Reviewerly/exHarmony huggingface dataset:
Robust Cross-View Embedding With Discriminant Structure for Multi-Label Classification

Label embedding is an important family of multi-label classification algorithms which can jointly extract the information of all labels for better performance. However, few works have been done to develop the multi-label embedding methods that can effectively deal with the interference of noisy data during training process. The noise often makes the labels of a few samples incorrect ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e.</i> , missing or mislabeled), which could lead to a poor learning performance. To address this issue, we propose a novel cross-view based model. It performs a robust and discriminant embedding, namely Robust Cross-view Embedding with Discriminant Structure for Multi-label Classification (RCEDS). In RCEDS, a novel hypergraph fusion technique is designed to explore and utilize the complementary between the feature space and the label space to make the proposed RCEDS robust. Meanwhile, we use double-side metric learning to mine the consistency between the feature space and the label space to effectively improve the discriminative ability of our proposed RCEDS. Furthermore, we conduct a deep extension of RCEDS and effectively apply it to image annotation. Extensive experimental results on data sets with many labels demonstrate that our proposed approach can attain better classification performance than the existing label embedding algorithms.
FedRAN

In this paper, we propose FedRAN, a mobile edge, federated learning system that incorporates differential privacy to improve the privacy integrity of sensitive edge information, preventing adversarial entities from exploiting the network interactions within a federated ecosystem to access private edge data, while tapping into the vast amounts of data generated from distributed endpoints. We deploy and evaluate FedRAN in a real controlled radio-frequency LTE environment, as opposed to a simulated one. We show that FedRAN's distributed model outperforms locally-constrained models on the MNIST handwritten digits dataset. Additionally, we explore a variety of differential privacy settings, in an effort, to enable a privacy preserving, large scale mobile edge computing ecosystem. To our knowledge, our work is the first evaluation of a federated learning system within a controlled radio-frequency LTE environment.
Mobile Devices and Portative Classroom Based on Raspberry Pi Computers

In this paper the possibility of using Raspberry Pi microcomputers for effective training in practical skills of operating systems and computer networks administration and configuration is described. The algorithm for assembling a mobile device based on it and installing the software is given. The necessary resources and stages of the developing of individual training kits are provided. To complete the training kits a required number of devices and MicroSD with necessary operating systems and working environments, extra peripheral items, and study materials were evaluated.
Social Characteristic-Based Propagation-Efficient PBFT Protocol to Broadcast in Unstructured Overlay Networks

Blockchain allows for secure management of a shared ledger by agreement protocols, where transactions are validated over network without central authorities. Although the agreement protocol has been thoroughly conducted of propagation and consensus researches, mobility of nodes in unstructured overlay networks has not received much attention. Besides, current dynamic propagation schemes waste travel hops and are low of delivery ratio. In this article, we propose a social characteristic-based propagation-efficient protocol NefSBFT to agree on system state plus consensus mechanisms in public blockchains. We devise a propagation technique (travel hops of at least <inline-formula><tex-math notation="LaTeX">$\frac{1}{3}$</tex-math></inline-formula> savings, delivery ratio above 0.93, etc.) for message multicasting when exploiting real nodes’ social characteristics of intermittent connectivity and frequent partitions. This propagation technique is executed in the improved FastBFT to achieve transaction ordering and block verification, thus, no controllable mobility is required during the whole system’s execution. NefSBFT achieves fast propagation, small message complexity and few resource consumption of travel hops and running nodes for complete protocol execution. We analyze NefSBFT’s security against DDOS attack of non-primary failure. The experiments show the performance tradeoff under different parameters, compare the propagation efficiency with Erlay and Flooding, and clarify NefSBFT’s impact on the whole system performance through comparison.
Learning-Based Light Field View Synthesis for Efficient Transmission and Storage

One of the main advantages of Light Field (LF) technology is that it provides a truly immersive experience, critical for computer vision, autonomous driving and medical applications. However, one of the main problems of light field is the size of the data captured, which significantly increases bandwidth requirements. In this paper, we introduce a learning-based LF view synthesis approach for efficient transmission and storage, fundamental for performing remote surgery and storing data. This is achieved by dropping specific views at the transmitting end and then efficiently synthesizing them at the receiver end. Our deep learning approach uses the epipolar image plane (EPI) information to ensure smooth disparity between the generated and original views. We consider plenoptic, synthetic LF content and camera array implementations which support different baseline settings. Experimental results show that our proposed method outperforms state-of-the-art light field view synthesis techniques, offering improved visual quality for the generated views.
STGAE: Spatial-Temporal Graph Auto-Encoder for Hand Motion Denoising

