Reviewerly/exHarmony huggingface dataset:
Precise and Robust Ship Detection for High-Resolution SAR Imagery Based on HR-SDNet

Ship detection in high-resolution synthetic aperture radar (SAR) imagery is a challenging problem in the case of complex environments, especially inshore and offshore scenes. Nowadays, the existing methods of SAR ship detection mainly use low-resolution representations obtained by classification networks or recover high-resolution representations from low-resolution representations in SAR images. As the representation learning is characterized by low resolution and the huge loss of resolution makes it difficult to obtain accurate prediction results in spatial accuracy; therefore, these networks are not suitable to ship detection of region-level. In this paper, a novel ship detection method based on a high-resolution ship detection network (HR-SDNet) for high-resolution SAR imagery is proposed. The HR-SDNet adopts a novel high-resolution feature pyramid network (HRFPN) to take full advantage of the feature maps of high-resolution and low-resolution convolutions for SAR image ship detection. In this scheme, the HRFPN connects high-to-low resolution subnetworks in parallel and can maintain high resolution. Next, the Soft Non-Maximum Suppression (Soft-NMS) is used to improve the performance of the NMS, thereby improving the detection performance of the dense ships. Then, we introduce the Microsoft Common Objects in Context (COCO) evaluation metrics, which provides not only the higher quality evaluation metrics average precision (AP) for more accurate bounding box regression, but also the evaluation metrics for small, medium and large targets, so as to precisely evaluate the detection performance of our method. Finally, the experimental results on the SAR ship detection dataset (SSDD) and TerraSAR-X high-resolution images reveal that (1) our approach based on the HRFPN has superior detection performance for both inshore and offshore scenes of the high-resolution SAR imagery, which achieves nearly 4.3% performance gains compared to feature pyramid network (FPN) in inshore scenes, thus proving its effectiveness; (2) compared with the existing algorithms, our approach is more accurate and robust for ship detection of high-resolution SAR imagery, especially inshore and offshore scenes; (3) with the Soft-NMS algorithm, our network performs better, which achieves nearly 1% performance gains in terms of AP; (4) the COCO evaluation metrics are effective for SAR image ship detection; (5) the displayed thresholds within a certain range have a significant impact on the robustness of ship detectors.
SpanMlt: A Span-based Multi-Task Learning Framework for Pair-wise Aspect and Opinion Terms Extraction

Aspect terms extraction and opinion terms extraction are two key problems of fine-grained Aspect Based Sentiment Analysis (ABSA).The aspect-opinion pairs can provide a global profile about a product or service for consumers and opinion mining systems.However, traditional methods can not directly output aspect-opinion pairs without given aspect terms or opinion terms.Although some recent co-extraction methods have been proposed to extract both terms jointly, they fail to extract them as pairs.To this end, this paper proposes an end-to-end method to solve the task of Pair-wise Aspect and Opinion Terms Extraction (PAOTE).Furthermore, this paper treats the problem from a perspective of joint term and relation extraction rather than under the sequence tagging formulation performed in most prior works.We propose a multi-task learning framework based on shared spans, where the terms are extracted under the supervision of span boundaries.Meanwhile, the pair-wise relations are jointly identified using the span representations.Extensive experiments show that our model consistently outperforms stateof-the-art methods.
InfoXLM: An Information-Theoretic Framework for Cross-Lingual Language Model Pre-Training

In this work, we present an informationtheoretic framework that formulates crosslingual language model pre-training as maximizing mutual information between multilingual-multi-granularity texts.The unified view helps us to better understand the existing methods for learning cross-lingual representations.More importantly, inspired by the framework, we propose a new pretraining task based on contrastive learning.Specifically, we regard a bilingual sentence pair as two views of the same meaning and encourage their encoded representations to be more similar than the negative examples.By leveraging both monolingual and parallel corpora, we jointly train the pretext tasks to improve the cross-lingual transferability of pre-trained models.Experimental results on several benchmarks show that our approach achieves considerably better performance.The code and pre-trained models are available at https://aka.ms/infoxlm.* Contribution during internship at Microsoft Research.Contact person: Li Dong and Furu Wei. and XLCO.We conduct extensive experiments on several cross-lingual understanding tasks, including cross-lingual natural language inference (Conneau et al., 2018), cross-lingual question answering (Lewis et al., 2020), and cross-lingual sentence retrieval (Artetxe and Schwenk, 2019).Experimental results show that INFOXLM outperforms strong baselines on all the benchmarks.Moreover, the analysis indicates that INFOXLM achieves better cross-lingual transferability.2 Related Work 2.1 Cross-Lingual LM Pre-Training Multilingual BERT (mBERT; Devlin et al. 2019) is pre-trained with the multilingual masked language modeling (MMLM) task on the monolingual text.mBERT produces cross-lingual representations and performs cross-lingual tasks surprisingly well (Wu and Dredze, 2019).XLM (Conneau and Lample, 2019) extends mBERT with the translation language modeling (TLM) task so that the model can learn cross-lingual representations from parallel corpora.Unicoder (Huang et al., 2019) tries several pre-training tasks to utilize parallel corpora.ALM (Yang et al., 2020) extends TLM to codeswitched sequences obtained from translation pairs.XLM-R (Conneau et al., 2020a) scales up MMLM pre-training with larger corpus and longer training.LaBSE (Feng et al., 2020) learns cross-lingual sentence embeddings by an additive translation ranking loss.In addition to learning cross-lingual encoders, several pre-trained models focus on generation.MASS (Song et al., 2019) and mBART (Liu et al., 2020) pretrain sequence-to-sequence models to improve machine translation.XNLG (Chi et al., 2020) focuses on the cross-lingual transfer of language generation, such as cross-lingual question generation, and abstractive summarization.
Graph-based deep learning for communication networks: A survey

