Reviewerly/exHarmony huggingface dataset:
Spatial Relation Graph and Graph Convolutional Network for Object Goal Navigation

This paper describes a framework for the object-goal navigation (ObjectNav) task, which requires a robot to find and move to an instance of a target object class from a random starting position. The framework uses a history of robot trajectories to learn a Spatial Relational Graph (SRG) and Graph Convolutional Network (GCN)-based embeddings for the likelihood of proximity of different semantically-labeled regions and the occurrence of different object classes in these regions. To locate a target object instance during evaluation, the robot uses Bayesian inference and the SRG to estimate the visible regions, and uses the learned GCN embeddings to rank visible regions and select the region to explore next. This approach is tested using the Matterport3D (MP3D) benchmark dataset of indoor scenes in AI Habitat, a visually realistic simulation environment, to report substantial performance improvement in comparison with state of the art baselines.
BCSChain: Blockchain-Based Ceramic Supply Chain

The ceramic supply chain is a complex network centered on ceramic products, encompassing business flow, logistics, information flow, and capital flow, and involving many participants and complex business processes. Currently, the key issues confronting the ceramic supply chain are lack of trust between different participants, financing difficulty for medium, small, and micro enterprises, inefficient transportation and distribution, and the inability to meet the diversified needs of customers. These pain points can be properly sloved by blockchain technology, with characteristics such as immutability and decentralization as well as the application of smart contracts. In this paper, a system architecture of Blockchain-Based Ceramic Supply Chain (BCSChain) is presented, and three applications of BCSChain in different scenarios are proposed, including logistics traceability, financing, and automatic trading. It is found that combing blockchain technology with ceramic supply chain can contribute to enable data sharing and information visualization, de-intermediation and data security, automated execution, as well as efficient collaboration.
Developing and Evaluating the ARSy: Augmented Reality Solar System for Students Elementary School Learning

The existence of smartphones has facilitated society in this era. Many schools are beginning to leave the outdated approach and adopt the new one. Individuals are forced to interact remotely and maintain their distance in crowded spaces due to the present pandemic, which makes it difficult for people to carry out activities and interact. Many educational materials are not delivered to pupils because teachers find it difficult to perform online learning. In the modern era, smartphones are outfitted with augmented reality technologies. Just by aiming the camera at the marker, users may see the learning material of 3D objects.Educational augmented reality (AR) technologies are an excellent approach to presenting and engaging students with any subject in the classroom. Textbooks and traditional teaching methods cannot compete with the advantages of this technology. We developed an augmented reality solar system application for elementary school thematic lessons and evaluated the application for elementary students. The application was tested using a user experience questionnaire, and the results showed a number above 2.00, indicating that it functioned very well and received positive feedback from respondents.
On the influence of head-mounted displays on quality rating of omnidirectional images

In this paper, we propose to study the influence of using head-mounted displays (HMDs) for visual quality rating of omnidirectional images and their impact on the final quality scores.Because of the used display technology, these devices introduce a significant impairment called screen door effect that may alter the quality of experience.Furthermore, the extended use of such a technology may produce cyber-sickness.In this study, a subjective experiment is designed and carried out using various HMDs with various types of content.The statistically analysed results revealed a significant difference between HMDs for quality rating tasks on the overall ratings as well as per individual distortions.These findings will contribute to the development of a reliable protocol for omnidirectional subjective quality assessment, and the constructed database will be used as a ground-truth for quality metrics development.
Lattice-Based Revocable Identity-Based Proxy Re-encryption with Re-encryption Verifiability

Identity-based proxy re-encryption (IB-PRE) is a type of public key cryptography that allows a proxy to convert a ciphertext under Alice’s identity into another ciphertext of the same message under Bob’s identity, but the proxy can not access the participants’ secret keys or underlying plaintext. As far as practical application is concerned, a key revocation mechanism is an essential feature of an identity-based encryption system. By extending IB-PRE scheme, we propose a new cryptographic primitive revocable identity-based proxy re-encryption with re-encryption verifiability (RIB-VPRE), which allows the RIB-VPRE scheme to support the users’ revocation, delegation of decryption rights and re-encryption verifiability at the same time. In this paper, we give the first concrete construction of collusion-resistant unidirectional RIB-VPRE on lattice, which is secure under the standard model based on learning with error (LWE) for both selective and adaptive identities.
SMOF: Squeezing More Out of Filters Yields Hardware-Friendly CNN Pruning