Hand object interaction in mixed reality (MR) relies on the accurate tracking and estimation of human hands, which provide users with a sense of immersion. However, raw captured hand motion data always contains errors such as joints occlusion, dislocation, high-frequency noise, and involuntary jitter. Denoising and obtaining the hand motion data consistent with the user’s intention are of the utmost importance to enhance the interactive experience in MR. To this end, we propose an end-to-end method for hand motion denoising using the spatial-temporal graph auto-encoder (STGAE). The spatial and temporal patterns are recognized simultaneously by constructing the consecutive hand joint sequence as a spatial-temporal graph. Considering the complexity of the articulated hand structure, a simple yet effective partition strategy is proposed to model the physic-connected and symmetry-connected relationships. Graph convolution is applied to extract structural constraints of the hand, and a self-attention mechanism is to adjust the graph topology dynamically. Combining graph convolution and temporal convolution, a fundamental graph encoder or decoder block is proposed. We finally establish the hourglass residual auto-encoder to learn a manifold projection operation and a corresponding inverse projection through stacking these blocks. In this work, the proposed framework has been successfully used in hand motion data denoising with preserving structural constraints between joints. Extensive quantitative and qualitative experiments show that the proposed method has achieved better performance than the state-of-the-art approaches.
A study on specific learning algorithms pertaining to classify lung cancer disease

Abstract Lung cancer is a worldwide precarious disease and it is encouraged by the abnormal growth of cells in bronchi. Spotting the cancer cells is unknown until it leads to respiration issues and the muddling of organs working. Due to problems, limited or incorrect selection of hypothesis space, and dropping into local minima, single learners often give erratic output in an existing approach. The ensemble method accomplished a dataset that is free and composed of computed tomography (CT) images. The annotation process reveals observed lung lesions and provides a degree of malignancy for each lesion. Detection of benign and malignant nodules is recognized using deep convolutional frameworks AlexNet, SqueezeNet, GoogleNet, ResNet, and Inception ResNet, achieves higher accuracy (93%) than other convolutional neural networks (CNNs). Eight machine learning methods are involved for achieving better performance. The prediction probability obtained from CNN is applied as input to support vector machines (SVM), K‐nearest neighbours (KNN), naive Bayes (NB), multi‐layer perceptron (MLP), decision trees (DT), gradient boosted regression trees (GBRT), and adaptive boosting. The composition of GoogleNet model and AdaBoost classifier reached the most coherent classification accuracy as 99%. This is one of the best ways to analyse early detection and it increases the survival rate. Therefore, the result from the proposed deep CNN and ML technique achieves better precision than sputum cytology, X‐Ray process, and earlier detection of lung cancer.
Fall Prevention and Detection in Smart Homes Using Monocular Cameras and an Interactive Social Robot

Falls are one of the greatest risks for older adults living at home. They are also one of the biggest factors impacting independence and quality of life. Falling and even the fear of falling can lead to serious physical and mental health issues. Moreover, some afflictions like the self-neglect syndrome make this risk even greater. In this paper, we introduce a system to manage the fall risk before and after it happens, using monocular cameras and an humanoid robot. The proposed system achieves a 75% detection of trip-hazard objects in a heavily cluttered environment, and an 86.11% accuracy in detecting falls after they happened.
(Server-Aided) Two-Party Multiplication of Encrypted Shares Using (<i>k, n</i>) Threshold Secret Sharing With <i>N ≥ k</i> Servers

Two-party computation allows two clients to jointly compute an arbitrary function of their inputs without revealing these inputs to each other.In this study, we adopt a server-aided model, in which a set of computing servers performs computation using the inputs of two clients.In the (k, n)-threshold secret sharing scheme, input s is divided into n shares and can be recovered from k shares, where k is a threshold.Typically, the multiplication of shares increases the polynomial's degree from (k-1) to (2k-2), thus increasing the number of shares required from k to 2k-1.Because each server typically holds only one share, the number of servers required also increases to 2k-1.Therefore, a set of n servers can compute multiplication securely only if the adversary corrupts at most k-1<n/2 servers.In this study, we differentiate N, which is the number of required servers, and n, which is the parameter of the (k, n) threshold secret sharing scheme.We propose a method of multiplication by using only N≥k servers.This is implemented by sending two shares of the same input to each server.In a "normal" method, sending multiple shares to one server violates security because k shares can be leaked from k-1 servers.We overcome this by implementing a different functionality, where each share is first encrypted with a different random number (encrypted share) before being sent to a server.Instead of the "normal shares" of ab, our protocol computes the encrypted shares of ab using the encrypted shares of a and b.We show that the proposed method is secure against a non-colluding semi-honest adversary.Moreover, we implement our method in MATLAB and show its efficiency.
Research progress of measurement-based quantum computation

