Reviewerly/exHarmony huggingface dataset:
A Question Answering-Based Framework for One-Step Event Argument Extraction

Event argument extraction, which aims to identify arguments of specific events and label their roles, is a challenging subtask of event extraction.Previous approaches solve this problem in a twostage manner that first extracts named entities as argument candidates and then determines their roles.However, many nested entities may be missed or wrongly predicted during the argument candidate extraction procedure, which substantially affects the performance of the downstream classifier.In this paper, we propose a novel one-step question answering based framework, which performs argument candidate extraction and argument role classification simultaneously to mitigate the error propagation problem in conventional two-stage methods.Since the conventional question answering based framework cannot be applied directly to this task, we design a Question Answering based Sequence Labeling (QA-SL) model to tackle inexistent argument roles and multiple argument token spans.Moreover, considering the overwhelming number of parameters in question answering based neural network models and the relatively small size of event extraction corpus, we fine-tune the pre-trained model from BERT to mitigate the data scarcity problem.Extensive experiments demonstrate the benefits of the proposed method, leading to a competitive performance compared with state-of-the-art methods.To the best of our knowledge, this is the first work to cast event argument extraction as a question answering task.
An Empirical Study of Transformer-Based Neural Language Model Adaptation

We explore two adaptation approaches of deep Transformer based neural language models (LMs) for automatic speech recognition. The first approach is a pretrain-finetune framework, where we first pretrain a Transformer LM on a large-scale text corpus from scratch and then adapt it to relatively small target domains via finetuning. The second approach is a mixer of dynamically weighted models that are separately trained on source and target domains, aiming to improve simple linear interpolation with dynamic weighting. We compare the two approaches with three baselines - without adaptation, merging data, and simple interpolation - on Switchboard (SWBD) and Wall Street Journal (WSJ). Experiments show that the mixer model generally performs better than baselines and finetuning. Compared with no adaptation, finetuning and the mixer approach obtain up to relative 11.5% and 14.1% WER reductions on SWBD, respectively. The mixer model also outperforms linear interpolation and merging data. On WSJ, the mixer approach achieves a new state-of-the-art WER result.
Analysis of research on amazon AWS cloud computing seller data security

We are not ready for big data [1]. Amazon Data Security RisksIn September 2018, Amazon announced that it had received a related report and said that it had launched an internal investigation.The focus of the inquiry was on internal employees receiving bribes to disclose company data and other confidential information, establishing an advantage for merchants who purchased the data.According to media reports, this practice is particularly evident in China, where the number of sellers is soaring.At the same time, the relatively low salaries of Amazon employees in China may encourage them to take risks.There are even Shenzhen Amazon employees who can provide internal sales data and email addresses of reviewers, as well as services to delete negative reviews and restore banned Amazon accounts, with prices ranging from $ 80 to $ 2,000.Also, according to several foreign media reports, the latest disclosed court documents showed that in November 2018 Amazon requested a UK judge to approve the search of account information of Barclays Bank and MasterCard-owned Prepay Technologies.Amazon believes it is being attacked on a large
A Joint Label Space for Generalized Zero-Shot Classification

The fundamental problem of Zero-Shot Learning (ZSL) is that the one-hot label space is discrete, which leads to a complete loss of the relationships between seen and unseen classes. Conventional approaches rely on using semantic auxiliary information, e.g. attributes, to re-encode each class so as to preserve the inter-class associations. However, existing learning algorithms only focus on unifying visual and semantic spaces without jointly considering the label space. More importantly, because the final classification is conducted in the label space through a compatibility function, the gap between attribute and label spaces leads to significant performance degradation. Therefore, this paper proposes a novel pathway that uses the label space to jointly reconcile visual and semantic spaces directly, which is named Attributing Label Space (ALS). In the training phase, one-hot labels of seen classes are directly used as prototypes in a common space, where both images and attributes are mapped. Since mappings can be optimized independently, the computational complexity is extremely low. In addition, the correlation between semantic attributes has less influence on visual embedding training because features are mapped into labels instead of attributes. In the testing phase, the discrete condition of label space is removed, and priori one-hot labels are used to denote seen classes and further compose labels of unseen classes. Therefore, the label space is very discriminative for the Generalized ZSL (GZSL), which is more reasonable and challenging for real-world applications. Extensive experiments on five benchmarks manifest improved performance over all of compared state-of-the-art methods.
Character of motional modes for entanglement and sympathetic cooling of mixed-species trapped-ion chains

