Reviewerly/exHarmony huggingface dataset:
An n/2 byzantine node tolerate blockchain sharding approach

Traditional Blockchain Sharding approaches can only tolerate up to n/3 of nodes being adversary because they rely on the hyper-geometric distribution to make a failure (an adversary does not have n/3 of nodes globally but can manipulate the consensus of a Shard) hard to happen. The system must maintain a large Shard size (the number of nodes inside a Shard) to sustain the low failure probability so that only a small number of Shards may exist. In this paper, we present a new approach of Blockchain Sharding that can withstand up to n/2 of nodes being bad. We categorise the nodes into different classes, and every Shard has a fixed number of nodes from different classes. We prove that this design is much more secure than the traditional models (only have one class) and the Shard size can be reduced significantly. In this way, many more Shards can exist, and the transaction throughput can be largely increased. The improved Blockchain Sharding approach is promising to serve as the foundation for decentralised autonomous organisations and decentralised database.
MultiDM-GCN: Aspect-guided Response Generation in Multi-domain Multi-modal Dialogue System using Graph Convolutional Network

In the recent past, dialogue systems have gained immense popularity and have become ubiquitous.During conversations, humans not only rely on languages but seek contextual information through visual contents as well.In every task-oriented dialogue system, the user is guided by the different aspects of a product or service that regulates the conversation towards selecting the product or service.In this work, we present a multi-modal conversational framework for a task-oriented dialogue setup that generates the responses following the different aspects of a product or service to cater to the user's needs.We show that the responses guided by the aspect information provide more interactive and informative responses for better communication between the agent and the user.We first create a Multi-domain Multimodal Dialogue (MDMMD) dataset having conversations involving both text and images belonging to the three different domains, such as restaurants, electronics, and furniture.We implement a Graph Convolutional Network (GCN) based framework that generates appropriate textual responses from the multi-modal inputs.The multi-modal information having both textual and image representation is fed to the decoder and the aspect information for generating aspect guided responses.Quantitative and qualitative analyses show that the proposed methodology outperforms several baselines for the proposed task of aspect-guided response generation.
Pauli Fusion: a Computational Model to Realise Quantum Transformations from ZX Terms

We present an abstract model of quantum computation, the "Pauli Fusion" model, whose primitive operations correspond closely to generators of the ZX calculus (a formal graphical language for quantum computing). The fundamental operations of Pauli Fusion are also straightforward abstractions of basic processes in some leading proposed quantum technologies. These operations have non-deterministic heralded effects, similarly to measurement-based quantum computation. We describe sufficient conditions for Pauli Fusion procedures to be deterministically realisable, so that it performs a given transformation independently of its non-deterministic outcomes. This provides an operational model to realise ZX terms beyond the circuit model.
DLNR-SIQA: Deep Learning-Based No-Reference Stitched Image Quality Assessment

Due to recent advancements in virtual reality (VR) and augmented reality (AR), the demand for high quality immersive contents is a primary concern for production companies and consumers. Similarly, the topical record-breaking performance of deep learning in various domains of artificial intelligence has extended the attention of researchers to contribute to different fields of computer vision. To ensure the quality of immersive media contents using these advanced deep learning technologies, several learning based Stitched Image Quality Assessment methods have been proposed with reasonable performances. However, these methods are unable to localize, segment, and extract the stitching errors in panoramic images. Further, these methods used computationally complex procedures for quality assessment of panoramic images. With these motivations, in this paper, we propose a novel three-fold Deep Learning based No-Reference Stitched Image Quality Assessment (DLNR-SIQA) approach to evaluate the quality of immersive contents. In the first fold, we fined-tuned the state-of-the-art Mask R-CNN (Regional Convolutional Neural Network) on manually annotated various stitching error-based cropped images from the two publicly available datasets. In the second fold, we segment and localize various stitching errors present in the immersive contents. Finally, based on the distorted regions present in the immersive contents, we measured the overall quality of the stitched images. Unlike existing methods that only measure the quality of the images using deep features, our proposed method can efficiently segment and localize stitching errors and estimate the image quality by investigating segmented regions. We also carried out extensive qualitative and quantitative comparison with full reference image quality assessment (FR-IQA) and no reference image quality assessment (NR-IQA) on two publicly available datasets, where the proposed system outperformed the existing state-of-the-art techniques.
Applying Text Analytics for Studying Research Trends in Dependability