Compared with the quantum gate circuit model, the measurement-based quantum computing model provides an alternative way to realize universal quantum computation, and relevant contents have been greatly enriched after nearly two decades of research and exploration. In this article, we review the research history and status of the measurement-based quantum computing model. First, we briefly introduce the basic theories of this model, including the concept and working principles of quantum graph states as resource states, the model’s computational universality and classical simulation methods, and relevant applications in the field of quantum information processing such as designing quantum algorithms and fault-tolerant error correction schemes. Then, from the perspective of quantum physical properties, which include the specific roles of quantum entanglement, contextuality, quantum correlations, symmetry-protected topological order, and quantum phases of matter as computing resources, the close relationship between measurement-based quantum computing model and quantum many-body system is presented. For example, a type of measurement-based computing model for exploiting quantum correlations can show a quantum advantage over the classical local hidden variable models, or certain symmetry-protected topological order states enable the universal quantum computation to be conducted by using only the measurements of single-qubit Pauli operators. Next, a variety of different technical routes and experimental progress of realizing the measurement-based quantum computing model are summarized, such as photonic systems, ion traps, superconducting circuits, etc. These achievements in various physical areas lay the foundation for future scalable and fault-tolerant quantum computers. Finally, we discuss and prospect the future research directions in this field thereby inspiring readers to further study and explore the relevant subjects.
LightRoAD: Lightweight Rowhammer Attack Detector

Dynamic Random Access Memory (DRAM)-based systems are widely used in mobile and portable applications where low-cost and high-storage memory capability are required. However, such systems are prone to attacks. A latent threat to DRAM-based system security is the so-called Rowhammer attacks. By repeatedly accessing memory, an attacker is able to perform unauthorized data modifications into physically adjacent memory locations. As a consequence, powerful privilege-escalation attacks can be achieved. Although most of the known countermeasures are based on refresh strategies or intensive address monitoring, their efficient and low-cost realization is still a challenge. In this work, we present LightRoad, a lightweight and flexible hardware detector for Rowhammer attacks. Additionally, we propose two variants that further extend the LightRoad security, namely LightRoAD+Sec and LightRoAD+PARA. Our experiments show that LightRoad and its variants are very efficient and effective to detect attacks while having an affordable cost that varies according to the desired security level.
Detecting entanglement of quantum channels

Entanglement is the crucial resource for different quantum information processing tasks. While conventional studies focus on the entanglement of bipartite or multipartite quantum states, recent works have extended the scenario to the entanglement of quantum channels, an operational quantification of the channel entanglement manipulation capability. Based on the recently proposed channel entanglement resource framework, here we study a further task of resource detection—witnessing entanglement of quantum channels. We first introduce the general framework and show how channel entanglement detection is related to the Choi state of the channel, enabling channel entanglement detection via conventional state entanglement detection methods. We also consider entanglement of multipartite quantum channels and use the stabilizer formalism to construct entanglement witnesses for circuits consisting of controlled-Z gates. We study the effectiveness of the proposed detection methods and compare their performance for several typical channels. Our work paves the way for systematic theoretical studies of channel entanglement and practical benchmarking of noisy intermediate scaled quantum devices.
Human readable network troubleshooting based on anomaly detection and feature scoring

Network troubleshooting is still a heavily human-intensive process. To reduce the time spent by human operators in the diagnosis process, we present a system based on (i) unsupervised learning methods for detecting anomalies in the time domain, (ii) an attention mechanism to rank features in the feature space and finally (iii) an expert knowledge module able to seamlessly incorporate previously collected domain-knowledge. In this paper, we thoroughly evaluate the performance of the full system and of its individual building blocks: particularly, we consider (i) 10 anomaly detection algorithms as well as (ii) 10 attention mechanisms, that comprehensively represent the current state of the art in the respective fields. Leveraging a unique collection of expert-labeled datasets worth several months of real router telemetry data, we perform a thorough performance evaluation contrasting practical results in constrained stream-mode settings, with the results achievable by an ideal oracle in academic settings. Our experimental evaluation shows that (i) the proposed system is effective in achieving high levels of agreement with the expert, and (ii) that even a simple statistical approach is able to extract useful information from expert knowledge gained in past cases, significantly improving troubleshooting performance.
Dual-Complementary Convolution Network for Remote-Sensing Image Denoising

