversarial attacks. Specifically, discretizing the input space (or allowed pixel levels from 256 values or 8-bit to 4 values or 2-bit) extensively improves the adversarial robustness of DLNs for a substantial range of perturbations for minimal loss in test accuracy. Furthermore, we find that Binary Neural Networks (BNNs) and related variants are intrinsically more robust than their full precision counterparts in adversarial scenarios. Combining input discretization with BNNs furthers the robustness even waiving the need for adversarial training for certain magnitude of perturbation values. We evaluate the effect of discretization on MNIST, CIFAR10, CIFAR100 and Imagenet datasets. Across all datasets, we observe maximal adversarial resistance with 2-bit input discretization that incurs an adversarial accuracy loss of just ~1-2% as compared to clean test accuracy.
HRAN: Hybrid Residual Attention Network for Single Image Super-Resolution

The extraction and proper utilization of convolution neural network (CNN) features have a significant impact on the performance of image super-resolution (SR). Although CNN features contain both the spatial and channel information, current deep techniques on SR often suffer to maximize performance due to using either the spatial or channel information. Moreover, they integrate such information within a deep or wide network rather than exploiting all the available features, eventually resulting in high computational complexity. To address these issues, we present a binarized feature fusion (BFF) structure that utilizes the extracted features from residual groups (RG) in an effective way. Each residual group (RG) consists of multiple hybrid residual attention blocks (HRAB) that effectively integrates the multiscale feature extraction module and channel attention mechanism in a single block. Furthermore, we use dilated convolutions with different dilation factors to extract multiscale features. We also propose to adopt global, short and long skip connections and residual groups (RG) structure to ease the flow of information without losing important features details. In the paper, we call this overall network architecture as hybrid residual attention network (HRAN). In the experiment, we have observed the efficacy of our method against the state-of-the-art methods for both the quantitative and qualitative comparisons.
A nanophotonic laser on a graph

Conventional nanophotonic schemes minimise multiple scattering to realise a miniaturised version of beam-splitters, interferometers and optical cavities for light propagation and lasing. Here instead, we introduce a nanophotonic network built from multiple paths and interference, to control and enhance light-matter interaction via light localisation. The network is built from a mesh of subwavelength waveguides, and can sustain localised modes and mirror-less light trapping stemming from interference over hundreds of nodes. With optical gain, these modes can easily lase, reaching ~100 pm linewidths. We introduce a graph solution to the Maxwell's equation which describes light on the network, and predicts lasing action. In this framework, the network optical modes can be designed via the network connectivity and topology, and lasing can be tailored and enhanced by the network shape. Nanophotonic networks pave the way for new laser device architectures, which can be used for sensitive biosensing and on-chip optical information processing.
PROUD: Verifiable Privacy-preserving Outsourced Attribute Based SignCryption supporting access policy Update for cloud assisted IoT applications

The ever-growing number of Internet connected devices poses several cybersecurity risks. Most of the exchanged data between the Internet of Things (IoT) devices are not adequately secured due to resource constraints on IoT devices. Attribute Based SignCryption (ABSC) is a powerful cryptographic mechanism suitable for distributed environments, providing flexible access control and data secrecy. However, it imposes high designcryption costs, and does not support access policy update (user addition/revocation). This paper presents PROUD, an ABSC solution, to securely outsource data designcryption process to edge servers in order to reduce the computation overhead on the user side. PROUD allows end-users to offload most of the designcryption overhead to an edge server and verify the correctness of the received partially designcrypted data from the edge server. Moreover, PROUD provides the access policy update feature with neither involving a proxy-server, nor re-signcrypting the signcrypted message and re-distributing the users’ secret keys. The access policy update feature in PROUD does not affect the size of the message received by the end-user which reduces the bandwidth and the storage usage. Our comprehensive theoretical and experimental analysis prove that PROUD outperforms existing schemes in terms of functionality, communication and computation overhead.
No-Reference Light Field Image Quality Assessment Based on Spatial-Angular Measurement

