Reviewerly/exHarmony huggingface dataset:
EmergencyNet: Efficient Aerial Image Classification for Drone-Based Emergency Monitoring Using Atrous Convolutional Feature Fusion

Deep learning-based algorithms can provide state-of-the-art accuracy for remote sensing technologies such as unmanned aerial vehicles (UAVs)/drones, potentially enhancing their remote sensing capabilities for many emergency response and disaster management applications. In particular, UAVs equipped with camera sensors can operating in remote and difficult to access disaster-stricken areas, analyze the image and alert in the presence of various calamities such as collapsed buildings, flood, or fire in order to faster mitigate their effects on the environment and on human population. However, the integration of deep learning introduces heavy computational requirements, preventing the deployment of such deep neural networks in many scenarios that impose low-latency constraints on inference, in order to make mission-critical decisions in real time. To this end, this article focuses on the efficient aerial image classification from on-board a UAV for emergency response/monitoring applications. Specifically, a dedicated Aerial Image Database for Emergency Response applications is introduced and a comparative analysis of existing approaches is performed. Through this analysis a lightweight convolutional neural network architecture is proposed, referred to as EmergencyNet, based on atrous convolutions to process multiresolution features and capable of running efficiently on low-power embedded platforms achieving upto 20× higher performance compared to existing models with minimal memory requirements with less than 1% accuracy drop compared to state-of-the-art models.
Robust Learning Through Cross-Task Consistency

Visual perception entails solving a wide set of tasks (e.g., object detection, depth estimation, etc). The predictions made for different tasks out of one image are not independent, and therefore, are expected to be 'consistent'. We propose a flexible and fully computational framework for learning while enforcing Cross-Task Consistency (X-TAC). The proposed formulation is based on 'inference path invariance' over an arbitrary graph of prediction domains. We observe that learning with cross-task consistency leads to more accurate predictions, better generalization to out-of-distribution samples, and improved sample efficiency. This framework also leads to a powerful unsupervised quantity, called 'Consistency Energy, based on measuring the intrinsic consistency of the system. Consistency Energy well correlates with the supervised error (r=0.67), thus it can be employed as an unsupervised robustness metric as well as for detection of out-of-distribution inputs (AUC=0.99). The evaluations were performed on multiple datasets, including Taskonomy, Replica, CocoDoom, and ApolloScape.
Privacy-Preserving News Recommendation Model Learning

News recommendation aims to display news articles to users based on their personal interest.Existing news recommendation methods rely on centralized storage of user behavior data for model training, which may lead to privacy concerns and risks due to the privacy-sensitive nature of user behaviors.In this paper, we propose a privacy-preserving method for news recommendation model training based on federated learning, where the user behavior data is locally stored on user devices.Our method can leverage the useful information in the behaviors of massive number users to train accurate news recommendation models and meanwhile remove the need of centralized storage of them.More specifically, on each user device we keep a local copy of the news recommendation model, and compute gradients of the local model based on the user behaviors in this device.The local gradients from a group of randomly selected users are uploaded to server, which are further aggregated to update the global model in the server.Since the model gradients may contain some implicit private information, we apply local differential privacy (LDP) to them before uploading for better privacy protection.The updated global model is then distributed to each user device for local model update.We repeat this process for multiple rounds.Extensive experiments on a real-world dataset show the effectiveness of our method in news recommendation model training with privacy protection.
Research and application of wireless sensor network technology in power transmission and distribution system

Power is an important part of the energy industry, relating to national economy and people's livelihood, and it is of great significance to ensure the security and stability in operation of power transmission and distribution system. Based on Wireless Sensor Network technology (WSN) and combined with the monitoring and operating requirements of power transmission and distribution system, this paper puts forward an application system for monitoring, inspection, security, and interactive service of layered power transmission and distribution system. Furthermore, this paper demonstrates the system verification projects in Wuxi, Jiangsu Province and Lianxiangyuan Community in Beijing, which have been widely used nationwide.
A Compact Hardware Implementation of CCA-Secure Key Exchange Mechanism CRYSTALS-KYBER on FPGA