Remote-sensing images serve as key data sources which play a crucial role in recording the target information of ground features. Due to the limitations of the existing imaging equipment, environments, and transmission conditions, the obtained remote-sensing images are usually contaminated by noise in real-world scenarios. To address this problem, we propose a dual-complementary convolution network (DCCNet), including structural and detailed subnetwork, for repairing the structure and details of noisy remote-sensing images. More specifically, they generate multiresolution inputs via discrete wavelet transform and shuffling operation, respectively. Since the convolution operation is imposed on low-resolution inputs, the network parameters are considerably reduced. Experimental evaluations demonstrate that our proposed network exhibits superior performance to other competing methods in remote-sensing public datasets. The code of the DCCNet is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/20155104009/DCCNet</uri> .
Deformation-Invariant Networks For Handwritten Text Recognition

Image deformations under simple geometric restrictions are crucial for Handwriting Text Recognition (HTR), since different writing styles can be viewed as simple geometrical deformations of the same textual elements. In this respect, the usefulness of including deformation invariance to an HTR system is indisputable. We explore different existing strategies for ensuring deformation invariance, including spatial transformers and deformable convolutions, under the context of text recognition, as well as introduce a new deformation-based algorithm, inspired by adversarial learning, which aims to reduce character output uncertainty during evaluation time. The resulting HTR system is shown to achieve state-of-the-art performance on the IAM and RIMES datasets.
The gas triangle and its challenges to the development of blockchain-powered applications

Ethereum is the most popular blockchain platform for the development of blockchain-powered applications (a.k.a, ). Developing a involves translating requests captured in the frontend of an application into contract transactions. However, transactions need to be payed for. Ethereum employs the gas system to charge transaction fees. The gas system has three key components, namely gas price, gas usage, and gas limit. We refer to these components and their interplay as the gas triangle. In this paper, we claim that the inherently complex gas triangle should not be exposed to end-users. We conduct two studies that provide empirical evidence to support our claim. In light of our results, we provide a list of recommendations to novice end-users. We conclude the paper with a list of research challenges that need to be tackled in order to support the development of next-generation that completely hide the gas triangle from end-users.
Learned Image Compression with Channel-Wise Grouped Context Modeling

Learned image compression has achieved improved rate-distortion performance with end-to-end optimized framework based on deep neural networks. However, context-based entropy modeling for learned image compression cannot simultaneously achieve enhanced efficiency and sufficiently exploiting the channel-wise correlations. In this paper, we propose a novel framework for learned image compression with channel-wise grouped context modeling. The proposed framework presents channel-wise grouping to explicitly exploit the channel-wise correlations and develop a grouped 3-D context model to achieve efficient entropy coding with a guarantee of rate-distortion performance. The proposed framework achieves competitive performance with a significantly reduced decoding complexity in comparison to 3-D context models.
Learning View Selection for 3D Scenes

Efficient 3D space sampling to represent an underlying 3D object/scene is essential for 3D vision, robotics, and beyond. A standard approach is to explicitly sample a dense collection of views and formulate it as a view selection problem, or, more generally, a set cover problem. In this paper, we introduce a novel approach that avoids dense view sampling. The key idea is to learn a view prediction network and a trainable aggregation module that takes the predicted views as input and outputs an approximation of their generic scores (e.g., surface coverage, viewing angle from surface normals). This methodology allows us to turn the set cover problem (or multi-view representation optimization) into a continuous optimization problem. We then explain how to effectively solve the induced optimization problem using continuation, i.e., aggregating a hierarchy of smoothed scoring modules. Experimental results show that our approach arrives at similar or better solutions with about 10 x speed up in running time, comparing with the standard methods.
A Foveated Video Quality Assessment Model Using Space-Variant Natural Scene Statistics

In Virtual Reality (VR) systems, head mounted displays (HMDs) are widely used to present VR contents. When displaying immersive (360° video) scenes, greater challenges arise due to limitations of computing power, frame rate, and transmission bandwidth. To address these problems, a variety of foveated video compression and streaming methods have been proposed, which seek to exploit the nonuniform sampling density of the retinal photoreceptors and ganglion cells, which decreases rapidly with increasing eccentricity. Creating foveated immersive video content leads to the need for specialized foveated video quality pridictors. Here we propose a No-Reference (NR or blind) method which we call “Space-Variant BRISQUE (SV-BRISQUE),” which is based on a new space-variant natural scene statistics model. When tested on a large database of foveated, compression-distorted videos along with human opinions of them, we found that our new model algorithm achieves state of the art (SOTA) performance with correlation 0.88 / 0.90 (PLCC / SROCC) against human subjectivity.