Researchers have proposed various structured Convolutional Neural Networks (CNNs) pruning strategies to make them work efficiently on edge devices. However, most of them focus on reducing the number of filter channels per layer without considering the redundancy within individual filter channels. In this work, we explore pruning from another dimension, the kernel size. We develop a CNN pruning framework called SMOF, which Squeezes More Out of Filters by reducing both kernel size and the number of filter channels. Notably, SMOF is friendly to standard hardware devices without any customized low-level implementations, and the pruning effort by kernel size reduction does not suffer from the fixed-size width constraint in SIMD units of general-purpose processors. The pruned networks can be deployed effortlessly with significant running time reduction. We also support these claims via extensive experiments on various CNN structures and general-purpose processors for mobile devices.
GAN-based face identity feature recovery for image inpainting

In recent years, image inpainting techniques using GAN have yielded impressive results. However, repairing complete structure features and filling realistic textures in images with large-damaged areas is still a challenge. To solve this problem, we propose a refinement network to the baseline model to form a new two-step adversarial model, Semantic Guidance Refinement GAN (SGR-GAN). The first step of our model is the baseline model that consists of a pyramid structure encoder pSp-encoder and a pre-trained StyleGAN to generate a coarse result image with rich structural features. Then the second step is our proposed network, Facial Identity Preservation (FIP), for face identity preservation. To generate a more complete structural feature image, we add the semantic features of the coarse result into the FIP generator as semantic guidance. We have evaluated our model on the public dataset CelebA, and the experimental results also show that our model outperforms compared methods.
The Mysteries of NFT Taxation and the Problem of Crypto Asset Tax Evasion

Cryptocurrencies have long captured the attention of the financial world, revolutionizing how the world does business by providing virtually costless transactions. More recently, however, a new digital token has taken its place on the world stage. Known as NFTs, non-fungible tokens have allowed for the reinvention of modern finance infrastructure consisting of sophisticated trading and loaning systems for different asset types. Despite cryptocurrencies’ and NFTs’ novelty and popularity, they are not immune to the U.S. Tax Code. The Internal Revenue Service (IRS) has provided guidance on the tax framework of cryptocurrencies, but the taxation of NFTs is still relatively unclear, leaving taxpayers to rely largely on the cryptocurrency tax framework to address NFT taxation. The cryptocurrency framework, however, does not fully address all issues that may arise in NFT taxation. Virtual currencies have drawn much excitement, sparking the popularity of cryptocurrencies and NFT investors but have also drawn the scrutiny and worry of tax regulators. The U.S. has been experiencing a significant tax gap between the money taxpayers make from the transactions of these crypto assets and the amount of taxes paid to the IRS. Specifically, to blame for this tax gap are the novelty of these crypto assets, their inherent anonymity, their cross-border nature, and their independence from governmental or financial institutions. This article discusses the taxation of cryptocurrencies, its influence on a potential NFT-specific tax framework, why crypto assets are the weapon of choice for tax evaders, and the possible solutions the U.S. can pursue to remedy crypto asset tax evasion.
SAR Image Segmentation Based on Fisher Vector Superpixel Generation and Label Revision

This paper addresses the problem of superpixel-bases segmentation of synthetic aperture radar (SAR) images. Most superpixel segmentation methods have difficulties in segmenting adjacent regions with similar gray values, due to only considering spatial and gray information. To solve this problem and improve segmentation accuracy, this paper proposes a SAR image segmentation method based on Fisher vector superpixel generation and label revision. Firstly, the Fisher vector is obtained by processing the Gaussian mixture function. By introducing the Fisher vector, a distance formula is constructed for superpixel segmentation. Therefore, the adjacent regions with similar gray values can be segmented effectively in the generated superpixel map. Secondly, the superpixels are clustered using the K-means algorithm to obtain the initial label map. Then, with extracted edge information as constraints, the pixel labels obtained by K-means are repaired pixel by pixel to get the middle label map, according to the number and gray value difference of labels. This overcomes influence of noise generated by K-means. Finally, the middle label map is relabeled using the region growth algorithm to divide pixel blocks. Isolated pixel blocks surrounded by similar labels are corrected, based on the gray mean difference. The final label result has a better segmentation accuracy. Experiments on synthetic SAR images and real images demonstrate that the proposed algorithm achieves higher segmentation accuracy than six state-of-the-art clustering algorithms for SAR image segmentation.
The Representation of Refined Neutrosophic Matrices By Refined Neutrosophic Linear Functions

The aim of this paper is to solve the problem of representing refined neutrosophic matrices by linear functions, where it describes the structure of refined neutrosophic linear transformations that represent refined neutrosophic matrices. On the other hand, this work introduces a novel algorithm to compute a basis of any refined neutrosophic vector space depending on the classical basis of its corresponding classical vector space.
Factors that Influence the Scientific Production of Teachers in Peru

