Reviewerly/exHarmony huggingface dataset:
HiAM: A Hierarchical Attention based Model for knowledge graph multi-hop reasoning

Learning to reason in large-scale knowledge graphs has attracted much attention from research communities recently. This paper targets a practical task of multi-hop reasoning in knowledge graphs, which can be applied in various downstream tasks such as question answering, and recommender systems. A key challenge in multi-hop reasoning is to synthesize structural information (e.g., paths) in knowledge graphs to perform deeper reasoning. Existing methods usually focus on connection paths between each entity pair. However, these methods ignore predecessor paths before connection paths and regard entities and relations within every single path as equally important. With our observations, predecessor paths before connection paths can provide more accurate semantic representations. Furthermore, entities and relations in a single path contribute variously to the right answers. To this end, we propose a novel model HiAM (Hierarchical Attention based Model) for knowledge graph multi-hop reasoning. HiAM makes use of predecessor paths to provide more accurate semantics for entities and explores the effects of different granularities. Firstly, we extract predecessor paths of head entities and connection paths between each entity pair. Then, a hierarchical attention mechanism is designed to capture the information of different granularities, including entity/relation-level and path-level features. Finally, multi-granularity features are fused together to predict the right answers. We go one step further to select the most significant path as the explanation for predicted answers. Comprehensive experimental results demonstrate that our method achieves competitive performance compared with the baselines on three benchmark datasets.
Heterogeneous Federated Learning Through Multi-Branch Network

Recently, federated learning has gained increasing attention for privacy-preserving computation since the learning paradigm allows to train models without the need for exchanging the data across different institutions distributively. However, heterogeneity of computational capabilities of edge devices is seldom discussed and analyzed in the current literature for heterogeneous federated learning. To address this issue, we propose a novel heterogeneous federated learning framework based on multi-branch deep neural network models which enable the selection of a proper sub-branch model for the client devices according to their computational capabilities. Meanwhile, we also present an aggregation method for model training, MFedAvg, that performs branch-wise averaging-based aggregation. With extensive experiments on MNIST, FashionMNIST, MedMNIST, and CIFAR-10, it demonstrates that our proposed approaches can achieve satisfactory performance with guaranteed convergence and effectively utilize all the available resources for training across different devices with lower communication cost than its homogeneous counterpart.
Towards Cross-Modal Forgery Detection and Localization on Live Surveillance Videos

The cybersecurity breaches render surveillance systems vulnerable to video forgery attacks, under which authentic live video streams are tampered to conceal illegal human activities under surveillance cameras. Traditional video forensics approaches can detect and localize forgery traces in each video frame using computationally-expensive spatial-temporal analysis, while falling short in real-time verification of live video feeds. The recent work correlates time-series camera and wireless signals to recognize replayed surveillance videos using event-level timing information but it cannot realize fine-grained forgery detection and localization on each frame. To fill this gap, this paper proposes Secure-Pose, a novel cross-modal forgery detection and localization system for live surveillance videos using WiFi signals near the camera spot. We observe that coexisting camera and WiFi signals convey common human semantic information and the presence of forgery attacks on video frames will decouple such information correspondence. Secure-Pose extracts effective human pose features from synchronized multi-modal signals and detects and localizes forgery traces under both inter-frame and intra-frame attacks in each frame. We implement Secure-Pose using a commercial camera and two Intel 5300 NICs and evaluate it in real-world environments. Secure-Pose achieves a high detection accuracy of 95.1% and can effectively localize tampered objects under different forgery attacks.
Simultaneous positive sequential vectors modeling and unsupervised feature selection via continuous hidden Markov models

Since positive data vectors are often naturally generated in various real-life applications, positive vectors modeling has become an important research topic. In this article, we tackle the problem of modeling positive sequential vectors through continuous hidden Markov models (HMMs). Motivated by several recent studies in which the generalized inverted Dirichlet (GID) distribution has provided better performance than the Gaussian distribution for modeling positive data, instead of adopting Gaussian mixture models (GMM) as the emission density for conventional continuous HMMs, we theoretically propose a novel HMM by considering the mixture of GID distributions as the emission density. Moreover, to cope with high-dimensional data which may contain irrelevant features, an unsupervised localized feature selection method is incorporated with our model, which results in a unified framework that can simultaneously perform positive sequential data modeling and feature selection. To learn the proposed model, we develop a convergence-guaranteed algorithm based on variational Bayes. The advantages of our model are demonstrated through both simulated data sets and a real-life application about human action recognition.
Non-Intrusive and High-Efficient Balance Tomography in the Lightning Network

