Reviewerly/exHarmony huggingface dataset:
GestureCanvas: A Programming by Demonstration System for Prototyping Compound Freehand Interaction in VR

As the use of hand gestures becomes increasingly prevalent in virtual reality (VR) applications, prototyping Compound Freehand Interactions (CFIs) effectively and efficiently has become a critical need in the design process. Compound Freehand Interaction (CFI) is a sequence of freehand interactions where each sub-interaction in the sequence conditions the next. Despite the need for interactive prototypes of CFI in the early design stage, creating them is effortful and remains a challenge for designers since it requires a highly technical workflow that involves programming the recognizers, system responses and conditionals for each sub-interaction. To bridge this gap, we present GestureCanvas, a freehand interaction-based immersive prototyping system that enables a rapid, end-to-end, and code-free workflow for designing, testing, refining, and subsequently deploying CFI by leveraging the three pillars of interaction models: event-driven state machine, trigger-action authoring, and programming by demonstration. The design of GestureCanvas includes three novel design elements — (i) appropriating the multimodal recording of freehand interaction into a CFI authoring workspace called Design Canvas, (ii) semi-automatic identification of the input trigger logic from demonstration to reduce the manual effort of setting up triggers for each sub-interaction, (iii) on the fly testing for independently validating the input conditionals in-situ. We validate the workflow enabled by GestureCanvas through an interview study with professional designers and evaluate its usability through a user study with non-experts. Our work lays the foundation for advancing research on immersive prototyping systems allowing even highly complex gestures to be easily prototyped and tested within VR environments.
Neighbor-Environment Observer: An Intelligent Agent for Immersive Working Companionship

Human-computer symbiosis is a crucial direction for the development of artificial intelligence. As intelligent systems become increasingly prevalent in our work and personal lives, it is important to develop strategies to support users across physical and virtual environments. While technological advances in personal digital devices, such as personal computers and virtual reality devices, can provide immersive experiences, they can also disrupt users' awareness of their surroundings and enhance the frustration caused by disturbances. In this paper, we propose a joint observation strategy for artificial agents to support users across virtual and physical environments. We introduce a prototype system, neighbor-environment observer (NEO), that utilizes non-invasive sensors to assist users in dealing with disruptions to their immersive experience. System experiments evaluate NEO from different perspectives and demonstrate the effectiveness of the joint observation strategy. A user study is conducted to evaluate its usability. The results show that NEO could lessen users' workload with the learned user preference. We suggest that the proposed strategy can be applied to various smart home scenarios.
RadarVR: Exploring Spatiotemporal Visual Guidance in Cinematic VR

In cinematic VR, viewers can only see a limited portion of the scene at any time. As a result, they may miss important events outside their field of view. While there are many techniques which offer spatial guidance (where to look), there has been little work on temporal guidance (when to look). Temporal guidance offers viewers a look-ahead time and allows viewers to plan their head motion for important events. This paper introduces spatiotemporal visual guidance and presents a new widget, RadarVR, which shows both spatial and temporal information of regions of interest (ROIs) in a video. Using RadarVR, we conducted a study to investigate the impact of temporal guidance and explore trade-offs between spatiotemporal and spatial-only visual guidance. Results show spatiotemporal feedback allows users to see a greater percentage of ROIs, with 81% more seen from their initial onset. We discuss design implications for future work in this space.
Self-Supervised SAR Anomaly Detection Guided with RX Detector

Anomaly detection in Synthetic Aperture Radar (SAR) images is an important topic. However, the task is challenging due to the scarcity of anomalous samples and the lack of annotated data, which has led most algorithms in this field to be unsupervised. To address the issue, this article proposes a new loss that adds prior information. One of the main functions of an autoencoder is to reconstruct the input data as accurately as possible after encoding them in a latent vector. The proposed loss function guides the network using the Reed-Xiaoli (RX) detector and replaces any pixels in the input data deemed too abnormal with normal surrounding values. This approach incorporates a priori information in addition to the assumption that anomalies are largely under-represented compared to the rest of the image. An ablation study demonstrates that the proposed loss function improves detection performance.
RSSOD-Bench: a Large-Scale Benchmark Dataset for Salient Object Detection in Optical Remote Sensing Imagery

We present the RSSOD-Bench dataset for salient object detection (SOD) in optical remote sensing imagery. While SOD has achieved success in natural scene images with deep learning, research in SOD for remote sensing imagery (RSSOD) is still in its early stages. Existing RSSOD datasets have limitations in terms of scale, and scene categories, which make them misaligned with real-world applications. To address these shortcomings, we construct the RSSOD-Bench dataset, which contains images from four different cities in the USA <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> . The dataset provides annotations for various salient object categories, such as buildings, lakes, rivers, highways, bridges, aircraft, ships, athletic fields, and more. The salient objects in RSSOD-Bench exhibit large-scale variations, cluttered backgrounds, and different seasons. Unlike existing datasets, RSSOD-Bench offers uniform distribution across scene categories. We benchmark 23 different state-of-the-art approaches from both the computer vision and remote sensing communities. Experimental results demonstrate that more research efforts are required for the RSSOD task.
MS2P: A True Multi-View Satellite Stereo Pipeline without Rectification of Push Broom Images

