Reviewerly/exHarmony huggingface dataset:
RFA-Net: Reconstructed Feature Alignment Network for Domain Adaptation Object Detection in Remote Sensing Imagery

With the development of deep learning, great progress has been made in object detection of remote sensing (RS) imagery. However, the object detector is hard to generalize well from one labeled dataset (source domain) to another unlabeled dataset (target domain) due to the discrepancy of data distribution (domain shift). Currently, adversarial-based domain adaptation methods align the semantic features of source and target domain features to alleviate the domain shift. But they fail to avoid the alignment of noisy background features and neglect the instance-level features, which are inappropriate for detection models that focus on instance location and classification. To mitigate domain shift existing in object detection, we propose a reconstructed feature alignment network (RFA-Net) for unsupervised cross-domain object detection in remote sensing imagery. The RFA-Net includes one sequential data augmentation module (SDA) deployed on data level for providing solid gains on unlabeled data, one sparse feature reconstruction module (SFR) deployed on feature level to intensify instance feature for feature alignment, and one pseudo-label generation module (PLG) deployed on label level for the supervision of the unlabeled target domain. Extensive experiments illustrate that our proposed RFA-Net is effective to alleviate the domain shift problem in domain adaptation object detection of RS imagery.
WASAI: uncovering vulnerabilities in Wasm smart contracts

WebAssembly (Wasm) smart contracts have shown growing popularity across blockchains (e.g., EOSIO) recently. Similar to Ethereum smart contracts, Wasm smart contracts suffer from various attacks exploiting their vulnerabilities. Even worse, few developers released the source code of their Wasm smart contracts for security review, raising the bar for uncovering vulnerable contracts. Although a few approaches have been proposed to detect vulnerable Wasm smart contracts, they have several major limitations, e.g., low code coverage, low accuracy and lack of scalability, unable to produce exploit payloads, etc. To fill the gap, in this paper, we design and develop WASAI, a new concolic fuzzer for uncovering vulnerabilities in Wasm smart contract after tackling several challenging issues. We conduct extensive experiments to evaluate WASAI, and the results show that it outperforms the state-of-the-art methods. For example, it achieves 2x code coverage than the baselines and surpasses them in detection accuracy, with an F1-measure of 99.2%. Moreover, WASAI can handle complicated contracts (e.g., contracts with obfuscation and sophisticated verification). Applying WASAI to 991 deployed smart contracts in the wild, we find that over 70% of smart contracts are vulnerable. By the time of this study, over 300 vulnerable contracts have not been patched and are still operating on the EOSIO Mainnet. One fake EOS vulnerability reported to the EOSIO ecosystem was recently assigned a CVE identifier (CVE-2022-27134).
An Entropy-View Secure Multiparty Computation Protocol Based on Semi-Honest Model

Data interaction scenarios involving multiple parties in network communities have problems of trust, data security, and reliability of the parties, and secure multiparty computation(SMPC) can effectively solve these problems. To address the security and fairness issues of SMPC, this study considers that semi-honest participants can lead to deviations in the security and fairness of the protocol, and combines information entropy and mutual information to present an n-round information exchange protocol in which each participant broadcasts a relevant information value in each round without revealing other information. The uncertainty of the correct outcome value is blurred by the interaction information in each round, and each participant is not sure of the correct outcome value until the end of the protocol, which effectively prevents malicious behavior and ensures the correct execution of the protocol. Security and fairness analysis shows that our protocol guarantees the security and relative fairness of the output obtained by the participants after completing the protocol.
A Label-Aware Autoregressive Framework for Cross-Domain NER

Cross-domain named entity recognition (NER) aims to borrow the entity information from the source domain to help the entity recognition in the target domain with limited labeled data.Despite the promising performance of existing approaches, most of them focus on reducing the discrepancy of token representation between source and target domains, while the transfer of the valuable label information is often not explicitly considered or even ignored.Therefore, we propose a novel autoregressive framework to advance cross-domain NER by first enhancing the relationship between labels and tokens and then further improving the transferability of label information.Specifically, we associate each label with an embedding vector, and for each token, we utilize a bidirectional LSTM (Bi-LSTM) to encode the labels of its previous tokens for modeling internal context information and label dependence.Afterward, we propose a Bi-Attention module that merges the token representation from a pre-trained model and the label features from the Bi-LSTM as the label-aware information, which is concatenated to the token representation to facilitate crossdomain NER.In doing so, label information contained in the embedding vectors can be effectively transferred to the target domain, and Bi-LSTM can further model the label relationship among different domains by pre-train and then fine-tune setting.Experimental results on several datasets confirm the effectiveness of our model, where our model achieves significant improvements over existing methods. 1
Semantic interdisciplinary evaluation of image captioning models

