Reviewerly/exHarmony huggingface dataset:
Oriented Feature Augmentation for Few-Shot Object Detection in Remote Sensing Images

In recent years, many researches have been conducted for few-shot object detection in image of natural scenery. But in the field of remote sensing images, this is a more challenging task. due to the fact that the objects have arbitrary direction in remote sensing image compared with the natural scenery images, which will lead to larger intra-class distance, and are more likely to cause category confusion. Toward this end, we proposed a method of oriented feature augmentation (OAF) on the basis of dual pipelines solution. The method is simple without bells and whistles. With the help of enhanced category features, the detector is easier to achieve classification and regression of remote sensing objects. Extensive experiments on NWPU VHR-10(v2) and DIOR dataset in various settings show that, our method achieves new state-of-the-art performance by a large margin, demonstrating its effectiveness and generalization ability.
The Promise for Reducing Healthcare Cost with Predictive Model: An Analysis with Quantized Evaluation Metric on Readmission

Quality of care data has gained transparency captured through various measurements and reporting. Readmission measure is especially related to unfavorable patient outcomes that directly bends the curve of healthcare cost. Under the Hospital Readmission Reduction Program, payments to hospitals were reduced for those with excessive 30-day rehospitalization rates. These penalties have intensified efforts from hospital stakeholders to implement strategies to reduce readmission rates. One of the key strategies is the deployment of predictive analytics stratified by patient population. The recent research in readmission model is focused on making its prediction more accurate. As cost-saving improvements through artificial intelligent-based health solutions are expected, the broad economic impact of such digital tool remains unknown. Meanwhile, reducing readmission rate is associated with increased operating expenses due to targeted interventions. The increase in operating margin can surpass native readmission cost. In this paper, we propose a quantized evaluation metric to provide a methodological mean in assessing whether a predictive model represents cost-effective way of delivering healthcare. Herein, we evaluate the impact machine learning has had on transitional care and readmission with proposed metric. The final model was estimated to produce net healthcare savings at over $1 million given a 50% rate of successfully preventing a readmission.
Reinforced Active Entity Alignment

Entity alignment (EA) is the task of detecting equivalent entities from different knowledge graphs (KGs). Although this problem has been intensively studied during the last few years, the majority of the state-of-the-arts heavily rely on the labeled data, which are difficult to obtain in practice. Therefore, it calls for the study of EA with scarce supervision. To resolve this issue, we put forward a reinforced active entity alignment framework to select the entities to be manually labeled with the aim of enhancing alignment performance with minimal labeling efforts. Under this framework, we further devise an unsupervised contrastive loss to contrast different views of entity representations and augment the limited supervision signals by exploiting the vast unlabeled data. We empirically evaluate our proposal on eight popular KG pairs, and the results demonstrate that our proposed model and its components consistently boost the alignment performance under scarce supervision.
Roadmap to FAIR Research Information in Open Infrastructures

The FAIR Principles were designed to improve the findability, accessibility, interoperability and reusability of data holdings by humans and machines. The principles can be applied to research information too. We present the results of the discussions that took place during the series of online workshops with experts on Research Information and FAIR Guiding Principles. We provide high-level criteria on how to foster findable, accessible, interoperable and reusable, and we hope that our roadmap for FAIR research information in open infrastructures bring many benefits to a diverse group of stakeholders of the scientific ecosystem.
Self-Supervised Learning on Users' Spontaneous Behaviors for Multi-Scenario Ranking in E-commerce

Multi-scenario Learning to Rank is essential for Recommender Systems, Search Engines and Online Advertising in e-commerce portals where the ranking models are usually applied in many scenarios. However, existing works mainly focus on learning the ranking model for a single scenario, and pay less attention to learning ranking models for multiple scenarios. We identify two practical challenges in industrial multi-scenario ranking systems: (1) The Feedback Loop problem that the model is always trained on the items chosen by the ranker itself. (2) Insufficient training data for small and new scenarios. To address the above issues, we present ZEUS, a novel framework that learns a Zoo of ranking modEls for mUltiple Scenarios based on pre-training on users' spontaneous behaviors (e.g. queries which are directly searched in the search box and not recommended by the ranking system). ZEUS decomposes the training process into two stages: self-supervised learning based pre-training and fine-tuning. Firstly, ZEUS performs self-supervised learning on users' spontaneous behaviors and generates a pre-trained model. Secondly, ZEUS fine-tunes the pre-trained model on users' implicit feedback in multiple scenarios. Extensive experiments on Alibaba's production dataset demonstrate the effectiveness of ZEUS, which significantly outperforms state-of-the-art methods. ZEUS averagely achieves 6.0%, 9.7%, 11.7% improvement in CTR, CVR and GMV respectively than state-of-the-art method.
Cross-Layer Deanonymization Methods in the Lightning Protocol

