Reviewerly/exHarmony huggingface dataset:
Normalized Diversification

Generating diverse yet specific data is the goal of the generative adversarial network (GAN), but it suffers from the problem of mode collapse. We introduce the concept of normalized diversity which force the model to preserve the normalized pairwise distance between the sparse samples from a latent parametric distribution and their corresponding high-dimensional outputs. The normalized diversification aims to unfold the manifold of unknown topology and non-uniform distribution, which leads to safe interpolation between valid latent variables. By alternating the maximization over the pairwise distance and updating the total distance (normalizer), we encourage the model to actively explore in the high-dimensional output space. We demonstrate that by combining the normalized diversity loss and the adversarial loss, we generate diverse data without suffering from mode collapsing. Experimental results show that our method achieves consistent improvement on unsupervised image generation, conditional image generation and hand pose estimation over strong baselines.
Eye Movements in Real-World Scene Photographs: General Characteristics and Effects of Viewing Task

The present study examines eye movement behavior in real-world scenes with a large (N=100) sample. We report baseline measures of eye movement behavior in our sample, including mean fixation duration, saccade amplitude, and initial saccade latency. We also characterize how eye movement behaviors change over the course of a 12 second trial. These baseline measures will be of use to future work studying eye movement behavior in scenes in a variety of literatures. We also examine effects of viewing task on when and where the eyes move in real-world scenes: participants engaged in a memorization and an aesthetic judgement task while viewing 100 scenes. While we find no difference at the mean-level between the two tasks, temporal-and distribution-level analyses reveal significant task-driven differences in eye movement behavior.
Lightweight and Accurate Recursive Fractal Network for Image Super-Resolution

Convolutional neural networks have recently achieved great success in image super-resolution (SR). However, we notice an interesting phenomenon that these SR models are getting bigger, deeper, and more complex. Extensive models promote the development of SR, but the effectiveness, reproducibility and practical application prospects of these new models need further verification. In this paper, we propose a lightweight and accurate SR framework, named Super-Resolution Recursive Fractal Network (SRRFN). SRRFN introduces a flexible and diverse fractal module, which enables it to construct infinitely possible topological sub-structure through a simple component. We also introduce the recursive learning mechanism to maximize the use of model parameters. Extensive experiments show that our SRRFN achieves favorable performance against state-of-the-art methods with fewer parameters and less execution time. All code is available at https://github.com/MIVRC/SRRFN-PyTorch.
A fair selection protocol for committee-based permissionless blockchains

Committee-based blockchain consensus protocols combine permissionless consensus and classical state machine replication protocols to process transactions efficiently. Due to corruptions by the adversary, reconfiguration mechanisms have to be deployed to update committee members. How to select enough fraction of honest nodes is a key issue that needs to be addressed. In this paper, we propose a fair selection protocol for reconfiguring a committee in a permissionless blockchain. Our fair selection protocol consists of two main phases: the mining process and the confirmation of the new nodes list. We analyze the impact of the network latency and give a rigorous proof of the mining process. Furthermore, we point out that in the process of confirming the new nodes list, “node censorship” by a malicious leader could lead to an increase in the proportion of new nodes controlled by an adversary. We propose a threshold-vote rule to defend against the node censorship attack and prove the security of the entire protocol, including safety and liveness properties. Our fair selection protocol could be implemented in committee-based permissionless blockchains according to different demands with proper parameters.
A Blockchain-based Framework for Central Bank Digital Currency

Cryptocurrency and blockchain technologies have developed in parallel in recent years, with technological breakthroughs in currency issuance, payment methods, and currency storage. However, the existing cryptocurrencies cannot replace fiat money. There is a huge gap between decentralized cryptocurrency and central bank digital currency, namely CBDC, in terms of monetary governance and circulation. In this paper, we propose the function and security requirements of CBDC, through a comprehensive analysis of the existing typical cryptocurrency and the prototype of the CBDC scheme. On this basis, we present a blockchain-based framework for CBDC with three layers, including supervisory layer, network layer and user layer, and describe the key business processes of the CBDC's entire lifecycle of issuance-circulation-withdrawal in detail. Finally, we take cross-border payment as an example to explain the transaction process of CBDC. We aim to provide theoretical guidance for CBDC design.
Efficient Graph Learning From Noisy and Incomplete Data