Light field image quality assessment (LFI-QA) is a significant and challenging research problem. It helps to better guide light field acquisition, processing and applications. However, only a few objective models have been proposed and none of them completely consider intrinsic factors affecting the LFI quality. In this paper, we propose a No-Reference Light Field image Quality Assessment (NR-LFQA) scheme, where the main idea is to quantify the LFI quality degradation through evaluating the spatial quality and angular consistency. We first measure the spatial quality deterioration by capturing the naturalness distribution of the light field cyclopean image array, which is formed when human observes the LFI. Then, as a transformed representation of LFI, the Epipolar Plane Image (EPI) contains the slopes of lines and involves the angular information. Therefore, EPI is utilized to extract the global and local features from LFI to measure angular consistency degradation. Specifically, the distribution of gradient direction map of EPI is proposed to measure the global angular consistency distortion in the LFI. We further propose the weighted local binary pattern to capture the characteristics of local angular consistency degradation. Extensive experimental results on four publicly available LFI quality datasets demonstrate that the proposed method outperforms state-of-the-art 2D, 3D, multi-view, and LFI quality assessment algorithms.
In-Memory Subgraph Matching: An In-depth Study

We study the performance of eight representative in-memory subgraph matching algorithms. Specifically, we put QuickSI, GraphQL, CFL, CECI, DP-iso, RI and VF2++ in a common framework to compare them on the following four aspects: (1) method of filtering candidate vertices in the data graph; (2) method of ordering query vertices; (3) method of enumerating partial results; and (4) other optimization techniques. Then, we compare the overall performance of these algorithms with Glasgow, an algorithm based on the constraint programming. Through experiments, we find that (1) the filtering method of GraphQL is competitive to that of the latest algorithms CFL, CECI and DP-iso in terms of pruning power; (2) the ordering methods in GraphQL and RI are usually the most effective; (3) the set intersection based local candidate computation in CECI and DP-iso performs the best in the enumeration; and (4) the failing sets pruning in DP-iso can significantly improve the performance when queries become large. Our source code is publicly available at https://github.com/RapidsAtHKUST/SubgraphMatching.
Application of Blockchain Technology in Healthcare: A Comprehensive Study

Blockchain technology has been emerged in the last decade and has gained a lot of interests from several sectors such as finance, government, energy, health, etc. This paper gives a broad ranging survey of the application of blockchain in healthcare domain. In fact, the ongoing research in this area is evolving rapidly. Therefore, we have identified several use cases in the state of art applying the blockchain technology, for instance for sharing electronic medical records, for remote patient monitoring, for drug supply chain, etc. We have focused also on identifying limitations of studied approaches and finally we have discussed some open research issues and the areas of future research.
Distillation-Based Semi-Supervised Federated Learning for Communication-Efficient Collaborative Training With Non-IID Private Data

This study develops a federated learning (FL) framework overcoming largely incremental communication costs due to model sizes in typical frameworks without compromising model performance. To this end, based on the idea of leveraging an unlabeled open dataset, we propose a distillation-based semi-supervised FL (DS-FL) algorithm that exchanges the outputs of local models among mobile devices, instead of model parameter exchange employed by the typical frameworks. In DS-FL, the communication cost depends only on the output dimensions of the models and does not scale up according to the model size. The exchanged model outputs are used to label each sample of the open dataset, which creates an additionally labeled dataset. Based on the new dataset, local models are further trained, and model performance is enhanced owing to the data augmentation effect. We further highlight that in DS-FL, the heterogeneity of the devices’ dataset leads to ambiguous of each data sample and lowing of the training convergence. To prevent this, we propose entropy reduction averaging, where the aggregated model outputs are intentionally sharpened. Moreover, extensive experiments show that DS-FL reduces communication costs up to 99 percent relative to those of the FL benchmark while achieving similar or higher classification accuracy.
Understanding host network stack overheads

Traditional end-host network stacks are struggling to keep up with rapidly increasing datacenter access link bandwidths due to their unsustainable CPU overheads. Motivated by this, our community is exploring a multitude of solutions for future network stacks: from Linux kernel optimizations to partial hardware offload to clean-slate userspace stacks to specialized host network hardware. The design space explored by these solutions would benefit from a detailed understanding of CPU inefficiencies in existing network stacks.
High-fidelity reconstruction of turbulent flow from spatially limited data using enhanced super-resolution generative adversarial network