Post-quantum cryptosystems should be prepared before the advent of powerful quantum computers to ensure information secure in our daily life. In 2016 a post-quantum standardization contest was launched by National Institute of Standards and Technology (NIST), and there have been lots of works concentrating on evaluation of these candidate protocols, mainly in pure software or through hardware-software co-design methodology on different platforms. As the contest progresses to third round in July 2020 with only 7 finalists and 8 alternate candidates remained, more dedicated and specific hardware designs should be considered to illustrate the intrinsic property of a certain protocol and achieve better performance. To this end, we present a standalone hardware design of CRYSTALS-KYBER, amodule learning-with-errors (MLWE) based key exchange mechanism (KEM) protocol within the 7 finalists on FPGA platform. Through elaborate scheduling of sampling and number theoretic transform (NTT) related calculations, decent performance is achieved with limited hardware resources. The way that Encode/Decode and the tweaked Fujisaki-Okamoto transform are implemented is demonstrated in detail. Analysis about minimizing memory footprint is also given out. In summary, we realize the adaptive chosen ciphertext attack (CCA) secure Kyber with all selectable module dimension k on the smallest Xilinx Artix-7 device. Our design computes key-generation, encapsulation (encryption) and decapsulation (decryption and reencryption) phase in 3768/5079/6668 cycles when k = 2, 6316/7925/10049 cycles when k = 3, and 9380/11321/13908 cycles when k = 4, consuming 7412/6785 LUTs, 4644/3981 FFs, 2126/1899 slices, 2/2 DSPs and 3/3 BRAMs in server/client with 6.2/6.0 ns critical path delay, outperforming corresponding high level synthesis (HLS) based designs or hardware-software co-designs to a large extent.
NetSquid, a NETwork Simulator for QUantum Information using Discrete events

In order to bring quantum networks into the real world, we would like to determine the requirements of quantum network protocols including the underlying quantum hardware. Because detailed architecture proposals are generally too complex for mathematical analysis, it is natural to employ numerical simulation. Here we introduce NetSquid, the NETwork Simulator for QUantum Information using Discrete events, a discrete-event based platform for simulating all aspects of quantum networks and modular quantum computing systems, ranging from the physical layer and its control plane up to the application level. We study several use cases to showcase NetSquid's power, including detailed physical layer simulations of repeater chains based on nitrogen vacancy centres in diamond as well as atomic ensembles. We also study the control plane of a quantum switch beyond its analytically known regime, and showcase NetSquid's ability to investigate large networks by simulating entanglement distribution over a chain of up to one thousand nodes.
The effect of digital marketing capabilities on organizational ambidexterity of the information technology sector

The aim of the study was to examine the impact of digital marketing capabilities on organizational ambidexterity by focusing on the Information Technology Sector in UAE. Data were primarily gathered through self-reported questionnaires created by Google Forms which were distributed to a purposive sample of managers at different levels via email. This study was conducted structural equation modeling (SEM) to test the hypotheses, which represents a contemporary statistical technique for testing and estimating the relationship between factors and variables. The results showed that the highest impact on organizational ambidexterity was for strategic approach and data content infrastructure, followed by integrating customers with employees, and finally the lowest impact belonged to the process of improving performance. Based on the study findings, the researcher hopes that the decision-makers and managers define all tasks, roles and work procedures in companies through digital marketing systems to improve their organizational ambidexterity and enhance their performance.
Explainable Recommendation through Attentive Multi-View Learning

Recommender systems have been playing an increasingly important role in our daily life due to the explosive growth of information. Accuracy and explainability are two core aspects when we evaluate a recommendation model and have become one of the fundamental trade-offs in machine learning. In this paper, we propose to alleviate the trade-off between accuracy and explainability by developing an explainable deep model that combines the advantages of deep learning-based models and existing explainable methods. The basic idea is to build an initial network based on an explainable deep hierarchy (e.g., Microsoft Concept Graph) and improve the model accuracy by optimizing key variables in the hierarchy (e.g., node importance and relevance). To ensure accurate rating prediction, we propose an attentive multi-view learning framework. The framework enables us to handle sparse and noisy data by co-regularizing among different feature levels and combining predictions attentively. To mine readable explanations from the hierarchy, we formulate personalized explanation generation as a constrained tree node selection problem and propose a dynamic programming algorithm to solve it. Experimental results show that our model outperforms state-of-the-art methods in terms of both accuracy and explainability.
Partial Multi-Label Learning via Credible Label Elicitation

In partial multi-label learning (PML), each training example is associated with multiple candidate labels which are only partially valid. The task of PML naturally arises in learning scenarios with inaccurate supervision, and the goal is to induce a multi-label predictor which can assign a set of proper labels for unseen instance. To learn from PML training examples, the training procedure is prone to be misled by the false positive labels concealed in candidate label set. In light of this major difficulty, a novel two-stage PML approach is proposed which works by eliciting credible labels from the candidate label set for model induction. In this way, most false positive labels are expected to be excluded from the training procedure. Specifically, in the first stage, the labeling confidence of candidate label for each PML training example is estimated via iterative label propagation. In the second stage, by utilizing credible labels with high labeling confidence, multi-label predictor is induced via pairwise label ranking with virtual label splitting or maximum a posteriori (MAP) reasoning. Extensive experiments on synthetic as well as real-world data sets clearly validate the effectiveness of credible label elicitation in learning from PML examples.
Beyond BLEU:Training Neural Machine Translation with Semantic Similarity