The Lightning Network (LN) is a second layer technology for solving the scalability problem of blockchain-based cryptocurrencies such as Bitcoin. The LN nodes (i.e., LN users), linked by payment channels, can make payments to each other directly or through multiple hops of payment channels, subject to the available balances of the serving channels. In current LN implementation, the channel capacity (i.e., the sum of the bidirectional balances in the channel) is open to the public, but the bidirectional balances are kept secret for privacy concerns. Nevertheless, the balances can be directly measured by conducting multiple fake payments to probe the precise value of the balance. Such a method, while effective, creates many fake invoices and incurs high cost when used for discovering balances for multiple users.
EdgeConv with Attention Module for Monocular Depth Estimation

Monocular depth estimation is an especially important task in robotics and autonomous driving, where 3D structural information is essential. However, extreme lighting conditions and complex surface objects make it difficult to predict depth in a single image. Therefore, to generate accurate depth maps, it is important for the model to learn structural information about the scene. We propose a novel Patch-Wise EdgeConv Module (PEM) and EdgeConv Attention Module (EAM) to solve the difficulty of monocular depth estimation. The proposed modules extract structural information by learning the relationship between image patches close to each other in space using edge convolution. Our method is evaluated on two popular datasets, the NYU Depth V2 and the KITTI Eigen split, achieving state-of-the-art performance. We prove that the proposed model predicts depth robustly in challenging scenes through various comparative experiments.
Automatic Generation of Unit Visualization-based Scrollytelling for Impromptu Data Facts Delivery

Data-driven scrollytelling has become a prevalent way of visual communication because of its comprehensive delivery of perspectives derived from the data. However, creating an expressive scrollytelling story requires both data and design literacy and is time-consuming. As a result, scrollytelling has been mainly used only by professional journalists to disseminate opinions. In this paper, we present an automatic method to generate expressive scrollytelling visualization, which can present easy-to-understand data facts through a carefully arranged sequence of views. The method first enumerates data facts of a given dataset, and scores and organizes them. The facts are further assembled, sequenced into a story, with reader input taken into consideration. Finally, visual graphs, transitions, and text descriptions are generated to synthesize the scrollytelling visualization. In this way, non-professionals can easily explore and share interesting perspectives from selected data attributes and fact types. We demonstrate the effectiveness and usability of our method through both use cases and an in-lab user study.
Construction of MDS Euclidean Self-Dual Codes via Two Subsets

The parameters of a q-ary MDS Euclidean self-dual codes are completely determined by its length and the construction of MDS Euclidean self-dual codes with new length has been widely investigated in recent years. In this paper, we give a further study on the construction of MDS Euclidean self-dual codes via generalized Reed-Solomon (GRS) codes and their extended codes. The main idea of our construction is to choose suitable evaluation points such that the corresponding (extended) GRS codes are Euclidean self-dual. Firstly, we consider the evaluation set consists of two disjoint subsets, one of which is based on the trace function, the other one is a union of a subspace and its cosets. Then four new families of MDS Euclidean self-dual codes are constructed. Secondly, we give a simple but useful lemma to ensure that the symmetric difference of two intersecting subsets of finite fields can be taken as the desired evaluation set. Based on this lemma, we generalize our first construction and provide two new families of MDS Euclidean self-dual codes. Finally, by using two multiplicative subgroups and their cosets which have nonempty intersection, we present three generic constructions of MDS Euclidean self-dual codes with flexible parameters. Several new families of MDS Euclidean self-dual codes are explicitly constructed.
Distributed Kalman estimation with decoupled local filters

We study a distributed Kalman filtering problem in which a number of nodes cooperate without central coordination to estimate a common state based on local measurements and data received from neighbors. This is typically done by running a local filter at each node using information obtained through some procedure for fusing data across the network. A common problem with existing methods is that the outcome of local filters at each time step depends on the data fused at the previous step. We propose an alternative approach to eliminate this error propagation. The proposed local filters are guaranteed to be stable under some mild conditions on certain global structural data, and their fusion yields the centralized Kalman estimate. The main feature of the new approach is that fusion errors introduced at a given time step do not carry over to subsequent steps. This offers advantages in many situations including when a global estimate is only needed at a rate slower than that of measurements or when there are network interruptions. If the global structural data can be fused correctly asymptotically, the stability of local filters is equivalent to that of the centralized Kalman filter. Otherwise, we provide conditions to guarantee stability and bound the resulting estimation error. Numerical experiments are given to show the advantage of our method over other existing alternatives.
Temporally Reliable Motion Vectors for Real‐time Ray Tracing

