Reviewerly/exHarmony huggingface dataset:
Instance Segmentation of Buildings Using Keypoints

Building segmentation is of great importance in the task of remote sensing imagery interpretation. However, the existing semantic segmentation and instance segmentation methods often lead to segmentation masks with blurred boundaries. In this paper, we propose a novel instance segmentation network for building segmentation in high-resolution remote sensing images. More specifically, we consider segmenting an individual building as detecting several keypoints. The detected keypoints are subsequently reformulated as a closed polygon, which is the semantic boundary of the building. By doing so, the sharp boundary of the building could be preserved. Experiments are conducted on selected Aerial Imagery for Roof Segmentation (AIRS) dataset, and our method achieves better performance in both quantitative and qualitative results with comparison to the state-of-the-art methods. Our network is a bottom-up instance segmentation method that could well preserve geometric details.
Creating an Open-Source Augmented Reality Remote Support Tool for Industry: Challenges and Learnings

Remote support tools offer great savings potential for maintenance work and simplify communication between on-site workers and remote experts in the back-office. Despite the predicted benefits and the existence of an increasing number of commercial augmented reality (AR) remote support tools, the technology still struggles with various challenges, mainly caused by technical limitations, safety concerns or industry-specific constraints. In this paper, we describe the implementation of an open source AR remote support application with a strong industry focus. The software was developed based on the feedback of four industry partners with different needs in the field of maintenance work. Therefore, we set the focus on configurability to provide the right tools for each use case. The developed remote support tool was then compared with traditional support measures in a user study. This paper presents the lessons learned from the expert discussions with our company partners and the results of the user study.
Cost-Aware Prediction of Uncorrected DRAM Errors in the Field

This paper presents and evaluates a method to predict DRAM uncorrected errors, a leading cause of hardware failures in large-scale HPC clusters. The method uses a random forest classifier, which was trained and evaluated using error logs from two years of production of the MareNostrum 3 supercomputer. By enabling the system to take measures to mitigate node failures, our method reduces lost compute time by up to 57%, a net saving of 21,000 node-hours per year. We release all source code as open source. We also discuss and clarify aspects of methodology that are essential for a DRAM prediction method to be useful in practice. We explain why standard evaluation metrics, such as precision and recall, are insufficient, and base the evaluation on a cost-benefit analysis. This methodology can help ensure that any DRAM error predictor is clear from training bias and has a clear cost-benefit calculation.
Learning Relation by Graph Neural Network for SAR Image Few-Shot Learning

Supervised deep learning models usually need large amounts of labeled data due to the data-driven training strategies, and its applicability to the newly emerging categories that lack annotated images is severely limited. In contrast, few-shot learning aims to recognize novel targets from very few labeled examples, so it will be a promising method for synthetic aperture radar (SAR) image interpretation, where numerous labeled data may not exist. In this paper, we introduced a few-shot learning method based on relation network and graph neural network (GNN). Relation network extracts the feature similarity between query samples and support samples through a convolutional neural network, and it has achieved good performance in few-shot learning problems. GNNs have received increasing attention in recent years, and they have shown superior performance in relation extraction. In this work, we replaced the relation module in the relation network with attention GNN, aiming to model the relationship between the samples more effectively and learn a better metric for feature similarity. Experiments on the MSTAR dataset demonstrate that the proposed method can better extract the relationship between query samples and support samples, thereby improving the performance for few-shot image classification tasks.
Extreme Learning Machine-Based Receiver for Multi-User Massive MIMO Systems

An extreme learning machine (ELM)-based receiver for multi-user massive MIMO systems is introduced. The proposed ELM combining method, defined in the complex plane, is designed to directly perform MIMO combining processing to the received uplink signals, based on the adoption of the pilot symbols as training data. Numerical results show that by appropriately setting the number of hidden neurons, the ELM achieves higher spectral efficiency and smaller BER, with fewer floating-point operations than the conventional linear MIMO receivers, namely the minimum mean squared error and maximum ratio receivers.
Efficient integration process of production data into Augmented Reality based maintenance of machine tools

