Reviewerly/exHarmony huggingface dataset:
Self-Correlation and Cross-Correlation Learning for Few-Shot Remote Sensing Image Semantic Segmentation

Remote sensing image semantic segmentation is an important problem for remote sensing image interpretation. Although remarkable progress has been achieved, existing deep neural network methods suffer from the reliance on massive training data. Few-shot remote sensing semantic segmentation aims at learning to segment target objects from a query image using only a few annotated support images of the target class. Most existing few-shot learning methods stem primarily from their sole focus on extracting information from support images, thereby failing to effectively address the large variance in appearance and scales of geographic objects. To tackle these challenges, we propose a Self-Correlation and Cross-Correlation Learning Network for the few-shot remote sensing image semantic segmentation. Our model enhances the generalization by considering both self-correlation and cross-correlation between support and query images to make segmentation predictions. To further explore the self-correlation with the query image, we propose to adopt a classical spectral method to produce a class-agnostic segmentation mask based on the basic visual information of the image. Extensive experiments on two remote sensing image datasets demonstrate the effectiveness and superiority of our model in few-shot remote sensing image semantic segmentation. The code is available at https://github.com/linhanwang/SCCNet.
GPT Applications in Relevance Model Training in Map Search

Understanding map queries and retrieving correct entity results are the two main relevance tasks in Map search. They are usually performed by a set of task specific machine learning models. Collecting large amount of high quality labelled data for training such models is a time-consuming and labor-intensive process. Although various methods have been studied for producing pseudo data labels, they are limited in their effectiveness when applied across different languages or tasks. The recently released Large Language models (LLMs), including ChatGPT and GPT-4 (GPT for short), have demonstrated state-of-the-art performance in text understanding by using simple prompt instructions with only a handful of examples for in-context learning. In this paper, we explore GPT as a cost-effective alternative for both data labeling and synthetic data generation, where we subsequently use data obtained from this approach to train various task specific models such as maps intent detection, address detection, address parsing, geo-entity ranking, and rank scores calibration. GPT demonstrates strong potential in generating otherwise hard-to-synthesize data. We observe significant accuracy and relevance improvement across all task specific models when trained or fine-tuned on data generated by GPT. Lastly, we propose a general framework combining labeled data from GPT with other sources and a prompt fine-tune structure to guide GPT model in completing a given task.
Map GPT Playground: Smart Locations and Routes with GPT

People often ask questions for which the answer contains multiple locations or locations with additional context. The questions can be very natural and easy to understand by other people, yet if formulated as map queries, today's map search engines struggle to understand them. Here we look into three categories of such map queries: 1) queries with multiple explicitly stated locations, e.g. 'Show me directions from A to B through C, D, and E'; 2) queries where the locations need to be inferred, e.g. 'Show me on the map all locations which James Bond visited in Casino Royale'; 3) queries of locations where we request additional geographical, historical or other context, e.g. 'Show me a map of wildlife in Australia'. We build a prototype system, called Map GPT Playground, and demonstrate with it how such queries can be seamlessly answered by combining the power of Large Language Models (LLM) with foundational maps services, such as geocoding, routing, etc. We describe the architecture of the system and reason over the abstractions needed for the system to be able to generalize across complex query intents and invoke suitable chains of services to fulfil them. Lastly, we demonstrate that in resolving complex location search queries, novel considerations emerge without prior analog, namely the set of returned locations needs to be spatially consistent and often to satisfy some inferred from the query temporal order.
NALSpatial: An Effective Natural Language Transformation Framework for Queries over Spatial Data

Spatial databases play a vital role in many applications that access spatial data via appropriate queries. However, most application users lack the expertise necessary for formulating spatial queries. To fill in this gap, we propose an effective framework called NALSpatial that translates natural language queries over spatial data into executable database queries. NALSpatial consists of two core phases. The natural language understanding phase extracts key entity information, comprehends the query intent and determines the query type. The key entities and query type are passed to the subsequent natural language translation phase, which employs entity mapping rules and structured language models to construct executable database queries accordingly. We implement NALSpatial on the open-source extensible database system SECONDO to support range queries, nearest neighbor queries, spatial joins and aggregation queries. Extensive experiments show that NALSpatial on average achieves response time of about 2.5 seconds, translatability of 95% and translation precision of 92%, outperforming state-of-the-art natural language transformation methods.
Saliency Driven Imagery Preprocessing for Efficient Compression - Industrial Paper

