Reviewerly/exHarmony huggingface dataset:
Mapping of Fire Detection Using Visible Infrared Imaging Radiometer Suite Satellite Imagery to Reduce the Risk of Environmental Damage

The frequency of fires increases and triggers environmental damage, so it needs detection to minimize negative impacts. Fire detection using S-NPP VIIRS satellite imagery. Based on the background, research was conducted with the title "Mapping of Fire Detection Using Visible Infrared Imaging Radiometer Suite Satellite Imagery to Reduce the Risk of Environmental Damage".&#x0D; Research in Banjarbaru City, South Kalimantan Province. Hotspot data from S-NPP VIIRS satellite image recording in 2012-2021. Data analysis is descriptive qualitative (describes the distribution of hotspots) and quantitatively (maps the frequency and distribution of hotspots) as the first step in detecting fires to reduce the risk of environmental damage. The risk of environmental damage is known using a questionnaire.&#x0D; The results of the study revealed that the highest number of hotspots and nominal and high confidence levels were in September (1370) and October (1050). The greater the number of hotspots with a nominal confidence level and high the potential for fires and environmental damage the higher. Environmental damage in the study area is quite high (&gt;75%). Research findings show that fire detection results can be used as a strategy to reduce the risk of environmental damage and minimize negative impacts.&#x0D;
PEMANFAATAN NFT SEBAGAI PELUANG BISNIS PADA ERA METAVERSE

In the metaverse era, there are several business opportunities that can be obtained by the people, one of which is NFT. NFT is an NFT or Non-Fungible Token is a token (based on program code on the blockchain) that represents digital files, including photos, images, songs, sound recordings, videos, and games. Thus, NFT is a digital-based asset so it is relatively easy to trade because it does not require real space which is more expensive. Meanwhile, the research method carried out is descriptive qualitative using secondary data based on phenomena by using analysis from relevant references. The result of his research is that NFT is one of the easiest business opportunities to do in the metaverse era.
Towards More Efficient Data Valuation in Healthcare Federated Learning Using Ensembling

Federated Learning (FL) wherein multiple institutions collaboratively train a machine learning model without sharing data is becoming popular. Participating institutions might not contribute equally - some contribute more data, some better quality data or some more diverse data. To fairly rank the contribution of different institutions, Shapley value (SV) has emerged as the method of choice. Exact SV computation is impossibly expensive, especially when there are hundreds of contributors. Existing SV computation techniques use approximations. However, in healthcare where the number of contributing institutions are likely not of a colossal scale, computing exact SVs is still exorbitantly expensive, but not impossible. For such settings, we propose an efficient SV computation technique called SaFE (Shapley Value for Federated Learning using Ensembling). We empirically show that SaFE computes values that are close to exact SVs, and that it performs better than current SV approximations. This is particularly relevant in medical imaging setting where widespread heterogeneity across institutions is rampant and fast accurate data valuation is required to determine the contribution of each participant in multi-institutional collaborative learning.
A comprehensive study of watermarking schemes for 3D polygon mesh objects

Three-dimensional (3D) objects have been used in machine design, architecture design, entertainment, cultural heritage, medical field, etc. during the last two decades. Increasing trends of 3D objects attract the researcher, academician, and industry persons for various processing operations on 3D objects. Extensive growth in specific areas requires intellectual property rights protection and authentication problems. The proposed survey paper provides a comprehensive study of various watermarking solution for 3D polygon mesh objects (3D PMO). This comprehensive survey includes an introduction, strength, and limitations to the relevant state of the art. The proposed study writes down various problems encountered, and their respective solution suggested in various state-of-art. The robustness of various algorithms is also studied against various attacks applied to watermarked models. Lastly, future scope and new directions for the robustness and optimisation of watermarks on various parameters are suggested.
Map of Research Infrastructure as a tool of the Information System for Research, Development, and Innovation

Research infrastructures play a key role in technological development supporting science, technology and innovation. For the scientific community, but also for the creation of state science policy, it is necessary to have an overview of the research infrastructures that are available. Information is required on what infrastructures are available, who manages them and what services they provide. The evidence of research infrastructures is an integral part of the R&D information system. Its development at national level follows from EU activities in this area. The software module for research infrastructure should meet the requirements of CRIS system. It should contain well-defined workflow and user interface. The second requirement for created data model has been to achieve the maximal level of CERIF compatibility, including linking with main CRIS entities. The article presents brief situation overview in Slovakia and the experience with the implementation of the Map of research infrastructure, and with its integration to the information system SK CRIS.
CM-UNet: ConvMixer UNet for Segmentation of Unknown Objects in Cluttered Scenes

