Reviewerly/exHarmony huggingface dataset:
USID-Net: Unsupervised Single Image Dehazing Network via Disentangled Representations

Captured images of outdoor scenes usually exhibit low visibility in cases of severe haze, which interferes with optical imaging and degrades image quality. Most of the existing methods solve the single-image dehazing problem by applying supervised training on paired images; however, in practice, the pairing of real-world images is not viable. Additionally, the processing speed of individual dehazing models is important in practical applications. In this study, a novel unsupervised single image dehazing network (USID-Net) based on disentangled representations without paired training images is explored. Furthermore, considering the trade-off between performance and memory storage, a compact multi-scale feature attention (MFA) module is developed, integrating multi-scale feature representation and attention mechanism to facilitate feature representation. To effectively extract haze information, a mechanism referred to as OctEncoder is designed to include multi-frequency representations that can capture more global information. Extensive experiments show that USID-Net achieves competitive dehazing results and a relatively high processing speed compared to state-of-the-art methods. The source code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/dehazing/USID-Net</uri> .
AdaFocus V2: End-to-End Training of Spatial Dynamic Networks for Video Recognition

Recent works have shown that the computational efficiency of video recognition can be significantly improved by reducing the spatial redundancy. As a representative work, the adaptive focus method (AdaFocus) has achieved a favorable trade-off between accuracy and inference speed by dynamically identifying and attending to the informative regions in each video frame. However, AdaFocus requires a complicated three-stage training pipeline (involving reinforcement learning), leading to slow convergence and is unfriendly to practitioners. This work reformulates the training of AdaFocus as a simple one-stage algorithm by introducing a differentiable interpolation-based patch selection operation, enabling efficient end-to-end optimization. We further present an improved training scheme to address the issues introduced by the one-stage formulation, including the lack of supervision, input diversity and training stability. Moreover, a conditional-exit technique is proposed to perform temporal adaptive computation on top of AdaFocus without additional training. Extensive experiments on six benchmark datasets (i.e., ActivityNet, FCVID, Mini-Kinetics, Something-Something V1&V2, and Jester) demonstrate that our model significantly outperforms the original AdaFocus and other competitive baselines, while being considerably more simple and efficient to train. Code is available at https://github.com/LeapLabTHU/AdaFocusV2.
A Transfer Learning-Based Multi-Instance Learning Method With Weak Labels

In multi-instance learning (MIL), labels are associated with bags rather than the instances in the bag. Most of the previous MIL methods assume that each bag has the actual label in the training set. However, from the process of labeling work, the label of a bag is always evaluated by the calculation of the labels obtained from a number of labelers. In the calculation, the weight of each labeler is always unknown and people always assign the weight for each labeler by random or equally, and this may result in the ambiguous labels for the bags, which is called weak labels here. In addition, we always meet the problem of knowledge transfer from the source task to the target task, and this leads to the study of multiple instance transfer learning. In this article, we propose a new framework called transfer learning-based multiple instance learning (TMIL) framework to address the problem of multiple instance transfer learning in which both the source task and the target task contain the weak labels. We first construct a TMIL model with weak labels, which can transfer knowledge from the source task to the target task where both source and target tasks contain weak labels. We then put forward an iterative framework to solve the transfer learning model with weak labels so that we can update the label of the bag to improve the performance of multiple instance learning. We then present the convergence analysis of the proposed method. The experiments show that the proposed method outperforms the existing multiple instance learning methods and can correct the initial labels to obtain the actual labels for the bags.
Blockchain-Based Federated Learning With Secure Aggregation in Trusted Execution Environment for Internet-of-Things

This article proposes a blockchain-based federated learning (FL) framework with Intel Software Guard Extension (SGX)-based trusted execution environment (TEE) to securely aggregate local models in Industrial Internet-of-Things (IIoTs). In FL, local models can be tampered with by attackers. Hence, a global model generated from the tampered local models can be erroneous. Therefore, the proposed framework leverages a blockchain network for secure model aggregation. Each blockchain node hosts an SGX-enabled processor that securely performs the FL-based aggregation tasks to generate a global model. Blockchain nodes can verify the authenticity of the aggregated model, run a blockchain consensus mechanism to ensure the integrity of the model, and add it to the distributed ledger for tamper-proof storage. Each cluster can obtain the aggregated model from the blockchain and verify its integrity before using it. We conducted several experiments with different CNN models and datasets to evaluate the performance of the proposed framework.
FedLess: Secure and Scalable Federated Learning Using Serverless Computing