The compression of satellite imagery remains an important research area as hundreds of terabytes of images are collected every day, which drives up storage and bandwidth costs. Although progress has been made in increasing the resolution of these satellite images, many downstream tasks are only interested in small regions of any given image. These areas of interest vary by task but, once known, can be used to optimize how information within the image is encoded. Whereas standard image encoding methods, even those optimized for remote sensing, work on the whole image equally, there are emerging methods that can be guided by saliency maps to focus on important areas. In this work we show how imagery preprocessing techniques driven by saliency maps can be used with traditional lossy compression coding standards to create variable rate image compression within a single large satellite image. Specifically, we use variable sized smoothing kernels that map to different quantized saliency levels to process imagery pixels in order to optimize downstream compression and encoding schemes.
Integrating Automated Annotation of Magnetic Prospection Data into GIS Workflows in Archaeology (demo paper)

Archaeological excavations play a major role in gaining knowledge about prehistoric landscapes and ways of living. However, archaeological excavations are destructive acts and very resource intensive, so they cannot be performed in every area of interest. Therefore prospection methods have been developed, where feedbacks of e.g. lidar, radar or magnetic sensors are utilized to get an overview of the distribution, extent and complexity of underground structures in larger areas. After automated pre-processing of the sensor data arrays (and images) of these, grid data is provided as an input for exploration, analysis and annotation using geographic information system tools like QGIS. Annotating the images has been a fully manual task, performed by domain scientists. In this work we demonstrate a tool that supports domain scientists through automated annotation prediction. The implementation is integrated in the prevalent scientific workflow using available input and required output formats. The implementation is based on a pre-trained Rotated Retina Net. The manually annotated data of underground house remains from one of three archaeological sites is then used to pre-process and augment a feasible amount of training data for this specific task. One challenge was that global normalization of pixel values in the images did not yield useful results, because of modern infrastructure (such as utility pipes) distorting the magnetic feedback. A separated portion of the annotated data has been used for a quantitative evaluation of model performance. The system has also been applied to two additional, formerly unseen and non-annotated datasets where predicted annotations were found to be valuable for domain scientists. The system's output data can be used in GIS tools to edit annotations by experts, explore the sites, identify promising excavation sites and perform e.g. cluster analysis on house sizes and other features.
Exploiting Polygon Metadata to Understand Raster Maps - Accurate Polygonal Feature Extraction

Locating undiscovered deposits of critical minerals requires accurate geological data. However, most of the 100,000 historical geological maps of the United States Geological Survey (USGS) are in raster format. This hinders critical mineral assessment. We target the problem of extracting geological features represented as polygons from raster maps. We exploit the polygon metadata that provides information on the geological features, such as the map keys indicating how the polygon features are represented, to extract the features. We present a metadata-driven machine-learning approach that encodes the raster map and map key into a series of bitmaps and uses a convolutional model to learn to recognize the polygon features. We evaluated our approach on USGS geological maps; our approach achieves a median F1 score of 0.809 and outperforms state-of-the-art methods by 4.52%.
Reconsidering utility: unveiling the limitations of synthetic mobility data generation algorithms in real-life scenarios

In recent years, there has been a surge in the development of models for the generation of synthetic mobility data. These models aim to facilitate the sharing of data while safeguarding privacy, all while ensuring high utility and flexibility regarding potential applications. However, current utility evaluation methods fail to fully account for real-life requirements.
Web-Based Traffic-Sign Detection

Detecting traffic signs on images has many applications within the transportation area, e.g., speed limit detection, navigation, asset management, and autonomous vehicles. A significant challenge in detecting traffic signs is that a large set of labeled images are needed to train, test, and validate an object detector. In this paper, we demonstrate a webapp that enables traffic-sign detection of 169 common traffic signs and downloads the results. Users can contribute to improving object detection by (1) annotating user-uploaded images, (2) verifying traffic signs detected on existing imagery, or (3) donating new imagery by uploading images, e.g., taken with smartphones. The set of detected traffic signs is stepwise increased by correcting mistakes in object detection using the online verification part of the system. The user can immediately download all verified traffic-sign objects. Users can also download 43,995 traffic-sign objects from 378 different classes. This existing set of traffic-sign objects is updated nightly with objects two or more users have verified. The demonstration includes detecting traffic signs on imagery uploaded by the audience and downloading the traffic sign detected, e.g., to be used internally in an organization.
SolarDetector: A Transformer-based Neural Network for the Detection and Masking of Solar Panels