In this study, we propose a new and true multi-view stereo pipeline (MS2P) to reconstruct surface model from push broom satellite images. The proposed method utilizes all unrectified multi-view stereo image at the same time, thus it is a true multi-view stereo (MVS) method. Using the rational polynomial coefficient (RPC) of the images, the ground-to-image geometry between all 3D cost volumes and multi-view images are defined. Then EnSoft3D, a true multi-view stereo algorithm, is used to iteratively compute and refine all height maps. In addition, VoteValue in the original EnSoft3D is adjusted to facilitate satellite images for more accurate height map generation. Experimental results show that an MVS method in the computer vision community can be used to reconstruct height map and surface model from multi-view unrectified satellite images. Based on experimental results using DFC19 dataset, the MSE and RMSE are about 0.572m and 2.09m in average.
Location Privacy Preservation of Geospatial Data Using Error Based Transformation

Geospatial data is obtained from a variety of sources, including satellite sensors, GPS, and observations that are mostly based on the ground or aerial sensors. Through a variety of consistent, reliable indications that square measures available for storing, managing, and sharing spatial information, attention towards security, access management, and privacy policies of spatial information are extremely low. The user is sharing its location perpetually in several Geo-social applications in terms of latitude and longitude to urge information regarding their encompassing with the assistance of other locations and their recommendations. But users' information is exploited by the pursuit of users' activities if there are no privacy preservation techniques applied to the current geo spatial information. It protects the spatial data from attackers while permitting licensed users to issue spatial queries expeditiously by the service supplier. The target of this paper is to develop techniques to produce protection and security at dissemination for geospatial information through error-based transformation.
Towards Geospatial Knowledge Graph Infused Neuro-Symbolic AI for Remote Sensing Scene Understanding

Deep learning has proven its effectiveness in numerous tasks for remote sensing scene understanding. However there is an increasing interest to explore fusion of domain-specific background information to the deep neural network to further improve its performance. Remote sensing researchers are also working towards developing models that generalize and adapt to multiple applications. Generalization challenges coupled with the scarcity of large corpora of high-quality noise-free labelled data, have together fueled an interest for leveraging background information. Knowledge graphs serve as excellent choice to represent domain-specific information in a structured, standardized and extensible manner. Integrating symbolic knowledge representations in the form of Knowledge Graph Embedding (KGE) to perform neuro-symbolic reasoning is an emerging research direction promising significant impacts. This vision paper seeks to position ideas and provoke early thoughts toward advancing neuro-symbolic artificial intelligence in the context of geospatial challenges. Specifically, it conceptualizes and elaborates on an architecture for infusing geospatial knowledge from knowledge graph in a deep neural network pipeline. As guiding case studies - land-use land-cover classification, object detection and instance segmentation can benefit from infusing spatio-contextual information with remote sensing imagery. The discussion further reflects on and articulates the challenges and explainable AI opportunities anticipated when scaling and maintaining large-scale geospatial knowledge graphs.
Deepfake Satellite Imagery Detection with Multi-Attention and Super Resolution

Deepfake satellite imagery detection is a crucial task in the era of digital deception, as the ability to manipulate and generate fake satellite images poses significant risks and negative impacts, such as disguising military activities, spreading mis-information, and undermining the trust in surveillance systems. To maintain the integrity of satellite data, we propose a novel approach detecting forged satellite images by leveraging novel technologies such as multi-attention and super resolution, claiming and supporting their fitness for this domain. We evaluate our approach on three fake satellite imagery datasets based on different generative models and sizes, obtaining 99.46%, 92.81%, and 99.50% accuracies. We perform comparisons to SOTA deepfake detectors where our detector overperforms the second best one by 15%. We also analyze attention maps for interpretibility of our detector, and conduct ablation studies to support our architectural choices.
Assessing Landsat-9 in Identifying Lithology, Using a Hybrid Metric-Learning and SVM Method Against Baseline Algorithms: A Case Study of the West African Craton

