Reviewerly/exHarmony huggingface dataset:
Kalman Filter for Linear Systems With Unknown Structural Parameters

This brief considers Kalman filter for linear systems with unknown structural parameters. We design a Bayesian parameter identification algorithm based on maximum likelihood (ML) criterion and expectation maximization (EM). Under the identification of structural parameter, we perform the Kalman filter to estimate the states. The Kalman filter and the parameter identification algorithm are interactive to estimate the states and the structural parameters. More specifically, first, we use EM algorithm together with Kalman smoother to estimate the structural parameters. Then, on the basis of results provided by the first step, we employ the classical Kalman filter to predict and update the states, which formulates the final proposed Kalman filter. Performance analysis and simulation results verify the presented filter.
Classical and quantum algorithms for constructing text from dictionary problem

We study algorithms for solving a problem of constructing a text (a long string) from a dictionary (a sequence of small strings). The problem has an application in bioinformatics and has a connection with the sequence assembly method for reconstructing a long DNA sequence from small fragments. Our problem is the construction a string t of length n using strings $$s^1,\dots , s^m$$ with possible overlapping. Firstly, we provide a classical (randomized) algorithm with running time $$O\left( n+L +m(\log n)^2\right) =\tilde{O}(n+L)$$ where L is the sum of lengths of $$s^1,\dots ,s^m$$ . Secondly, we provide a quantum algorithm with running time $$O\left( n +\log n\cdot (\log m+\log \log n)\cdot \sqrt{m\cdot L}\right) =\tilde{O}\left( n +\sqrt{m\cdot L}\right) $$ . Additionally, we show that the lower bound for a classical (randomized or deterministic) algorithm is $$\varOmega (n+L)$$ . Thus, our classical algorithm is optimal up to a log factor, and our quantum algorithm shows a speed-up when compared with any classical (randomized or deterministic) algorithm in the case of non-constant length of strings in the dictionary.
A novel domain and event adaptive tweet augmentation approach for enhancing the classification of crisis related tweets

One of the purposes of detecting the crisis related tweets is the ability to single out the tweets that provide information about the helps needed and offered. Classification of such tweets is difficult because of the unavailability of sufficient annotated tweets in those categories. To facilitate such classifications, a domain and event adaptive augmentation approach is proposed. The main objective of the research is to enhance the classification of crisis related tweets that have less training samples. The proposed algorithms are designed to integrate the innate domain and event information during the selection of words for augmentation. Components such as CrisisLex lexicon, Word2Vec embeddings and WordNet are utilized for the proposed augmentation. Experimentation is carried out to substantiate the benefits of augmentation. Results indicate increased performance of the classifier when provided with the expanded dataset including the augmented and original tweets. To combat the problem of overfitting and class imbalance arising due to the lesser training samples, a novel tweets augmentation algorithm can be utilized. The advantage in the proposed algorithms is the ability to retain the structure and inherent nature of the tweets during the augmentation.
Generalized spaces of pointwise regularity: toward a general framework for the WLM

Abstract In this work we generalize the spaces <?CDATA ${T}_{u}^{p}$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"> <mml:msubsup> <mml:mrow> <mml:mi>T</mml:mi> </mml:mrow> <mml:mrow> <mml:mi>u</mml:mi> </mml:mrow> <mml:mrow> <mml:mi>p</mml:mi> </mml:mrow> </mml:msubsup> </mml:math> introduced by Calderón and Zygmund using pointwise conditions emanating from generalized Besov spaces. We give conditions binding the functions belonging to these spaces and their wavelet coefficients. Next, we propose a multifractal formalism based on such spaces which generalizes the so-called wavelet leaders method and show that it is satisfied on a prevalent set.
Transfer Learning for Pose Estimation of Illustrated Characters

Human pose information is a critical component in many downstream image processing tasks, such as activity recognition and motion tracking. Likewise, a pose estimator for the illustrated character domain would provide a valuable prior for assistive content creation tasks, such as reference pose retrieval and automatic character animation. But while modern data-driven techniques have substantially improved pose estimation performance on natural images, little work has been done for illustrations. In our work, we bridge this domain gap by efficiently transfer-learning from both domain-specific and task-specific source models. Additionally, we upgrade and expand an existing illustrated pose estimation dataset, and introduce two new datasets for classification and segmentation subtasks. We then apply the resultant state-of-the-art character pose estimator to solve the novel task of pose-guided illustration retrieval. All data, models, and code will be made publicly available.
Reducing traffic burstiness for MPTCP in data center networks