The traditional cloud-centric approach for Deep Learning (DL) requires training data to be collected and processed at a central server which is often challenging in privacy-sensitive domains like healthcare. Towards this, a new learning paradigm called Federated Learning (FL) has been proposed that brings the potential of DL to these domains while addressing privacy and data ownership issues. FL enables clients to learn a shared ML model while keeping the data local. However, conventional FL systems face challenges such as scalability, complex infrastructure management, and wasted compute and incurred costs due to idle clients. These challenges of FL systems closely align with the core problems that serverless computing and Function-as-a-Service (FaaS) platforms aim to solve. These include rapid scalability, no infrastructure management, automatic scaling to zero for idle clients, and a pay-per-use billing model. To this end, we present a novel system and framework for serverless FL, called FedLess. Our system supports multiple commercial and self-hosted FaaS providers and can be deployed in the cloud, on-premise in institutional data centers, and on edge devices. To the best of our knowledge, we are the first to enable FL across a large fabric of heterogeneous FaaS providers while providing important features like security and Differential Privacy. We demonstrate with comprehensive experiments that the successful training of DNNs for different tasks across up to 200 client functions and more is easily possible using our system. Furthermore, we demonstrate the practical viability of our methodology by comparing it against a traditional FL system and show that it can be cheaper and more resource-efficient.
RBGNet: Ray-based Grouping for 3D Object Detection

As a fundamental problem in computer vision, 3D object detection is experiencing rapid growth. To extract the point-wise features from the irregularly and sparsely distributed points, previous methods usually take a feature grouping module to aggregate the point features to an object candidate. However, these methods have not yet leveraged the surface geometry of foreground objects to enhance grouping and 3D box generation. In this paper, we propose the RBGNet framework, a voting-based 3D detector for accurate 3D object detection from point clouds. In order to learn better representations of object shape to enhance cluster features for predicting 3D boxes, we propose a ray-based feature grouping module, which aggregates the point-wise features on object surfaces using a group of determined rays uniformly emitted from cluster centers. Considering the fact that foreground points are more meaningful for box estimation, we design a novel foreground biased sampling strategy in downsample process to sample more points on object surfaces and further boost the detection performance. Our model achieves state-of-the-art 3D detection performance on ScanNet V2 and SUN RGB-D with remarkable performance gains. Code will be available at https://github.com/Haiyang-W/RBGNet.
Weakly Supervised Temporal Action Localization via Representative Snippet Knowledge Propagation

Weakly supervised temporal action localization aims to localize temporal boundaries of actions and simultaneously identify their categories with only video-level category labels. Many existing methods seek to generate pseudo labels for bridging the discrepancy between classification and localization, but usually only make use of limited contextual information for pseudo label generation. To alleviate this problem, we propose a representative snippet summarization and propagation framework. Our method seeks to mine the representative snippets in each video for propagating information between video snippets to generate better pseudo labels. For each video, its own representative snippets and the representative snippets from a memory bank are propagated to update the input features in an intra and inter-video manner. The pseudo labels are generated from the temporal class activation maps of the updated features to rectify the predictions of the main branch. Our method obtains superior performance in comparison to the existing methods on two benchmarks, THUMOS14 and ActivityNet1.3, achieving gains as high as 1.2% in terms of average mAP on THUMOS14. Our code is available at https://github.com/LeonHLJ/RSKP.
Iteratively Designing Gesture Vocabularies: A Survey and Analysis of Best Practices in the HCI Literature

Gestural interaction has evolved from a set of novel interaction techniques developed in research labs, to a dominant interaction modality used by millions of users everyday. Despite its widespread adoption, the design of appropriate gesture vocabularies remains a challenging task for developers and designers. Existing research has largely used Expert-Led, User-Led, or Computationally-Based methodologies to design gesture vocabularies. These methodologies leverage the expertise, experience, and capabilities of experts, users, and systems to fulfill different requirements. In practice, however, none of these methodologies provide designers with a complete, multi-faceted perspective of the many factors that influence the design of gesture vocabularies, largely because a singular set of factors has yet to be established. Additionally, these methodologies do not identify or emphasize the subset of factors that are crucial to consider when designing for a given use case. Therefore, this work reports on the findings from an exhaustive literature review that identified 13 factors crucial to gesture vocabulary design and examines the evaluation methods and interaction techniques commonly associated with each factor. The identified factors also enable a holistic examination of existing gesture design methodologies from a factor-oriented viewpoint and highlighting the strengths and weaknesses of each methodology. This work closes with proposals of future research directions of developing an iterative user-centered and factor-centric gesture design approach as well as establishing an evolving ecosystem of factors that are crucial to gesture design.
VolumeFusion: Deep Depth Fusion for 3D Scene Reconstruction

