Reviewerly/exHarmony huggingface dataset:
Reasoning Step-by-Step: Temporal Sentence Localization in Videos via Deep Rectification-Modulation Network

Temporal sentence localization in videos aims to ground the best matched segment in an untrimmed video according to a given sentence query.Previous works in this field mainly rely on single-step attentional frameworks to align the temporal boundaries by a soft selection.Although they focus on the visual content relevant to the query, these attention strategies are insufficient to model complex video contents and restrict the higher-level reasoning demand for temporal relation.In this paper, we propose a novel deep rectification-modulation network (RMN), transforming this task into a multi-step reasoning process by repeating rectification and modulation.In each rectification-modulation layer, unlike existing methods directly conducting the cross-modal interaction, we first devise a rectification module to correct implicit attention misalignment which focuses on wrong position during the interaction process.Then, a modulation module is developed to model the frame-to-frame relation with the help of specific sentence information for better correlating and composing the video contents over time.With multiple such layers cascaded in depth, our RMN progressively refines video and query interactions, thus enabling a further precise localization.Experimental evaluations on three public datasets show that the proposed method achieves state-of-the-art performance.
More Data, More Relations, More Context and More Openness: A Review and Outlook for Relation Extraction

Relational facts are an important component of human knowledge, which are hidden in vast amounts of text. In order to extract these facts from text, people have been working on relation extraction (RE) for years. From early pattern matching to current neural networks, existing RE methods have achieved significant progress. Yet with explosion of Web text and emergence of new relations, human knowledge is increasing drastically, and we thus require "more" from RE: a more powerful RE system that can robustly utilize more data, efficiently learn more relations, easily handle more complicated context, and flexibly generalize to more open domains. In this paper, we look back at existing RE methods, analyze key challenges we are facing nowadays, and show promising directions towards more powerful RE. We hope our view can advance this field and inspire more efforts in the community.
Construction of Virtual Reality-Interactive Classroom Based on Deep Learning Algorithm

The traditional classroom has been impacted by the digital teaching resources. Students are no longer satisfied with the traditional teaching mode of teacher teaching and student learning. Combined with the characteristics of a virtual reality-interactive classroom, the design of a virtual reality-interactive classroom based on the deep learning algorithm is proposed. This paper divides the teaching activities of the VR-interactive classroom into two parts: in-class learning activities and after-class learning activities. The software is used to design the interactive test. The emphasis and difficulty in the virtual reality-interactive classroom are taken as the development object to realize the construction of the virtual reality-interactive classroom. The simulation results show that the statistical output of teaching quality evaluation can be obtained from the quantitative regression analysis of the factors involved in VR classroom participation.
Aschern at SemEval-2020 Task 11: It Takes Three to Tango: RoBERTa, CRF, and Transfer Learning

We describe our system for SemEval-2020 Task 11 on Detection of Propaganda Techniques in News Articles.We developed ensemble models using RoBERTa-based neural architectures, additional CRF layers, transfer learning between the two subtasks, and advanced post-processing to handle the multi-label nature of the task, the consistency between nested spans, repetitions, and labels from similar spans in training.We achieved sizable improvements over baseline fine-tuned RoBERTa models, and the official evaluation ranked our system 3 rd (almost tied with the 2nd) out of 36 teams on the span identification subtask with an F1 score of 0.491, and 2 nd (almost tied with the 1st) out of 31 teams on the technique classification subtask with an F1 score of 0.62.
Facial image inpainting using attention-based multi-level generative network

Facial image inpainting is a challenging task since the facial images lose much content causing blur and unnaturalness. In this paper, we propose facial image inpainting using attention-based multi-level generative network. We adopt multi-level feature processing to reduce the training and testing time while enhancing the performance by reducing the number of channels of each convolutional layer in the generator. We combine attention with multi-level feature processing to maintain soft correlation with the surrounding content. For network optimization, we utilize two kinds of loss functions: content and texture. Content loss consists of mean absolute error (MAE) and edge-preserving losses, and produces inpainting results close to the ground truth. Texture loss consists of adversarial and perceptual losses to fine-tune the texture synthesis. Besides, we use the edge-preserving loss to take edge and patch similarity. Comparative experiments indicate that the proposed method produces photo-realistic and plausible inpainting results in random masks as well as outperforms the state-of-the-art ones in terms of quantitative measurements and subjective evaluations.
HopRetriever: Retrieve Hops over Wikipedia to Answer Complex Questions