Modern industrial and data center networks adopt multipath TCP (MPTCP) to achieve low communication latency. To make full use of multiple paths, MPTCP divides one TCP connection into multiple subflows. Due to the connection-level ACK blocking, however, MPTCP suffers from the traffic burstiness, which leads to large queueing delay, frequent packet drops, and even TCP timeout. To mitigate the traffic burstiness caused by ACK blocking, we propose MPTCP-RB (Multipath TCP with Reduced Burstiness), which interleaves consecutive packets across multiple subflows, instead of assigning consecutive packets to individual subflows. MPTCP-RB is implemented as a loadable Linux kernel module, working on any traditional MPTCP-based schemes without any other modifications on existing network stacks. The testbed and NS2 experiments show that, MPTCP-RB reduces the average flow completion time by up to 91% compared with the state-of-the-art MPTCP-based schemes.
Semantic-Based Explainable AI: Leveraging Semantic Scene Graphs and Pairwise Ranking to Explain Robot Failures

When interacting in unstructured human environments, occasional robot failures are inevitable. When such failures occur, everyday people, rather than trained technicians, will be the first to respond. Existing natural language explanations hand-annotate contextual information from an environment to help everyday people understand robot failures. However, this methodology lacks generalizability and scalability. In our work, we introduce a more generalizable semantic explanation framework. Our framework autonomously captures the semantic information in a scene to produce semantically descriptive explanations for everyday users. To generate failure-focused explanations that are semantically grounded, we lever-ages both semantic scene graphs to extract spatial relations and object attributes from an environment, as well as pairwise ranking. Our results show that these semantically descriptive explanations significantly improve everyday users’ ability to both identify failures and provide assistance for recovery than the existing state-of-the-art context-based explanations.
Attribute-Aware Feature Encoding for Object Recognition and Segmentation

Existing multi-task models for object recognition and segmentation have verified the effectiveness of joint optimization of two semantic tasks. However, learning discriminative representations with insufficient training data and redundant contextual information from the background remains challenging. Semantic attributes are designed as powerful and informative mid-level features that 1) share information across categories to model the interclass correlation and that 2) can be localized in the object region to benefit foreground extraction. This paper introduces a novel attribute-aware feature encoding (AFE) module to a multi-task network for object recognition and segmentation with the aim of improving both semantic tasks by regularizing feature encoding with auxiliary attribute learning. Intuitively, attribute learning in our method not only provides extra supervision signals to capture interclass correlation in object classification but also refines the output of object segmentation via weakly supervised attribute localization. The experimental results on two public benchmarks show that our method yields remarkable improvement in both semantic tasks and auxiliary attribute estimation over existing methods.
Multi-layer adversarial domain adaptation with feature joint distribution constraint

The domain-invariant constraint is usually exerted on a single layer of a convolutional neural network (CNN). This scheme may help the constrained layer learn domain-invariant features at the cost of sacrificing the other layers’ learning ability. In this paper, we propose a novel method called multi-layer adversarial domain adaptation (MLADA), which incorporates information of all the layers by a hierarchical scheme. To the convolutional layers of the CNN model, a feature-level domain classifier is introduced to learn domain-invariant representation. To the fully connected layer of the CNN model, a prediction-level domain classifier is set to reduce domain discrepancy in the decision layer. An union domain classifier is then mounted to balance the joint distribution constraints between the feature-level and the prediction-level domain classifiers. So the MLADA can gain domain-invariant representation through sufficient training over all layers of the model. Experimental results indicate that MLADA outperforms other methods on multiple classic tasks in domain adaptation. The ablation study proves the necessity of three domain classifiers in the architecture of MLADA.
Optimization of CNN model for image classification

Convolutional Neural Networks (CNNs) are particularly precise in several fields, especially computer vision where image classification is one of the most researched and commercialized application. Deploying these models on embedded devices requires high throughput and low latency even with limited resources and energy budgets. The complexity of the architecture of CNN models implies a very high computation cost. We are looking in this paper for determining the optimal topology (the number of layers and the number of neurons per layer) that allows us to reduce the model and deploy it in embedded platforms. We have proposed a small CNN architecture that achieves high level accuracy 81.50% on CIFAR-10 with fewer parameters based on the growing approach. For more optimization we used the pruning technique and the results showed that with more optimal architecture we obtained 82.43% accuracy and 15% reduction of the number of parameters.
Automatic configuration of the Cassandra database using irace