To reconstruct a 3D scene from a set of calibrated views, traditional multi-view stereo techniques rely on two distinct stages: local depth maps computation and global depth maps fusion. Recent studies concentrate on deep neural architectures for depth estimation by using conventional depth fusion method or direct 3D reconstruction network by regressing Truncated Signed Distance Function (TSDF). In this paper, we advocate that replicating the traditional two stages framework with deep neural networks improves both the interpretability and the accuracy of the results. As mentioned, our network operates in two steps: 1) the local computation of the local depth maps with a deep MVS technique, and, 2) the depth maps and images’ features fusion to build a single TSDF volume. In order to improve the matching performance between images acquired from very different viewpoints (e.g., large-baseline and rotations), we introduce a rotation-invariant 3D convolution kernel called PosedConv. The effectiveness of the proposed architecture is underlined via a large series of experiments conducted on the ScanNet dataset where our approach compares favorably against both traditional and deep learning techniques.
An Improved LPI Radar Waveform Recognition Framework With LDC-Unet and SSR-Loss

Low probability of intercept (LPI) radar signals have been widely used in modern radars due to the advantages of being hardly intercepted by non-cooperative receivers. Therefore, the waveform recognition of LPI radar signals has recently gained increasing attention. In this letter, an improved LPI radar waveform recognition framework with local dense connection U-net and novel loss function is proposed. Specifically, radar signals are transformed into time-frequency images (TFIs) through time-frequency analysis techniques. Then, the local dense connection U-net is proposed to reduce interference of noise and enhance TFIs features. Finally, the recognition task is implemented through a deep convolutional neural network, which is trained with a noise-robust loss function called SSR-loss. Compared with other existing LPI radar waveform recognition frameworks, our method can obtain a significant improvement in recognition accuracy under noise conditions. When signal-to-noise ratio (SNR) is as low as −10 dB, the overall probability of successful recognition on twelve kinds of typical modulated signals can reach up to 91.17%.
A Look Behind the Curtain: Traffic Classification in an Increasingly Encrypted Web

Traffic classification is essential in network management for operations ranging from capacity planning, performance monitoring, volumetry, and resource provisioning, to anomaly detection and security. Recently, it has become increasingly challenging with the widespread adoption of encryption in the Internet, e.g., as a de-facto in HTTP/2 and QUIC protocols. In the current state of encrypted traffic classification using Deep Learning (DL), we identify fundamental issues in the way it is typically approached. For instance, although complex DL models with millions of parameters are being used, these models implement a relatively simple logic based on certain header fields of the TLS handshake, limiting model robustness to future versions of encrypted protocols. Furthermore, encrypted traffic is often treated as any other raw input for DL, while crucial domain-specific considerations exist that are commonly ignored. In this paper, we design a novel feature engineering approach that generalizes well for encrypted web protocols, and develop a neural network architecture based on Stacked Long Short-Term Memory (LSTM) layers and Convolutional Neural Networks (CNN) that works very well with our feature design. We evaluate our approach on a real-world traffic dataset from a major ISP and Mobile Network Operator. We achieve an accuracy of 95% in service classification with less raw traffic and smaller number of parameters, out-performing a state-of-the-art method by nearly 50% fewer false classifications. We show that our DL model generalizes for different classification objectives and encrypted web protocols. We also evaluate our approach on a public QUIC dataset with finer and application-level granularity in labeling, achieving an overall accuracy of 99%.
Towards Privacy-Preserving Medical Cloud Computing Using Homomorphic Encryption

Personal health monitoring tools, such as commercially available wireless ECG patches, can significantly reduce healthcare costs by allowing patient monitoring outside the healthcare organizations. These tools transmit the acquired medical data into the cloud, which could provide an invaluable diagnosis tool for healthcare professionals. Despite the potential of such systems to revolutionize the medical field, the adoption of medical cloud computing in general has been slow due to the strict privacy regulations on patient health information. We present a novel medical cloud computing approach that eliminates privacy concerns associated with the cloud provider. Our approach capitalizes on Fully Homomorphic Encryption (FHE), which enables computations on private health information without actually observing the underlying data. For a feasibility study, we present a working implementation of a long-term cardiac health monitoring application using a well-established open source FHE library.
Entanglement-Enhanced Quantum Metrology in Colored Noise by Quantum Zeno Effect

In open quantum systems, the precision of metrology inevitably suffers from the noise. In Markovian open quantum dynamics, the precision can not be improved by using entangled probes although the measurement time is effectively shortened. However, it was predicted over one decade ago that in a non-Markovian one, the error can be significantly reduced by the quantum Zeno effect (QZE) [Chin, Huelga, and Plenio, Phys. Rev. Lett. 109, 233601 (2012)PRLTAO0031-900710.1103/PhysRevLett.109.233601]. In this work, we apply a recently developed quantum simulation approach to experimentally verify that entangled probes can improve the precision of metrology by the QZE. Up to n=7 qubits, we demonstrate that the precision has been improved by a factor of n^{1/4}, which is consistent with the theoretical prediction. Our quantum simulation approach may provide an intriguing platform for experimental verification of various quantum metrology schemes.
Making the Most of Text Semantics to Improve Biomedical Vision–Language Processing

