Reviewerly/exHarmony huggingface dataset:
Path Planning for Searching Submarine With Cooperative Coverage of Fixed-Wing UAVs Cluster in Complex Boundary Sea Area

Aiming at the problem of using fixed-wing UAVs (clusters) for rapid coverage search of submarines in large-scale sea areas, the Strategy of using a single UAV for Coverage Searching in form of Dubins Curve(SCSDC) and the Strategy of using fixed-wing UAVs cluster for cooperative Coverage Searching in form of Parallel Intervals(SCSPI) were proposed after selective removal of concave points in complex irregular concave polygonal areas. Firstly, the cost mathematical model of cooperative search for multiple UAVs was established. At the same time, considering the limitation of turning radius of UAVs, a Dubins curve coverage search strategy was proposed to avoid search blind areas. Secondly, the judgment conditions of “edge length threshold” and “angle threshold” were introduced, and the method of selective removal of concave points was proposed to transform the irregular concave polygon area into a regular convex polygonal area. Then, in order to reduce the searching cost of UAV’s “Ω” turning type and improve the timeliness of its submarine search, a strategy of UAV cluster cooperative coverage search in the form of parallel interval was proposed. Finally, a comparative verification experiment was designed for the strategy of SCSDC and SCSPI in a complex irregular polygon search area. The simulation results show that selection of the two different area coverage search strategies needs to be determined according to actual combat requirements. The experimental results well verify the effectiveness and application scope of the two area coverage search strategies, and provide information support for subsequent anti-submarine action.
Remote Sensing Image Super-Resolution Adversarial Network Based on Reverse Feature Fusion and Residual Feature Dilation

Enhancing the resolution of images by super-resolution reconstruction algorithm is less costly compared with the way of upgrading hardware devices.At present, the image super-resolution algorithm can recover the texture details of the image well, but the performance is relatively general on the remote sensing images with lower contrast and more complex texture, and the reconstructed images are prone to noise, missing texture and checkerboard effect.This paper proposes a super-resolution adversarial network based on inverse feature fusion for the characteristics of remote sensing images, which combines the high-level semantics of the image with the low-level semantics to make the reconstructed images perform better in overall better performance, and enhance the local details of the reconstructed image by feature expansion module.Experiments are carried out on RSSCN7 Dataset and representative algorithms are used for comparison.Experimental results show that RFSRNet and RFSRGAN offer better results than other methods, and ablation study show proposed modules are effective in enhancing the perceived similarity.
Dynamic Cascade Query Selection for Oriented Object Detection

Most of the existing object detection methods have complicated hand-designed components, such as non-maximum suppression procedures and manual resizing of anchor boxes. Based on DETR, this paper not only eliminates the need for manual component adjustment, but also solves three problems of poor remote sensing image for directional object capture, slow DETR convergence, and the same attention allocated by different layers of Decoder. First, the D-Angle module is used to align the rotating object region while accelerating the convergence using the a priori angle. Then the overall computation of the model is reduced by using Adaptive Proposal Selection(APS) in the cascade structure. Finally, the Adaptive Query Selection(AQS) module is applied so that Decoder in different layers get different attention weights to optimize the layer-by-layer fine-tuning process. In this paper, the effectiveness of the proposed method is verified using two public datasets, DOTA and HRSC2016.
Stochastic Event-Triggered Sequential Fusion Filtering for USV Cooperative Localization

This paper deals with the cooperative localization of maneuvering unmanned surface vessel (USV) based on multi-sensor fusion estimation, in which a sequential fusion filter is designed to estimate the real-time position of the USV. To avoid excessive communication consumption between sensors and the fusion filter, a stochastic event-triggered communication mechanism is adopted to ensure necessary measurements transmission. With the aid of the classical framework of sequential Bayesian filtering, an event-triggered sequential fusion filter is constructed by co-designing the stochastic event-triggered communication mechanism and the sequential filter, where a technique of unscented transformation with the sequential idea is used to resolve the intractable problem caused by nonlinear measurement models. Furthermore, a sufficient condition is established to ensure the boundedness of the fusion covariance. Finally, the effectiveness and superiority of the designed fusion filter is verified both by numerical simulation and practical experiment of a real USV tracking system.
The Multi-Agent Programming Contest 2022

We present the 16th edition of the Multi-Agent Programming Contest, an annual competition designed to increase interest and further research in the area of Multi-Agent Systems development. This is the third version of the Agents Assemble scenario in which the agents inhabit a grid and must build specific shapes out of blocks scattered in the environment to win a match. Six teams from five different countries were involved in the contest. Each team has shown a solid performance. Given the current state of the Multi-Agent Programming Contest, we envision the matches in the next edition to be run in a more automated fashion. This way, decentralized agents can be enforced, and network latency avoided, while simulations can run for a longer time and with various different parameters.
The 16th Edition of the Multi-Agent Programming Contest - The GOAL-DTU Team