The dependability of systems and networks has been the target of research for many years now. In the 1970s, what is now known as the top conference on dependability—The IEEE/IFIP International Conference on Dependable Systems and Networks (DSN)—emerged gathering international researchers and sparking the interest of the scientific community. Although it started in niche systems, nowadays dependability is viewed as highly important in most computer systems. The goal of this work is to analyze the research published in the proceedings of well-established dependability conferences (i.e., DSN, International Symposium on Software Reliability Engineering (ISSRE), International Symposium on Reliable Distributed Systems (SRDS), European Dependable Computing Conference (EDCC), Latin-American Symposium on Dependable Computing (LADC), Pacific Rim International Symposium on Dependable Computing (PRDC)), while using Natural Language Processing (NLP) and namely the Latent Dirichlet Allocation (LDA) algorithm to identify active, collapsing, ephemeral, and new lines of research in the dependability field. Results show a strong emphasis on terms, like ‘security’, despite the general focus of the conferences in dependability and new trends that are related with ’machine learning’ and ‘blockchain’. We used the PRDC conference as a use case, which showed similarity with the overall set of conferences, although we also found specific terms, like ‘cyber-physical’, being popular at PRDC and not in the overall dataset.
An Image Clustering Auto-Encoder Based on Predefined Evenly-Distributed Class Centroids and MMD Distance

In this paper, we propose a novel, effective and simpler end-to-end image clustering auto-encoder algorithm: ICAE. The algorithm uses predefined evenly-distributed class centroids (PEDCC) as the clustering centers, which ensures the inter-class distance of latent features is maximal, and adds data distribution constraint, data augmentation constraint, auto-encoder reconstruction constraint and Sobel smooth constraint to improve the clustering performance. Specifically, we perform one-to-one data augmentation to learn the more effective features. The data and the augmented data are simultaneously input into the autoencoder to obtain latent features and the augmented latent features whose similarity are constrained by an augmentation loss. Then, making use of the maximum mean discrepancy distance, we combine the latent features and augmented latent features to make their distribution close to the PEDCC distribution (uniform distribution between classes, Dirac distribution within the class) to further learn clustering-oriented features. At the same time, the MSE of the original input image and reconstructed image is used as reconstruction constraint, and the Sobel smooth loss to build generalization constraint to improve the generalization ability. Finally, extensive experiments on three common datasets MNIST, Fashion-MNIST, COIL20 are conducted. The experimental results show that the algorithm has achieved the best clustering results so far. In addition, we can use the predefined PEDCC class centers, and the decoder to clearly generate the samples of each class. The code can be downloaded at https://github.com/zyWang-Power/Clustering!
Learning Numeral Embedding

Word embedding is an essential building block for deep learning methods for natural language processing.Although word embedding has been extensively studied over the years, the problem of how to effectively embed numerals, a special subset of words, is still underexplored.Existing word embedding methods do not learn numeral embeddings well because there are an infinite number of numerals and their individual appearances in training corpora are highly scarce.In this paper, we propose two novel numeral embedding methods that can handle the out-of-vocabulary (OOV) problem for numerals.We first induce a finite set of prototype numerals using either a selforganizing map or a Gaussian mixture model.We then represent the embedding of a numeral as a weighted average of the prototype number embeddings.Numeral embeddings represented in this manner can be plugged into existing word embedding learning approaches such as skip-gram for training.We evaluated our methods and showed its effectiveness on four intrinsic and extrinsic tasks: word similarity, embedding numeracy, numeral prediction, and sequence labeling.
Document-Level Definition Detection in Scholarly Documents: Existing Models, Error Analyses, and Future Directions

The task of definition detection is important for scholarly papers, because papers often make use of technical terminology that may be unfamiliar to readers.Despite prior work on definition detection, current approaches are far from being accurate enough to use in realworld applications.In this paper, we first perform in-depth error analysis of the current best performing definition detection system and discover major causes of errors.Based on this analysis, we develop a new definition detection system, HEDDEx, that utilizes syntactic features, transformer encoders, and heuristic filters, and evaluate it on a standard sentence-level benchmark.Because current benchmarks evaluate randomly sampled sentences, we propose an alternative evaluation that assesses every sentence within a document.This allows for evaluating recall in addition to precision.HEDDEx outperforms the leading system on both the sentence-level and the document-level tasks, by 12.7 F1 points and 14.4 F1 points, respectively.We note that performance on the high-recall document-level task is much lower than in the standard evaluation approach, due to the necessity of incorporation of document structure as features.We discuss remaining challenges in document-level definition detection, ideas for improvements, and potential issues for the development of reading aid applications.
Summarizing Chinese Medical Answer with Graph Convolution Networks and Question-focused Dual Attention