The West African Craton hosts economic minerals such as diamonds, gold, and bauxite. Yet, it remains underexplored as poor socio-political and environmental conditions limit traditional exploration in the area. This study assesses the performance of the Landsat-9 OLI-2/TIR-2 in mapping lithologies and highlighting potential metallogenic points using remote sensing techniques. Specifically, we perform classification tasks on preprocessed Landsat-9 dataset to classify the lithological features using a hybrid machine learning (ML) module integrating Local Fisher Discriminant Analysis (LFDA) and Support Vector Machine (SVM) against three (3) baseline ML algorithms. The aim is to enhance computing proficiency and improve classification precision. The accuracy results show that the proposed LFDA and SVM algorithm performs better than Random Forest (RF) and KNN. The results distinguish nine lithological units with a 0.9992 overall accuracy. Additionally, Landsat-9 OLI-2/TIR-2 proves efficient in attaining high accuracy in conducting geological exploration work in terrains under unfavorable conditions.
Super-Resolution of Remote Sensing Imagery Using Implicit Degradation Modeling

Super-resolution (SR) techniques are widely used in remote sensing image analysis to enhance image resolution. However, existing SR methods often struggle to accurately model the complex image degradation processes encountered in remote sensing images. In this paper, we propose a simple yet effective SR method specifically designed for remote sensing images. The proposed method incorporates a quality degradation modeling network and a SR network to bridge the gap between low-resolution (LR) and high-resolution (HR) images. Experimental results using Korea Multi-purpose Satellite (KOMPSAT)-3/3A and WorldView-3 images demonstrate that the proposed method significantly improves the quality of the source images by leveraging the high-resolution characteristics of the HR images.
MMS:Multi-Source Mutual Supervision Semantic Segmentation

How to use multi-source data for semantic segmentation is a hot topic. In this article, a new multi-source image data semantic segmentation based on multi-source mutual supervision (MMS) has been proposed. First, multi-source data from the same region are trained separately using identical segmentation networks for initialization; then, MMS performs mutual supervision training on these initialized networks, thus adaptively cooperating differences in information distribution between multiple sources of image data, and combines real label constraints output consistency across different networks. Experimental results demonstrate that the proposed MMS strategy can effectively coordinate information between multi-source data, improve segmentation accuracy, and is effective in different semantic segmentation networks and different types of data sets.
Application of artificial intelligence in computer network technology

With the development of science and technology, artificial intelligence has been widely used in many fields, smart TVs, intelligent sweeping robots, intelligent voice, etc. are quiet trigger great changes in human society. At the same time, people are also exploring the integration and development of computer network technology and artificial intelligence, in order to use artificial intelligence advantages improve the accuracy and efficiency of data processing. The author first analyzes the concept of artificial intelligence, and then analyzes artificial intelligence in computer network technology in detail. The application of artificial intelligence in the field of computer is finally discussed.
Transformer and CNN Hybrid Network for Super-Resolution Semantic Segmentation of Remote Sensing Imagery

Super-resolution semantic segmentation (SRSS) based on Convolutional neural network (CNN) cannot establish long-range dependencies due to limited receptive field, which limits the SRSS to obtain accurate high-resolution (HR) segmentation results from the low-resolution (LR) input images. In this paper, we design a Transformer and CNN hybrid SRSS network that consists of two branches: Transformer and CNN hybrid SRSS branch and super-resolution guided branch. In the Transformer and CNN hybrid SRSS branch, Transformer extracts global context information from the feature map of the CNN, while skip connection is used to retain the local context information extracted from the CNN and combines both features to further improve the segmentation performance. In addition, the super-resolution guided branch is designed to supplement rich structure information and guide the semantic segmentation (SS). We test the proposed method on the ISPRS Vaihingen benchmark data set, and our network is superior to other state-of-the-art methods.
Atmospheric Motion Vector Retrieval Using the Total Variation-Based Optical Flow Method

Atmospheric motion vector (AMV) retrieval from water vapor measurements is important in climate research and weather forecasting. However, conventional feature tracking methods for AMV retrievals generate velocity fields with gaps and large errors. In this work, we test the optical flow algorithm by generating a nature run of a convective weather phenomenon, which provides water vapor variables and wind vector fields at various pressure levels. We show that our optical flow algorithm generates superior performance when compared with traditional feature tracking algorithms used in operational centers, generating dense AMVs with no gaps and significantly improving AMV accuracy. The optical flow algorithm performs well down to very low wind speeds and does not require a low-wind cutoff threshold. In our studies, we considered various measurement configurations, including water vapor retrievals at different temporal resolutions and found that the optical flow algorithm is not sensitive to the time interval between images.
Deep Learning-Based Cloud Detection in High-Resolution Satellite Imagery Using Various Open-Source Cloud Images