We provide an overview of the GOAL-DTU system for the Multi-Agent Programming Contest, including the overall strategy and how the system is designed to apply this strategy. Our agents are implemented using the GOAL programming language. We evaluate the performance of our agents in the contest and, finally, we discuss how to improve the system based on an analysis of its strengths and weaknesses.
XMQAs: Constructing Complex-Modified Question-Answering Dataset for Robust Question Understanding

Question understanding is an important issue to the success of a Knowledge-based Question Answering (KBQA) system.However, the existing study does not pay enough attention to this issue given that the questions in the existing KBQA datasets are usually expressed in simple and straightforward way. This is not in line with the actual linguistic conventions, which often use a lot of modifiers. To facilitate the study on evaluating and enhancing the question understanding ability of the KBQA systems, this paper proposes to construct a complex-modified question-answering (XMQAs) dataset based on existing KBQA datasets. With the help of knowledge bases and dictionaries, three kinds of modifiers are defined and applied to original simple-expressed questions. These modifiers could make the expression of these questions complex without changing their semantics. Based on XMQAs, we then propose a novel question understanding algorithm upon existing KBQA models, which greatly improves the robustness of their question understanding abilities. We conduct extensive experiments on XMQAs and two widely acknowledged KBQA datasets. The empirical results demonstrate that our proposed algorithm can improve the performance of KBQA models on not only the complex-modified questions, but also simple-expressed questions.
NL3DLogTNN: An Effective Hyperspectral Image Denoising Method Combined Non-Local Self-Similarity and Low-Fibered- Rank Regularization

Hyperspectral image denoising is an important research topic in the field of remote sensing image processing. Recently, methods based on non-local low-rank tensor approximation have gained widespread attention towing to their ability to fully exploit non-local self-similarity. However, existing non-local low-rank tensor approximation methods fall short in capturing the correlations between various modes in hyperspectral images, thus failing to achieve the optimal approximation. To solve this issue, a novel three-directional log-based tensor nuclear norm (3DLogTNN)-based non-local hyperspectral image denoising model NL3DLogTNN is proposed. The correlation between the various modes of the model was obtained by performing TNN decomposition in three directions on the extracted non-local comparable blocks, better capturing the global low-rank property of the image. To effectively solve the proposed NL3DLogTNN model, we developed an approximate alternating direction method of multipliers (ADMM)-based methodology and offered a thorough numerical convergence proof. Extensive experiments are conducted on hyperspectral image datasets with simulated noise and real-world noise, which demonstrated that the proposed NL3DLogTNN model outperforms state-of-the-art methods in terms of quantitative and visual performance evaluation.
LI(A)RA Team - A Declarative and Distributed Implementation for the MAPC 2022

With the increasing computational power and the evolution of the distributed computing paradigm and artificial intelligence techniques and methodologies, computing is becoming increasingly distributed and intelligent. The multi-agent paradigm is one of the most powerful paradigms for implementing distributed artificial intelligence. There are multiple (multi-)agent-oriented programming languages, platforms and methodologies to implement these systems, and the Multi-Agent Programming Contest aims to provide challenging scenarios in which researchers can explore their preferred programming languages, platforms and methodologies to implement multi-agent systems, collecting benchmarks, etc. In this paper, we describe the multi-agent system implemented by the LI(A)RA team for the Multi-Agent Programming Contest 2022, including details about the system’s implementation, the technologies and methodologies used, and also discuss the team’s results.
An LLVM-Inspired Framework for Unified Processing of Multimodal Time-Series Data

Groups of sensors collecting time-series data in a variety of modalities are widely used for monitoring humans, environments, and equipment. Datasets with multimodal sensor data pose several challenges not present in many image or language datasets; most notably, there are few de-facto standards on how the data should be organized and packaged. In this work, we present a framework inspired by the LLVM Compiler architecture that streamlines sensor data processing for machine learning applications. Specifically, we define standardized, intermediate representations that can be easily transformed for input to data preprocessing and model training steps. By standardizing and preserving time and subject information, our method supports robust label verification and multiple means of subject-independent cross-validation. We demonstrate the validity of our framework using seven different datasets, all containing time-series data and representing a variety of sensors, modalities, domains, and collection environments.
Secret Image Sharing Schemes: A Comprehensive Survey