Database systems play a central role in modern data-centered applications. Their performance is thus a key factor in the efficiency of data processing pipelines. Modern database systems expose several parameters that users and database administrators can configure to tailor the database settings to the specific application considered. While this task has traditionally been performed manually, in the last years several methods have been proposed to automatically find the best parameter configuration for a database. Many of these methods, however, use statistical models that require high amounts of data and fail to represent all the factors that impact the performance of a database, or implement complex algorithmic solutions. In this work we study the potential of a simple model-free general-purpose configuration tool to automatically find the best parameter configuration of a database. We use the irace configurator to automatically find the best parameter configuration for the Cassandra NoSQL database using the YCBS benchmark under different scenarios. We establish a reliable experimental setup and obtain speedups of up to 30% over the default configuration in terms of throughput, and we provide an analysis of the configurations obtained.
User Interface Research in Web Extended Reality

With computer technology rapidly expands out in recent years, there is a significant trend that Virtual Reality, Augmented Reality, and Mixed Reality technologies turn into the public. The paper starts from a literature review to discuss Web Extended Reality and its critical characteristics in user interface. Our research discussed the user interface layout and interactive mode by wide research. The paper reveals not only the traditional literature review but also contains a data experiment study. By analyzing how the platform limitation restricts the user interface in Web Extended Reality, we designed a simulated solar system as a case experiment. We summarized its rendering performance data and discussed the limitation in various textures and materials. The paper uses the collected articles and experimental data to discuss industrial developing trends and indicates the possible future view based on the data study result.
Vulnerability Assessment on Ethereum Based Smart Contract Applications

A Smart Contract is an agreement in the form of computer code that is made between two individuals. In a blockchain environment, smart contracts executed and stored in a shared ledger that are not modifiable. Ethereum is one of the major platforms used for smart contracts, where solidity basically is a high-level programming language used in the Ethereum to build smart contracts. Recent vulnerabilities found by the coders were not updated in analysis tool (SmartCheck) and therefore incapable of detecting vulnerabilities. No definitions of patterns were existing to detect these vulnerabilities. This paper focuses on the improvement of the Smartcheck analysis method to convert the source code of solidity into an intermediate representation based on XML and verifies this against the XPath patterns. Moreover, the latest vulnerabilities were listed to create new patterns to detect such vulnerabilities. The proposed method was evaluated with real world datasets and the results were compared with similar tools.
Following the video surveillance and personal video cameras: New tools and innovations to health monitor the earthquake wave field

Video cameras are becoming essential tools for obtaining real-time information on the mechanical performance of structures and their contents during seismic events as well as information on the moving properties of propagating media, such as tsunamis, landslides, and water sloshing. Their recorded images also provide important clues on human behavior during shaking. In this paper a large set of situations obtained from published YouTube videos, involving structures, natural outfits, human behavior are presented. Video cameras cannot replace laboratory static tests or tests on shaking tables, pseudo-dynamic sub-structure testing, wind tunnels or the channel propagation of waves. However, information that is collected over time and well used is of great value, as it shows the real world without any shortcomings provoked by “similarity laws”, “boundary conditions”, or a “friction and nonlinear hypothesis”. This information should be collected even if it only serves as an “inspiration” to researchers by supporting new ideas that only visualization can provide. This type of information can be considered a random visual health monitoring system. The present paper pretends to show the added value brought by video and personal cameras to the health monitoring of the wave field. We supplement the presented observations with practical recommendations supported in a simple analysis. Once treated, the images can be used in presentations to illustrate different phenomena related to the wave propagation and effects on the built environment and can also serve as an inspiration for mathematical modeling and/or can supply information that otherwise would be lost.
A Blockchain-aided Self-Sovereign Identity Framework for Edge-based UAV Delivery System

Edge computing is becoming more and more popular in both academics and industries. With the booming of edge computing technology, the Unmanned Aerial Vehicle (UAV) based delivery system is expected to achieve higher efficiency and low latency. However, the UAV often collects user-specific data during the delivery process, the data-leakage or security/privacy breaching could occur during the data-sharing process between the edge nodes and UAV devices. Privacy-preserving issues are further refraining from the popularity of the UAV-based logistic systems. It is believed that the UAV tracking and identity verification system can provide imminent access-level security and privacy protection, which is urgently required to eliminate the practical concerns under the edge computing-based environment. To the best knowledge of authors, for the first time, this paper proposes a Self-Sovereign Identity (SSI) integrated framework with the latest Blockchain technology for UAV-based delivery system. It is expected to protect the edge computing-based UAV delivery system against security flaws and privacy concerns. In this work, the benefits of using SSI with Blockchain technology are analyzed, the efficiency of identifying and authenticating UAVs and their respective users is further experimented and discussed. The experimental results show that the integrated SSI framework with Blockchain can effectively improve the efficiency of the user identity management system as well as the identity verification process in the delivery process.
High-impact hate speech by persons of authority: A lower threshold needed?