Collecting supporting evidence from large corpora of text (e.g., Wikipedia) is of great challenge for open-domain Question Answering (QA). Especially, for multi-hop open-domain QA, scattered evidence pieces are required to be gathered together to support the answer extraction. In this paper, we propose a new retrieval target, hop, to collect the hidden reasoning evidence from Wikipedia for complex question answering. Specifically, the hop in this paper is defined as the combination of a hyperlink and the corresponding outbound link document. The hyperlink is encoded as the mention embedding which models the structured knowledge of how the outbound link entity is mentioned in the textual context, and the corresponding outbound link document is encoded as the document embedding representing the unstructured knowledge within it. Accordingly, we build HopRetriever which retrieves hops over Wikipedia to answer complex questions. Experiments on the HotpotQA dataset demonstrate that HopRetriever outperforms previously published evidence retrieval methods by large margins. Moreover, our approach also yields quantifiable interpretations of the evidence collection process.
Training-Free Uncertainty Estimation for Dense Regression: Sensitivity as a Surrogate

Uncertainty estimation is an essential step in the evaluation of the robustness for deep learning models in computer vision, especially when applied in risk-sensitive areas. However, most state-of-the-art deep learning models either fail to obtain uncertainty estimation or need significant modification (e.g., formulating a proper Bayesian treatment) to obtain it. Most previous methods are not able to take an arbitrary model off the shelf and generate uncertainty estimation without retraining or redesigning it. To address this gap, we perform a systematic exploration into training-free uncertainty estimation for dense regression, an unrecognized yet important problem, and provide a theoretical construction justifying such estimations. We propose three simple and scalable methods to analyze the variance of outputs from a trained network under tolerable perturbations: infer-transformation, infer-noise, and infer-dropout. They operate solely during the inference, without the need to re-train, re-design, or fine-tune the models, as typically required by state-of-the-art uncertainty estimation methods. Surprisingly, even without involving such perturbations in training, our methods produce comparable or even better uncertainty estimation when compared to training-required state-of-the-art methods. Code is available at https://github.com/lumi9587/train-free-uncertainty.
A Hybrid Architecture combining Artificial intelligence and Blockchain for IoT Applications

As Internet of Things (IoT) revolutionized over the years, it has become a crucial part in many application such as smart city and smart transportation, ensuring smooth functioning of the human life in a more reliable manner. As the amount of data that is to be collected, stored and analyzed increases, there is need for a system that perform these tasks in a more efficient manner. Artificial intelligence plays a crucial part in delivering these requirements. But when AI is involved, it will also result in the use of big tools that are necessary for tackling the issue at hand. Hence in order to ensure that AI can function in a smooth manner, blockchain support is introduced such that it develops a decentralized architecture. Block chain will provide a safe and secure environment to share the information and resources and can also address the drawbacks of Artificial intelligence. The purpose of this proposed work is to develop a hybrid architecture, combining the advantages of blockchain and artificial intelligence to enable big data analysis. We have performed an analysis and results have been observed to conclude the impact of blockchain in Artificial Intelligence in the aspect of latency and accuracy. The results indicate that the proposed architecture excels over the other architectures and can also overcome some of the challenges prevalent.
Face Image Publication Based on Differential Privacy

As an information carrier, face images contain abundant sensitive information. Due to its natural weak privacy, direct publishing may divulge privacy. Anonymization Technology and Data Encryption Technology are limited by the background knowledge and attack means of attackers, which cannot completely content the needs of face image privacy protection. Therefore, this paper proposes a face image publishing SWP (sliding window publication) algorithm, which satisfies the differential privacy. Firstly, the SWP translates the image gray matrix into a one-dimensional ordered data stream by using image segmentation technology. The purpose of this step is to transform the image privacy protection problem into the data stream privacy protection problem. Then, the sliding window model is used to model the data flow. By comparing the similarity of data in adjacent sliding windows, the privacy budget is dynamically allocated, and Laplace noise is added. In SWP, the data in the sliding window comes from the image. To present the image features contained in the data more comprehensively and use the privacy budget more reasonably, this paper proposes a fusion similarity measurement EM (exact mechanism) mechanism and a dynamic privacy budget allocation DA (dynamic allocation) mechanism. Also, for further improving the usability of human face images and reducing the impact of noise, a sort-SWP algorithm based on the SWP method is proposed in the paper. Through the analysis, it can be seen that ordered input can further improve the usability of the SWP algorithm, but direct sorting of data will destroy the <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mi>ε</mi> </math> -differential privacy. Therefore, this paper proposes a sorting method-SAS method, which satisfies the <math xmlns="http://www.w3.org/1998/Math/MathML" id="M2"> <mi>ε</mi> </math> -differential privacy; SAS obtain an initial sort by using an exponential mechanism firstly. And then an approximate correct sort is obtained by using the Annealing algorithm to optimize the initial sort. Compared with LAP algorithm and SWP algorithm, the average accuracy rate of sort-SWP algorithm in ORL, Yale is increased by 56.63% and 21.55%, the recall rate is increased by 6.85% and 3.32%, and F1-sroce is improved by 55.62% and 16.55%.
Transfer learning of chaotic systems