As the global transition towards renewable energy sources accelerates, solar power becomes an increasingly important solution. Identifying and understanding the current distribution of solar panel installations is crucial for future planning and decision-making process. This paper introduces SolarDetector, a transformer-based neural network model, which we developed and fine-tuned for the accurate detection of solar panels. It achieves 91.0% mIoU for the task of masking solar panels on SWISSIMAGE dataset.
Efficient and Accurate Object Extraction from Scanned Maps by Leveraging External Data and Learning Representative Context

This paper introduces innovative approaches for extracting geographic objects from scanned map images. Overcoming the challenges associated with labor-intensive data labeling and inaccurate extraction results, we present two data labeling methods and an accurate-enhancing extraction method. The experiment in this paper shows that our approaches outperform the baselines.
Faster high-accuracy log-concave sampling via algorithmic warm starts

It is a fundamental problem to understand the complexity of high-accuracy sampling from a strongly log-concave density $\pi$ on $\mathbb{R}^{d}$. Indeed, in practice, high-accuracy samplers such as the Metropolis-adjusted Langevin algorithm (MALA) remain the de facto gold standard; and in theory, via the proximal sampler reduction, it is understood that such samplers are key for sampling even beyond log-concavity (in particular, for sampling under isoperimetric assumptions).This paper improves the dimension dependence of this sampling problem to $\widetilde{O}\left(d^{1 / 2}\right)$, whereas the previous best result for MALA was $\widetilde{O}(d)$. This closes the long line of work on the complexity of MALA, and moreover leads to state-of-the-art guarantees for high-accuracy sampling under strong log-concavity and beyond (thanks to the aforementioned reduction).Our starting point is that the complexity of MALA improves to $\widetilde{O}\left(d^{1 / 2}\right)$, but only under a warm start (an initialization with constant Rényi divergence w.r.t. $\pi$). Previous algorithms for finding a warm start took $O(d)$ time and thus dominated the computational effort of sampling. Our main technical contribution resolves this gap by establishing the first $\widetilde{O}\left(d^{1 / 2}\right)$ Rényi mixing rates for the discretized underdamped Langevin diffusion. For this, we develop new differential-privacy-inspired techniques based on Rényi divergences with Orlicz-Wasserstein shifts, which allow us to sidestep longstanding challenges for proving fast convergence of hypocoercive differential equations.
Analisis Resiko Keamanan Informasi Website Repository Digital Library Menggunakan Framework ISO/IEC 27001 &amp; 27002: Studi Kasus Perguruan tinggi X

The continuous evolution of digital repositories in the era of globalization, especially in context of higher education digital libraries, poses security risks that raise concerns among users. Existence of sensitive user data that requires protection by universities adds to this concern. This research aims to conduct comprehensive analysis of information security risks associated with digital library repository websites. This research seeks to identify potential vulnerabilities, threats that could compromise the confidentiality, integrity and availability of digital assets stored in repositories. Through detailed risk analysis, this research provides actionable insights and recommendations to improve the information security posture of digital libraries using the ISO/IEC 27001 and 27002 IT governance framework specifically tailored to information security standards. This research uses a literature review and interviews with responsible parties at the University of X's digital library repository. Findings show that the use of tools such as Acunetix helps identify vulnerabilities in web repositories. Risk mitigation in digital library web repositories involves the application of ISO/IEC 27001, 27002 standards, which results in specific risk mitigation actions. For example, universities should create policies to monitor information technology assets, ensuring regular monitoring to protect technology assets. In addition, for Database Management System (DBMS) management (e.g., MySQL, PostgreSQL, Oracle, Ms SQL Server), colleges must facilitate easy access and storage of information. By implementing the recommendations obtained from this research, higher education institutions can ensure safe environment for users accessing digital library web repositories, thereby reducing concerns about the security of their information.
Real-time violence detection using CNN and LSTM

Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Twitter Facebook Reddit LinkedIn Tools Icon Tools Reprints and Permissions Cite Icon Cite Search Site Citation Swati Shilaskar, Abhishek Rajput, Aditya Rasal, Sambhodhi Umare, Varun Shelke, Shripad Bhatlawande; Real-time violence detection using CNN and LSTM. AIP Conf. Proc. 22 December 2023; 2938 (1): 020004. https://doi.org/10.1063/5.0181589 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAIP Publishing PortfolioAIP Conference Proceedings Search Advanced Search |Citation Search
logicDT: a procedure for identifying response-associated interactions between binary predictors