While most neural machine translation (NMT) systems are still trained using maximum likelihood estimation, recent work has demonstrated that optimizing systems to directly improve evaluation metrics such as BLEU can substantially improve final translation accuracy.However, training with BLEU has some limitations: it doesn't assign partial credit, it has a limited range of output values, and it can penalize semantically correct hypotheses if they differ lexically from the reference.In this paper, we introduce an alternative reward function for optimizing NMT systems that is based on recent work in semantic similarity.We evaluate on four disparate languages translated to English, and find that training with our proposed metric results in better translations as evaluated by BLEU, semantic similarity, and human evaluation, and also that the optimization procedure converges faster.Analysis suggests that this is because the proposed metric is more conducive to optimization, assigning partial credit and providing more diversity in scores than BLEU. 1
Smart Contract Based Data Trading Mode Using Blockchain and Machine Learning

There are two traditional data trading modes, the hosting mode, and the aggregation mode, which depend on the trusted third parties to a large extent. The hosting mode is that the data are completely hosted in the data trading center, so the data trading center retains the data. On the surface, the aggregation mode is that the data trading center is not to retain the data of trading, but actually, it has the ability to retain the data. There is a fundamental difference between the ability to retain the data and the inability to retain the data. These two trading modes cause the data owners to be afraid to share data trading. In this paper, we propose a solution to the data trading mode based on the smart contract using blockchain and machine learning. Our solution takes advantage of the immutability, tamper-proof and traceability of blockchain, the programmability of smart contract, and the verification of data availability by the similarity learning to propose a challenge response mechanism between the data purchaser and the data owner, an off-chain download mechanism between the data purchaser and the data storage service provider, and an arbitration mechanism for the controversy resolution of the data trading. The challenge response mechanism is used to authenticate and authorize the data owner, the off-chain download mechanism is used to authenticate and authorize the data purchaser to download the purchased data, and the similarity learning is used to deal with the controversy over the data availability in the data trading. The design and implementation of data trading smart contract successfully achieved the goal of removing the trusted third party in the data trading, and thus, the problem that the data trading center has the ability to retain the data in the process of the data trading is solved, as well as the automatic payment by using the Ethereum encrypted currency among the trading participants is realized. This paper presents the whole process of smart contract from the design and implementation to the test completion and provides the security analysis and performance evaluation. The full code of smart contract and the ABI interface have been uploaded to the GitHub for the public release.
Cryptanalysis of random-phase-encoding-based optical cryptosystem via deep learning

Random Phase Encoding (RPE) techniques for image encryption have drawn increasing attention during the past decades. We demonstrate in this contribution that the RPE-based optical cryptosystems are vulnerable to the chosen-plaintext attack (CPA) with deep learning strategy. A deep neural network (DNN) model is employed and trained to learn the working mechanism of optical cryptosystems, and finally obtaining a certain optimized DNN that acts as a decryption system. Numerical simulations were carried out to verify its feasibility and reliability of not only the classical Double RPE (DRPE) scheme but also the security-enhanced Tripe RPE (TRPE) scheme. The results further indicate the possibility of reconstructing images (plaintexts) outside the original data set.
A Deep Neural Network Based on an Attention Mechanism for SAR Ship Detection in Multiscale and Complex Scenarios

Synthetic aperture radar (SAR) ship detection based on deep learning has been widely applied in recent years. However, two main obstacles are hindering SAR ship detection. First, the identification of ships in a port is seriously disrupted by the presence of onshore buildings. It is difficult for the existing detection algorithms to effectively distinguish the targets from such a complex background. Additionally, it appears more complicated to accurately locate densely arranged ships. Second, the ships in SAR images exist at a variety of scales due to multiresolution imaging modes and the variety of ship shapes; these pose a much greater challenge to ship detection. To solve the above problems, this paper proposes an object detection network combined with an attention mechanism to accurately locate targets in complex scenarios. To address the diverse scales of ship targets, we construct a loss function that incorporates the generalized intersection over union (GIoU) loss to reduce the scale sensitivity of the network. For the final processing of the results, soft nonmaximum suppression (Soft-NMS) is also introduced into the model to reduce the number of missed detections for ship targets in the presence of severe overlap. The experimental results reveal that the proposed model exhibits excellent performance on the extended SAR ship detection dataset (SSDD) while achieving real-time detection.
Omnidirectional Scene Text Detection with Sequential-free Box Discretization