In our day-to-day life, synchronizing vision and language aspects plays a crucial role in solving various real-time challenges. Image captioning is one of them, and it aims to recognise objects, activities, and their relationships in order to provide a syntactically and semantically correct visual description. There are existing works of image captioning in various directions, such as news, fashion, art, and medical domains. The core architectural idea of image captioning is based on merging CNN, RNN, and transformer models. In practice, there are many conceivable combinations, and brute forcing all of them would take a long time. As we know, there is no work on interpreting image captioning models across various usecases. In this research article, we examine and analyze different image captioning models used across various domains, and multiple insights are extracted to determine the best combinational architecture for a new application without ignoring contextual semantics. We examined numerous designs and determined that LSTM is best for image captioning across several domains.
Event-Driven Model Predictive Control With Deep Learning for Wastewater Treatment Process

Wastewater treatment processes (WWTPs) have been considered as complex control problems, because effluent water standard, stability and multioperational conditions need to be taken into account. In this article, an event-driven model predictive control with deep learning (EMPC-DL) is proposed for the control problems to improve the running performance of WWTPs. First, several events are defined based on different operational conditions reflected by operational data. Then, an event-driven deep belief network (EDBN) is developed based on deep learning to approximate the nonlinear characteristics of the WWTPs. Second, a quadratic optimization is designed to solve the control law of MPC based on the predictive output of the EDBN. The major advantage of quadratic optimization is its efficiency, which is achieved by an efficient strategy that only needs one-step prediction of EDBN during one-time rolling optimization. Third, this article gives convergence and stability analysis of EMPC-DL. Finally, the feasibility and applicability of EMPC-DL are demonstrated on the benchmark simulation model No. 1 (BSM1). The experimental results show that EMPC-DL achieves the more satisfactory performance in modeling, controlling, and tracking water quality parameters than its peers.
Poor English skills? New AIs help researchers to write better

Machine-learning tools can correct grammar and advise on the style and tone of presentations — but they must be used with caution. Machine-learning tools can correct grammar and advise on the style and tone of presentations — but they must be used with caution.
Methodology of formation of modeling skills based on a constructive approach (on the example of GeoGebra)

Author’s methodology of forming modeling skills involves 4 steps: Step 1 – the teacher step by step constructs the curve by means of cloud based service GeoGebra; Step 2 – the teacher offers a description-definition of the curve and provides a ready-made algorithm by which students model the curve independently in GeoGebra; Step 3 – the teacher offers an algorithm for constructing a curve model, and students need to characterize the properties of the curve or give its definition based on the results, Step 4 – students are offered definitions of curves that they have to model in GeoGebra). An example of realization of the author’s methodology is given, the pedagogical experiment on proof of its effectiveness is described.
Improving the content of training future translators in the aspect of studying modern CAT tools

The article deals with the search for improving the content of training for future translators, taking into account the expansion of the use of information technologies in the field of translation. The results of a study of curriculums for translators at the universities of Europe, America and Asia are presented. The use of CAT systems in the work of translation agencies is shown. The presentation of various CAT systems in training programs for translators and their use in the market of translation services is analyzed. It has been established that both university curricula and translation agencies are oriented, as a rule, not to one, but to several CAT systems. The results of a student survey based on their practice in translation agencies are presented. Recommendations have been developed regarding the inclusion of the most common CAT systems in the training program for translators. The expediency of studying not just one, but several CAT systems is substantiated. The necessity of studying both desktop and cloud CAT systems is indicated.
Consumer acceptance of autonomous delivery robots for last-mile delivery: Technological and health perspectives

The unprecedented outbreak of the novel coronavirus has led to a great shift toward online retailing and accelerated the need for contactless delivery. This study investigates how technological and health belief factors influence consumer acceptance of autonomous delivery robots (ADRs). Anchored in four behavioral theories [i.e., technology acceptance model, health belief model, perceived value (VAL) theory and trust theory], a synthesized model is developed. A total of 500 valid responses were collected through an online questionnaire in Singapore, and structural equation modeling was conducted to examine the responses. The results revealed that perceived ease of use (EOU), perceived usefulness (UFN), perceived susceptibility (SUS), perceived severity (SEV), self-efficacy (SEL) and cues to action (CUE) have a positive and significant influence on consumers’ perceptions of the value of ADRs. The total effect analysis also showed that perceived VAL strongly affects consumer acceptance of ADRs. Academically, this study introduces both technological and health belief factors to explain consumer acceptance of ADRs. It also provides recommendations for policymakers and autonomous delivery robot developers on policy formulation, public communication, product design and infrastructure development.
Early Adopter or Follower? The Strategic Equilibrium of Blockchain Technology Adoption Strategy for Competing Agri-Food Supply Chains