Abstract With the increasing requirements on machine tools such as higher complexity, individuality and digitization, their maintenance becomes also more complex, which increases time consumption and demands special knowledge. The maintenance engineer can be supported in this process using the Augmented Reality (AR) technology, mobile devices and suitable production data. This requires the integration and adaptation of the maintenance documentation as well as further production systems and their data such as the CAD system, product data management (PDM) system, manufacturing execution system (MES) and CNC. For this purpose, a method is developed that allows an efficient integration process of various production data into an AR supported maintenance documentation. This includes concepts for the integration process of different production systems and data into an AR maintenance system as well as the design of the AR maintenance system architecture. Basis is the analysis of the required production and AR systems, data and processes. The aim of the method is to reduce the high expenditure of the overall planning process and the necessary expertise of the maintenance planner of the AR technology. The developed method is verified with different maintenance scenarios, production systems and data as well as machine tools and AR devices.
A Generative Adversarial Networks for Log Anomaly Detection

Detecting anomaly logs is a great significance step for guarding system faults. Due to the uncertainty of abnormal log types, lack of real anomaly logs and accurately labeled log datasets. Existing technologies cannot be enough for detecting complex and various log point anomalies by using human-defined rules. We propose a log anomaly detection method based on Generative Adversarial Networks (GAN). This method uses the Encoder-Decoder framework based on Long Short-Term Memory (LSTM) network as the generator, takes the log keywords as the input of the encoder, and the decoder outputs the generated log template. The discriminator uses the Convolutional Neural Networks (CNN) to identify the difference between the generated log template and the real log template. The model parameters are optimized automatically by iteration. In the stage of anomaly detection, the probability of anomaly is calculated by the Euclidean distance. Experiments on real data show that this method can detect log point anomalies with an average precision of 95%. Besides, it outperforms other existing log-based anomaly detection methods.
Rating prediction based on combination of review mining and user preference analysis

Review websites allow users to share their reviews of products or businesses, give ratings to products or businesses, and interact with other users. Due to the rapid growth of online review data, users face the problem of information overload. To resolve this problem, many researches have proposed various recommendation methods based on the analysis of users’ ratings. Besides user ratings, the review websites contain unstructured textual data and information of different aspects which has different impact and importance to both users and businesses. It may lead to inaccurate rating predictions, as it is difficult to know users’ preferring aspects and their corresponding importance by analyzing users’ rating data. To resolve the above-stated problems, this research proposed aspect-based rating prediction methods, i.e., ARPM and ARPM-Social, which integrate aspect detection and sentiment analysis to generate user preference and business performance, combined with the results of social behavior analysis to predict the ratings of the businesses that users will be interested in the future. Based on the experimental results, the proposed methods perform better than other traditional rating-based prediction methods. They can effectively analyze various aspects and sentiments from the reviews of users and businesses, as well as improve the accuracy of rating predictions.
Robot Calligraphy using Pseudospectral Optimal Control in Conjunction with a Novel Dynamic Brush Model

Chinese calligraphy is a unique art form with great artistic value but difficult to master. In this paper, we formulate the calligraphy writing problem as a trajectory optimization problem, and propose an improved virtual brush model for simulating the real writing process. Our approach is inspired by pseudospectral optimal control in that we parameterize the actuator trajectory for each stroke as a Chebyshev polynomial. The proposed dynamic virtual brush model plays a key role in formulating the objective function to be optimized. Our approach shows excellent performance in drawing aesthetically pleasing characters, and does so much more efficiently than previous work, opening up the possibility to achieve real-time closed-loop control.
λ-NIC: Interactive Serverless Compute on Programmable SmartNICs

There is a growing interest in serverless compute, a cloud computing model that automates infrastructure resource- allocation and management while billing customers only for the resources they use. Workloads like stream processing benefit from high elasticity and fine-grain pricing of these serverless frameworks. However, so far, limited concurrency and high latency of server CPUs prohibit many interactive workloads (e.g., web servers and database clients) from taking advantage of serverless compute to achieve high performance.In this paper, we argue that server CPUs are ill-suited to run serverless workloads (i.e., lambdas) and present λ-NIC, an open- source framework, that runs interactive workloads directly on a SmartNIC; more specifically an ASIC-based NIC that consists of a dense grid of Network Processing Unit (NPU) cores. λ- NIC leverages SmartNIC's proximity to the network and a vast array of NPU cores to simultaneously run thousands of lambdas on a single NIC with strict tail-latency guarantees. To ease the development and deployment of lambdas, λ-NIC exposes an event-based programming abstraction, Match+Lambda, and a machine model that allows developers to compose and execute lambdas on SmartNICs easily. Our evaluation shows that λ- NIC achieves up to 880x and 736x improvements in workloads' response latency and throughput, respectively, while significantly reducing host CPU and memory usage.
Generating Minimum-Snap Quadrotor Trajectories Really Fast