Modular mixed-species ion-trap networks are a promising framework for scalable quantum information processing, where one species acts as a memory qubit and another as a communication qubit. This architecture requires high-fidelity mixed-species entangling gates to transfer information from communication to memory qubits through their collective motion. We investigate the character of the motional modes of a mixed-species ion chain for entangling operations and also sympathetic cooling. We find that the laser power required for high-fidelity entangling gates based on transverse modes is at least an order of magnitude higher than that based on axial modes for widely different masses of the two species. We also find that for even moderate mass differences, the transverse modes are much harder to cool than the axial modes regardless of the ion chain configuration. Therefore, transverse modes conventionally used for operations in single-species ion chains may not be well suited for mixed-species chains with widely different masses.
Enabling Fast and Universal Audio Adversarial Attack Using Generative Model

Recently, the vulnerability of deep neural network (DNN)-based audio systems to adversarial attacks has obtained increasing attention. However, the existing audio adversarial attacks allow the adversary to possess the entire user's audio input as well as granting sufficient time budget to generate the adversarial perturbations. These idealized assumptions, however, make the existing audio adversarial attacks mostly impossible to be launched in a timely fashion in practice (e.g., playing unnoticeable adversarial perturbations along with user's streaming input). To overcome these limitations, in this paper we propose fast audio adversarial perturbation generator (FAPG), which uses generative model to generate adversarial perturbations for the audio input in a single forward pass, thereby drastically improving the perturbation generation speed. Built on the top of FAPG, we further propose universal audio adversarial perturbation generator (UAPG), a scheme to craft universal adversarial perturbation that can be imposed on arbitrary benign audio input to cause misclassification. Extensive experiments on DNN-based audio systems show that our proposed FAPG can achieve high success rate with up to 214X speedup over the existing audio adversarial attack methods. Also our proposed UAPG generates universal adversarial perturbations that can achieve much better attack performance than the state-of-the-art solutions.
Semantic Integration of Heterogeneous Databases of Same Domain Using Ontology

Heterogeneous database integration is the study of integrating data from multiple databases. Integrating the heterogeneous database of the same domain has three main challenges that make the heterogeneity problem difficult to solve. The three problems are Semantic, Syntactic and Structural Heterogeneity. Conventional heterogeneous database integration schemes, like De-duplication Techniques, Data Warehouse, and Information Retrieval (IR) Search technique lack the capability to solve the integration of databases completely. The only reason is they cannot deal with Semantic heterogeneity problems efficiently. The semantic Web ontology model is experimented and discussed in the article, which is based on the query execution model. The ontology modeling is divided into two phases, initially to translate the database rules according to ontology rules to find an abstract ontology model. Secondly, to extend the abstract ontology model according to the databases. The method facilitates to apply similarly SPQRAL queries to search the data in the databases. Therefore, the Jena API is used to retrieve semantically similar records. The experiment is based on the two heterogeneous Universities Library Databases. The results show the effectiveness and scalability of the methodology.
An Adaptive Image Watermarking Method Combining SVD and Wang-Landau Sampling in DWT Domain

To keep a better trade-off between robustness and imperceptibility is difficult for traditional digital watermarks. Therefore, an adaptive image watermarking method combining singular value decomposition (SVD) and the Wang–Landau (WL) sampling method is proposed to solve the problem. In this method, the third-level approximate sub-band obtained by applying the three-level wavelet transform is decomposed by SVD to obtain the principal component, which is firstly selected as the embedded position. Then, the information is finally embedded into the host image by the scaling factor. The Wang–Landau sampling method is devoted to finding the best embedding coefficient through the proposed objective evaluation function, which is a global optimization algorithm. The embedding strength is adaptively adjusted by utilizing the historical experience, which overcomes the problem of falling into local optimization easily in many traditional optimization algorithms. To affirm the reliability of the proposed method, several image processing attacks are applied and the experimental results are given in detail. Compared with other existing related watermarking techniques based on both qualitative and quantitative evaluation parameters, such as peak signal to noise ratio (PSNR) and normalized cross-correlation (NC), this method has been proven to achieve a trade-off between robustness and invisibility.
Image Denoising With Deep Convolutional Neural and Multi-Directional Long Short-Term Memory Networks Under Poisson Noise Environments