Communication networks are important infrastructures in contemporary society. There are still many challenges that are not fully solved and new solutions are proposed continuously in this active research area. In recent years, to model the network topology, graph-based deep learning has achieved the state-of-the-art performance in a series of problems in communication networks. In this survey, we review the rapidly growing body of research using different graph-based deep learning models, e.g. graph convolutional and graph attention networks, in various problems from different types of communication networks, e.g. wireless networks, wired networks, and software defined networks. We also present a well-organized list of the problem and solution for each study and identify future research directions. To the best of our knowledge, this paper is the first survey that focuses on the application of graph-based deep learning methods in communication networks involving both wired and wireless scenarios. To track the follow-up research, a public GitHub repository is created, where the relevant papers will be updated continuously.
Attribute-Based Privacy-Preserving Data Sharing for Dynamic Groups in Cloud Computing

The sharing of personal data with multiple users from different domains has been benefited considerably from the rapid advances of cloud computing, and it is highly desirable to ensure the sharing file should not be exposed to the unauthorized users or cloud providers. Unfortunately, issues such as achieving the flexible access control of the sharing file, preserving the privacy of the receivers, forming the receiver groups dynamically, and high efficiency in encryption/decryption still remain challenging. To deal with these challenges, we provide a novel anonymous attribute-based broadcast encryption (A <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> B <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> E) which features the property of hidden access policy and enables the data owner to share his/her data with multiple participants who are inside a predefined receiver set and fulfill the access policy. We first suggest a concrete A <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> B <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> E scheme together with the rigorous and formal security proof without the support of the random oracle model. Then, we design an efficient and secure data sharing system by incorporating the A <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> B <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> E scheme, verifiable outsourcing decryption technique for attribute-based encryption, and the idea of online/offline attribute-based encryption. Extensive security analysis and performance evaluation demonstrate that our data sharing system is secure and practical.
A Cross-Domain Recommender System With Kernel-Induced Knowledge Transfer for Overlapping Entities

The aim of recommender systems is to automatically identify user preferences within collected data, then use those preferences to make recommendations that help with decisions. However, recommender systems suffer from data sparsity problem, which is particularly prevalent in newly launched systems that have not yet had enough time to amass sufficient data. As a solution, cross-domain recommender systems transfer knowledge from a source domain with relatively rich data to assist recommendations in the target domain. These systems usually assume that the entities either fully overlap or do not overlap at all. In practice, it is more common for the entities in the two domains to partially overlap. Moreover, overlapping entities may have different expressions in each domain. Neglecting these two issues reduces prediction accuracy of cross-domain recommender systems in the target domain. To fully exploit partially overlapping entities and improve the accuracy of predictions, this paper presents a cross-domain recommender system based on kernel-induced knowledge transfer, called KerKT. Domain adaptation is used to adjust the feature spaces of overlapping entities, while diffusion kernel completion is used to correlate the non-overlapping entities between the two domains. With this approach, knowledge is effectively transferred through the overlapping entities, thus alleviating data sparsity issues. Experiments conducted on four data sets, each with three sparsity ratios, show that KerKT has 1.13%-20% better prediction accuracy compared with six benchmarks. In addition, the results indicate that transferring knowledge from the source domain to the target domain is both possible and beneficial with even small overlaps.
A hierarchical gravitational search algorithm with an effective gravitational constant