Object segmentation in cluttered environments is a fundamental pre-processing step for many perception-related tasks such as vision-based robotic grasping. Most of the existing object segmentation methods are incapable of precisely segmenting unknown objects, particularly in scenarios exhibiting significant occlusion. In this paper, we propose a novel approach for refining the segmentation of unknown objects in cluttered scenes. More specifically, a ConvMixer-based UNet model is designed to enhance the segmentation mask and boundary of unknown objects appearing in cluttered scenes. In our model, we leverage the object’s semantic and localization information, which are essential for successful segmentation, using a ConvMixer-based Cross Fusion (CMCF) module. Furthermore, we propose to use patch embedding as a pre-processing step, where input data is rearranged to expedite processing and improve the efficiency of the system. CM-UNet was trained and extensively tested on various challenging publicly available datasets, including unknown objects in un-structured scenes. Thorough evaluations, in terms of segmentation accuracy and processing efficiency, were conducted against state-of-the-art solutions, where the superiority of our model was proven. CM-UNet has shown its ability to efficiently improve the segmentation accuracy of unknown objects in cluttered scenes, even in presence of occlusion.
Few-Shot Incremental Learning for Label-to-Image Translation

Label-to-image translation models generate images from semantic label maps. Existing models depend on large volumes of pixel-level annotated samples. When given new training samples annotated with novel semantic classes, the models should be trained from scratch with both learned and new classes. This hinders their practical applications and motivates us to introduce an incremental learning strategy to the label-to-image translation scenario. In this paper, we introduce a few-shot incremental learning method for label-to-image translation. It learns new classes one by one from a few samples of each class. We propose to adopt semantically-adaptive convolution filters and normalization. When incrementally trained on a novel semantic class, the model only learns a few extra parameters of class-specific modulation. Such design avoids catastrophic forgetting of already-learned semantic classes and enables label-to-image translation of scenes with increasingly rich content. Furthermore, to facilitate few-shot learning, we propose a modulation transfer strategy for better initialization. Extensive experiments show that our method outperforms existing related methods in most cases and achieves zero forgetting.
The Architecture Decentralised Autonomous Organisation - A stigmergic exploration in architectural collaboration

We present ÂArchiDAOÂ, a decentralised Autonomous Organisation, i.e an architecture studio run on via smart contracts on the Ethereum blockchain. The objective of the paper is to offer a concise framework for the transformation of the way architectural work is conducted, via stigmergic principles, realised on smart contracts. The paper follows a systematic review of the Viable System Model, collective authorship in architectural design and previous DAO software stacks to extract a set of stigmergic principles. We conclude the paper with a discussion and a proposal for a basic set of smart contracts that can regulate the ArchiDAO operations.
Joint Learning of Discriminative Metric Space From Multi-Context Visual Scene for Unsupervised Salient Object Detection

Mimicking the biological visual attention mechanism to discriminate visually appealing regions in natural scenes has been a hot research topic in recent years.However, designing computational models with self-driven capability for open world scenarios remains a challenging task which deserves to be further studied.In this paper, we propose an unsupervised learning approach to detect salient objects from images by fully exploiting the multi-context semantic information of the scenes.Specifically, a selfdriven model combing the idea of discriminative metric learning and structured sparse constraint is designed to find an optimal semantic mapping space for robust scene specific saliency prediction from complex environments.Meanwhile, a heuristic alternating optimization algorithm is developed to remove the ambiguity in the coarse geometric prior to generate a fine-grained discriminative model for saliency.On the basis of this, multi-context visual scenes are jointly modeled and fused to capture the image hierarchical structures for high-quality saliency map generation.Finally, we conduct experiments to verify the effectiveness of the proposed approach on four saliency benchmark datasets and compare it with 18 stateof-the-art saliency detection methods.Both qualitative saliency map and quantitative numerical index results indicate that our method has superior detection performance than the other counterparts under diversified scenes.Also, the proposed approach is applied to model wide synthetic aperture radar images for rapid target detection and promising results are obtained.
Self-calibrated dilated convolutional neural networks for SAR image despeckling