The safeguarding of digitized data against unwanted access and modification has become an issue of utmost importance as a direct result of the rapid development of network technology and internet applications.In response to this challenge, numerous secret image sharing (SIS) schemes have been developed.SIS is a method for protecting sensitive digital images from unauthorized access and alteration.The secret image is fragmented into a large number of arbitrary shares, each of which is designed to prevent the disclosure of any information to the trespassers.In this paper, we present a comprehensive survey on SIS schemes along with their pros and cons.We review various existing verifiable secret image sharing (VSIS) schemes that are immune to different types of cheating.We have identified various aspects of developing secure and efficient SIS schemes.In addition to that, a comparison and contrast of several SIS methodologies based on various properties is included in this survey work.We also highlight some of the applications based on SIS.Finally, we present open challenges and future directions in the field of SIS.
A Visualization Bookmarklet for Assessing the Readability of Texts on the Web

Readability is an important goal of texts on the web. Manual inspection of readability can be time consuming. Several readability indices have been proposed but their calculations often require text to be copied into specific tools. We therefore developed a bookmarklet which allows the readability of websites to be visualized directly in the browser. The background of different passages of text is marked according to their readability with heat-map colors, using established readability indices. Frequent words and difficult words are marked with colors.
Critical Risks Associated with Blockchain Adoption in China’s Construction Supply Chain

With the successful landing of blockchain in more and more industries, the construction industry is on a path to finding possible application scenarios of blockchain. Blockchain, described as “open and transparent”, “safe and trustworthy”, and “immutable and traceable”, is expected to break the barriers to trust in the construction supply chain (CSC). However, the application of any new technology will generate more unknown risks due to the increase of uncertainties. As a result, the objective of this paper is to find the critical risks associated with blockchain adoption in China’s CSC. Through a literature review, this research identified 26 risks of blockchain application in the CSC, including seven types of risks: technical risk, industry risk, social risk, cost risk, management risk, legal risk, and force majeure risk. Then, a risk network was set up to analyze the importance of risk nodes by considering risk associations. Results showed that “Technical suitability”, “Public attitude”, and “Information asymmetry” were perceived to be the critical risks. This research had great reference significance to the future development of blockchain applications in China’s CSC.
Encoding Semantic Attributes - Towards Explainable AI in Industry

The transformation of industrial environments is progressing at a fast pace as more and more autonomous systems are installed and operated. Save and explainable AI algorithms are thus essential, especially for collaborative interactive systems that operate in human spaces. We propose the “Semantic Encoder”, a 2D-vision based CNN model trained on a purely synthetic dataset, to address the explainability aspect by extracting semantic descriptions of real objects based on their visual appearances. We can use the extracted semantic information to simply describe depicted samples or to differentiate between normal and anomalous samples, with the possibility to explain what caused the anomaly detection. The semantic description can be further used to sort samples by classifying them or to find a sample with specific semantic properties. We evaluate the Semantic Encoder with respect to its informative power by comparing the computed semantic features with features extracted by a VGG-16 model and classical image processing methods. The results are quantified based on the Generalized Discriminative Value (GDV). We also investigate how accurately anomalous samples are detected by computing ROC and PR curves. We use the semantic parameters to understand what causes good and inaccurate anomaly detection decisions. In addition, we evaluate the quality of the classification based sorting by examining confusion matrices and classification accuracy.
Face expression image detection and recognition based on big data technology

This research addresses the deficiencies in current dynamic sequence facial expression recognition methods, which suffer from limited accuracy and effectiveness. The primary objective is to introduce an innovative approach that leverages big data technology for improved facial expression detection and recognition. The methodology encompasses several vital steps. The integral graph method is employed to capture dynamic sequences of facial expressions, and a weak facial feature classifier is utilized for image preprocessing. To enhance accuracy, a dynamic sequence model is devised for feature extraction. The study combines the personalized learning algorithm with the optical flow technique to pinpoint critical facial expression junctures and facilitate dynamic sequence recognition. The investigation reveals the inadequacy of current dynamic sequence facial expression recognition methods in accurately categorizing expressions. The proposed approach yields promising results, achieving a peak expression division accuracy of 91.78% in simulations. Notably, the personalized learning recognition method demonstrates enhanced robustness in categorizing expressions, effectively capturing intricate facial details and augmenting overall recognition efficacy. This research thus contributes to advancing facial expression recognition technology, addressing critical shortcomings in current methods.
AUC-Oriented Domain Adaptation: From Theory to Algorithm