Can a neural network trained by the time series of system A be used to predict the evolution of system B? This problem, knowing as transfer learning in a broad sense, is of great importance in machine learning and data mining yet has not been addressed for chaotic systems. Here, we investigate transfer learning of chaotic systems from the perspective of synchronization-based state inference, in which a reservoir computer trained by chaotic system A is used to infer the unmeasured variables of chaotic system B, while A is different from B in either parameter or dynamics. It is found that if systems A and B are different in parameter, the reservoir computer can be well synchronized to system B. However, if systems A and B are different in dynamics, the reservoir computer fails to synchronize with system B in general. Knowledge transfer along a chain of coupled reservoir computers is also studied, and it is found that, although the reservoir computers are trained by different systems, the unmeasured variables of the driving system can be successfully inferred by the remote reservoir computer. Finally, by an experiment of chaotic pendulum, we demonstrate that the knowledge learned from the modeling system can be transferred and used to predict the evolution of the experimental system.
Towards Efficient Local Causal Structure Learning

Local causal structure learning aims to discover and distinguish direct causes (parents) and direct effects (children) of a variable of interest from data. While emerging successes have been made, existing methods need to search a large space to distinguish direct causes from direct effects of a target variable T. To tackle this issue, we propose a novel Efficient Local Causal Structure learning algorithm, named ELCS. Specifically, we first propose the concept of N-structures, then design an efficient Markov Blanket (MB) discovery subroutine to integrate MB learning with N-structures to learn the MB of T and simultaneously distinguish direct causes from direct effects of T. With the proposed MB subroutine, ELCS starts from the target variable, sequentially finds MBs of variables connected to the target variable and simultaneously constructs local causal structures over MBs until the direct causes and direct effects of the target variable have been distinguished. Using eight Bayesian networks the extensive experiments have validated that ELCS achieves better accuracy and efficiency than the state-of-the-art algorithms.
Video Data Compression by Progressive Iterative Approximation

In the present paper, the B-spline curve is used for reducing the entropy of video data.We consider the color or luminance variations of a spatial position in a series of frames as input data points in Euclidean space R or R3.The progressive and iterative approximation (PIA) method is a direct and intuitive way of generating curve series of high and higher fitting accuracy.The video data points are approximated using progressive and iterative approximation for least square (LSPIA) fitting.The Lossless video data compression is done through storing the B-spline curve control points (CPs) and the difference between fitted and original video data.The proposed method is applied to two classes of synthetically produced and naturally recorded video sequences and makes a reduction in the entropy of both.However, this reduction is higher for syntactically created than those naturally produced.The comparative analysis of experiments on a variety of video sequences suggests that the entropy of output video data is much less than that of input video data.
Performance Optimization for Drift-Robust Fidelity Improvement of Two-Qubit Gates

Quantum system characterization techniques represent the front line in the identification and mitigation of noise in quantum computing, but can be expensive in terms of quantum resources and time to repeatedly employ. Another challenging aspect is that parameters governing the performance of various operations tend to drift over time, and monitoring these is hence a difficult task. One of the most promising characterization techniques, gate set tomography (GST), provides a self-consistent estimate of the completely positive, trace-preserving (CPTP) maps for a complete set of gates, as well as preparation and measurement operators. We develop a method for performance optimization seeded by tomography (POST), which couples the power of GST with a classical optimization routine to achieve a consistent gate improvement in just a short number of steps within a given calibration cycle. By construction, the POST procedure finds the best available gate operation given the hardware, and is therefore robust to the effects of drift. Further, in comparison to other quantum error mitigation techniques, it builds upon a one-time application of GST. To demonstrate the performance of this method on a real quantum computer, we map out the operations of six qubit pairs on the superconducting \emph{ibmq\_poughkeepsie} quantum device. Under the restriction of logical-only control, we monitor the performance of the POST approach on a chosen CNOT gate over a period of six weeks. In this time, we achieve a consistent improvement in gate fidelity, averaging a fidelity increase of 21.1\% as measured by randomized benchmarking. The POST approach should find wide applicability as it is hardware agnostic, and can be applied at the upper logical level or at a deeper pulse control level.
FedQPL