We consider the problem of learning a graph from a given set of smooth graph signals. Our graph learning approach is formulated as a constrained quadratic program in the edge weights. We provide an implicit characterization of the optimal solution and propose a tailored ADMM algorithm to solve this problem efficiently. Several nearest neighbor and smoothness based graph learning methods are shown to be special cases of our approach. Specifically, our algorithm yields an efficient but extremely accurate approximation to b-matched graphs. We then propose a generalization of our scheme that can deal with noisy and incomplete data via joint graph learning and signal inpainting. We compare the performance of our approach with state-of-the art methods on synthetic data and on real-world data from the Austrian National Council.
Exploring Age Differences in Motivations for and Acceptance of Chatbot Communication in a Customer Service Context

This qualitative interview study explores age differences in perceptions of chatbot communication in a customer service context. Socioemotional selectivity theory and research into technology acceptance suggest that older adults may differ from younger adults in motivations to use chatbots, and in perceived complexity and security of this chatbot communication. The in-depth interviews with older adults (54–81 years; N = 7) and younger adults (19–30 years; N = 7) revealed that both groups were aligned in their prime motivation: They used chatbots to get their (simple) customer queries answered in a fast and convenient manner. However, they seemed to differ in their need for additional human contact. In both age groups, there were participants for whom it was easy to communicate with chatbots, and the two groups were united in their frustrations when the chatbot did not understand and answer their queries. They were aligned as well in the difficulty they experienced in assessing the security of the chatbot. The two age groups may differ in the factors that contribute to perceived ease of use and perceived security. Directions for future research and implications for the implementation of chatbots for customer service are discussed.
A Machine Learning Based Approach to Identify SQL Injection Vulnerabilities

This paper presents a machine learning classifier designed to identify SQL injection vulnerabilities in PHP code. Both classical and deep learning based machine learning algorithms were used to train and evaluate classifier models using input validation and sanitization features extracted from source code files. On ten-fold cross validations a model trained using Convolutional Neural Network(CNN) achieved the highest precision (95.4%), while a model based on Multilayer Perceptron(MLP) achieved the highest recall (63.7%) and the highest f-measure (0.746).
Human Behavior Recognition from Multiview Videos

With the proliferation of deep learning techniques, a significant number of applications related to home care systems have emerged recently. In particular, detecting abnormal events in a smart home environment has been extensively studied. In this paper, we adopt deep learning techniques, including convolutional neural networks (CNNs) and long short-term memory (LSTM) networks, to construct deep networks to learn the long-term dependencies from videos for human behavior recognition in a multiview framework. We adopt two cameras as our sensors to efficiently overcome the problem of occlusions and contour ambiguity for improving the accuracy performance of the multiview framework. After performing a series of image preprocessing on the raw data, we obtain human silhouette images as the input to our training model. In addition, because real-world datasets are complicated for analysis, labeling data is time consuming. Therefore, we present an image clustering method based on a stacked convolutional autoencoder (SCAE), which generates clustering labels for autolabeling. Finally, we set up our experimental environment as a normal residence to collect a large dataset, and the experimental results demonstrate the novelty of our proposed models.
Spatially weighted order binary pattern for color texture classification

In this paper, we propose a novel descriptor called spatially weighted order binary pattern (SWOBP) for color texture classification. The SWOBP descriptor not only encodes color order information in different channels but also encodes color order relationships in the spatial domain. To achieve these goals, we introduce a color gradient channel to complement the traditional color channels and explore a multi-channel color order pattern to jointly encode inter-channel features. Furthermore, we decompose local color differences into spatially weighted binary templates and use them to encode color order information in a local neighborhood. Finally, we aggregate all the encoded features into image histograms as texture descriptor. Experiments on five benchmark databases demonstrate that the proposed SWOBP descriptor achieves the state-of-the-art performance for color texture classification.
High coherence superconducting microwave cavities with indium bump bonding

Low-loss cavities are important in building high-coherence superconducting quantum computers. Generating high-quality joints between parts is crucial for the realization of a scalable quantum computer using the circuit quantum electrodynamics (cQED) framework. In this paper, we adapt the technique of indium bump bonding to cQED to realize high-quality superconducting microwave joints between chips. We use this technique to fabricate compact superconducting cavities in the multilayer microwave integrated quantum circuit (MMIQC) architecture and achieve single photon quality factors over 300 × 106 or single-photon lifetimes approaching 5 ms. To quantify the performance of the resulting seam, we fabricate microwave stripline resonators in multiple sections connected by different numbers of bonds, resulting in a wide range of seam admittances. The measured quality factors combined with the designed seam admittances allow us to bound the conductance of the seam at gseam≥2×1010/(Ω m). Such a conductance should enable the construction of micromachined superconducting cavities with a quality factor of at least a billion. These results demonstrate the capability to construct very high-quality microwave structures within the MMIQC architecture.
Quantification of Morphological Features in Non-Contrast-Enhanced Ultrasound Microvasculature Imaging