The Area Under the ROC curve (AUC) is a crucial metric for machine learning, which is often a reasonable choice for applications like disease prediction and fraud detection where the datasets often exhibit a long-tail nature. However, most of the existing AUC-oriented learning methods assume that the training data and test data are drawn from the same distribution. How to deal with domain shift remains widely open. This paper presents an early trial to attack AUC-oriented Unsupervised Domain Adaptation (UDA) (denoted as AUCUDA hence after). Specifically, we first construct a generalization bound that exploits a new distributional discrepancy for AUC. The critical challenge is that the AUC risk could not be expressed as a sum of independent loss terms, making the standard theoretical technique unavailable. We propose a new result that not only addresses the interdependency issue but also brings a much sharper bound with weaker assumptions about the loss function. Turning theory into practice, the original discrepancy requires complete annotations on the target domain, which is incompatible with UDA. To fix this issue, we propose a pseudo-labeling strategy and present an end-to-end training framework. Finally, empirical studies over five real-world datasets speak to the efficacy of our framework.
Converting Excel date serial numbers to Python datetime format

Excel stores dates in a very odd way: a serial number of days since 1900.
Softcomputing Approach to Music Generation

The goal of the paper is to create a system for music generation based on data representing sounds in a selected style. Data representing music is understood as sequences of notes. Information such as pitch, duration, or point in time is processed. The style means a set of features to create the impression of similarity both in standard music notation and in the perception of the sound material by the listener. Analyzed issues include features and standards of notation of musical compositions, ways of representing music by a computer, methods of music data processing, and principles and methods of creating intelligent systems. This approach aims to combine two separate fields. The effect of such a procedure is to make another step on the way to combine the knowledge of artists with the knowledge of researchers.
Identification of the Language Using Statistical and Neural Approaches

Language classification is one of the tasks in Natural Language Processing domain that has been analyzed over the years using various approaches. The main goal of this task is to for selected two languages define their similarity. In our research, we compare two approaches: the statistical and neural methods. For this purpose, varied randomly selected Wikipedia articles in 24 European languages were used as a data set. Firstly, the frequency of unigrams, bigrams, as well as trigrams, have been created, next, using Kullback-Leibler divergence, distributions were compared and presented on the similarity graph. Additionally, the assignment to language family groups has been presented in the form of a graph and evaluated. We compare this approach with the application of neural network architecture. We evaluated the quality of the classification for narrowing test datasets. The contribution of this research is a comparison of the statistical and neural approach for the strict domain of the Latin alphabet, as well as the identification of the language family groups based on classification models. The results of the experiments show that while the text is reduced to a length of few words, the method based on n-grams distributions outperforms recurrent neural network.
UVAM: The Unified Visual Analytics Model. The Unified Visualization Model Revisited

Nowadays, the explosive growth of data generated from numerous sources results in ever-increasing volumes of data that are, therefore, difficult to understand, explore and analyze in order to extract information from them. The contribution of visualization to the exploration and understanding of these large datasets is very significant. Various application domains often require different visual representations, although several share the same intermediate steps, transformations, and/or manipulations of such data. These shared aspects lead to the requirement for a consistent and extensible visual analytics model across all application domains. In this context, we introduce the Unified Visual Analytics Model (UVAM), which combines the Unified Visualization Model (UVM) with the specific features of visual analytics. The UVAM is a state model represented as a flow between the many states that the data passes through throughout the process. It includes the user interactions with the data and its intermediate representations and how the user controls the transformations and, subsequently, modifications of the visualizations. This paper illustrates how the model includes various visual analytics processes and describes in detail a UVAM case study.
An Analysis of Satellite-Based Machine Learning Models to Estimate Global Solar Irradiance at a Horizontal Plane

Accurate solar resource information is a fundamental requirement for solar energy ventures. The lack of precision in solar radiation data can significantly affect the success of the projects. Argentina has solar radiation ground measurement networks. The information obtained through this method is limited due to its spatial sparsity, since it is only possible to measure with appropriate quality in some sites across the territory. To overcome this limitation, it is common to generate models capable of estimating solar radiation through satellite images, which provide spatial resolution. This work develops and validates an empirical model for this purpose based on Machine Learning (ML), demonstrating that it is a useful and accurate tool to be considered. This allows ventures that make use of this type of energy to have greater certainty in the availability of the resource, and therefore in the decision-making process. Variables obtained from images of the geostationary meteorological satellite GOES-16, McClear clear-sky model estimates, and geometrically calculated information are used as input to the algorithms. The results of the ML models are compared with estimates from pre-existing models for the region that incorporate physical modelings, such as Heliosat-4 and CIM-ESRA. The evaluation shows a higher performance of the ML methods when multi-scale satellite information is used as input. The incorporation of multi-scale satellite data is not yet implemented in solar radiation physical modeling, which is an advantage of ML modeling.