The purpose of the research is to determine the factors that influence the scientific production of teachers in Peru.Approached from the positivist paradigm, quantitative approach and basic type, descriptive-explanatory, the sample made up of 215 university professors calculated with probabilistic sampling, was used in the questionnaire and the survey as an instrument and technique, respectively.Content and construct validity were performed, as well as the reliability of the instrument through Cronbach's alpha, Omega and Theta.The factors that influence scientific production are: institutional factor, personal factor and the stimulus factor.It is concluded that the predominant factor that influences scientific production is the stimulus and the predominant indicators are incentives, prizes, bonuses, knowledge and participation in research methodology courses, empowerment or individual capacities and the personal motivation of teachers in scientific production.
SAR Time-Series Despeckling via Nonlocal Total Variation Regularized Robust PCA

Through the development of Synthetic Aperture Radar (SAR) technology, it is now possible to observe dynamic processes on the earth with fine temporal resolution by forming SAR time series. Nonetheless, such sequential images remain difficult to interpret due to the speckle effect. Despeckling them is further complicated by outliers caused by abrupt changes in weather conditions or the appearance of objects. In spite of the fact that many state-of-the-art methods can achieve excellent filtering performances over stable areas, they often result in artifacts in those areas where outliers existed at the time of acquisition. To simultaneously mitigate the speckle noise and extract outliers, we propose a novel SAR time series despeckling method based on nonlocal total variation regularized robust principle component analysis, which is termed SAR-NL-TVRPCA. By comparing it to other state-of-the-art methods, the effectiveness of its despeckling has been validated in real data experiments. Furthermore, the extracted outliers can provide insight into abrupt changes occurring throughout the observation period, which provides byproducts for further analysis.
Efficient Generative Face Completion with Perceptual Deblurring

Blurry textures in the inpainted regions or content inconsistencies between the inpainted and known regions may arise with existing deep-learning based completion approaches. A two-stage face completion framework with perceptual deblurring to address these problems is presented. In the first stage, a GAN is employed to inpaint masked regions with coarse facial features by integrating PSNR loss, SSIM loss and adversarial loss. In the second stage, a perceptual deblurring GAN is developed to further refine the coarsely reconstructed facial features in order to achieve clear facial textures and natural content consistencies. The quantitative, qualitative and ablation experiments are performed on the public face dataset CelebAHQ. Extensive experimental results show that the proposed algorithm is superior to the compared state-of-the-art image completion algorithms on the metrics of PSNR and SSIM.
Semantic Segmentation of FOD Using an Improved Deeplab V3+ Model

Foreign Object Debris (FOD) can cause security risks in the process of airplane take-off and landing, so the detection of FOD on the airport runway is a critical responsibility. In this article, we propose a multi-category semantic segmentation of FOD based on CBAM-Deeplab V3+. The dataset adopted in this study is a three-channel dataset obtained by an 3D laser scanning camera. The target region of image segmentation is manually calibrated on the grayscale images. Firstly, a soft attention mechanism is applied to the grayscale images as weight vectors after preprocessing the depth images. Secondly, we use the Resnet101 residual network as the backbone network of the CBAM-Deeplab V3+, with a convolutional block attention module (CBAM) applied in the spatial pyramid pooling part (ASPP) to highlight features. Thirdly, the loss function is calculated and the model is optimized using the focal loss function with dynamic weight. In this study, the segmentation effect of the model is evaluated by four kinds of common foreign objects in the airport, including wood products, screws and nuts, stones, pliers. Experiment results show that our method achieves 93.42% Mean Pixel Accuracy (MPA) and 73.69% Mean Intersection over Union (MIoU). The proposed method has achieved satisfactory results in the field of FOD image segmentation and can be further applied to clear the FOD objects using a mobile manipulator.
Forecasting Covid-19 Time Series Data using the Long Short-Term Memory (LSTM)

Confirmed statistical data of Covid-19 cases that have accumulated sourced from (https://corona.riau.go.id/datastatistik/) in Riau Province on June 7, 2021, there were 63441 cases, on June 14, 2021, it increased to 65883 cases, on June 21, 2021, it increased to 67910, and on June 28, 2021, it increased to 69830 cases.Since the beginning of this pandemic outbreak, it has been observed that the case data continues to increase every week until this July.This study predicts cases of Covid-19 time series data in Riau Province using the LSTM algorithm, with a dataset of 64 lines.Long-Short Term Memory has the ability to store memory information for patterns in the data for a long time at the same time.Tests predicting historical data for Covid-19 cases in Riau Province resulted in the lowest RMSE value in the training data, which was 8.87, and the test data, which was 13.00, in the death column.The evaluation of the best MAPE value in the training data, which is 0.23%, is in the recovered column, and the evaluation of the best MAPE value in the test data, which is 0.27%, in the positive_number column.In the test to predict the next 30 days using the LSTM model that has been trained, it was found that the performance evaluation of the prediction results for the positive_number column and the death column was very good, the recovery column was categorized as good, the independent_isolation column and the care_rs column were categorized as poor.
Scaling-Up Generalized Planning as Heuristic Search with Landmarks