Scene text in the wild is commonly presented with high variant characteristics. Using quadrilateral bounding box to localize the text instance is nearly indispensable for detection methods. However, recent researches reveal that introducing quadrilateral bounding box for scene text detection will bring a label confusion issue which is easily overlooked, and this issue may significantly undermine the detection performance. To address this issue, in this paper, we propose a novel method called Sequential-free Box Discretization (SBD) by discretizing the bounding box into key edges (KE) which can further derive more effective methods to improve detection performance. Experiments showed that the proposed method can outperform state-of-the-art methods in many popular scene text benchmarks, including ICDAR 2015, MLT, and MSRA-TD500. Ablation study also showed that simply integrating the SBD into Mask R-CNN framework, the detection performance can be substantially improved. Furthermore, an experiment on the general object dataset HRSC2016 (multi-oriented ships) showed that our method can outperform recent state-of-the-art methods by a large margin, demonstrating its powerful generalization ability.
Multi-authority Attribute-Based Keyword Search over Encrypted Cloud Data

Searchable Encryption (SE) is an important technique to guarantee data security and usability in the cloud at the same time. Leveraging Ciphertext-Policy Attribute-Based Encryption (CP-ABE), the Ciphertext-Policy Attribute-Based Keyword Search (CP-ABKS) scheme can achieve keyword-based retrieval and fine-grained access control simultaneously. However, the single attribute authority in existing CP-ABKS schemes is tasked with costly user certificate verification and secret key distribution. In addition, this results in a single-point performance bottleneck in distributed cloud systems. Thus, in this paper, we present a secure Multi-authority CP-ABKS (MABKS) system to address such limitations and minimize the computation and storage burden on resource-limited devices in cloud systems. In addition, the MABKS system is extended to support malicious attribute authority tracing and attribute update. Our rigorous security analysis shows that the MABKS system is selectively secure in both selective-matrix and selective-attribute models. Our experimental results using real-world datasets demonstrate the efficiency and utility of the MABKS system in practical applications.
Measurement reduction in variational quantum algorithms

Variational quantum algorithms are promising applications of noisy intermediate-scale quantum (NISQ) computers. These algorithms consist of a number of separate prepare-and-measure experiments that estimate terms in a Hamiltonian. The number of terms can become overwhelmingly large for problems at the scale of NISQ hardware that may soon be available. We approach this problem from the perspective of contextuality, and use unitary partitioning (developed independently by Izmaylov et al. [J. Chem. Theory Comput. 16, 190 (2020)]) to define variational quantum eigensolver procedures in which additional unitary operations are appended to the ansatz preparation to reduce the number of terms. This approach may be scaled to use all coherent resources available after ansatz preparation. We also study the use of asymmetric qubitization to implement the additional coherent operations with lower circuit depth. We investigate this technique for lattice Hamiltonians, random Pauli Hamiltonians, and electronic structure Hamiltonians. Using this technique, we find a constant factor speedup for lattice and random Pauli Hamiltonians. For electronic structure Hamiltonians, we prove that linear term reduction with respect to the number of orbitals, which has been previously observed in numerical studies, is always achievable. For systems represented on 10--30 qubits, we find that there is a reduction in the number of terms by approximately an order of magnitude. Applied to the plane-wave dual basis representation of fermionic Hamiltonians, however, unitary partitioning offers only a constant factor reduction. Finally, we show that noncontextual Hamiltonians may be reduced to effective commuting Hamiltonians using unitary partitioning.
Tree Transformer: Integrating Tree Structures into Self-Attention

Pre-training Transformer from large-scale raw texts and fine-tuning on the desired task have achieved state-of-the-art results on diverse NLP tasks.However, it is unclear what the learned attention captures.The attention computed by attention heads seems not to match human intuitions about hierarchical structures.This paper proposes Tree Transformer, which adds an extra constraint to attention heads of the bidirectional Transformer encoder in order to encourage the attention heads to follow tree structures.The tree structures can be automatically induced from raw texts by our proposed "Constituent Attention" module, which is simply implemented by self-attention between two adjacent words.With the same training procedure identical to BERT, the experiments demonstrate the effectiveness of Tree Transformer in terms of inducing tree structures, better language modeling, and further learning more explainable attention scores 1 .
Evaluating the Holevo Cramér-Rao Bound for Multiparameter Quantum Metrology

Only with the simultaneous estimation of multiple parameters are the quantum aspects of metrology fully revealed. This is due to the incompatibility of observables. The fundamental bound for multiparameter quantum estimation is the Holevo Cramér-Rao bound (HCRB) whose evaluation has so far remained elusive. For finite-dimensional systems we recast its evaluation as a semidefinite program, with reduced size for rank-deficient states. We show that it also satisfies strong duality. We use this result to study phase and loss estimation in optical interferometry and three-dimensional magnetometry with noisy multiqubit systems. For the former, we show that, in some regimes, it is possible to attain the HCRB with the optimal (single-copy) measurement for phase estimation. For the latter, we show a nontrivial interplay between the HCRB and incompatibility and provide numerical evidence that projective single-copy measurements attain the HCRB in the noiseless 2-qubit case.