Reviewerly/exHarmony huggingface dataset:
M<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si1.svg"><mml:msup><mml:mrow /><mml:mn>2</mml:mn></mml:msup></mml:math>RNet: Multi-modal and multi-scale refined network for RGB-D salient object detection

Salient object detection is a fundamental topic in computer vision, which has promising application prospects. The previous methods based on RGB-D may potentially suffer from the incompatibility of multi-modal feature fusion and the insufficiency of multi-scale feature aggregation. To tackle these two dilemmas, we propose a novel multi-modal and multi-scale refined network (M2RNet). Specifically, three essential components are presented in this network. The nested dual attention module (NDAM) explicitly exploits the combined features of RGB and depth flows. The adjacent interactive aggregation module (AIAM) gradually integrates the neighbor features of high, middle and low levels. The joint hybrid optimization loss (JHOL) makes the predictions have a prominent outline. Extensive experiments quantitatively and qualitatively demonstrate that our method outperforms other state-of-the-art approaches.
Privacy-Preserving Redactable Blockchain for Internet of Things

In the traditional blockchain system, data is public and cannot be redacted. With the development of blockchain technology, the problem that the data cannot be altered will be more serious once it is written on the chain. Recently, some redactable blockchain schemes have been proposed. However, most of the schemes are based on the public blockchain, and the users’ identities and transaction data may be disclosed. To solve the problem of privacy disclosure, we propose a privacy-preserving transaction-level redactable blockchain. In the proposed scheme, symmetric encryption and ring signature are used to protect transaction data and the users’ identities, respectively. In order to prove the legality of data redaction, the transaction sender can reveal the invalid users’ identities and transaction data in an anonymous environment. To construct a transaction-level redactable blockchain, the users only need to replace a single transaction to complete the data redaction instead of replacing the entire block. The experimental results show that the proposed scheme saves 20% of the redaction time compared to the previous privacy-preserving blockchains, so the redaction efficiency is higher.
<scp>MetaInfoChain</scp>: Bi‐layered blockchain consensus for metadata aggregation in<scp>IoT</scp>and cloud environments

Abstract Internet of Things (IoT) and cloud metadata have highly sensitive details and facts about data and objects; it usually holds confidential information about the data for which there are access restrictions. Hence, it forms the most abundant source of information about any digital object from which the actual data can be rebuilt or duplicated with the same level of originality. However, it appears to be an emerging threat vector in IoT and cloud environments. If a misfeasor acquires access to the metadata information, then there arises a security threat. This article proposes a novel method on a bifold layered blockchain network, referred to as MetaInfoChain, to enhance the integrity and access controllability of safe storage. By default, blockchain protects the data while a permissioned blockchain forms an access control layer over the core blockchain. The acquired instance, compute, and the unique credentials sign device metadata to enforce controllability and validated by minters and add it to the base layer of the MetaInfoChain. Subsequently, we propose a consensus attainment algorithm to build an unrefined block on a candidate block comprising metadata packages. Consequently, all the nodes receive an unrefined block replica, which can afford absolute integrity. The top layer of MetaInfoChain is sensible to generate tamper‐resistant metadata by executing hybrid PoS‐PoW tasks on the unrefined block, causing robust integrity. After accomplishing all the checks, it endorses the MetaInfoChain to append the block with 35% block time efficiency in the base layer with a 56% throughput hike.
A Deep Graph Wavelet Convolutional Neural Network for Semi-supervised Node Classification

Graph convolutional neural network provides good solutions for node classification and other tasks with non-Euclidean data. There are several graph convolutional models that attempt to develop deep networks but do not cause serious over-smoothing at the same time. Considering that the wavelet transform generally has a stronger ability to extract useful information than the Fourier transform, we propose a new deep graph wavelet convolutional network (DeepGWC) for semi-supervised node classification tasks. Based on the optimized static filtering matrix parameters of vanilla graph wavelet neural networks and the combination of Fourier bases and wavelet ones, DeepGWC is constructed together with the reuse of residual connection and identity mappings in network architectures. Extensive experiments on three benchmark datasets including Cora, Citeseer, and Pubmed are conducted. The experimental results demonstrate that our DeepGWC outperforms existing graph deep models with the help of additional wavelet bases and achieves new state-of-the-art performances eventually.
geoGAT: Graph Model Based on Attention Mechanism for Geographic Text Classification