Abstract Real‐time ray tracing (RTRT) is being pervasively applied. The key to RTRT is a reliable denoising scheme that reconstructs clean images from significantly undersampled noisy inputs, usually at 1 sample per pixel as limited by current hardware's computing power. The state of the art reconstruction methods all rely on temporal filtering to find correspondences of current pixels in the previous frame, described using per‐pixel screen‐space motion vectors. While these approaches are demonstrated powerful, they suffer from a common issue that the temporal information cannot be used when the motion vectors are not valid, i.e. when temporal correspondences are not obviously available or do not exist in theory. We introduce temporally reliable motion vectors that aim at deeper exploration of temporal coherence, especially for the generally‐believed difficult applications on shadows, glossy reflections and occlusions, with the key idea to detect and track the cause of each effect. We show that our temporally reliable motion vectors produce significantly better temporal results on a variety of dynamic scenes when compared to the state of the art methods, but with negligible performance overhead.
AI-based Test Automation: A Grey Literature Analysis

This paper provides the results of a survey of the grey literature concerning the use of artificial intelligence to improve test automation practices. We surveyed more than 1,200 sources of grey literature (e.g., blogs, white-papers, user manuals, StackOverflow posts) looking for highlights by professionals on how AI is adopted to aid the development and evolution of test code. Ultimately, we filtered 136 relevant documents from which we extracted a taxonomy of problems that AI aims to tackle, along with a taxonomy of AI-enabled solutions to such problems. Manual code development and automated test generation are the most cited problem and solution, respectively. The paper concludes by distilling the six most prevalent tools on the market, along with think-aloud reflections about the current and future status of artificial intelligence for test automation.
A Multimodal Memes Classification: A Survey and Open Research Issues

Memes are graphics and text overlapped so that together they present concepts that become dubious if one of them is absent. It is spread mostly on social media platforms, in the form of jokes, sarcasm, motivating, etc. After the success of BERT in Natural Language Processing (NLP), researchers inclined to Visual-Linguistic (VL) multimodal problems like memes classification, image captioning, Visual Question Answering (VQA), and many more. Unfortunately, many memes get uploaded each day on social media platforms that need automatic censoring to curb misinformation and hate. Recently, this issue has attracted the attention of researchers and practitioners. State-of-the-art methods that performed significantly on other VL dataset, tends to fail on memes classification. In this context, this work aims to conduct a comprehensive study on memes classification, generally on the VL multimodal problems and cutting edge solutions. We propose a generalized framework for VL problems. We cover the early and next-generation works on VL problems. Finally, we identify and articulate several open research issues and challenges. This is the first study that presents the generalized view of the advanced classification techniques concerning memes classification to the best of our knowledge. We believe this study presents a clear road-map for the Machine Learning (ML) research community to implement and enhance memes classification techniques.
Research on Knowledge Driven Intelligent Question Answering System for Electric Power Customer Service

The electric power customer service domain-specific knowledge graph aims to describe the concepts, entities, events and their relationships in the electric power customer service business field, with a structured manner, and provide a more effective data organization, management, and cognitive ability for the customer service business. This paper proposes a method for constructing a knowledge graph in the field of electric customer service, and constructs a knowledge graph with over 15,000 entities and 20,000 relationships. Based on the knowledge graph, an intelligent question answering application architecture is designed, which consists of multiple functional modules such as dialogue process configuration, natural language processing, and business process processing. It provides more efficient and open knowledge retrieval services for the electric customer service business, and improves the intelligence level of customer service question answering.
Data Shepherding in Nanotechnology. The Initiation

In this paper we describe the pragmatic approach of initiating, designing and implementing the Data Management Plan (DMP) and the data FAIRification process in the multidisciplinary Horizon 2020 nanotechnology project, Anticipating Safety Issues at the Design Stage of NAno Product Development (ASINA). We briefly describe the general DMP requirements, emphasizing that the initial steps in the direction towards data FAIRification must be conceptualized and visualized in a systematic way. We demonstrate the use of a generic questionnaire to capture primary data and metadata description from our consortium (data creators/experimentalists and data analysts/modelers). We then display the interactive process with external FAIR data initiatives (data curators/quality assessors), regarding guidance for data and metadata capturing and future integration into repositories. After the preliminary data capturing and FAIRification template is formed, the inner-communication process begins between the partners, which leads to developing case-specific templates. This paper assists future data creators, data analysts, stewards and shepherds engaged in the multi-faceted data shepherding process, in any project, by providing a roadmap, demonstrated in the case of ASINA.
CONSK-GCN: Conversational Semantic- and Knowledge-Oriented Graph Convolutional Network for Multimodal Emotion Recognition

