Reviewerly/exHarmony huggingface dataset:
Self-supervised Representation Learning on Dynamic Graphs

Graph representation learning has now become the de facto standard when dealing with graph-structured data. Using powerful tools from deep learning and graph neural networks, recent works have applied graph representation learning to time-evolving dynamic graphs and showed promising results. However, all the previous dynamic graph models require labeled samples to train, which might be costly to acquire in practice. Self-supervision offers a principled way of utilizing unlabeled data and has achieved great success in computer vision community. In this paper we propose debiased dynamic graph contrastive learning (DDGCL), the first self-supervised representation learning framework on dynamic graphs. The proposed model extends the contrastive learning idea to dynamic graphs via contrasting two nearby temporal views of the same node identity, with a time-dependent similarity critic. Inspired by recent theoretical developments contrastive learning, we propose a novel debiased GAN-type contrastive loss as the learning objective in order to correct the sampling bias occurred in negative sample construction process. We conduct extensive experiments on benchmark datasets via testing the DDGCL framework under two different self-supervision schemes: pretraining and finetuning and multi task learning. The results show that using a simple time-aware GNN encoder, the performance of downstream tasks is significantly improved under either scheme to closely match, or even outperform state-of-the-art dynamic graph models with more elegant encoder architectures. Further empirical evaluations suggest that the proposed approach offers more performance improvement than previously established self-supervision mechanisms over static graphs.
The processing of emoji-word substitutions: A self-paced-reading study

In computer-mediated communication, emojis can be used for various purposes. As small graphical images, many emojis depict abstract or concrete objects ideogrammatically. We report on a self-paced reading experiment of sentences containing emojis. We tested to what extent emojis encode lexical meanings when used in a sentence context. First, we confirm earlier findings that sentence comprehension does not suffer when emojis replace words. Second, we show that in addition to the graphically encoded concept, emojis in some cases enable the retrieval of an entire lexical entry, including the phonological value of the associated word. This means that even emojis showing a homophonous noun to the target word, such as “palm (tree)” for “palm (of hand)” can be interpreted correctly in context. Based on measured differences in the reading times between words, emojis depicting the intended target referent, and emojis depicting a homophonous noun, we propose a context dependent account of emoji interpretation.
A Blockchain-Based Dynamic Consent Architecture to Support Clinical Genomic Data Sharing (ConsentChain): Proof-of-Concept Study

In clinical genomics, sharing of rare genetic disease information between genetic databases and laboratories is essential to determine the pathogenic significance of variants to enable the diagnosis of rare genetic diseases. Significant concerns regarding data governance and security have reduced this sharing in practice. Blockchain could provide a secure method for sharing genomic data between involved parties and thus help overcome some of these issues.This study aims to contribute to the growing knowledge of the potential role of blockchain technology in supporting the sharing of clinical genomic data by describing blockchain-based dynamic consent architecture to support clinical genomic data sharing and provide a proof-of-concept implementation, called ConsentChain, for the architecture to explore its performance.The ConsentChain requirements were captured from a patient forum to identify security and consent concerns. The ConsentChain was developed on the Ethereum platform, in which smart contracts were used to model the actions of patients, who may provide or withdraw consent to share their data; the data creator, who collects and stores patient data; and the data requester, who needs to query and access the patient data. A detailed analysis was undertaken of the ConsentChain performance as a function of the number of transactions processed by the system.We describe ConsentChain, a blockchain-based system that provides a web portal interface to support clinical genomic sharing. ConsentChain allows patients to grant or withdraw data requester access and allows data requesters to query and submit access to data stored in a secure off-chain database. We also developed an ontology model to represent patient consent elements into machine-readable codes to automate the consent and data access processes.Blockchains and smart contracts can provide an efficient and scalable mechanism to support dynamic consent functionality and address some of the barriers that inhibit genomic data sharing. However, they are not a complete answer, and a number of issues still need to be addressed before such systems can be deployed in practice, particularly in relation to verifying user credentials.
The Intriguing Relation Between Counterfactual Explanations and Adversarial Examples

Abstract The same method that creates adversarial examples (AEs) to fool image-classifiers can be used to generate counterfactual explanations (CEs) that explain algorithmic decisions. This observation has led researchers to consider CEs as AEs by another name. We argue that the relationship to the true label and the tolerance with respect to proximity are two properties that formally distinguish CEs and AEs. Based on these arguments, we introduce CEs, AEs, and related concepts mathematically in a common framework. Furthermore, we show connections between current methods for generating CEs and AEs, and estimate that the fields will merge more and more as the number of common use-cases grows.
Adapted transformer network for news recommendation