We propose an algorithm for generating minimum-snap trajectories for quadrotors with linear computational complexity with respect to the number of segments in the spline trajectory. Our algorithm is numerically stable for large numbers of segments and is able to generate trajectories of more than 500, 000 segments. The computational speed and numerical stability of our algorithm makes it suitable for real-time generation of very large scale trajectories. We demonstrate the performance of our algorithm and compare it to existing methods, in which it is both faster and able to calculate larger trajectories than state-of-the-art. We also show the feasibility of the trajectories experimentally with a long quadrotor flight.
A Study of C/C++ Code Weaknesses on Stack Overflow

Stack Overflow hosts millions of solutions that aim to solve developers’ programming issues. In this crowdsourced question answering process, Stack Overflow becomes a code hosting website where developers actively share its code. However, code snippets on Stack Overflow may contain security vulnerabilities, and if shared carelessly, such snippets can introduce security problems in software systems. In this paper, we empirically study the prevalence of the <i>Common Weakness Enumeration</i> – CWE, in code snippets of C/C++ related answers. We explore the characteristics of <inline-formula><tex-math notation="LaTeX">$Code_w$</tex-math></inline-formula> , i.e., code snippets that have CWE instances, in terms of the types of weaknesses, the evolution of <inline-formula><tex-math notation="LaTeX">$Code_w$</tex-math></inline-formula> , and who contributed such code snippets. We find that: 1) 36 percent (i.e., 32 out of 89) CWE types are detected in <inline-formula><tex-math notation="LaTeX">$Code_w$</tex-math></inline-formula> on Stack Overflow. Particularly, CWE-119, i.e., <i>improper restriction of operations within the bounds of a memory buffer</i> , is common in both answer code snippets and real-world software systems. Furthermore, the proportion of <inline-formula><tex-math notation="LaTeX">$Code_w$</tex-math></inline-formula> doubled from 2008 to 2018 after normalizing by the total number of C/C++ snippets in each year. 2) In general, code revisions are associated with a reduction in the number of code weaknesses. However, the majority of <inline-formula><tex-math notation="LaTeX">$Code_w$</tex-math></inline-formula> had weaknesses introduced in the first version of the code, and these <inline-formula><tex-math notation="LaTeX">$Code_w$</tex-math></inline-formula> were never revised since then. Only 7.5 percent of users who contributed C/C++ code snippets posted or edited code with weaknesses. Users contributed less code with CWE weakness when they were more active (i.e., they either revised more code snippets or had a higher reputation). We also find that some users tended to have the same CWE type repeatedly in their various code snippets. Our empirical study provides insights to users who share code snippets on Stack Overflow so that they are aware of the potential security issues. To understand the community feedback about improving code weaknesses by answer revisions, we also conduct a qualitative study and find that 62.5 percent of our suggested revisions are adopted by the community. Stack Overflow can perform CWE scanning for all the code that is hosted on its platform. Further research is needed to improve the quality of the crowdsourced knowledge on Stack Overflow.
Modelling Attacks in Blockchain Systems using Petri Nets

Blockchain technology has evolved through many changes and modifications, such as smart-contracts since its inception in 2008. The popularity of a blockchain system is due to the fact that it offers a significant security advantage over other traditional systems. However, there have been many attacks in various blockchain systems, exploiting different vulnerabilities and bugs, which caused a significant financial loss. Therefore, it is essential to understand how these attacks in blockchain occur, which vulnerabilities they exploit, and what threats they expose. Another concerning issue in this domain is the recent advancement in the quantum computing field, which imposes a significant threat to the security aspects of many existing secure systems, including blockchain, as they would invalidate many widely-used cryptographic algorithms. Thus, it is important to examine how quantum computing will affect these or other new attacks in the future. In this paper, we explore different vulnerabilities in current blockchain systems and analyse the threats that various theoretical and practical attacks in the blockchain expose. We then model those attacks using Petri nets concerning current systems and future quantum computers.
Residual attention graph convolutional network for web services classification