In the area of geographic information processing, there are few researches on geographic text classification. However, the application of this task in Chinese is relatively rare. In our work, we intend to implement a method to extract text containing geographical entities from a large number of network texts. The geographic information in these texts is of great practical significance to transportation, urban and rural planning, disaster relief, and other fields. We use the method of graph convolutional neural network with attention mechanism to achieve this function. Graph attention networks (GAT) is an improvement of graph convolutional neural networks (GCN). Compared with GCN, the advantage of GAT is that the attention mechanism is proposed to weight the sum of the characteristics of adjacent vertices. In addition, We construct a Chinese dataset containing geographical classification from multiple datasets of Chinese text classification. The Macro-F Score of the geoGAT we used reached 95% on the new Chinese dataset.
Vertical federated DNN training

In the training process of distributed machine learning, the data possessed at distinct companies usually contain different features. Labels may even be lacked at certain companies. Therefore, one option is that multiple companies perform joint training in the form of federated learning (FL). Existing studies on FL confines to traditional machine learning (ML), including linear regression, logistic regression, and decision tree. In this work, vertical federated learning is extended from traditional ML to deep neural networks (DNN). A privacy-preserving DNN model training scheme based on homomorphic encryption is proposed for vertically segmented datasets. It is shown that the proposed federated DNN scheme can achieve similar precision as that of the traditional centralized DNN scheme.
Duco

Human environments are filled with large open spaces that are separated by structures like walls, facades, glass windows, etc. Most often, these structures are largely passive offering little to no interactivity. In this paper, we present Duco, a large-scale electronics fabrication robot that enables room-scale &amp; building-scale circuitry to add interactivity to vertical everyday surfaces. Duco negates the need for any human intervention by leveraging a hanging robotic system that automatically sketches multi-layered circuity to enable novel large-scale interfaces. The key idea behind Duco is that it achieves single-layer or multi-layer circuit fabrication on 2D surfaces as well as 2D cutouts that can be assembled into 3D objects by loading various functional inks (e.g., conductive, dielectric, or cleaning) to the wall-hanging drawing robot, as well as employing an optional laser cutting head as a cutting tool. Our technical evaluation shows that Duco's mechanical system works reliably on various surface materials with a wide range of roughness and surface morphologies. The system achieves superior mechanical tolerances (0.1mm XY axis resolution and 1mm smallest feature size). We demonstrate our system with five application examples, including an interactive piano, an IoT coffee maker controller, an FM energy-harvester printed on a large glass window, a human-scale touch sensor and a 3D interactive lamp.
Investigating Accuracy and Diversity in Heterogeneous Ensembles for Breast Cancer Classification

Breast Cancer (BC) is one of the most common forms of cancer among women. Detecting and accurately diagnosing breast cancer at an early phase increase the chances of women’s survival. For this purpose, various single classification techniques have been investigated to diagnosis BC. Nevertheless, none of them proved to be accurate in all circumstances. Recently, a promising approach called ensemble classifiers have been widely used to assist physicians accurately diagnose BC. Ensemble classifiers consist on combining a set of single classifiers by means of an aggregation layer. The literature in general shows that ensemble techniques outperformed single ones when ensemble members are accurate (i.e. have the lowest percentage error) and diverse (i.e. the single classifiers make uncorrelated errors on new instances). Hence, selecting ensemble members is often a crucial task since it can lead to the opposite: single techniques outperformed their ensemble. This paper evaluates and compares ensemble members’ selection based on accuracy and diversity with ensemble members’ selection based on accuracy only. A comparison with ensembles without member selection was also performed. Ensemble performance was assessed in terms of accuracy, F1-score. Q statistics diversity measure was used to calculate the classifiers diversity. The experiments were carried out on three well-known BC datasets available from online repositories. Seven single classifiers were used in our experiments. Skott Knott test and Borda Count voting system were used to assess the significance of the performance differences and rank ensembles according to theirs performances. The findings of this study suggest that: (1) Investigating both accuracy and diversity to select ensemble members often led to better performance, and (2) In general, selecting ensemble members using accuracy and/or diversity led to better ensemble performance than constructing ensembles without members’ selection.
MetaVD: A Meta Video Dataset for enhancing human action recognition datasets

Numerous practical datasets have been developed to recognize human actions from videos. However, many of them were constructed by collecting videos within a limited domain; thus, a model trained using one of the existing datasets often fails to classify videos in a different domain accurately. A possible solution for this drawback is to enhance the domain of each action label, i.e., to import videos associated with a given action label from the other datasets, and then, to train a model using the enhanced dataset. To realize this solution, we constructed a meta video dataset from the existing datasets for human action recognition, referred to as MetaVD. MetaVD comprises six popular human action recognition datasets, which we integrated by annotating 568,015 relation labels in total. These relation labels reflect equality, similarity, and hierarchy between action labels of the original datasets. We further present simple yet effective dataset enhancement methods using MetaVD, which are useful for training models with higher generalization performance, as established by experiments on human action classification. As a further contribution of MetaVD, we show that its analysis can provide useful insight into the datasets.
Cyber Security Risk Analysis for a Virtual Assistant G2C Digital Service Using FAIR Model