Synthetic aperture radar (SAR) imaging process is essentially disturbed by speckle noise due to its unique mechanism. Speckle noise causes severe degradation of SAR image quality, which significantly limits its practical application. Recently, convolutional neural networks (CNNs) have indicated good potential for various image processing tasks. In this article, we propose a self-calibrated dilated convolutional neural network for SAR image despeckling called SAR-SCDCNN. The main body of SAR-SCDCNN is formed by seven self-calibrated blocks (SeCaBlock). Firstly, in each SeCaBlock, the input features are split into two branches: one represents the contextual features in the original space, and another represents those in the long-range space. Then, the down-up sampling operation and the convolutions with hybrid dilated rates are employed to enlarge the receptive field. The weights for calculating features themselves are adaptively extracted in the second branch. And then, two branch features are concatenated and fused through a convolution. Finally, a skip connection is used between the input and output of each SeCaBlock to give full play to the expressive power of the deep neural network and enhance training stability. Experiments on synthetic speckled and real SAR images are conducted to perform objective quantitative and subjective visual evaluations of image quality. Results show that our proposed method can effectively suppress speckle noise and adequately preserve detailed features.
Phase-Programmable Gaussian Boson Sampling Using Stimulated Squeezed Light

We report Gaussian boson sampling experiments which produce up to 113 photon detection events out of a 144-mode photonic circuit. We reveal genuine high-order correlation our samples, which is evidence of robustness against classical simulation.
Optimizations of Modified Machine Learning Algorithms Using K-Fold Cross Validations for Wheat Productivity: A Hyper Parametric Approach

D ata science has been emerged as a well-defined multi-disciplinary field that comprises of statistics, computer science and machine learning methodologies enabled to learn optimized information can be inferred inside from the data (Hui et al., 2019;Igual and Segui, 2017).Data science deals with the massive amount of data by adding the methods from computer science to the repertoire of statistics (Cielen
Robotic Interestingness via Human-Informed Few-Shot Object Detection

Interestingness recognition is crucial for decision making in autonomous exploration for mobile robots. Previous methods proposed an unsupervised online learning approach that can adapt to environments and detect interesting scenes quickly, but lack the ability to adapt to human-informed interesting objects. To solve this problem, we introduce a human-interactive framework, AirInteraction, that can detect human-informed objects via few-shot online learning. To reduce the communication bandwidth, we first apply an online unsupervised learning algorithm on the unmanned vehicle for interestingness recognition and then only send the potential interesting scenes to a base-station for human inspection. The human operator is able to draw and provide bounding box annotations for particular interesting objects, which are sent back to the robot to detect similar objects via few-shot learning. Only using few human-labeled examples, the robot can learn novel interesting object categories during the mission and detect interesting scenes that contain the objects. We evaluate our method on various interesting scene recognition datasets. To the best of our knowledge, it is the first human-informed few-shot object detection framework for autonomous exploration.
The Problem of Detecting Boxers in the Boxing Ring

Modern technology is strongly associated with sports. A perfect example of machine learning in sports is a support of detection of specific events or situations. Such a problem is present in boxing, where boxers’ moves need to be precisely detected. However video analysis is labor intensive but may provide valuable information. The paper presents the problem of processing recordings of boxing boxers, in which the dynamics is at an extremely high level and some events last for fractions of seconds. Additionally, the competition is often watched by spectators blocking the view. The goal of this paper is to present accurate, precise and quick method of detecting the presence of pugilists in the ring. This will allow to evaluate and score the boxing fight later. To validate the experiment, relevant material had to be collected – the authors recorded real boxing bouts and prepared the complete training set. The proposed solution will be used to automatically filter-out uninteresting parts of footage, where boxers are not engaged in close-combats situation.
Selection criteria for cloud-based learning technologies for the development of professional competencies in statistics bachelors

This article presents the findings of an expert evaluation of current cloud-based learning technologies according to defined criteria and scientifically supports the selection criteria for cloud-based learning technologies for the development of professional competencies of bachelor's degree statistics majors. Information-didactic, functional, and technological criteria were established for the selection of cloud-based learning technologies for the development of professional competences of bachelor's degree statistics majors. The method of expert evaluation was used to implement the choice of cloud-based learning technologies for the formation of professional competences of bachelor's degree statistics majors, and effective use in the process of formation of relevant competencies. The expert evaluation was conducted in two stages: the first stage chose the cloud-based learning technologies that the author deemed to be the most appropriate, and the second stage identified those cloud-based learning technologies that should be used in the educational process as a way to develop professional skills for Bachelor of Statistics graduates. According to the study, CoCalc and Wolfram|Alpha are the cloud-based learning tools that are most suitable, practical, and efficient for the development of professional capabilities of upcoming bachelor's degree recipients in statistics.
Portability and Explainability of Synthesized Formula-based Heuristics