Federations of RDF data sources provide great potential when queried for answers and insights that cannot be obtained from one data source alone. A challenge for planning the execution of queries over such a federation is that the federation may be heterogeneous in terms of the types of data access interfaces provided by the federation members. This challenge has not received much attention in the literature. This paper provides a solid formal foundation for future approaches that aim to address this challenge. Our main conceptual contribution is a formal language for representing query execution plans; additionally, we identify a fragment of this language that can be used to capture the result of selecting relevant data sources for different parts of a given query. As technical contributions, we show that this fragment is more expressive than what is supported by existing source selection approaches, which effectively highlights an inherent limitation of these approaches. Moreover, we show that the source selection problem is NP-hard and in σP2, and we provide an extensive set of rewriting rules that can be used as a basis for query optimization.
Average Localised Proximity: A new data descriptor with good default one-class classification performance

One-class classification is a challenging subfield of machine learning in which so-called data descriptors are used to predict membership of a class based solely on positive examples of that class, and no counter-examples. A number of data descriptors that have been shown to perform well in previous studies of one-class classification, like the Support Vector Machine (SVM), require setting one or more hyperparameters. There has been no systematic attempt to date to determine optimal default values for these hyperparameters, which limits their ease of use, especially in comparison with hyperparameter-free proposals like the Isolation Forest (IF). We address this issue by determining optimal default hyperparameter values across a collection of 246 one-class classification problems derived from 50 different real-world datasets. In addition, we propose a new data descriptor, Average Localised Proximity (ALP) to address certain issues with existing approaches based on nearest neighbour distances. Finally, we evaluate classification performance using a leave-one-dataset-out procedure, and find strong evidence that ALP outperforms IF and a number of other data descriptors, as well as weak evidence that it outperforms SVM, making ALP a good default choice.
Average skew information-based coherence and its typicality for random quantum states

Abstract We study the average skew information-based coherence for both random pure and mixed states. The explicit formulae of the average skew information-based coherence are derived and shown to be the functions of the dimension N of the state space. We demonstrate that as N approaches to infinity, the average coherence is 1 for random pure states, and a positive constant less than 1/2 for random mixed states. We also explore the typicality of average skew information-based coherence of random quantum states. Furthermore, we identify a coherent subspace such that the amount of the skew information-based coherence for each pure state in this subspace can be bounded from below almost always by a fixed number that is arbitrarily close to the typical value of coherence.
Specular Reflection Image Enhancement Based on a Dark Channel Prior

In this paper, we propose a specular highlight image enhancement algorithm based on a dark channel prior to solve the problem of information loss in specular highlight images in real scenes. First, the algorithm is based on the dark channel prior algorithm, and a moving window minimum filter is used to estimate the global illumination component. Then, a weighted function based on the local pixel color difference is introduced to solve the halo artifacts in the image. Then, the improved guided filter algorithm is used to optimize the transmittance, which improves the computational efficiency of the algorithm. Finally, the brightness of the image is adjusted by the CLAHE algorithm to enhance the local details of the image. Experimental results show that this method can effectively enhance the information in specular highlight images, and its processing results are obviously better than those of other algorithms.
Recent Advances in Adversarial Training for Adversarial Robustness

Adversarial training is one of the most effective approaches defending against adversarial examples for deep learning models. Unlike other defense strategies, adversarial training aims to promote the robustness of models intrinsically. During the last few years, adversarial training has been studied and discussed from various aspects. A variety of improvements and developments of adversarial training are proposed, which were, however, neglected in existing surveys. For the first time in this survey, we systematically review the recent progress on adversarial training for adversarial robustness with a novel taxonomy. Then we discuss the generalization problems in adversarial training from three perspectives. Finally, we highlight the challenges which are not fully tackled and present potential future directions.
Exploring Soft Concepts with Hard Corpus-Analytic Methods

Corpus-analytic studies are currently experiencing a veritable boom in the social sciences.However, three crucial barriers impede the methodological quality and long-term reputation of these promising new technologies: Firstly, creating and pre-processing very large text corpora is still a laborious and costly enterprise.Secondly, the semantically valid operationalization of complex theoretical concepts remains a problem.Thirdly, scholars need flexible data output and visualization options to connect the data generated by computational methods with the discipline's existing research.We will conclude that it is possible to solve these problems.However, hermeneutically sensitive uses of computational methods will take much more time, work, and creativity than often assumed.The reflected appropriation of big data methods in the social sciences has only just begun.Zusammenfassung: In diesem Beitrag argumentieren wir,