During development and deployment of Government-to-Citizen (G2C) e-services, stakeholders must take cybersecurity risks into account. Recently, Estonia proposed a novel e-service delivery through the use of virtual assistants. Cybersecurity is important to any organization due to the prevalence of attacks in recent times. Organizations in both the public sector and private sector typically have a program that determines, calculates and treats risks. These programs are identified as “risk management,” and cybersecurity risk assessment and analysis are a key part of these organizations. When correctly implemented, these programs can help decision makers allocate their cyber defense budgets in a more effective manner.The researchers conducted interviews with seven cybersecurity experts discussing cybersecurity data, metrics and risk assessment and analysis methods. These interviews informed the use of the Open Group’s Factor Analysis of Information Risk (FAIR) model in this paper.Because Estonia is one of the first countries in the world to prioritize e-service development in this domain, there is a literature gap pertaining to cybersecurity risk analyses of virtual assistant enabled e-services. Although a multiplicity of methods exist for risk assessment and analysis, this research follows the FAIR method to assess a G2C e-service which uses a third party provider for an Amazon Alexa skill that has become corrupted due to malware infection. The FAIR method projects the risk for a particular vulnerability in terms of dollar amount loss over time. It states the annualized loss exposure in terms of a minimum loss, a maximum loss, and a most likely amount of loss.Aggregated global public sector cyber breach frequency data was taken from the Privacy Rights Clearinghouse database and loss magnitude data was taken from the Cyentia IRIS 2020 report which utilized the Advisen cyber loss database. Based upon a Project Evaluation and Review Technique (PERT) distribution Monte Carlo simulation of this data, the annualized loss exposure projection for the public sector entity was $0 minimum likely value, most likely value $70,500 and maximum likely value of $6,000,000. Future work proposes more country and organization specific data for FAIR methodology use for G2C virtual assistant e-services analysis. The value of the work is that it shows how public sector agencies could use the FAIR model to make their cybersecurity risk models more quantitative than is generally currently practiced even in cases of new types of developments and services in order to aid decision-makers in allocation of funding to better defend against the most impactful risks.
Modeling Dynamic User Interests: A Neural Matrix Factorization Approach

We propose an interpretable model that combines the simplicity of matrix factorization with the flexibility of neural networks to model evolving user interests by efficiently extracting nonlinear patterns from massive text data collections.
Scaling Blockchain by Autonomous Sidechains

Blockchain is one of the most promising technologies of the future. But till date, it is mostly used for transactions that involve mostly cryptocurrencies. To expand the scope of blockchain beyond cryptocurrency, it is really important to rethink the structure of existing blockchain architecture. One of the major factors which hold blockchain back from being an option to host real-world applications is its lack of Scalability. This paper presents the solution to scale blockchain using the concept of Autonomous Sidechains based on the Plasma framework. Instead of forcing the implementation of all transactions to the mainchain, we propose to create a new sidechain for every decentralized application. The sidechains created are autonomous with respect to each other, with mainchain acting as a backbone of the entire network. This concept increases the scalability of the blockchain by increasing the number of transactions network can process at a time.
Evaluation of Proprietary Social VR Platforms for Use in Distance Learning

Distance learning in form of video chats or the streaming of recorded lectures are common ways for schools and universities to maintain teaching under the restrictions of the global SARS-CoV-2 pandemic. One possibility to improve distance learning can be the use of virtual reality (VR). To this day there are no common guidelines for educators for using VR, and therefore no recommendations to decide which platform to use and what specific requirements they entail. This paper presents an evaluation of currently available proprietary social VR platforms for the use in distance learning. Based on results of expert interviews (\(n=4\)) with educators and an online survey (\(n=92\)) of students, we identified ten relevant criteria for teaching in VR. We compiled a list of 155 currently available proprietary social VR platforms, which we filtered and evaluated. The results indicate that current social VR platforms can provide an easy and affordable way for educators to access the usage of VR in education, although we could not determine a single best social VR platform. Our recommendations rather provide different most suitable platforms for particular learning applications depending to respective educational requirements. Our research provides a guideline for interested educators in order to find an affordable entry into the usage of VR in education. Building on this, future research can shed light on questions of didactic effects, user-friendliness, or data protection.
Impact of Spatial Frequency Based Constraints on Adversarial Robustness