Gravitational search algorithm (GSA) inspired by the law of gravity is a swarm intelligent optimization algorithm. It utilizes the gravitational force to implement the interaction and evolution of individuals. The conventional GSA achieves several successful applications, but it still faces a premature convergence and a low search ability. To address these two issues, a hierarchical GSA with an effective gravitational constant (HGSA) is proposed from the viewpoint of population topology. Three contrastive experiments are carried out to analyze the performances between HGSA and other GSAs, heuristic algorithms and particle swarm optimizations (PSOs) on function optimization. Experimental results demonstrate the effective property of HGSA due to its hierarchical structure and gravitational constant. A component-wise experiment is also established to further verify the superiority of HGSA. Additionally, HGSA is applied to several real-world optimization problems so as to verify its good practicability and performance. Finally, the time complexity analysis is discussed to conclude that HGSA has the same computational efficiency in comparison with other GSAs.
Analysis of Blockchain Solutions for IoT: A Systematic Literature Review

The Internet of Things (IoT) aims at connecting things to the Internet in a peer-to-peer paradigm for data collecting and data sharing in our daily life.A blockchain is an immutable append-only ledger maintained by a peer-to-peer network, where the whole network needs to reach a consensus on the transactional data stored on the ledger.With the decentralization nature, the design of IoT and blockchain aligns with each other well.Blockchain has been integrated with the IoT to solve the existing IoT problems.Our research focuses on analyzing the solutions proposed in academia and the methodologies used to integrate blockchain with the IoT.Through conducting a systematic literature review (SLR) on peerreviewed, published articles on blockchain-based solutions for IoT, we gather the knowledge on current technical approaches implemented to integrate blockchain into the IoT.Majority of the research in this space is either at a conceptual level or at a very early stage.However, we only found 35 published papers with the real implementation of blockchain in the IoT platforms.We elicit the challenges of the IoT that were being addressed, and the detailed design of the blockchain-based solutions from two perspectives, namely data management and thing management.The evaluation methods and metrics used by those works are also being recorded and analyzed.In addition to the analysis of the literature, we provide our insights on improving the existing solutions and research methodology based on our expertise and experience on the blockchain.
Ultrasound prostate segmentation based on multidirectional deeply supervised V‐Net

Transrectal ultrasound (TRUS) is a versatile and real-time imaging modality that is commonly used in image-guided prostate cancer interventions (e.g., biopsy and brachytherapy). Accurate segmentation of the prostate is key to biopsy needle placement, brachytherapy treatment planning, and motion management. Manual segmentation during these interventions is time-consuming and subject to inter- and intraobserver variation. To address these drawbacks, we aimed to develop a deep learning-based method which integrates deep supervision into a three-dimensional (3D) patch-based V-Net for prostate segmentation.We developed a multidirectional deep-learning-based method to automatically segment the prostate for ultrasound-guided radiation therapy. A 3D supervision mechanism is integrated into the V-Net stages to deal with the optimization difficulties when training a deep network with limited training data. We combine a binary cross-entropy (BCE) loss and a batch-based Dice loss into the stage-wise hybrid loss function for a deep supervision training. During the segmentation stage, the patches are extracted from the newly acquired ultrasound image as the input of the well-trained network and the well-trained network adaptively labels the prostate tissue. The final segmented prostate volume is reconstructed using patch fusion and further refined through a contour refinement processing.Forty-four patients' TRUS images were used to test our segmentation method. Our segmentation results were compared with the manually segmented contours (ground truth). The mean prostate volume Dice similarity coefficient (DSC), Hausdorff distance (HD), mean surface distance (MSD), and residual mean surface distance (RMSD) were 0.92 ± 0.03, 3.94 ± 1.55, 0.60 ± 0.23, and 0.90 ± 0.38 mm, respectively.We developed a novel deeply supervised deep learning-based approach with reliable contour refinement to automatically segment the TRUS prostate, demonstrated its clinical feasibility, and validated its accuracy compared to manual segmentation. The proposed technique could be a useful tool for diagnostic and therapeutic applications in prostate cancer.
Statistical correlations between locally randomized measurements: A toolbox for probing entanglement in many-body quantum states

We develop a general theoretical framework for measurement protocols employing statistical correlations of randomized measurements. We focus on locally randomized measurements implemented with local random unitaries in quantum lattice models. In particular, we discuss the theoretical details underlying the recent measurement of the second R\'enyi entropy of highly mixed quantum states consisting of up to ten qubits in a trapped-ion quantum simulator [Brydges et al., Science 364, 260 (2019)]. We generalize the protocol to access the overlap of quantum states, prepared sequentially in one experiment. Furthermore, we discuss proposals for quantum state tomography based on randomized measurements within our framework and the respective scaling of statistical errors with system size.
A Secure Communicating Things Network Framework for Industrial IoT using Blockchain Technology