Online search engines are a popular source of medical information for users, where users can enter questions and obtain relevant answers. It is desirable to generate answer summaries for online search engines, particularly summaries that can reveal direct answers to questions. Moreover, answer summaries are expected to reveal the most relevant information in response to questions; hence, the summaries should be generated with a focus on the question, which is a challenging topic-focused summarization task. In this paper, we propose an approach that utilizes graph convolution networks and question-focused dual attention for Chinese medical answer summarization. We first organize the original long answer text into a medical concept graph with graph convolution networks to better understand the internal structure of the text and the correlation between medical concepts. Then, we introduce a question-focused dual attention mechanism to generate summaries relevant to questions. Experimental results demonstrate that the proposed model can generate more coherent and informative summaries compared with baseline models.
A Study on Encodings for Neural Architecture Search

Neural architecture search (NAS) has been extensively studied in the past few years. A popular approach is to represent each neural architecture in the search space as a directed acyclic graph (DAG), and then search over all DAGs by encoding the adjacency matrix and list of operations as a set of hyperparameters. Recent work has demonstrated that even small changes to the way each architecture is encoded can have a significant effect on the performance of NAS algorithms. In this work, we present the first formal study on the effect of architecture encodings for NAS, including a theoretical grounding and an empirical study. First we formally define architecture encodings and give a theoretical characterization on the scalability of the encodings we study Then we identify the main encoding-dependent subroutines which NAS algorithms employ, running experiments to show which encodings work best with each subroutine for many popular algorithms. The experiments act as an ablation study for prior work, disentangling the algorithmic and encoding-based contributions, as well as a guideline for future work. Our results demonstrate that NAS encodings are an important design decision which can have a significant impact on overall performance. Our code is available at this https URL.
Subgraph Neural Networks

Deep learning methods for graphs achieve remarkable performance on many node-level and graph-level prediction tasks. However, despite the proliferation of the methods and their success, prevailing Graph Neural Networks (GNNs) neglect subgraphs, rendering subgraph prediction tasks challenging to tackle in many impactful applications. Further, subgraph prediction tasks present several unique challenges: subgraphs can have non-trivial internal topology, but also carry a notion of position and external connectivity information relative to the underlying graph in which they exist. Here, we introduce SubGNN, a subgraph neural network to learn disentangled subgraph representations. We propose a novel subgraph routing mechanism that propagates neural messages between the subgraph's components and randomly sampled anchor patches from the underlying graph, yielding highly accurate subgraph representations. SubGNN specifies three channels, each designed to capture a distinct aspect of subgraph topology, and we provide empirical evidence that the channels encode their intended properties. We design a series of new synthetic and real-world subgraph datasets. Empirical results for subgraph classification on eight datasets show that SubGNN achieves considerable performance gains, outperforming strong baseline methods, including node-level and graph-level GNNs, by 19.8% over the strongest baseline. SubGNN performs exceptionally well on challenging biomedical datasets where subgraphs have complex topology and even comprise multiple disconnected components.
EmoTag1200: Understanding the Association between Emojis and Emotions

Given the growing ubiquity of emojis in language, there is a need for methods and resources that shed light on their meaning and communicative role.One conspicuous aspect of emojis is their use to convey affect in ways that may otherwise be non-trivial to achieve.In this paper, we seek to explore the connection between emojis and emotions by means of a new dataset consisting of human-solicited association ratings.We additionally conduct experiments to assess to what extent such associations can be inferred from existing data in an unsupervised manner.Our experiments show that this succeeds when high-quality wordlevel information is available.
Not one but many Tradeoffs

Data holders are increasingly seeking to protect their user's privacy, whilst still maximizing their ability to produce machine learning (ML) models with high quality predictions. In this work, we empirically evaluate various implementations of differential privacy (DP), and measure their ability to fend off real-world privacy attacks, in addition to measuring their core goal of providing accurate classifications. We establish an evaluation framework to ensure each of these implementations are fairly evaluated. Our selection of DP implementations add DP noise at different positions within the framework, either at the point of data collection/release, during updates while training of the model, or after training by perturbing learned model parameters. We evaluate each implementation across a range of privacy budgets and datasets, each implementation providing the same mathematical privacy guarantees. By measuring the models' resistance to real world attacks of membership and attribute inference, and their classification accuracy. we determine which implementations provide the most desirable tradeoff between privacy and utility. We found that the number of classes of a given dataset is unlikely to influence where the privacy and utility tradeoff occurs, a counter-intuitive inference in contrast to the known relationship of increased privacy vulnerability in datasets with more classes. Additionally, in the scenario that high privacy constraints are required, perturbing input training data before applying ML modeling does not trade off as much utility, as compared to noise added later in the ML process.
MovieChats: Chat like Humans in a Closed Domain