Adversarial examples mainly exploit changes to input pixels to which humans are not sensitive to, and arise from the fact that models make decisions based on uninterpretable features.Interestingly, cognitive science reports that the process of interpretability for human classification decision relies predominantly on low spatial frequency components.In this paper, we investigate the robustness to adversarial perturbations of models enforced during training to leverage information corresponding to different spatial frequency ranges.We show that it is tightly linked to the spatial frequency characteristics of the data at stake.Indeed, depending on the data set, the same constraint may results in very different level of robustness (up to 0.41 adversarial accuracy difference).To explain this phenomenon, we conduct several experiments to enlighten influential factors such as the level of sensitivity to high frequencies, and the transferability of adversarial perturbations between original and low-pass filtered inputs.
Partially Observable Reinforcement Learning for Dialog-based Interactive Recommendation

A dialog-based interactive recommendation task is where users can express natural-language feedback when interacting with the recommender system. However, the users' feedback, which takes the form of natural-language critiques about the recommendation at each iteration, can only allow the recommender system to obtain a partial portrayal of the users' preferences. Indeed, such partial observations of the users' preferences from their natural-language feedback make it challenging to correctly track the users' preferences over time, which can result in poor recommendation performances and a less effective satisfaction of the users' information needs when in presence of limited iterations. Reinforcement learning, in the form of a partially observable Markov decision process (POMDP), can simulate the interactions between a partially observable environment (i.e. a user) and an agent (i.e. a recommender system). To alleviate such a partial observation issue, we propose a novel dialog-based recommendation model, the Estimator-Generator-Evaluator (EGE) model, with Q-learning for POMDP, to effectively incorporate the users' preferences over time. Specifically, we leverage an Estimator to track and estimate users' preferences, a Generator to match the estimated preferences with the candidate items to rank the next recommendations, and an Evaluator to judge the quality of the estimated preferences considering the users' historical feedback. Following previous work, we train our EGE model by using a user simulator which itself is trained to describe the differences between the target users' preferences and the recommended items in natural language. Thorough and extensive experiments conducted on two recommendation datasets – addressing images of fashion products (namely dresses and shoes) – demonstrate that our proposed EGE model yields significant improvements in comparison to the existing state-of-the-art baseline models.
Monocular and Binocular Interactions Oriented Deformable Convolutional Networks for Blind Quality Assessment of Stereoscopic Omnidirectional Images

Stereoscopic omnidirectional content, as a novel visual media, has drawn wide attention in recent years due to its ability in providing strong immersive experience. Since Stereoscopic Omnidirectional Images (SOIs) involve the properties from panoramic and stereoscopic visual perception, it is very challenging to establish an efficient and effective visual quality evaluation model for SOIs. To better measure the user’s experience in virtual reality, we put forward a novel deep learning framework to assess the quality of SOIs in this paper. Firstly, the deformable convolutions instead of standard convolutions are adopted to ensure the invariant receptive fields of convolutional kernels on Equi-Rectangular Projection (ERP). Secondly, according to the stereoscopic property, we use binocular-difference information and a coarse-to-fine mechanism to construct the binocular feature extraction network. Thirdly, a three-channel network involving left-view, right-view and binocular-difference channels is presented to simulate the process of monocular and binocular interactions, in which independent quality labels are provided for each channel to reflect the individual effect of monocular and binocular visions on the whole visual quality. Finally, experimental results on two available benchmark databases demonstrate the superiority of the proposed metric over the state-of-the-art blind quality assessment models in predicting the quality of SOIs. Moreover, our model is efficient in computational cost as the feature extraction is directly applied on ERP images.
3D Hand Pose Estimation on Conventional Capacitive Touchscreens

Contemporary mobile devices with touchscreens capture the X/Y position of finger tips on the screen and pass these coordinates to applications as though the input were points in space. Of course, human hands are much more sophisticated, able to form rich 3D poses capable of far more complex interactions than poking at a screen. In this paper, we describe how conventional capacitive touchscreens can be used to estimate 3D hand pose, enabling richer interaction opportunities. Importantly, our software-only approach requires no special or new sensors, either internal or external. As a proof of concept, we use an off-the-shelf Samsung Tablet flashed with a custom kernel. After describing our software pipeline, we report findings from our user study, we conclude with several example applications we built to illustrate the potential of our approach.