In this study, a deep learning-based approach is applied with the aim of reconstructing high-resolution turbulent flow fields using minimal flow fields data. A multi-scale enhanced super-resolution generative adversarial network with a physics-based loss function is introduced as a model to reconstruct the high-resolution flow fields. The model capability to reconstruct high-resolution laminar flows is examined using data of laminar flow around a square cylinder. The results reveal that the model can accurately reproduce the high-resolution flow fields even when limited spatial information is provided. The case of turbulent channel flow is used to assess the ability of the model to reconstruct the high-resolution wall-bounded turbulent flow fields. The instantaneous and statistical results obtained from the model agree well with the ground truth data, indicating that the model can successfully learn to map the coarse flow fields to the high-resolution once. Furthermore, the computational cost of the proposed model, which is examined carefully, is found to be effectively low. This demonstrates that using high-fidelity training data with physics-guided generative adversarial network-based models can be practically efficient in reconstructing high-resolution turbulent flow fields from extremely coarse data.
Dynamically Generated Logical Qubits

We present a quantum error correcting code with dynamically generated logical qubits. When viewed as a subsystem code, the code has no logical qubits. Nevertheless, our measurement patterns generate logical qubits, allowing the code to act as a fault-tolerant quantum memory. Our particular code gives a model very similar to the two-dimensional toric code, but each measurement is a two-qubit Pauli measurement.
A Novel Method to Solve Real Time Security Issues in Software Industry Using Advanced Cryptographic Techniques

In recent times, the utility and privacy are trade-off factors with the performance of one factor tends to sacrifice the other. Therefore, the dataset cannot be published without privacy. It is henceforth crucial to maintain an equilibrium between the utility and privacy of data. In this paper, a novel technique on trade-off between the utility and privacy is developed, where the former is developed with a metaheuristic algorithm and the latter is developed using a cryptographic model. The utility is carried out with the process of clustering, and the privacy model encrypts and decrypts the model. At first, the input datasets are clustered, and after clustering, the privacy of data is maintained. The simulation is conducted on the manufacturing datasets over various existing models. The results show that the proposed model shows improved clustering accuracy and data privacy than the existing models. The evaluation with the proposed model shows a trade-off privacy preservation and utility clustering in smart manufacturing datasets.
Quantum computational advantage via high-dimensional Gaussian boson sampling

Photonics is a promising platform for demonstrating a quantum computational advantage (QCA) by outperforming the most powerful classical supercomputers on a well-defined computational task. Despite this promise, existing proposals and demonstrations face challenges. Experimentally, current implementations of Gaussian boson sampling (GBS) lack programmability or have prohibitive loss rates. Theoretically, there is a comparative lack of rigorous evidence for the classical hardness of GBS. In this work, we make progress in improving both the theoretical evidence and experimental prospects. We provide evidence for the hardness of GBS, comparable to the strongest theoretical proposals for QCA. We also propose a QCA architecture we call high-dimensional GBS, which is programmable and can be implemented with low loss using few optical components. We show that particular algorithms for simulating GBS are outperformed by high-dimensional GBS experiments at modest system sizes. This work thus opens the path to demonstrating QCA with programmable photonic processors.
Motion planning and control for mobile robot navigation using machine learning: a survey

Moving in complex environments is an essential capability of intelligent mobile robots. Decades of research and engineering have been dedicated to developing sophisticated navigation systems to move mobile robots from one point to another. Despite their overall success, a recently emerging research thrust is devoted to developing machine learning techniques to address the same problem, based in large part on the success of deep learning. However, to date, there has not been much direct comparison between the classical and emerging paradigms to this problem. In this article, we survey recent works that apply machine learning for motion planning and control in mobile robot navigation, within the context of classical navigation systems. The surveyed works are classified into different categories, which delineate the relationship of the learning approaches to classical methods. Based on this classification, we identify common challenges and promising future directions.
Search-Based Drama Management in the Interactive Fiction Anchorhead

Drama managers guide a user through a story experience by modifying the experience in reaction to the user's actions. Search-based drama management (SBDM) casts the dramamanagement problem as a search problem: Given a set of plot points, a set of actions the drama manager can take, and an evaluation of story quality, search can be used to optimize the user's experience. SBDM was first investigated by Peter Weyhrauch in 1997, but little explored since. We return to SBDM to investigate algorithmic and authorship issues, including the extension of SBDM to different kinds of stories, especially stories with subplots and multiple endings, and issues of scalability. In this paper we report on experiments applying SBDM to an abstract story search space based on the text-based interactive fiction Anchorhead. We describe the features employed by the story evaluation function, investigate design issues in the selection of a set of drama management actions, and report results for drama managed versus unmanaged stories for a simulated random user
Deterministic Single-Ion Implantation of Rare-Earth Ions for Nanometer-Resolution Color-Center Generation