Online news recommendation aims to provide personalized news for users according to their interests. Existing methods usually learn user preference from their historical reading records in a static and independent way, which ignore the dynamic interaction with the target candidate news. In fact, it is important to fully capture the semantic interaction between user?s historical news and candidate news since the user?s interests would be different in terms of different candidate news. In this paper, we propose a novel news recommendation model with an adapted transformer network. There are three parts in our approach, i.e., a news encoder to learn the semantic features of news, a user encoder to learn the initial representations of users, an adapted transformer module to learn the deep interaction between users and candidate news. The core is that we effectively integrate the historical clicked news and the candidate news into the transformer framework to capture their inherent relatedness. Besides, an additive attention layer is proposed to learn different informativeness of words since different words are differently useful for news or users? representation. We conduct extensive experiments on a real-world dataset from MSN news and the results indicates the effectiveness of our proposed approach.
Simulating quantum repeater strategies for multiple satellites

A global quantum repeater network involving satellite-based links is likely to have advantages over fiber-based networks in terms of long-distance communication, since the photon losses in free space scale only polynomially with the distance -- compared to the exponential losses in optical fibers. To simulate the performance of such networks, we have introduced a scheme of large-scale event-based Monte Carlo simulation of quantum repeaters with multiple memories that can faithfully represent loss and imperfections in these memories. In this work, we identify the quantum key distribution rates achievable in various satellite and ground station geometries for feasible experimental parameters. The power and flexibility of the simulation toolbox allows us to explore various strategies and parameters, some of which only arise in these more complex, multi-satellite repeater scenarios. As a primary result, we conclude that key rates in the kHz range are reasonably attainable for intercontinental quantum communication with three satellites, only one of which carries a quantum memory.
Analyzing Patient Trajectories With Artificial Intelligence

In digital medicine, patient data typically record health events over time (eg, through electronic health records, wearables, or other sensing technologies) and thus form unique patient trajectories. Patient trajectories are highly predictive of the future course of diseases and therefore facilitate effective care. However, digital medicine often uses only limited patient data, consisting of health events from only a single or small number of time points while ignoring additional information encoded in patient trajectories. To analyze such rich longitudinal data, new artificial intelligence (AI) solutions are needed. In this paper, we provide an overview of the recent efforts to develop trajectory-aware AI solutions and provide suggestions for future directions. Specifically, we examine the implications for developing disease models from patient trajectories along the typical workflow in AI: problem definition, data processing, modeling, evaluation, and interpretation. We conclude with a discussion of how such AI solutions will allow the field to build robust models for personalized risk scoring, subtyping, and disease pathway discovery.
Counterfactual Review-based Recommendation

Incorporating review information into the recommender system has been demonstrated to be an effective method for boosting the recommendation performance. Previous research mainly focus on designing advanced architectures to better profile the users and items. However, the review information in realities can be highly sparse and imbalanced, which poses great challenges for effective user/item representations and satisfied performance enhancement. To alleviate this problem, in this paper, we propose to improve review-based recommendation by counterfactually augmenting the training samples. We focus on a common setting --- feature-aware recommendation, and the main building block of our idea lies in the counterfactual question: "what would be the user's decision if her feature-level preference had been different?''. When augmenting the training samples, we actively change the user preference (also called intervention), and predict the user feedback on the items based on pre-trained recommender models. Instead of changing the user preference in a random manner, we design a learning-based method to discover the samples which are more effective for model optimization. In order to improve the sample qualities, we propose two strategies --- constrained feature perturbation and frequency-based sampling --- to equip our model. Since the sample generation model can be not perfect, we theoretically analyze the relation between the model prediction error and the number of generated samples. As a byproduct, our framework can explain the user pair-wise preference, which is complementary to the traditional point-wise explanations. Extensive experiments demonstrate that our model can significantly improve the performance of the state-of-the-art methods.
VLG-Net: Video-Language Graph Matching Network for Video Grounding