Abstract This paper discusses a global trend in the approach to hate speech. It describes how the international human rights organisations are recently addressing the dynamics of hate speech and how academic thinking is stretching the framework of the justification of hate speech regulations. This work analyses the aspect of cause and effect in the light of the role of the speaker; examining the academic argument that content expressed by public figures of authority have a higher impact, in particular in the context of the digital media ecosystem, with a social media dominance.
Robotic Assistant Agent for Student and Machine Co-Learning on AI-FML Practice with AIoT Application

In this paper, the Robotic Assistant Agent for student and machine co-learning on AI-FML practice with AIoT application is presented. The structure of AI-FML contains three parts, including fuzzy logic, neural network, and evolutionary computation. Besides, the Robotic Assistant Agent (RAA) can assist students and machines in co-learning English and AI-FML practice based on the robot Kebbi Air and AIoT-FML learning tool. Since Sept. 2019, we have introduced an Intelligent Speaking English Assistant (ISEA) App and AI-FML platform to English and computer science learning classes at two elementary schools in Taiwan. We use the collected English-learning data to train a predictive regression model based on students' monthly examination scores. In Jan. 2021, we further combined the developed AI-FML platform with a novel AIoT-FML learning tool to enhance students' interests in learning English and AI-FML with basic hands-on practice. The proposed RAA is responsible for reasoning students' learning performance and showing the results on the AIoT-FML learning tool after communicating with the AI-FML platform. The experimental results and the collection of students' feedback show that this kind of learning model is popular with elementary-school and high-school students, and the learning performance of elementary-school students is improved.
Avoiding conflict and minimising exposure: Face-work on Twitter

In a context where Twitter has come under criticism for enabling and encouraging hostile communication, this article explores how users adopt a ‘Twitter face’ when navigating interactions on the platform. Extending Goffman’s observation that face-work is applicable to both immediate and mediated interaction, this article provides a novel application of face-work on Twitter. Reporting on data from an online questionnaire completed by general Twitter users, we explore how uncivil interaction is experienced on the platform and the interaction strategies users employ to protect their face. We examine how interactions on the platform can lead to a ritual break-down that generate forms of alienation arising from aggressive uses of face-work. We contend that attempts to enhance Twitter as a medium by limiting and restricting particular interactions are ultimately attempts at shaping Twitter’s affordances. In analysing user experience, our discussion considers how incivility is responded to and how the platform encourages users to engage in the avoidance components of face-work, while simultaneously inhibiting the easy adoption of its restorative dimensions. While both dimensions of face-work are vital, the downplaying of restorative aspects of face-work arguably undermines Twitter’s efforts to encourage inclusive interactions across the platform.
From Learning-to-Match to Learning-to-Discriminate: Global Prototype Learning for Few-shot Relation Classification

Few-shot relation classification has attracted great attention recently, and is regarded as an effective way to tackle the long-tail problem in relation classification. Most previous works on few-shot relation classification are based on learning-to-match paradigms, which focus on learning an effective universal matcher between the query and one target class prototype based on inner-class support sets. However, the learning-to-match paradigm focuses on capturing the similarity knowledge between query and class prototype, while fails to consider discriminative information between different candidate classes. Such information is critical especially when target classes are highly confusing and domain shifting exists between training and testing phases. In this paper, we propose the Global Transformed Prototypical Networks (GTPN), which learns to build a few-shot model to directly discriminate between the query and all target classes with both inner-class local information and inter-class global information. Such learning-to-discriminate paradigm can make the model concentrate more on the discriminative knowledge between all candidate classes, and therefore leads to better classification performance. We conducted experiments on standard FewRel benchmarks. Experimental results show that GTPN achieves very competitive performance on few-shot relation classification and reached the best performance on the official leaderboard of FewRel 2.0 ( https://thunlp.github.io/2/fewrel2_da.html ).
Product rating estimation using machine learning

Now-a-days several product reviews are available online, and it's difficult for a typical future customer to travel through all of these reviews and understand the gist of all the reviews to determine whether the product is a fit for their respective needs. It will be difficult to investigate the merchandise review just from the rating given from the surveys as they will be misleading for the choice makers. Almost all e-commerce websites contain an online reviews section in which the customers who bought that particular product can post online reviews for that particular product. The customers can post a review coupled with a rating publicly. In some instances, there's a mismatch between the review and the corresponding rating. The final rating of a product is the average of the entire user provided ratings, which could lead to slightly, but significantly misleading overall rating. To eliminate the influence of reviews with mismatched ratings, this research work attempts to perform sentiment analysis for the product reviews using deep learning to estimate the rating of the product.