Removal Poisson noise poses a very challenging technical issue because it is difficult to capture noise characteristics. This induces from the fact that Poisson noises from different sources affect each image pixel proportional to the pixel level. This paper addresses a new image denoising method for removing Poisson noise based on the Deep Convolutional Neural and Multi-directional Long-Short Term Memory Networks. The architecture of the proposed network contains some Convolutional Neural Network (CNN) layers and multi-directional Long-Short Term Memory (LSTM) layers. CNN layers are responsible to extract image features and to estimate some noise bases existed in images. The multi-directional LSTM layers are used to effectively capture and learn the statistics of residual noise components, which possess long-range correlations and appear sparse in the spatial domain. Moreover, designing deep learning models for image denoising involves several hyperparameters such as a number of layers. To select proper hyperparameters, it is beneficial to investigate what is the best image denoising performance we can achieve under different model complexities. Moreover knowing and realizing how far the employing image denoising algorithm can do to the optimal result makes us possible to design the efficient image denoising algorithm. We utilize the Blahut-Arimoto algorithm to derive numerically distortion-mutual information function of image denoising algorithm. The derived function serves as the distortion lower bound given the mutual information between the original image and the denoised image. Based on the knowledge of distortion-mutual information function, we can decide how deep the CNN layers should be deployed in our image denoising algorithm before applying the multi-directional LSTM layers. From our experiments, the proposed image denoising algorithm can outperform other algorithms in both subjective and objective qualities.
Use What You Have: Video retrieval using representations from collaborative experts.

The rapid growth of video on the internet has made searching for video content using natural language queries a significant challenge. Human-generated queries for video datasets `in the wild' vary a lot in terms of degree of specificity, with some queries describing specific details such as the names of famous identities, content from speech, or text available on the screen. Our goal is to condense the multi-modal, extremely high dimensional information from videos into a single, compact video representation for the task of video retrieval using free-form text queries, where the degree of specificity is open-ended. For this we exploit existing knowledge in the form of pre-trained semantic embeddings which include 'general' features such as motion, appearance, and scene features from visual content. We also explore the use of more 'specific' cues from ASR and OCR which are intermittently available for videos and find that these signals remain challenging to use effectively for retrieval. We propose a collaborative experts model to aggregate information from these different pre-trained experts and assess our approach empirically on five retrieval benchmarks: MSR-VTT, LSMDC, MSVD, DiDeMo, and ActivityNet. Code and data can be found at this http URL. This paper contains a correction to results reported in the previous version.
Regional geochemical survey of concealed sandstone-type uranium deposits using fine-grained soil and groundwater in the Erlian basin, north-east China

The Erlian basin is a very important uranium-bearing basin in China; that is located in the famous Central Asian Mobile Belt. It contains several concealed sandstone-type uranium deposits in sandstone and mudstone of Mesozoic-Cenozoic strata. Sampling of soil profiles over the Nuheting concealed U deposit in this basin shows that fine-grained soil collected from the horizon at depth of 15–30 cm has an obvious geochemical anomaly relative to background areas. Regional geochemical survey using fine-grained soil at a density of approximately 1 site per 4 km2 and groundwater at a density of approximately 1 site per 16 km2 or greater was carried out in the Erlian basin in an area of approximately 6400 km2. The survey results indicate that geochemical anomalies of U in the soil have a corresponding relationship with uranium-bearing geological bodies, including concealed uranium ore mineralization and granitic intrusions. Furthermore, groundwater chemistry determined from samples collected from wells is an effective supplementary means for exploration of concealed sandstone-type U deposits. The soil and groundwater geochemical anomalies may reduce the drilling density required to explore regionally, making them two effective and potentially cost-saving alternatives to regional geochemical exploration for concealed sandstone-type uranium deposits in basins. The formation of large-scale regional U anomalies in soil is a complicated process. A uranium vertical migration model for sandstone-type uranium was built for this study, where the water plays an important role in carrying soluble U in the form of uranyl through transport channels from mineralized bodies to Earth's surface under the action of groundwater motion and evapotranspiration. The study of the methods and anomaly formation mechanism is necessary for sandstone-type uranium exploration and will greatly enhance U resource exploration in covered arid terrains worldwide.
Aggregating Customer Review Attributes for Online Reputation Generation