Single dopant atoms or dopant-related defect centers in a solid state matrix are of particular importance among the physical systems proposed for quantum computing and communication, due to their potential to realize a scalable architecture compatible with electronic and photonic integrated circuits. Here, using a deterministic source of single laser-cooled Pr+ ions, we present the fabrication of arrays of praseodymium color centers in yttrium-aluminum-garnet substrates. The beam of single Pr+ ions is extracted from a Paul trap and focused down to 30(9) nm. Using a confocal microscope, we determine a conversion yield into active color centers of up to 50% and realize a placement precision of 34 nm.Received 28 March 2019DOI:https://doi.org/10.1103/PhysRevLett.123.106802© 2019 American Physical SocietyPhysics Subject Headings (PhySH)Research AreasColor centersQuantum information with solid state qubitsPhysical SystemsRare-earth doped crystalsTrapped ionsTechniquesFocused ion beamIon implantationOptical microscopyAccelerators & BeamsCondensed Matter, Materials & Applied PhysicsQuantum Information
Initial coin offerings (ICOs): market cycles and relationship with bitcoin and ether

Abstract We apply a vector autoregression (VAR) model to investigate the market cycles of Initial Coin Offerings (ICOs) as well as their relationships with bitcoin and ether. Our sample covers 104 weekly observations between January 2017 and December 2018. Our results show that ICO market cycles exist and that shocks to the growth rates of ICO volumes are persistent. In addition, shocks in cryptocurrency returns have a substantial and positive effect on ICO volumes. In contrast, the volatility of cryptocurrency returns does not significantly affect ICO volumes. Our results are robust to using (i) the number of successfully completed ICO campaigns instead of ICO volumes and (ii) ICO data from a different data source. Our study has implications for financial practice, in particular for cryptocurrency investors and entrepreneurial firms conducting ICOs.
Neural News Recommendation with Topic-Aware News Representation

News recommendation can help users find interested news and alleviate information overload.The topic information of news is critical for learning accurate news and user representations for news recommendation.However, it is not considered in many existing news recommendation methods.In this paper, we propose a neural news recommendation approach with topic-aware news representations.The core of our approach is a topic-aware news encoder and a user encoder.In the news encoder we learn representations of news from their titles via CNN networks and apply attention networks to select important words.In addition, we propose to learn topic-aware news representations by jointly training the news encoder with an auxiliary topic classification task.In the user encoder we learn the representations of users from their browsed news and use attention networks to select informative news for user representation learning.Extensive experiments on a real-world dataset validate the effectiveness of our approach.
Unsupervised Video Summarization with Attentive Conditional Generative Adversarial Networks

With the rapid growth of video data, video summarization technique plays a key role in reducing people's efforts to explore the content of videos by generating concise but informative summaries. Though supervised video summarization approaches have been well studied and achieved state-of-the-art performance, unsupervised methods are still highly demanded due to the intrinsic difficulty of obtaining high-quality annotations. In this paper, we propose a novel yet simple unsupervised video summarization method with attentive conditional Generative Adversarial Networks (GANs). Firstly, we build our framework upon Generative Adversarial Networks in an unsupervised manner. Specifically, the generator produces high-level weighted frame features and predicts frame-level importance scores, while the discriminator tries to distinguish between weighted frame features and raw frame features. Furthermore, we utilize a conditional feature selector to guide GAN model to focus on more important temporal regions of the whole video frames. Secondly, we are the first to introduce the frame-level multi-head self-attention for video summarization, which learns long-range temporal dependencies along the whole video sequence and overcomes the local constraints of recurrent units, e.g., LSTMs. Extensive evaluations on two datasets, SumMe and TVSum, show that our proposed framework surpasses state-of-the-art unsupervised methods by a large margin, and even outperforms most of the supervised methods. Additionally, we also conduct the ablation study to unveil the influence of each component and parameter settings in our framework.