There are significant differences in microvascular morphological features in diseased tissues, such as cancerous lesions, compared to noncancerous tissue.Quantification of microvessel morphological features could play an important role in disease diagnosis and tumor classification.However, analyzing microvessel morphology in ultrasound Doppler is a challenging task due to limitations associated with this technique.Our main objective is to provide methods for quantifying morphological features of microvasculature obtained by ultrasound Doppler imaging.To achieve this goal, we propose multiple image enhancement techniques and appropriate morphological feature extraction methods that enable quantitative analysis of microvasculature structures.Vessel segments obtained by the skeletonization of the regularized microvasculature images are further analyzed to satisfy other constraints, such as vessel segment diameter and length.Measurements of some morphological metrics, such as tortuosity, depend on preserving large vessel trunks.To address this issue, additional filtering methods are proposed.These methods are tested on in vivo images of breast lesion and thyroid nodule microvasculature, and the outcomes are discussed.Initial results show that using vessel morphological features allows for differentiation between malignant and benign breast lesions (p-value < 0.005) and thyroid nodules (p-value < 0.01).This paper provides a tool for the quantification of microvasculature images obtained by non-contrast ultrasound imaging, which may serve as potential biomarkers for the diagnosis of some diseases.
AdapNet: Adaptability Decomposing Encoder–Decoder Network for Weakly Supervised Action Recognition and Localization

The point process is a solid framework to model sequential data, such as videos, by exploring the underlying relevance. As a challenging problem for high-level video understanding, weakly supervised action recognition and localization in untrimmed videos have attracted intensive research attention. Knowledge transfer by leveraging the publicly available trimmed videos as external guidance is a promising attempt to make up for the coarse-grained video-level annotation and improve the generalization performance. However, unconstrained knowledge transfer may bring about irrelevant noise and jeopardize the learning model. This article proposes a novel adaptability decomposing encoder-decoder network to transfer reliable knowledge between the trimmed and untrimmed videos for action recognition and localization by bidirectional point process modeling, given only video-level annotations. By decomposing the original features into the domain-adaptable and domain-specific ones based on their adaptability, trimmed-untrimmed knowledge transfer can be safely confined within a more coherent subspace. An encoder-decoder-based structure is carefully designed and jointly optimized to facilitate effective action classification and temporal localization. Extensive experiments are conducted on two benchmark data sets (i.e., THUMOS14 and ActivityNet1.3), and the experimental results clearly corroborate the efficacy of our method.
Efficient Sampling and Structure Learning of Bayesian Networks

Bayesian networks are probabilistic graphical models widely employed to understand dependencies in high-dimensional data, and even to facilitate causal discovery. Learning the underlying network structure, which is encoded as a directed acyclic graph (DAG) is highly challenging mainly due to the vast number of possible networks in combination with the acyclicity constraint. Efforts have focused on two fronts: constraint-based methods that perform conditional independence tests to exclude edges and score and search approaches which explore the DAG space with greedy or MCMC schemes. Here, we synthesize these two fields in a novel hybrid method which reduces the complexity of MCMC approaches to that of a constraint-based method. Individual steps in the MCMC scheme only require simple table lookups so that very long chains can be efficiently obtained. Furthermore, the scheme includes an iterative procedure to correct for errors from the conditional independence tests. The algorithm offers markedly superior performance to alternatives, particularly because it also offers the possibility to sample DAGs from their posterior distribution, enabling full Bayesian model averaging for much larger Bayesian networks. Supplementary materials for this article are available online.
An Examination of Motivation and Routine Activity Theory to Account for Cyberattacks Against Dutch Web Sites

This study provides a partial test of the relationship between actor motivations and target suitability using a routine activity framework to understand a form of cybercrime called web defacements. Specifically, the relationships between the visibility, inertia, value, and accessibility of the target in online spaces relative to the unique nonmonetary motivations of the attacker were examined. This study utilized a sample of 138,361 web defacements performed against websites hosted within the Netherlands IP space from January 2011 to April 2017. Seven multinomial logistic regression models were conducted for each self-identified motive for the attack, clustered by attacker to minimize the size of standard errors. The findings demonstrated partial support for aspects of routine activity theory to account for differences in offender motivation, suggesting web defacements are similar to other forms of cybercrime. At the same time, motivations differentially shape target selection.
Generalized optical encryption framework based on Shearlets for medical image