More and more attention has been paid to web service classification as it can improve the quality of service discovery and management in the service repository, and can be widely used to locate developers' desired services. Although traditional classification method based on supervised learning model to this task shows promising results, it still suffered from the following shortcomings: (i) the performance of conventional machine learning methods highly depends on the quality of manual feature engineering; (ii) some classification methods (such as CNN, RNN, etc.) are usually limited to very shallow models due to the vanishing gradient problem and cannot extract more features, which have great impact on the accuracy of web service classification. To overcome these challenges, a novel web service classification model named Residual Attention Graph Convolutional Network (RAGCN) is proposed. Firstly, adding an attention mechanism to the graph convolutional network can assign different weights to the neighborhood nodes without complicated matrix operations or relying on understanding the entire graph structure. Secondly, using residual learning to deepen the depth of the model can extract more features. The comprehensive experimental results on real dataset show that the proposed model outperforms the state-of-the-art approaches and proves its potentially good interpretability for graphical analysis.
Victoria Theatre virtual reality: A digital heritage case study and user experience design

This research study centres on a digital reconstruction of the heritage building, Victoria Theatre in Newcastle, Australia. The Virtual Reality project is called ‘Visualising the Victoria.’ The research focuses specifically on the on-going development and user experience aspects of using a transmedia virtual reality approach to preserving the heritage of this site. Traditional room-scale VR locomotion with 6 degrees of freedom relies heavily on ‘teleport-style’ method for users moving around the virtual space. However, this can be physically and mentally challenging for some new VR users and users with limited mobility. To explore different options, we seek for various interaction design alternatives and customisation that might work alongside the teleport-style method for improved perceived ease-of-use and usefulness of the VR system. A user experience study is included in this article. The focus of this usability pilot study is to provide an on-going understanding of feasible interaction design approaches to be included in digital heritage VR projects. In this paper we describe the case study, prototype, a user evaluation study and directions for future work.
Self-Supervised Online Reward Shaping in Sparse-Reward Environments

We introduce Self-supervised Online Reward Shaping (SORS), which aims to improve the sample efficiency of any RL algorithm in sparse-reward environments by automatically densifying rewards. The proposed framework alternates between classification-based reward inference and policy update steps—the original sparse reward provides a self-supervisory signal for reward inference by ranking trajectories that the agent observes, while the policy update is performed with the newly inferred, typically dense reward function. We introduce theory that shows that, under certain conditions, this alteration of the reward function will not change the optimal policy of the original MDP, while potentially increasing learning speed significantly. Experimental results on several sparse-reward environments demonstrate that, across multiple domains, the proposed algorithm is not only significantly more sample efficient than a standard RL baseline using sparse rewards, but, at times, also achieves similar sample efficiency compared to when hand-designed dense reward functions are used.
Query Suggestions as Summarization in Exploratory Search

Query suggestions have been shown to benefit users performing information retrieval tasks. In exploratory search, however, users may lack the necessary domain knowledge to assess the relevance of query suggestions with respect to their information needs. In this article, we investigate the use of alternative queries in exploratory search. Alternative queries are queries that would retrieve similar search results to those currently visible on-screen. They are independent of the original search query and can, therefore, be updated dynamically as users scroll through search results. In addition to being follow-on queries, alternative queries serve as keyword summaries of the current search results page to help users assess whether results are inline with their search intents. We investigated the use of alternative queries in scientific literature search and their impact on user behavior and perception. In a user study, participants inspected half as many documents per query when alternative queries were present, but were exposed to over 40% more search results overall. Despite using them extensively as follow-on queries, user feedback focused on the summarization properties offered by alternative queries; finding it reassuring that documents were relevant to their search goals.
High-Speed Modular Multiplier for Lattice-Based Cryptosystems

Thanks to the inherent post-quantum resistant properties, lattice-based cryptography has gained increasing attention in various cryptographic applications recently. To facilitate the practical deployment, efficient hardware architectures are demanded to accelerate the operations and reduce the computational resources, especially for the polynomial multiplication, which is the bottleneck of lattice-based cryptosystems. In this brief, we present a novel high-speed modular multiplier architecture for polynomial multiplication. The proposed architecture employs a divide and conquer strategy and exploits a special modulus to increase the parallelism and speed up the calculation, while enabling wider applications across various cryptosystems. The experimental results show that our design achieves around 27% and 39% reduction on the area consumption and delay, respectively, compared to prior works.