Cloud cover is a significant obstacle to use optical satellite imagery. Therefore, various studies have been proposed to accurately detect clouds and evaluate satellite image quality. In particular, with the advancement of deep learning technology, many cloud detection studies are being conducted. However, a large volume of high-quality data is required to develop an effective deep learning model training. Thus, in this study, we compare the performance of deep learning cloud detection models for according to the diversity of sensors and resolutions of training data. For conducting the study, five case dataset combinations were constructed and trained with HRNet (High-Resolution Network). The performance evaluation of the trained models was conducted using test images from the KOMPSAT and PlanetScope satellites. As a mean of achieving high cloud detection results, it was found that selecting and using high-quality data is more effective than simply increasing the number of training data.
Joint Multi-Modal Self-Supervised Pre-Training in Remote Sensing: Application to Methane Source Classification

With the current ubiquity of deep learning methods to solve computer vision and remote sensing specific tasks, the need for labelled data is growing constantly. However, in many cases, the annotation process can be long and tedious depending on the expertise needed to perform reliable annotations. In order to alleviate this need for annotations, several self-supervised methods have recently been proposed in the literature. The core principle behind these methods is to learn an image encoder using solely unlabelled data samples. In earth observation, there are opportunities to exploit domain-specific remote sensing image data in order to improve these methods. Specifically, by leveraging the geographical position associated with each image, it is possible to cross reference a location captured from multiple sensors, leading to multiple views of the same locations. In this paper, we briefly review the core principles behind so-called joint-embeddings methods and investigate the usage of multiple remote sensing modalities in self-supervised pre-training. We evaluate the final performance of the resulting encoders on the task of methane source classification.
SPEECH BIOMETRICS: A Comprehensive Deep Learning-based Speaker Identification System

Speaker recognition is a fascinating research area that has grown significantly in the last few years. The recognition rate of this technology depends on several factors such as pitch, frequency, handset type, and background noise, among others. Automatic Speaker Recognition (ASR) is the process of identifying and verifying a person based on their speech waves and speaker-specific information. It is used in various fields, including access control for different voice-based services, such as phone dialing, voice mail, database services, telephone shopping and banking services by telephone. Speaker recognition is also used in the security monitoring of remote access to computers and confidential information. Speaker recognition technology plays an important role in forensics. It is a valuable tool that is used to identify suspects or witnesses based on their speech recordings. Therefore, understanding the basics of ASR, including feature extraction techniques and speaker modeling, is crucial to achieve accurate speaker recognition. The objective of the paper is to evaluate and compare the performance of Siamese Neural Networks (SNN) and Gaussian Mixture Models (GMM) in the context of speaker recognition. Specifically, the paper aims to assess their effectiveness in distinguishing between four different speakers using audio inputs and preprocessing techniques, with a focus on achieving low Equal Error Rates (EER). The paper employed a comparative methodology where it utilized two different audio inputs from four distinct speakers. Preprocessing was performed on the audio data using the MFCC (Mel-Frequency Cepstral Coefficients) algorithm, followed by the removal of silent segments. The processed audio data were then input into both a Siamese Neural Network and a Gaussian Mixture Model. The Siamese Neural Network and GMM were used as the models for speaker recognition. The SNN achieved an EER of 4.88%, while the GMM achieved a slightly higher EER of 6.55%. In summary, speaker recognition has several important applications in our daily lives, and the accuracy of the technology depends on various factors. ASR is a fascinating field of research that continues to evolve, and understanding its basics is essential to achieve accurate and reliable results.
Saliency-Guided Attention-Based Feature Pyramid Network for Ship Detection in SAR Images

We report a saliency-guided attention-based feature pyramid network (SA-FPN) for ship detection from synthetic aperture radar (SAR) images. The two key contributions are – 1) the saliency-guided technique and 2) the attention-based means. The former offers one unsupervised visual saliency map that can guide FPN to focus more on regions of interest (ROIs). The latter offers one supervised non-local feature self-attention map that can improve FPN’s global representation ability. We offer an effective combination scheme of the two. Experimental results on the open SSDD dataset reveal SA-FPN’s advanced SAR ship detection performance. Furthermore, the ablation studies can confirm the two contributions' effectiveness.
Generating High-Resolution SAR Images with Loss Function Customization

High-resolution SAR images have become essential data in various fields such as object detection and disaster analysis. However, obtaining high-resolution images is relatively difficult and expensive, leading to ongoing research on methods to generate them. Recently, deep learning has been explored to convert low-resolution images into high-resolution ones. In this paper, we utilize deep learning to generate high-resolution images, and we modify and combine loss functions to enhance the model’s performance. The employed loss function incorporates various functions such as SSIM, MS-SSIM, L1, and L2. The experimental results demonstrate that utilizing the combined loss function outperforms using the existing loss function alone for image generation. When comparing the indicators, the combined loss function yields an SSIM value of 0.6098 and a DISTS value of 0.0542, indicating a 5% improvement over the original loss function.