In this paper, we face the problem of generating reputation for movies, products, hotels, restaurants and services by mining customer reviews expressed in natural language. To the best of our knowledge, previous studies on reputation generation for online entities have primarily examined semantic and sentiment orientation of customer reviews, disregarding other useful information that could be extracted from reviews, such as review helpfulness and review time. Therefore, we propose a new approach that combines review helpfulness, review time, review attached rating and review sentiment orientation for the purpose of generating a single reputation value toward various entities. The contribution of the paper is threefold. First, we design two equations to compute review helpfulness and review time scores, and we fine-tune Bidirectional Encoder Representations from Transformers (BERT) model to predict the review sentiment orientation probability. Second, we design a formula to assign a numerical score to each review. Then, we propose a new formula to compute reputation value toward the target entity (movie, product, hotel, restaurant, service, etc). Finally, we propose a new form to visualize reputation that depicts numerical reputation value, opinion categories, top positive review and top negative review. Experimental results coming from several real-world data sets of miscellaneous domains collected from IMDb, TripAdvisor and Amazon websites show the effectiveness of the proposed method in generating and visualizing reputation compared to three state-of-the-art reputation systems.
An adaptive method based on contextual anomaly detection in Internet of Things through wireless sensor networks

With the widespread propagation of Internet of Things through wireless sensor networks, massive amounts of sensor data are being generated at an unprecedented rate, resulting in very large quantities of explicit or implicit information. When analyzing such sensor data, it is of particular importance to detect accurately and efficiently not only individual anomalous behaviors but also anomalous events (i.e. patterns of behaviors). However, most previous work has focused only on detecting anomalies while generally ignoring the correlations between them. Even in approaches that take into account correlations between anomalies, most disregard the fact that the anomaly status of sensor data changes over time. In this article, we propose an unsupervised contextual anomaly detection method in Internet of Things through wireless sensor networks. This method accounts for both a dynamic anomaly status and correlations between anomalies based contextually on their spatial and temporal neighbors. We then demonstrate the effectiveness of the proposed method in an anomaly detection model. The experimental results show that this method can accurately and efficiently detect not only individual anomalies but also anomalous events.
Coherent Ising Machines with Error Correction Feedback

Abstract A nonequilibrium open‐dissipative neural network, such as a coherent Ising machine based on mutually coupled optical parametric oscillators, has been proposed and demonstrated as a novel computing machine for hard combinatorial optimization problems. However, there is a challenge in the previously proposed approach: The machine can be trapped by local minima which increases exponentially with a problem size. This leads to erroneous solutions rather than correct answers. In this paper, it is shown that it is possible to overcome this problem partially by introducing error detection and correction feedback mechanism. The proposed machine achieves efficient sampling of degenerate ground states and low‐energy excited states via its inherent exploration property during a solution search process.
Efficient Visual Tracking With Stacked Channel-Spatial Attention Learning

Template based learning, particularly Siamese networks, has recently become popular due to balancing accuracy and speed. However, preserving tracker robustness against challenging scenarios with real-time speed is a primary concern for visual object tracking. Siamese trackers confront difficulties handling target appearance changes continually due to less discrimination ability learning between target and background information. This paper presents stacked channel-spatial attention within Siamese networks to improve tracker robustness without sacrificing fast-tracking speed. The proposed channel attention strengthens target-specific channels increasing their weight while reducing the importance of irrelevant channels with lower weights. Spatial attention is focusing on the most informative region of the target feature map. We integrate the proposed channel and spatial attention modules to enhance tracking performance with end-to-end learning. The proposed tracking framework learns what and where to highlight important target information for efficient tracking. Experimental results on widely used OTB100, OTB50, VOT2016, VOT2017/18, TC-128, and UAV123 benchmarks verified the proposed tracker achieved outstanding performance compared with state-of-the-art trackers.
An Improved Squirrel Search Algorithm With Reproductive Behavior

The squirrel search algorithm (SSA) is a recently proposed nature-inspired algorithm based on the dynamic foraging and gliding behavior of squirrels.Because of its simplicity and stability, the squirrel algorithm has attracted increasing research interest.However, the lack of exploration ability of the SSA may lead to premature convergence to the local optimum.To overcome this disadvantage, an improved SSA with reproductive behavior (RSSA) is proposed to solve the numerical optimization problem.First, the reproductive behavior of the invasive weed algorithm (IWO) is introduced to the conventional SSA to generate offspring individuals, and these offspring individuals are scattered into the search space by Gaussian distribution to complete the location update.This method makes it possible for individuals with poor fitness to enter the next generation search, improving the exploration ability of the SSA.Second, an adaptive step strategy is proposed to adaptively adjust the search step of squirrels according to the distance between each squirrel and other family members.This strategy effectively balances the exploration and exploitation of the algorithm.Finally, the performance of the proposed RSSA algorithm is evaluated using Wilcoxon's test on unimodal, multimodal, fixed-dimensional multimodal and CEC 2014 benchmark functions.Experimental results and statistical tests show that RSSA has better performance in terms of convergence, accuracy, and search capability compared with other state-of-the-art algorithms.