Despite the touting value of blockchain technology (BT), many agri-food supply chains are still struggling to the adoption of it. In this article, incorporating the benefit and cost associated with BT, we seek to answer whether and under what conditions can two competing agri-food supply chains benefit from the adoption of BT, and how government choose optimal subsidy scheme to promote the adoption of BT. The findings suggest that the Nash equilibrium outcome toward BT adoption strategy will be greatly affected by key parameters, such as competitive intensity, the growth rate of the market size and the investment cost with the adoption of BT, and the planting cost of the agri-food. Specifically, early adopter can always snatch more benefit from the adoption of the BT than the follower, and the gap between them will be increased in the competition intensity. In addition, the decisions selected by agri-food supply chains does not always benefit the consumer surplus and the social welfare, which call for further government subsidy scheme to promote the adoption of BT. The findings provide important implications for both the industrial managers and policy makers on how to benefit from BT in the digital transformation era.
Learning to Attack Real-World Models for Person Re-identification via Virtual-Guided Meta-Learning

Recent advances in person re-identification (re-ID) have led to impressive retrieval accuracy. However, existing re-ID models are challenged by the adversarial examples crafted by adding quasi-imperceptible perturbations. Moreover, re-ID systems face the domain shift issue that training and testing domains are not consistent. In this study, we argue that learning powerful attackers with high universality that works well on unseen domains is an important step in promoting the robustness of re-ID systems. Therefore, we introduce a novel universal attack algorithm called ``MetaAttack'' for person re-ID. MetaAttack can mislead re-ID models on unseen domains by a universal adversarial perturbation. Specifically, to capture common patterns across different domains, we propose a meta-learning scheme to seek the universal perturbation via the gradient interaction between meta-train and meta-test formed by two datasets. We also take advantage of a virtual dataset (PersonX), instead of real ones, to conduct meta-test. This scheme not only enables us to learn with more comprehensive variation factors but also mitigates the negative effects caused by biased factors of real datasets. Experiments on three large-scale re-ID datasets demonstrate the effectiveness of our method in attacking re-ID models on unseen domains. Our final visualization results reveal some new properties of existing re-ID systems, which can guide us in designing a more robust re-ID model. Code and supplemental material are available at \url{https://github.com/FlyingRoastDuck/MetaAttack_AAAI21}.
Domain Adaptation for Time-Series Classification to Mitigate Covariate Shift

The performance of a machine learning model degrades when it is applied to data from a similar but different domain than the data it has initially been trained on. To mitigate this domain shift problem, domain adaptation (DA) techniques search for an optimal transformation that converts the (current) input data from a source domain to a target domain to learn a domain-invariant representation that reduces domain discrepancy. This paper proposes a novel supervised DA based on two steps. First, we search for an optimal class-dependent transformation from the source to the target domain from a few samples. We consider optimal transport methods such as the earth mover's distance, Sinkhorn transport and correlation alignment. Second, we use embedding similarity techniques to select the corresponding transformation at inference. We use correlation metrics and higher-order moment matching techniques. We conduct an extensive evaluation on time-series datasets with domain shift including simulated and various online handwriting datasets to demonstrate the performance.
Gromov-Wasserstein Guided Representation Learning for Cross-Domain Recommendation

Cross-Domain Recommendation (CDR) has attracted increasing attention in recent years as a solution to the data sparsity issue. The fundamental paradigm of prior efforts is to train a mapping function based on the overlapping users/items and then apply it to the knowledge transfer. However, due to the commercial privacy policy and the sensitivity of user data, it is unrealistic to explicitly share the user mapping relations and behavior data. Therefore, in this paper, we consider a more practical cross-domain scenario, where there is no explicit overlap between the source and target domains in terms of users/items. Since the user sets of both domains are drawn from the entire population, there may be commonalities between their user characteristics, resulting in comparable user preference distributions. Thus, without the mapping relations at user level, it is feasible to model this distribution-level relation to transfer knowledge between domains. To this end, we propose a novel framework that improves the effect of representation learning on the target domain by aligning the representation distributions between the source and target domains. In addition, GWCDR can be easily integrated with existing single-domain collaborative filtering methods to achieve cross-domain recommendation. Extensive experiments on two pairs of public bidirectional datasets demonstrate the effectiveness of our proposed framework in enhancing the recommendation performance.
Deep Learning Channel Estimation for OFDM 5G Systems with Different Channel Models