Being able to perform in-depth chat with humans in a closed domain is a precondition before an open-domain chatbot can ever be claimed.In this work, we take a close look at the movie domain and present a large-scale high-quality corpus with fine-grained annotations in hope of pushing the limit of moviedomain chatbots.We propose a unified, readily scalable neural approach which reconciles all subtasks like intent prediction and knowledge retrieval.The model is first pretrained on the huge general-domain data, then finetuned on our corpus.We show this simple neural approach trained on high-quality data is able to outperform commercial systems replying on complex rules.On both the static and interactive tests, we find responses generated by our system exhibits remarkably good engagement and sensibleness close to human-written ones.We further analyze the limits of our work and point out potential directions for future work 1 .
Long-distance dissipation-assisted transport of entangled states via a chiral waveguide

This work proposes a scheme for long-distance, high-fidelity transport of entangled states between nodes of a quantum network. The dissipation-assisted transport is achieved using chiral waveguide QED and ring resonators. Transport of various entangled states such as Bell states, multipartite W-states and Dicke states are demonstrated with high fidelity
Expert Level Evaluations for Explainable AI (XAI) Methods in the Medical Domain

The recently emerged field of explainable artificial intelligence (XAI) attempts to shed lights on 'black box' Machine Learning (ML) models in understandable terms for human. As several explanation methods are developed alongside different applications for a black box model, the need for expert-level evaluation in inspecting their effectiveness becomes inevitable. This is significantly important for sensitive domains such as medical applications where evaluation of experts is essential to better understand how accurate the results of complex ML are and debug the models if necessary. The aim of this study is to experimentally show how the expert-level evaluation of XAI methods in a medical application can be utilized and aligned with the actual explanations generated by the clinician. To this end, we collect annotations from expert subjects equipped with an eye-tracker while they classify medical images and devise an approach for comparing the results with those obtained from XAI methods. We demonstrate the effectiveness of our approach in several experiments.
PerfVis

Developers are usually unaware of the impact of code changes to the performance of software systems. Although developers can analyze the performance of a system by executing, for instance, a performance test to compare the performance of two consecutive versions of the system, changing from a programming task to a testing task would disrupt the development flow. In this paper, we propose the use of a city visualization that dynamically provides developers with a pervasive view of the continuous performance of a system. We use an immersive augmented reality device (Microsoft HoloLens) to display our visualization and extend the integrated development environment on a computer screen to use the physical space. We report on technical details of the design and implementation of our visualization tool, and discuss early feedback that we collected of its usability. Our investigation explores a new visual metaphor to support the exploration and analysis of possibly very large and multidimensional performance data. Our initial result indicates that the city metaphor can be adequate to analyze dynamic performance data on a large and non-trivial software system.
A Novel Horizon Picking Method on Sub-Bottom Profiler Sonar Images

Traditional manual horizon picking is time-consuming and laborious, while automatic picking methods often suffer from the limited scope of their applications and the discontinuity of picked results. In this paper, we propose a novel method for automatic horizon picking from sub-bottom profiles (SBP) by an improved filtering algorithm. First, a clear and fine SBP image is formed using an intensity transformation method. On this basis, a novel filtering method is proposed by improving the multi-scale enhancement filtering algorithm to obtain clear horizons from an SBP image. The improvement is performed by applying a vertical suppression weighting term based on the form of logistic function, which is constructed by using the eigenvectors from the Hessian matrix. Then, the filtered image is segmented using a threshold method, and the horizon points in the SBP image are picked. After that, a horizon linking method is applied, which uses the horizon directions to refine the picked horizon points. The proposed method has been verified experimentally, and accurate and continuous horizons were obtained. Finally, the proposed method is discussed and some conclusions are drawn.
Interactive Color Image Segmentation using HSV Color Space

The primary goal of this research work is to extract only the essential foreground fragments of a color image through segmentation. This technique serves as the foundation for implementing object detection algorithms. The color image can be segmented better in HSV color space model than other color models. An interactive GUI tool is developed in Python and implemented to extract only the foreground from an image by adjusting the values for H (Hue), S (Saturation) and V (Value). The input is an RGB image and the output will be a segmented color image.