Emotion recognition in conversations (ERC) has received significant attention in recent years due to its widespread applications in diverse areas, such as social media, health care, and artificial intelligence interactions. However, different from nonconversational text, it is particularly challenging to model the effective context-aware dependence for the task of ERC. To address this problem, we propose a new Conversational Semantic- and Knowledge-oriented Graph Convolutional Network (ConSK-GCN) approach that leverages both semantic dependence and commonsense knowledge. First, we construct the contextual inter-interaction and intradependence of the interlocutors via a conversational graph-based convolutional network based on multimodal representations. Second, we incorporate commonsense knowledge to guide ConSK-GCN to model the semantic-sensitive and knowledge-sensitive contextual dependence. The results of extensive experiments show that the proposed method outperforms the current state of the art on the IEMOCAP dataset.
Webapp Service for Booking Handyman Using Mongodb, Express JS, React JS, Node JS

In recent days, there is a rapid increase in the need for handyman services around the world. If any issue is unfortunately encountered in the home, some issues may be hectic and people can't be repaired on their own, people are busy with their schedule, hence they need workers to maintain and repair their homes. It is tough to find workers offline at the correct time and cost. Therefore, this online website makes it easier to book your own workers at the correct time and cost, it makes the workers available in just one click at your doorstep. Handyman workers have a separate login to showcase themselves by adding the works and skills they have. It also helps the professionals to gain opportunities and money based on their work. There are several categories and services, on the time of users' login for the need for the services, the workers are listed based on location and cost with their name and contact. Creating a website using React JS makes it faster, boosts productivity, and is SEO friendly. MongoDB is a schema-less database and with ease of scale-out, hence it is easier to manage data. With this, a user can avoid delay and difficulty.
JPEG Robust Invertible Grayscale

Invertible grayscale is a special kind of grayscale from which the original color can be recovered. Given an input color image, this seminal work tries to hide the color information into its grayscale counterpart while making it hard to recognize any anomalies. This powerful functionality is enabled by training a hiding sub-network and restoring sub-network in an end-to-end way. Despite its expressive results, two key limitations exist: 1) The restored color image often suffers from some noticeable visual artifacts in the smooth regions. 2) It is very sensitive to JPEG compression, i.e., the original color information cannot be well recovered once the intermediate grayscale image is compressed by JPEG. To overcome these two limitations, this article introduces adversarial training and JPEG simulator respectively. Specifically, two auxiliary adversarial networks are incorporated to make the intermediate grayscale images and final restored color images indistinguishable from normal grayscale and color images. And the JPEG simulator is utilized to simulate real JPEG compression during the online training so that the hiding and restoring sub-networks can automatically learn to be JPEG robust. Extensive experiments demonstrate that the proposed method is superior to the original invertible grayscale work both qualitatively and quantitatively while ensuring the JPEG robustness. We further show that the proposed framework can be applied under different types of grayscale constraints and achieve excellent results.
Equivariant Adversarial Network for Image-to-image Translation

Image-to-Image translation aims to learn an image from a source domain to a target domain. However, there are three main challenges, such as lack of paired datasets, multimodality, and diversity, that are associated with these problems and need to be dealt with. Convolutional neural networks (CNNs), despite of having great performance in many computer vision tasks, they fail to detect the hierarchy of spatial relationships between different parts of an object and thus do not form the ideal representative model we look for. This article presents a new variation of generative models that aims to remedy this problem. We use a trainable transformer, which explicitly allows the spatial manipulation of data within training. This differentiable module can be augmented into the convolutional layers in the generative model, and it allows to freely alter the generated distributions for image-to-image translation. To reap the benefits of proposed module into generative model, our architecture incorporates a new loss function to facilitate an effective end-to-end generative learning for image-to-image translation. The proposed model is evaluated through comprehensive experiments on image synthesizing and image-to-image translation, along with comparisons with several state-of-the-art algorithms.