Abstract At cellular wireless communication systems, channel estimation (CE) is one of the key techniques that are used in Orthogonal Frequency Division Multiplexing modulation (OFDM). The most common methods are Decision‐Directed Channel Estimation, Pilot-Assisted Channel Estimation (PACE) and blind channel estimation. Among them, PACE is commonly used and has a steadier performance. Applying deep learning (DL) methods in CE is getting increasing interest of researchers during the past 3 years. The main objective of this paper is to assess the efficiency of DL-based CE compared to the conventional PACE techniques, such as least-square (LS) and minimum mean-square error (MMSE) estimators. A simulation environment to evaluate OFDM performance at different channel models has been used. A DL process that estimates the channel from training data is also employed to get the estimated impulse response of the channel. Two channel models have been used in the comparison: Tapped Delay Line and Clustered Delay Line channel models. The performance is evaluated under different parameters including number of pilots (64 pilots or 8 pilots), number of subcarriers (64), the length of cyclic prefix (16 or 0 samples) and carrier frequency (4 GHz) through computer simulation using MATLAB. From the simulation results, the trained DL estimator provides better results in estimating the channel and detecting the transmitted symbols compared to LS and MMSE estimators although, the complexity of the proposed LSTM estimator exceeds the equivalent LS estimator. Furthermore, the DL estimator also demonstrates its effectiveness with various pilot densities and with different cyclic prefix periods.
FAIR principles for AI models with a practical application for accelerated high energy diffraction microscopy

Abstract A concise and measurable set of FAIR (Findable, Accessible, Interoperable and Reusable) principles for scientific data is transforming the state-of-practice for data management and stewardship, supporting and enabling discovery and innovation. Learning from this initiative, and acknowledging the impact of artificial intelligence (AI) in the practice of science and engineering, we introduce a set of practical, concise, and measurable FAIR principles for AI models. We showcase how to create and share FAIR data and AI models within a unified computational framework combining the following elements: the Advanced Photon Source at Argonne National Laboratory, the Materials Data Facility, the Data and Learning Hub for Science, and funcX, and the Argonne Leadership Computing Facility (ALCF), in particular the ThetaGPU supercomputer and the SambaNova DataScale ® system at the ALCF AI Testbed. We describe how this domain-agnostic computational framework may be harnessed to enable autonomous AI-driven discovery.
Improved Heralded Single-Photon Source with a Photon-Number-Resolving Superconducting Nanowire Detector

Deterministic generation of single photons is essential for many quantum information technologies. A bulk optical nonlinearity emitting a photon pair, where the measurement of one of the photons heralds the presence of the other, is commonly used with the caveat that the single-photon emission rate is constrained due to a trade-off between multiphoton events and pair emission rate. Using an efficient and low noise photon-number-resolving superconducting nanowire detector we herald, in real time, a single photon at telecommunication wavelength. We perform a second-order photon correlation ${g}^{2}(0)$ measurement of the signal mode conditioned on the measured photon number of the idler mode for various pump powers and demonstrate an improvement of a heralded single-photon source. We develop an analytical model using a phase-space formalism that encompasses all multiphoton effects and relevant imperfections, such as loss and multiple Schmidt modes. We perform a maximum-likelihood fit to test the agreement of the model to the data and extract the best-fit mean photon number $\ensuremath{\mu}$ of the pair source for each pump power. A maximum reduction of $0.118\ifmmode\pm\else\textpm\fi{}0.012$ in the photon ${g}^{2}(0)$ correlation function at $\ensuremath{\mu}=0.327\ifmmode\pm\else\textpm\fi{}0.007$ is obtained, indicating a strong suppression of multiphoton emissions. For a fixed ${g}^{2}(0)=7\ifmmode\times\else\texttimes\fi{}{10}^{\ensuremath{-}3}$, we increase the single pair generation probability by 25%. Our experiment, built using fiber-coupled and off-the-shelf components, delineates a path to engineering ideal sources of single photons.
A particle swarm optimizer with dynamic balance of convergence and diversity for large-scale optimization

Particle swarm optimization is found ineffective in large-scale optimization. The main reason is that particle swarlarge-scalem optimization cannot effectively balance convergence and diversity. This paper proposes a particle swarm optimizer with a dynamic balance of convergence and diversity (PSO-DBCD). In the proposed algorithm, a competitive multi-swarm mechanism is put forward, based on which a convergence-guiding learning strategy is proposed for the management of convergence pressure. Furthermore, an entropy-based local diversity measurement is proposed to measure the local diversity of particles. Afterwards, a diversity-guiding learning strategy is proposed based on the local diversity information to further improve the diversity preservation ability of the algorithm. Theoretical analyses are presented to investigate the characteristics of PSO-DBCD. Comprehensive experiments are conducted based on the benchmarks posted on CEC 2013 and several state-of-the-art algorithms to test the performance and scalability of the proposed algorithm. The PSO-DBCD exhibits evident advantages over the compared algorithms in the optimization results with respect to the statistical test results. The proposed strategies are demonstrated to be effective in managing the convergence speed and the swarm diversity. Lastly, a case study of centralized electric vehicle charging optimization shows that PSO-DBCD can reduce the cost of charging for people who use electric vehicles.