Bitcoin (BTC) pseudonyms (layer 1) can effectively be deanonymized using heuristic clustering techniques. However, while performing transactions off-chain (layer 2) in the Lightning Network (LN) seems to enhance privacy, a systematic analysis of the anonymity and privacy leakages due to the interaction between the two layers is missing. We present (Please, find the full version of this paper with appendix at https://arxiv.org/abs/2007.00764 .) clustering heuristics that group BTC addresses, based on their interaction with the LN, as well as LN nodes, based on shared naming and hosting information. We also present linking heuristics that link $$45.97$$ % of all LN nodes to $$29.61$$ % BTC addresses interacting with the LN. These links allow us to attribute information (e.g., aliases, IP addresses) to $$21.19$$ % of the BTC addresses contributing to their deanonymization. Further, these deanonymization results suggest that the security and privacy of LN payments are weaker than commonly believed, with LN users being at the mercy of as few as five actors that control 36 nodes and over $$33\%$$ of the total capacity. Overall, this is the first paper to present a method for linking LN nodes with BTC addresses across layers and to discuss privacy and security implications.
On the Adaptivity of Unscented Particle Filter for GNSS/INS Tightly-Integrated Navigation Unit in Urban Environment

Tight integration algorithms fusing Global Navigation Satellite System (GNSS) and Inertial Navigation System (INS) have become popular in many high-accuracy positioning and navigation applications.Despite their reliability, common integration architectures can still run into accuracy drops under challenging navigation settings.The growing computational power of low-cost, embedded systems has allowed for the exploitation of several advanced Bayesian state estimation algorithms, such as the Particle Filter (PF) and its hybrid variants, e.g.Unscented Particle Filter (UPF).Although sophisticated, these architectures are not immune from multipath scattering and Non-Line-of-Sight (NLOS) signal receptions, which frequently corrupt satellite measurements and jeopardise GNSS/INS solutions.Hence, a certain level of modelling adaptivity should be granted to avoid severe drifts in the estimated states.Given these premises, the paper presents a novel Adaptive Unscented Particle Filter (AUPF) architecture leveraging two cascading stages to cope with disruptive, biased GNSS input observables in harsh conditions.A INS-based signal processing block is implemented upstream of a Redundant Measurement Noise Covariance Estimation (RMNCE) stage to strengthen the adaptation of observables' statistics and improve the state estimation.An experimental assessment is provided for the proposed robust AUPF that demonstrates a 10 % average reduction of the horizontal position error above the 75-th percentile.In addition, a comparative analysis both with previous adaptive architectures and a plain UPF is carried out to highlight the improved performance of the proposed methodology.
Analyzing the Effect of Masking Length Distribution of MLM: An Evaluation Framework and Case Study on Chinese MRC Datasets

Machine reading comprehension (MRC) is a challenging natural language processing (NLP) task. It has a wide application potential in the fields of question answering robots, human-computer interactions in mobile virtual reality systems, etc. Recently, the emergence of pretrained models (PTMs) has brought this research field into a new era, in which the training objective plays a key role. The masked language model (MLM) is a self-supervised training objective widely used in various PTMs. With the development of training objectives, many variants of MLM have been proposed, such as whole word masking, entity masking, phrase masking, and span masking. In different MLMs, the length of the masked tokens is different. Similarly, in different machine reading comprehension tasks, the length of the answer is also different, and the answer is often a word, phrase, or sentence. Thus, in MRC tasks with different answer lengths, whether the length of MLM is related to performance is a question worth studying. If this hypothesis is true, it can guide us on how to pretrain the MLM with a relatively suitable mask length distribution for MRC tasks. In this paper, we try to uncover how much of MLM’s success in the machine reading comprehension tasks comes from the correlation between masking length distribution and answer length in the MRC dataset. In order to address this issue, herein, (1) we propose four MRC tasks with different answer length distributions, namely, the short span extraction task, long span extraction task, short multiple-choice cloze task, and long multiple-choice cloze task; (2) four Chinese MRC datasets are created for these tasks; (3) we also have pretrained four masked language models according to the answer length distributions of these datasets; and (4) ablation experiments are conducted on the datasets to verify our hypothesis. The experimental results demonstrate that our hypothesis is true. On four different machine reading comprehension datasets, the performance of the model with correlation length distribution surpasses the model without correlation.
Distillation Remote Sensing Object Counting via Multi-Scale Context Feature Aggregation