Grounding language queries in videos aims at identifying the time interval (or moment) semantically relevant to a language query. The solution to this challenging task demands understanding videos’ and queries’ semantic content and the fine-grained reasoning about their multi-modal interactions. Our key idea is to recast this challenge into an algorithmic graph matching problem. Fueled by recent advances in Graph Neural Networks, we propose to leverage Graph Convolutional Networks to model video and textual information as well as their semantic alignment. To enable the mutual exchange of information across the modalities, we design a novel Video-Language Graph Matching Network (VLG-Net) to match video and query graphs. Core ingredients include representation graphs built atop video snippets and query tokens separately and used to model intra-modality relationships. A Graph Matching layer is adopted for cross-modal context modeling and multi-modal fusion. Finally, moment candidates are created using masked moment attention pooling by fusing the moment’s enriched snippet features. We demonstrate superior performance over state-of-the-art grounding methods on three widely used datasets for temporal localization of moments in videos with language queries: ActivityNet-Captions, TACoS, and DiDeMo.
A Survey on Blockchain-Based Telecommunication Services Marketplaces

Digital marketplaces were created recently to accelerate the delivery of applications and services to customers. Their appealing feature is to activate and dynamize the demand, supply, and development of digital goods, applications, or services. By being an intermediary between producer and consumer, the primary business model for a marketplace is to charge the producer with a commission on the amount paid by the consumer. However, most of the time, the commission is dictated by the marketplace facilitator itself and creates an imbalance in value distribution, where producer and consumer sides suffer monetarily. In order to eliminate the need for a centralized entity between the producer and consumer, a blockchain-based decentralized digital marketplace concept was introduced. It provides marketplace actors with the tools to perform business transactions in a trusted manner and without the need for an intermediary. In this work, we provide a survey on Telecommunication Services Marketplaces (TSMs) which employ blockchain technology as the main trust enabling entity in order to avoid any intermediaries. We provide an overview of scientific and industrial proposals on the blockchain-based online digital marketplaces at large, and TSMs in particular. We consider in this study the notion of <i>telecommunication services</i> as any service enabling the capability for information transfer and, increasingly, information processing provided to a group of users by a telecommunications system. We discuss the main standardization activities around the concepts of TSMs and provide particular use-cases for the TSM business transactions such as SLA settlement. Also, we provide insights into the main foundational services provided by the TSM, as well as a survey of the scientific and industrial proposals for such services. Finally, a prospect for future developments is given.
Transfer Beyond the Field of View: Dense Panoramic Semantic Segmentation via Unsupervised Domain Adaptation

Autonomous vehicles clearly benefit from the expanded Field of View (FoV) of 360° sensors, but modern semantic segmentation approaches rely heavily on annotated training data which is rarely available for <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">panoramic</i> images. We look at this problem from the perspective of domain adaptation and bring <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">panoramic</i> semantic segmentation to a setting, where labelled training data originates from a different distribution of conventional <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">pinhole</i> camera images. To achieve this, we formalize the task of unsupervised domain adaptation for panoramic semantic segmentation and collect DensePass - a novel densely annotated dataset for panoramic segmentation under cross-domain conditions, specifically built to study the Pinhole <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\rightarrow$ </tex-math></inline-formula> PANORAMIC domain shift and accompanied with pinhole camera training examples obtained from Cityscapes. DensePass covers both, labelled- and unlabelled 360° images, with the labelled data comprising 19 classes which explicitly fit the categories available in the source ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e.</i> pinhole) domain. Since data-driven models are especially susceptible to changes in data distribution, we introduce P2PDA - a generic framework for Pinhole <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\rightarrow$ </tex-math></inline-formula> Panoramic semantic segmentation which addresses the challenge of domain divergence with different variants of attention-augmented domain adaptation modules, enabling the transfer in output-, feature-, and feature confidence spaces. P2PDA intertwines uncertainty-aware adaptation using confidence values regulated on-the-fly through attention heads with discrepant predictions. Our framework facilitates context exchange when learning domain correspondences and dramatically improves the adaptation performance of accuracy- and efficiency-focused models. Comprehensive experiments verify that our framework clearly surpasses unsupervised domain adaptation- and specialized panoramic segmentation approaches as well as state-of-the-art semantic segmentation methods.
Synergistic Attention for Ship Instance Segmentation in SAR Images