Abstract Interactions between predictors play an important role in many applications. Popular and successful tree-based supervised learning methods such as random forests or logic regression can incorporate interactions associated with the considered outcome without specifying which variables might interact. Nonetheless, these algorithms suffer from certain drawbacks such as limited interpretability of model predictions and difficulties with negligible marginal effects in the case of random forests or not being able to incorporate interactions with continuous variables, being restricted to additive structures between Boolean terms, and not directly considering conjunctions that reveal the interactions in the case of logic regression. We, therefore, propose a novel method called logic decision trees (logicDT) that is specifically tailored to binary input data and helps to overcome the drawbacks of existing methods. The main idea consists of considering sets of Boolean conjunctions, using these terms as input variables for decision trees, and searching for the best performing model. logicDT is also accompanied by a framework for estimating the importance of identified terms, i.e., input variables and interactions between input variables. This new method is compared to other popular statistical learning algorithms in simulations and real data applications. As these evaluations show, logicDT is able to yield high prediction performances while maintaining interpretability.
Human-centered Behavioral and Physiological Security

We propose a paradigm shift in human-centered security research in which users’ objective behavior and physiological states move into focus. This proposal is motivated by the fact that many personal and wearable devices today come with capabilities that allow researchers to assess users’ behavior and physiology in real-time. We expect substantial advances due to the ability to develop more targeted approaches to human-centered security in which solutions are targeted at individuals’ literacy, skills, and context. To this end, the main contribution of this work is a research space: we first provide an overview of common human-centered attacks that could be better understood and addressed through our approach. Based on this overview, we then showcase how specific security habits can benefit from the knowledge of users’ current state. Our work is complemented by a discussion of the implications and research directions enabled through this novel paradigm.
Endoprocess: Programmable and Extensible Subprocess Isolation

Modern applications combine multiple components into single processes, leading to complex tradeoffs between isolation, performance, and programmability. We present the Endoprocess, a unique, microkernel-based approach for protection within process spaces. An endoprocess safely multiplexes process resources by exporting a low-level abstraction, the subprocess, that is transparently overlaid on existing process interfaces (like mmap, mprotect, etc), and provides extensibility and programmability through custom application-layer modules. We report experimental results of an initial prototype and highlight several application domains. Overall, the endoprocess presents a path for protection within processes while remaining compatible with existing OS abstractions and multiplexing them in a secure and extensible way.
The Construction of Personalized English Writing Platform Driven by Deep Learning

The deep learning driver can help to realize a personalized online English writing correction platform, which shows its unique convenience, comprehensiveness and pertinence characteristics. Based on the deep learning algorithm, the personalized English writing correction platform for English is implemented by the GoogLeNet model. The training accuracy of the platform is 96.2% and the test accuracy is 95.2%. The system can effectively stimulate the interest of students in English writing, improve their writing enthusiasm and writing efficiency, and enhance the independent learning ability of students. On the other hand, the system also greatly reduces the burden of teachers correcting compositions, helps teachers free from the tedious correcting task, so as to pay more attention to the feedback, and leave more time and energy to guide and help students.
The impact of augmented reality on learning curves and mental workload: a preliminary experimental study

The learning process has always been fundamental in the industrial environment to correctly learn the right process and to perform it faster, increasing efficiency by also minimizing errors, consequently. Nowadays, new technologies that are emerging in this field are based on augmented reality, and, through a motion capture architecture, it is possible to real-time follow the operators’ activities and guide them in the next ones, improving the learning process. Therefore, this paper presents an architecture setup and the first preliminary tests, realized in the Industrial Plants and Logistics Laboratory of the University of Padua, in order to study the benefits that this type of technology can provide. The main findings are the decrease in the time required to learn the job along with a smaller operator’s cognitive workload during the training.
Scalable variable selection for two-view learning tasks with projection operators

Abstract In this paper we propose a novel variable selection method for two-view settings, or for vector-valued supervised learning problems. Our framework is able to handle extremely large scale selection tasks, where number of data samples could be even millions. In a nutshell, our method performs variable selection by iteratively selecting variables that are highly correlated with the output variables, but which are not correlated with the previously chosen variables. To measure the correlation, our method uses the concept of projection operators and their algebra. With the projection operators the relationship, correlation, between sets of input and output variables can also be expressed by kernel functions, thus nonlinear correlation models can be exploited as well. We experimentally validate our approach, showing on both synthetic and real data its scalability and the relevance of the selected features.