Multi-modal data abounds in biomedicine, such as radiology images and reports. Interpreting this data at scale is essential for improving clinical care and accelerating clinical research. Biomedical text with its complex semantics poses additional challenges in vision–language modelling compared to the general domain, and previous work has used insufficiently adapted models that lack domain-specific language understanding. In this paper, we show that principled textual semantic modelling can substantially improve contrastive learning in self-supervised vision–language processing. We release a language model that achieves state-of-the-art results in radiology natural language inference through its improved vocabulary and novel language pretraining objective leveraging semantics and discourse characteristics in radiology reports. Further, we propose a self-supervised joint vision–language approach with a focus on better text modelling. It establishes new state of the art results on a wide range of publicly available benchmarks, in part by leveraging our new domain-specific language model. We release a new dataset with locally-aligned phrase grounding annotations by radiologists to facilitate the study of complex semantic modelling in biomedical vision–language processing. A broad evaluation, including on this new dataset, shows that our contrastive learning approach, aided by textual-semantic modelling, outperforms prior methods in segmentation tasks, despite only using a global-alignment objective.
Against statistical significance testing in corpus linguistics

Abstract In the first volume of Corpus Linguistics and Linguistic Theory , Gries (2005. Null-hypothesis significance testing of word frequencies: A follow-up on Kilgarriff. Corpus Linguistics and Linguistic Theory 1(2). doi:10.1515/cllt.2005.1.2.277. http://www.degruyter.com/view/j/cllt.2005.1.issue-2/cllt.2005.1.2.277/cllt.2005.1.2.277.xml : 285) asked whether corpus linguists should abandon null-hypothesis significance testing. In this paper, I want to revive this discussion by defending the argument that the assumptions that allow inferences about a given population – in this case about the studied languages – based on results observed in a sample – in this case a collection of naturally occurring language data – are not fulfilled. As a consequence, corpus linguists should indeed abandon null-hypothesis significance testing.
Energy-Aware Service Selection and Adaptation in Wireless Sensor Networks with QoS Guarantee

Workflow-based Service-oriented WSNs have recently received a lot of attention from both academia and industry. With well-defined service components, various flexible WSN applications can be developed. However, the key characteristic of WSNs is resource constraints. Sensor nodes in WSNs have limited storage, computation and especially limited energy. As service components in WSNs rely on the data gathered by sensor nodes, they are also resource-constrained. Unfortunately, traditional workflow technologies (i.e., service selection, composition and adaptation) ignore the residual energy of services. This will bring unbalanced energy consumption and furthermore shorten the network lifetime. In order to resolve this issue, we proposed an energy-aware QoS-guaranteed workflow management mechanism. In this mechanism, a new QoS model is first presented to improve the QoS evaluation. Then, based on this QoS model, an efficient service selection schema, which considers both the energy and the QoS of services, is proposed. Furthermore, an adaptation mechanism for balanced energy consumption is also proposed. Experimental evaluations demonstrate the capability of our proposed approach.
A multi-criteria ranking algorithm (MCRA) for determining breast cancer therapy

Breast cancer is the leading cause of cancer deaths among women. The selection of an effective, patient-specific treatment plan for breast cancer has been a challenge for physicians because the decision process involves a vast number of treatment alternatives as well as treatment decision criteria, such as the stage of the cancer (e.g., in situ, invasive, metastasis), tumor characteristics, biomarker-related risks, and patient-related risks. Furthermore, every patient's case is unique, requiring a patient-specific treatment plan, while there is no standard procedure even for a particular stage of the breast cancer. In this paper, we first determine a comprehensive set of criteria for selecting the best breast cancer therapy by interviewing medical oncologists and reviewing the literature. We then present two analytical hierarchy process (AHP) models for quantifying the weights of criteria for breast cancer treatment in two sequential steps: primary and secondary treatment therapy. Using the weights of criteria from the AHP model, we propose a new multi-criteria ranking algorithm (MCRA), which evaluates a large variety of patient scenarios and provides the best patient-tailored breast cancer treatment alternatives based on the input of nine medical oncologists. We then validate the predictions of the multi-criteria ranking algorithm by comparing treatment ranks of the algorithm with ranks of five different oncologists, and show that algorithm rankings match or are statistically significantly correlated with the overall expert ranking in most cases. Our multi-criteria ranking algorithm could be used as an accessible decision-support tool to aid oncologists and educate patients for determining appropriate and effective treatment alternatives for breast cancer. Our approach is also general in the sense that it could be adapted to solve other complex decision-making problems in medicine, healthcare, as well as other service and manufacturing industries.