This paper takes account of the fact that there is a lack of consideration for imaging methods and target characteristics of synthetic aperture radar (SAR) images among existing instance segmentation methods designed for optical images. Thus, we propose a method for SAR ship instance segmentation based on the synergistic attention mechanism which not only improves the performance of ship detection with multi-task branches but also provides pixel-level contours for subsequent applications such as orientation or category determination. The proposed method—SA R-CNN—presents a synergistic attention strategy at the image, semantic, and target level with the following module corresponding to the different stages in the whole process of the instance segmentation framework. The global attention module (GAM), semantic attention module (SAM), and anchor attention module (AAM) were constructed for feature extraction, feature fusion, and target location, respectively, for multi-scale ship targets under complex background conditions. Compared with several state-of-the-art methods, our method reached 68.7 AP in detection and 56.5 AP in segmentation on the HRSID dataset, and showed 91.5 AP in the detection task on the SSDD dataset.
Analysis of Image Classification using SVM

Image classification is one of the classical image processing problems. There are various approaches such as Support Vector Machine, Artificial Neural Networks, Convolutional Neural Networks, K-Nearest Neighbors and Decision Tree for solving this problem. In this paper Support Vector Machine (SVM) is used to classify Images and we are trying to understand SVM and then understand how to draw a decision boundary and try to make it optimal and use it for classification. Two Datasets “Dogs Vs Cats” and “Color Classification” are used in this paper.
SeSy: Linguistic Steganalysis Framework Integrating Semantic and Syntactic Features

With the rapid development of natural language processing technology and linguistic steganography, linguistic steganalysis gains considerable interest in recent years. Current advanced methods dominantly focus on statistical features in semantic view yet ignore syntax structure of text, which leads to limited performance to some newly statistically indistinguishable steganography algorithms. To fill this gap, in this paper, we propose a novel linguistic steganalysis framework named SeSy to integrate both <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">se</b> mantic and <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">sy</b> ntactic features. Specifically, we propose to employ transformer-architecture language model as semantics extractor and leverage a graph attention network to retain syntactic features. Extensive experimental results show that owing to additional syntactic information, the SeSy framework effectively brings about remarkable improvement to current advanced linguistic steganalysis methods.
An LSTM-based distributed scheme for data transmission reduction of IoT systems

The growth of the number of connected devices in Internet of Things (IoT) systems causes a huge increase in network traffic. Thus, there is a significant demand for systems that can predict the measurements of the distributed IoT-based applications to mitigate the increasing network traffic. Existing methods utilized a distributed scheme, i.e., dual prediction schemes (DPS), to achieve this task. The idea of this scheme is based on deploying a predictive model on the data sources (i.e., sensors) and the fusion center in a distributive manner. The state-of-the-art results can be simply reached using predictive models, e.g., adaptive filters. Deep learning-based approaches are not well utilized to address this problem. In this context, we proposed a distributed scheme utilizing fog and edge computing technologies. The proposed DPS includes an LSTM predictive model to reduce the data transmission instances of connected IoT devices; it is called LSTM-DPS. In addition, we proposed an updating mechanism that updates the LSTM model according to a set of tracking parameters that observes the model behavior during the deployment due to the changes of data properties through time. The proposed updating mechanism guarantees that the deployed LSTM model is identical at the data source and fusion center. The LSTM-DPS is evaluated using two real datasets. The obtained results show that the LSTM-DPS outperforms state-of-the-art methods in terms of communication reduction ratios.
Comparative Perceptual Assessment of Visual Signals Using Free Energy Features

In this paper, we put forward the concept of comparative perceptual quality assessment (C-PQA), which refers to the judgment of relative qualities of two visual signals of the same content, but subject to different types and levels of distortions. While it is straightforward for human observers to fulfill the CPQA task in daily lives, it remains a difficult challenge for the current research of perceptual quality assessment (PQA). Among the existing PQA algorithms, the full-reference (FR) and reducedreference (RR) methods both need prior knowledge of the original images while the no-reference (NR) algorithms usually work with a single input image. C-PQA is inherently different from those existing methods in that it takes an image pair as input and predicts their relative quality without using any knowledge about the original image. In this paper, we propose a brain theory inspired approach to C-PQA that emulates the process of comparing the relative quality of two visual stimuli as performed by the human visual system (HVS) within the framework of free energy minimization. The brain's internal generative models initialized on the inputs are then used to explain both images. During the internal generative modeling, a group of features are extracted and then integrated to determine the relative quality of two images. We designed a dedicated image database to test the proposed C-PQA algorithm. Experimental results show that the proposed method achieves up to 98% prediction accuracy in line with the subjective ratings, outperforming many state of the art PQA algorithms.