The security of medical image data for transmission and storage is a critical issue. Texture information of medical images is important for diagnosis. Since Shearlets is particularly effective in characterizing texture information, in this paper, we propose a generalized optical encryption framework based on Shearlets and double random phase encoding (DRPE) especially for medical images. In the proposed encryption framework, the secret medical image is first decomposed into n sub-images with shearlet transform. Then, the sub-images or pixel positions are shuffled by scrambling algorithm. Subsequently, the shuffled sub-images are synthesized into one image. Finally, the synthesized image is encoded with DRPE. The security level of the proposed encryption framework can be improved by changing the parameters of shearlet transform and selecting different methods for scrambling pixels and synthesizing multiple images to single image. Extensive simulation results have shown the performance of the proposed optical encryption framework. The proposed encryption framework can be improved along with novelty scrambling method and reversible synthesize methods.
Automatic Generation of Multi-Precision Multi-Arithmetic CNN Accelerators for FPGAs

Modern deep Convolutional Neural Networks (CNNs) are computationally demanding, yet real applications often require high throughput and low latency. To help tackle these problems, we propose Tomato, a framework designed to automate the process of generating efficient CNN accelerators. The generated design is pipelined and each convolution layer uses different arithmetics at various precisions. Using Tomato, we showcase state-of-the-art multi-precision multi-arithmetic networks, including MobileNet-V1, running on FPGAs. To our knowledge, this is the first multi-precision multi-arithmetic autogeneration framework for CNNs. In software, Tomato fine-tunes pretrained networks to use a mixture of short powers-of-2 and fixed-point weights with a minimal loss in classification accuracy. The fine-tuned parameters are combined with the templated hardware designs to automatically produce efficient inference circuits in FPGAs. We demonstrate how our approach significantly reduces model sizes and computation complexities, and permits us to pack a complete ImageNet network onto a single FPGA without accessing off-chip memories for the first time. Furthermore, we show how Tomato produces implementations of networks with various sizes running on single or multiple FPGAs. To the best of our knowledge, our automatically generated accelerators outperform closest FPGA-based competitors by at least 2-4× for lantency and throughput; the generated accelerator runs ImageNet classification at a rate of more than 3000 frames per second.
Novel trajectory privacy-preserving method based on clustering using differential privacy

With the development of location-aware technology, a large amount of location data of users is collected by the trajectory database. If these trajectory data are directly used for data mining without being processed, it will pose a threat to the user's personal privacy. At the moment, differential privacy is favored by experts and scholars because of its strict mathematical rigor, but how to apply differential privacy technology to trajectory clustering analysis is a difficult problem. To solve the problems in which existing trajectory privacy-preserving models have poor data availability or difficulty to resist complex privacy attacks, we devise novel trajectory privacy-preserving method based on clustering using differential privacy. More specifically, Laplacian noise is added to the count of trajectory location in the cluster to resist the continuous query attack. Then, radius-constrained Laplacian noise is added to the trajectory location data in the cluster to avoid too much noise affecting the clustering effect. According to the noise location data and the count of noise location, the noise clustering center in the cluster is obtained. Finally, it is considered that the attacker can associate the user trajectory with other information to form secret reasoning attack, and secret reasoning attack model is proposed. And we use the differential privacy technology to give corresponding resistance. Experimental results using the open data show that the proposed algorithm can not only effectively protect the private information of the trajectory data, but also ensure the data availability in cluster analysis. And compared with other algorithms, our algorithm has good effect on some evaluation indicators.
Leveraging Hierarchical Representations for Preserving Privacy and Utility in Text

Guaranteeing a certain level of user privacy in an arbitrary piece of text is a challenging issue. However, with this challenge comes the potential of unlocking access to vast data stores for training machine learning models and supporting data driven decisions. We address this problem through the lens of dx-privacy, a generalization of Differential Privacy to non Hamming distance metrics. In this work, we explore word representations in Hyperbolic space as a means of preserving privacy in text. We provide a proof satisfying dx-privacy, then we define a probability distribution in Hyperbolic space and describe a way to sample from it in high dimensions. Privacy is provided by perturbing vector representations of words in high dimensional Hyperbolic space to obtain a semantic generalization. We conduct a series of experiments to demonstrate the tradeoff between privacy and utility. Our privacy experiments illustrate protections against an authorship attribution algorithm while our utility experiments highlight the minimal impact of our perturbations on several downstream machine learning models. Compared to the Euclidean baseline, we observe > 20x greater guarantees on expected privacy against comparable worst case statistics.