Communicating Things Network (CTN) is the latest paradigm in the development of smart technologies. CTN comprises a network of physical devices capable of extracting and sharing digital information. The aim of CTN is to develop smart appliances that boost productivity and provide real-time data rapidly than any structure or a network that is dependent on human interference. Interconnected physical objects in the network communicate with each other and facilitate intelligent decision-making by monitoring and analysing their surroundings. In today's era, CTNs are playing a significant role in daily activities by providing a substantial reduction in costs with increased visibility and efficiency in all aspects of businesses and individuals. In this manuscript, we have considered an important application of CTNs (i.e., IoT) and have proposed a secure Hybrid Industrial IoT framework using the Blockchain technique. We have used a hybrid industrial architecture where different branches of a company are located in more than one country. Although IoT devices are used in many organizations and assist in reducing their production costs along with improving quality, several threats can occur in IoT devices initiated by various intruders. Intruders may compromise IoT devices with the purpose of performing malicious activities. For example, a company's employee may steal some product or may rest during working hours. To prevent these issues, the Blockchain technology is considered as the best technique that provides secrecy and protects the control system in real-time conditions. In this manuscript, we have used a Blockchain mechanism to extract information from IoT devices and store extracted records into Blockchain to maintain transparency among various users located at different places. Furthermore, the experimentation of the proposed framework has been performed against the internal communication of Blockchain where IoT devices are compromised by several intruders. Results have been analysed against the conventional approach and validated with improved simulated results that offer an 89% success rate over user request time, falsification attack, black hole attack, and probabilistic authentication scenarios because of the Blockchain technology.
Generating Responses with a Specific Emotion in Dialog

It is desirable for dialog systems to have capability to express specific emotions during a conversation, which has a direct, quantifiable impact on improvement of their usability and user satisfaction.After a careful investigation of real-life conversation data, we found that there are at least two ways to express emotions with language.One is to describe emotional states by explicitly using strong emotional words; another is to increase the intensity of the emotional experiences by implicitly combining neutral words in distinct ways.We propose an emotional dialogue system (EmoDS) that can generate the meaningful responses with a coherent structure for a post, and meanwhile express the desired emotion explicitly or implicitly within a unified framework.Experimental results showed EmoDS performed better than the baselines in BLEU, diversity and the quality of emotional expression.
Deep Learning for Picking Seismic Arrival Times

Abstract Arrival times of seismic phases contribute substantially to the study of the inner working of the Earth. Despite great advances in seismic data collection, the usage of seismic arrival times is still insufficient because of the overload manual picking tasks for human experts. In this work we employ a deep‐learning method (PickNet) to automatically pick much more P and S wave arrival times of local earthquakes with a picking accuracy close to that by human experts, which can be used directly to determine seismic tomography. A large number of high‐quality seismic arrival times obtained with the deep‐learning model may contribute greatly to improve our understanding of the Earth's interior structure.
Logic Attention Based Neighborhood Aggregation for Inductive Knowledge Graph Embedding

Knowledge graph embedding aims at modeling entities and relations with low-dimensional vectors. Most previous methods require that all entities should be seen during training, which is unpractical for real-world knowledge graphs with new entities emerging on a daily basis. Recent efforts on this issue suggest training a neighborhood aggregator in conjunction with the conventional entity and relation embeddings, which may help embed new entities inductively via their existing neighbors. However, their neighborhood aggregators neglect the unordered and unequal natures of an entity’s neighbors. To this end, we summarize the desired properties that may lead to effective neighborhood aggregators. We also introduce a novel aggregator, namely, Logic Attention Network (LAN), which addresses the properties by aggregating neighbors with both rules- and network-based attention weights. By comparing with conventional aggregators on two knowledge graph completion tasks, we experimentally validate LAN’s superiority in terms of the desired properties.
CGMH: Constrained Sentence Generation by Metropolis-Hastings Sampling

In real-world applications of natural language generation, there are often constraints on the target sentences in addition to fluency and naturalness requirements. Existing language generation techniques are usually based on recurrent neural networks (RNNs). However, it is non-trivial to impose constraints on RNNs while maintaining generation quality, since RNNs generate sentences sequentially (or with beam search) from the first word to the last. In this paper, we propose CGMH, a novel approach using Metropolis-Hastings sampling for constrained sentence generation. CGMH allows complicated constraints such as the occurrence of multiple keywords in the target sentences, which cannot be handled in traditional RNN-based approaches. Moreover, CGMH works in the inference stage, and does not require parallel corpora for training. We evaluate our method on a variety of tasks, including keywords-to-sentence generation, unsupervised sentence paraphrasing, and unsupervised sentence error correction. CGMH achieves high performance compared with previous supervised methods for sentence generation. Our code is released at https://github.com/NingMiao/CGMH