Remote sensing object counting is an important issue in remote sensing analysis. Remote sensing object counting has many challenges, such as large-scale variations and complex backgrounds. The previous counting methods have many shortboards, such as only focusing on local appearance features of target scenes and ignoring the self-supervision ability of the network itself. To remedy the above problems, in this article, we propose a novel remote sensing object counting method, which contains the adaptive multi-scale context aggregation module (AMCAM) and the self-context distillation module (SCDM). The AMCAM can model and fuse context information from different receptive fields effectively. It also keeps detailed information through multiple pixel attention (PA) modules step by step. The SCDM can improve the representation learning without adding any additional supervision information. SCDM uses feature maps from the deeper layer of the network to supervise feature maps from the earlier layer of the network. Our method has achieved good performance on the remote sensing object counting dataset, RSOC, and mainstream crowd counting datasets, such as ShanghaiTech and UCF-QNRF datasets.
Improving readout in quantum simulations with repetition codes

Abstract Near term quantum computers suffer from the presence of different noise sources. In order to mitigate for this effect and acquire results with significantly better accuracy, there is the urge of designing efficient error correction or error mitigation schemes. The cost of such techniques is usually high in terms of resource requirements, either in hardware or at the algorithmic level. In this work, we follow a pragmatic approach and we use repetition codes as scalable schemes with the potential to provide more accurate solutions to problems of interest in quantum chemistry and physics. We investigate different repetition code layouts and we propose a circular repetition scheme with connectivity requirements that are native on IBM Quantum hardware. We showcase our approach in multiple IBM Quantum devices and validate our results using a simplified theoretical noise model. We highlight the effect of using the proposed scheme in an electronic structure variational quantum eigensolver calculation and in the simulation of time evolution for a quantum Ising model.
Publicly Auditable MPC-as-a-Service with succinct verification and universal setup

In recent years, multiparty computation as a service (MPCaaS) has gained popularity as a way to build distributed privacy-preserving systems like blockchain trusted parameter setup ceremonies, and digital asset auctions. We argue that for many such applications, we should also require that the MPC protocol is publicly auditable, meaning that anyone can check the given computation is carried out correctly – even if the server nodes carrying out the computation are all corrupt. In a nutshell, the way to make an MPC protocol auditable is to combine an underlying MPC protocol with a verifiable computing proof (in particular, a SNARK). Building a general purpose MPCaaS from existing constructions would require us to perform a costly “trusted setup” every time we wish to run a new or modified application. To address this, we provide the first efficient construction for auditable MPC that has a one time universal setup. Despite improving the trusted setup, we match the state-of-the-art in asymptotic performance: the nodes incur a linear computation overhead and constant round communication overhead compared to the underlying MPC, and the audit size and verification are logarithmic in the application circuit size. We also provide an implementation and benchmarks that support our asymptotic analysis in example applications. Furthermore, compared with existing auditable MPC protocols, besides offering a universal setup our construction also has a 3x smaller proof, 3x faster verification time and comparable prover time.
An NLP-based Tool for Software Artifacts Analysis

Software developers rely on various repositories and communication channels to exchange relevant information about their ongoing tasks and the status of overall project progress. In this context, semi-structured and unstructured software artifacts have been leveraged by researchers to build recommender systems aimed at supporting developers in different tasks, such as transforming user feedback in maintenance and evolution tasks, suggesting experts, or generating software documentation. More specifically, Natural Language (NL) parsing techniques have been successfully leveraged to automatically identify (or extract) the relevant information embedded in unstructured software artifacts. However, such techniques require the manual identification of patterns to be used for classification purposes. To reduce such a manual effort, we propose an NL parsing-based tool for software artifacts analysis named NEON that can automate the mining of such rules, minimizing the manual effort of developers and researchers. Through a small study involving human subjects with NL processing and parsing expertise, we assess the performance of NEON in identifying rules useful to classify app reviews for software maintenance purposes. Our results show that more than one-third of the rules inferred by NEON are relevant for the proposed task. Demo webpage: https://github.com/adisorbo/NEON_tool
Joint tracking sequence and dwell time allocation for multi-target tracking with phased array radar

Phased array radars (PAR) are demonstrated to provide enhanced performance for target tracking due to their agile beamforming. In this paper, a joint tracking sequence and dwell time allocation (JTSDTA) strategy is proposed for multi-target tracking (MTT) based on a PAR system. At its core, for the first time, the implementations of target tracking and searching are considered to be interleaved, and the effect of the revisit time on the tracking performance is emerged when arranging the sequence of target tracking. In this context, a time-aware posterior Cramér–Rao lower bound is presented to quantify the individual tracking performance. Then, the JTSDTA is formulated as an optimization problem with respect to the coupled integer and continuous variables. A two steps-based decoupling algorithm is proposed to solve the problem efficiently. Numerical simulations are presented to demonstrate the validity of the proposed JTSDTA strategy.
Basic Artificial Intelligence Techniques