Landmarks are one of the most effective search heuristics for classical planning, but largely ignored in generalized planning. Generalized planning (GP) is usually addressed as a combinatorial search in a given space of algorithmic solutions, where candidate solutions are evaluated w.r.t. the instances they solve. This type of solution evaluation ignores any sub-goal information that is not explicit in the representation of the planning instances, causing plateaus in the space of candidate generalized plans. Furthermore, node expansion in GP is a run-time bottleneck since it requires evaluating every child node over the entire batch of classical planning instances in a GP problem. In this paper we define a landmark counting heuristic for GP (that considers sub-goal information that is not explicitly represented in the planning instances), and a novel heuristic search algorithm for GP (that we call PGP) and that progressively processes subsets of the planning instances of a GP problem. Our two orthogonal contributions are analyzed in an ablation study, showing that both improve the state-of-the-art in GP as heuristic search, and that both benefit from each other when used in combination.
Image-based Handwriting Analysis for Disease Diagnosis

Handwriting movement is an activity dominated and controlled by human advanced nervous system, which depends on coordination and cooperation of multiple parts of the whole body, and the analysis of handwriting movement has been applied in many fields. In disease diagnosis, handwriting tremor is common in neurodegenerative diseases(NDs), such as Parkinson's disease(PD) and Alzheimer's disease(AD). In this paper, the image-based handwriting trajectories of mild and severe Parkinson's disease patients and healthy people with no tremor are collected, and the features of images are extracted. Utilizing corner detection method to judge the fluctuation of trajectory; 2-dimensional Discrete Fourier Transform (2D-DFT) is applied to transform it into frequency-domain space, and texture features of amplitude spectrum are extracted based on gray level co-occurrence matrix (GLCM). Finally, the machine learning method is used to classify the above features, so as to realize the diagnosis of diseases.
EG-SNIK: A Free Viewing Egocentric Gaze Dataset and Its Applications

Egocentric vision data captures the first person perspective of a visual stimulus and helps study the gaze behavior in more natural contexts. In this work, we propose a new dataset collected in a free viewing style with an end-to-end data processing pipeline. A group of 25 participants provided their gaze information wearing Tobii Pro Glasses 2 set up at a museum. The gaze stream is post-processed for handling missing or incoherent information. The corresponding video stream is clipped into 20 videos corresponding to 20 museum exhibits and compensated for user’s unwanted head movements. Then, I-VT algorithm is used to identify eye movements as fixations and saccades. Based on the velocity of directional shifts of the eye, the I-VT algorithm classifies the eye movements into either fixations or saccades. Representative scanpaths are built by generalizing multiple viewers’ gazing styles for all exhibits. The application of our dataset is demonstrated for characterizing the inherent gaze dynamics using state trajectory estimator based on ancestor sampling (STEAS) model in solving gaze data classification and retrieval problems.
Fully Continuous Leakage-Resilient Certificate-Based Signcryption Scheme for Mobile Communications

Due to the popularity of mobile communication, many computing devices are exposed to remote environments without physical protection so that these devices easily suffer from leakage attacks (e.g., side-channel attacks). Under such leakage attacks, when a computing device performs some cryptographic algorithm, an adversary may acquire partial bits of secret keys participated in this cryptographic algorithm. To resist leakage attacks, researchers offer leakage-resilient cryptography as a solution. A signcryption scheme combines signing and encrypting processes to simultaneously provide both authentication and confidentiality, which is an important cryptographic primitive. Indeed, many leakage-resilient signcryption schemes under various public key system (PKS) settings were proposed. Unfortunately, these schemes still have two shortcomings, namely, bounded leakage resilience and conditionally continuous leakage resilience. In this paper, a “fully” continuous leakage-resilient certificate-based signcryption (FCLR-CBSC) scheme is proposed. Security analysis is formally proved to show that our scheme possesses both authentication and confidentiality against two types of adversaries in the certificate-based PKS setting. Performance analysis and simulation experience show that our scheme is suited to run on both a PC and a mobile device.