Heuristic search is a key component of automated planning and pathfinding. It is guided by a heuristic function which estimates remaining solution cost. Traditionally heuristic functions for pathfinding have been human-designed or pre-computed for a specific search graph. The former tend to be compact, human-readable but generic. The latter offer better guidance but require per-graph pre-computation and have a substantial memory cost. We aim to retain compactness and readability of human-designed heuristics and increase their performance. We adopt the recently published approach of representing heuristic functions as algebraic formulae and automatically synthesizing them for video-game maps. Whereas published work merely randomly sampled the space of formula-based heuristic functions, we implement and evaluate a parameterized synthesis algorithm that unifies and generalizes the stochastic sampling, simulated annealing and a basic genetic algorithm. We tune the parameters for better synthesis performance and then, using maps from multiple video games, show that heuristics synthesized for maps from one game still outperform the baseline search (A* with weighted Manhattan distance) on maps from a different game. We analyze a frequently synthesized formula and explain how, despite having a higher error than the Manhattan distance, it takes advantage of the structure in video-game pathfinding problems and speeds up A*.
3D Single-Object Tracking with Spatial-Temporal Data Association

This paper proposes a novel 3D single-object tracker to more stably, accurately, and faster track objects, even if they are temporarily missed. Our idea is to utilize spatial-temporal data association to achieve object tracking robustly, and it consists of two main parts. We firstly employ a temporal motion model cross frames to estimate the object's temporal information and update the region of interest(ROI). The advanced detector only focuses on ROI rather than the whole scene to generate the spatial position. Second, we introduce a new pairwise evaluation system to exploit spatial-temporal data association in point clouds. The proposed evaluation system considers detection confidence, orientation offset, and objects distance to more stably achieve object matching. Then, we update the predicted state based on the pairwise spatial-temporal data. Finally, we utilize the previous trajectory to enhance the accuracy of static tracking in the refinement scheme. Experiments on the KITTI and nuScenes tracking datasets demonstrate that our method outperforms other state-of-the-art methods by a large margin (a 10% improvement and 280 FPS on a single NVIDIA 1080Ti GPU). Compared with multi-object tracking, our tracker also has superiority.
A Comparative Study of Classification and Clustering Methods from Text of Books

Book collections in libraries are an important means of information, but without proper assignment of books into appropriate categories, searching for books on similar topics is very troublesome for both librarians and readers. This is a difficult problem due to the analysis of large sets of real text data, such as the content of books. For this purpose, we propose to create an appropriate model system, the use of which will allow for automatic assignment of books to appropriate categories by analyzing the text from the content of the books. Our research was tested on a database consisting of 552 documents. Each document contains the full content of the book. All books are from Project Gutenberg in the Art, Biology, Mathematics, Philosophy, or Technology category. Well-known techniques of natural language processing (NLP) were used for the proper preprocessing of the book content and for data analysis. Then, two different machine learning approaches were used: classification (as supervised learning) and clustering (as unsupervised learning) in order to properly assign books to selected categories. Measures of accuracy, precision and recall were used to evaluate the quality of classification. In our research, good classification results were obtained, even above 90% accuracy. Also, the use of clustering algorithms allowed for effective assignment of books to categories.
The Improvement of C4.5 Algorithm Accuracy in Predicting Forest Fires Using Discretization and AdaBoost

Data mining is a process used to help analyze data obtained from certain circumstances with a mathematical approach. The decision tree is an algorithm that is often used in data mining. One of the Decision tree algorithms is the C4.5 algorithm. Data mining consists of preprocessing, data mining, pattern evaluation, and knowledge presentation in its application. Forest fire data used were taken from the UCI Machine Learning Repository. Data normalization, data transformation, and discretization are used to preprocess data in research. To improve accuracy, the C4.5 algorithm can be combined with AdaBoost. This study aims to determine how the application of discretization to the C4.5 algorithm with AdaBoost predicts forest fires and determines the increase in its accuracy. Based on the results of ten k-fold cross-validations, the highest accuracy value obtained is 98.04%. The implementation of discretization and AdaBoost increased the accuracy of forest fire predictions by 13.42%.
Data Crawling and Research Based on Topic Web Crawler

With the popularity of big data, efficient acquisition of existing massive data and multi-angle analysis has become a key technology. In this paper, compared with the traditional general web crawler, the main web crawler strategy adopted in network crawling can be more efficient for grasping targets, so as to carry out data grasping operations more efficiently. Based on the film review data on Douban.com, this paper analyzes and studies the film review data without damaging the operation of the website. By using jieba, matplotlib, wordcloud and other libraries in python library, data can be visualized into wordcloud map, pie chart and line chart, which is helpful for users to see key words and the proportion of favorable and negative comments in movie works directly, which is of great significance to users’ preference selection. It also has certain reference significance for accurate software recommendation in the era of big data.