Natural language processing (NLP) is a subfield of computer science and linguistics that can be applied to extract meaningful information from radiology reports. Symbolic NLP is rule based and well suited to problems that can be explicitly defined by a set of rules. Statistical NLP is better situated to problems that cannot be well defined and requires annotated or labeled examples from which machine learning algorithms can infer the rules. Both symbolic and statistical NLP have found success in a variety of radiology use cases. More recently, deep learning approaches, including transformers, have gained traction and demonstrated good performance.
Automatic Detection of Acute Lymphoblastic Leukemia Using UNET Based Segmentation and Statistical Analysis of Fused Deep Features

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Low NOx combustion optimization based on partial dimension opposition-based learning particle swarm optimization

NOx emissions emitted from coal-fired power plants are threatening human beings. Combustion optimization is an important technology to reduce NOx emissions. However, this technology is troubled with premature when swarm algorithms are employed. This paper studied low NOx combustion optimization based on partial dimension opposition-based learning particle swarm optimization. In this algorithm, opposition-based learning is carried out with some learning probability for each dimension of global best particle. The proposed algorithm was applied to the combustion optimization of some 600 MW boiler. Results show that median optimized NOx emissions and mean optimized NOx emissions are lower when the learning probability is located between 0.1 and 0.3. Median optimized NOx emissions and mean optimized NOx emissions based on partial dimension opposition-based learning particle swarm optimization are lowest among the studied algorithms in most cases with limited iterations. Up to 37.72 mg/Nm3 median NOx emissions can be further reduced by partial dimension opposition-based learning particle swarm optimization compared with the optimized result provided by particle swarm optimization in some case. Besides, low NOx combustion optimization based on partial dimension opposition-based learning particle swarm optimization is most stable among the studied algorithms.
SAHEFT: Security Aware Heterogeneous Earliest Finish Time Workflow Allocation Strategy for IaaS Cloud Environment

Security is one of the most emerging issues in the real-life scientific workflow's applications such as online marketing, big data, digital transaction etc. Cloud computing infrastructure is a platform that provides dynamically scalable on demand services. However, cloud users are to pay attention to the cost incurred with pay-per-use pricing model by leasing virtual machines (VMs) from cloud centers. IT companies and scientists are facing challenges of demanded Quality of service (QoS) in IaaS cloud environment. Therefore, many researchers making effort to achieve better performance for secure workflow allocation on heterogeneous virtual machines. In this paper, a Security Aware Heterogeneous Earliest Finish Time (SAHEFT), a list-based workflow allocation strategy for single workflow to optimize guarantee ratio has been proposed for IaaS cloud environment. SAHEFT has been compared with a standard algorithm, namely HEFT, to evaluate the performance. Experimental results shows the significantly improved performance of SAHEFT over HEFT by 32.11% and 27.08% on account of security overhead and guarantee ratio respectively.
General Purpose Atomic Crosschain Transactions

The General Purpose Atomic Crosschain Transaction protocol allows composable programming across multiple Ethereum blockchains. It allows for inter-contract and inter-blockchain function calls that are both synchronous and atomic: if one part fails, the whole call execution tree of function calls is rolled back. The protocol operates on existing Ethereum blockchains without modification. It works for both public permissioned and consortium blockchains. This paper describes the protocol, analyses it in terms of Gas usage and Finalised Block Periods for three scenarios: reading a value from one blockchain to another, writing a value from one blockchain to another, and a trade finance system involving five contracts on five blockchains with a complex call execution tree, and provides an initial security analysis that shows that the protocol has Safety and Liveness properties.
Redactable Transactions in Consortium Blockchain: Controlled by Multi-authority CP-ABE

The immutability of blockchain means that data in blockchain cannot be modified once confirmed. It guarantees the reliability and integrity of blockchain. However, absolute immutability is not conducive to timely correction of blockchain. Currently, there are some researches on redactable blockchain. They replaced hash functions with chameleon hash functions or proposed policy-based chameleon hashes, which may lead to the centralization of redaction right or single point of failure.We propose a multi-authority policy-based chameleon hash by combining chameleon-hashes with ephemeral trapdoors and multi-authority attribute-based encryption, and prove its security. Users who satisfy the access policies can perform modification operations while the rest have no permission. In addition, we give a proof-of-concept implementation of a redactable blockchain, building on Hyperledger Fabric source code. It only requires minimal changes to the current transaction structure and hash computation, etc. Our results show that the latency is still in millisecond with 20000 concurrent redactable transactions.