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International symposium on Applied Machine Intelligence and Informatics (SAMI)

作者： Michal Kvet Department of Informatics Žilinská unvierzita v Žiline Fakulta riadenia a informatiky Žilina Slovakia

ISBN: (数字)9798350379365

ISBN: (纸本)9798350379372

Increasingly expanding data analytics brings the need to store a large and structurally diverse amount of data. Related to this is the design of analytical data structures and performance optimization. Individual data are typically aggregated in reports. The goal of this paper is to create a methodology for using aggregation functions in analytical-transactional SQL databases. We focus on the impact of the used parameter format and values, as well as grant total calculation technique with the aim of minimizing processing costs and time.

关键词： Structured Query Language data analysis databases Aggregates data aggregation data structures Informatics Optimization Machine intelligence Meteorology

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Evaluation of strategies for efficient rate-distortion NeRF streaming

Evaluation of strategies for efficient rate-distortion NeRF ...

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ieee International symposium on Multimedia

作者： Pedro Martin António Rodrigues João Ascenso Maria Paula Queluz Instituto de Telecomunicações - Instituto Superior Técnico Lisbon Portugal

ISBN: (数字)9798331511111

ISBN: (纸本)9798331511128

Neural Radiance Fields (NeRF) have revolutionized the field of 3D visual representation by enabling highly realistic and detailed scene reconstructions from a sparse set of images. NeRF uses a volumetric functional representation that maps 3D points to their corresponding colors and opacities, enabling photorealistic view synthesis from arbitrary viewpoints. Despite its advancements, the efficient streaming of NeRF content remains a significant challenge due to the large amount of data involved. This paper investigates the rate-distortion performance of two NeRF streaming strategies: pixel-based and neural network (NN) parameter-based streaming. While in the former, images are coded and then transmitted throughout the network, in the latter, the respective NeRF model parameters are coded and transmitted instead. This work also highlights the trade-offs in complexity and performance, demonstrating that the NN parameter-based strategy generally offers superior efficiency, making it suitable for one-to-many streaming scenarios.

关键词： visualization Three-dimensional displays Image color analysis Rate-distortion Artificial neural networks Streaming media Neural radiance field Encoding Complexity theory Image reconstruction

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analysis of Calibration Requirements in Image Recognition Tasks with Time-Encoded ANNs

Analysis of Calibration Requirements in Image Recognition Ta...

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ieee International symposium on Circuits and Systems (ISCAS)

作者： Carlos Perez Eric Gutierrez Dpto. Tecnología Electrónica Universidad Carlos III de Madrid Madrid Spain

ISBN: (数字)9798350356830

ISBN: (纸本)9798350356847

Power and area efficiency of time-encoded neural networks make them highly suitable for smart portable applications. The main limitation here is the high sensitivity against circuit impairments like mismatch effects or PVT variations, which are usually mitigated by means of calibration circuitry. The purpose of this manuscript is to propose a novel time-encoded unit for Artificial Neural Networks, and make an in-depth analysis on how these effects degrade the accuracy in classification tasks. As an application example, image recognition tasks are selected using MNIST dataset. It is checked that the proposed unit does not require calibration to compensate mismatch effects, can be implemented with small devices, and shows accuracy degradation due to PVT variations that can be overcome with simple calibration or by increasing the number of bits used for the inputs and outputs of the unit. With a very simple neural network structure, 94.1 %-accuracy is achieved with a worst case simulated TOPS/W value of 24, and an area of 0.039 mm 2 in a 65 nm CMOS design node, with a data representation of 5 bits in the inputs and outputs of the unit and 4 bits used for the weights.

关键词： Degradation Image recognition Accuracy Sensitivity data visualization Artificial neural networks Energy efficiency Calibration Complexity theory Edge computing

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TSEA: An Open Source Python-Based Annotation Tool for Time Series data

TSEA: An Open Source Python-Based Annotation Tool for Time S...

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16th ieee International symposium on Medical Measurements and Applications (ieee MeMeA)

作者： Selzler, Roger Chan, Adrian D. C. Green, James R. Carleton Univ Dept Syst & Comp Engn Ottawa ON Canada

ISBN: (纸本)9781665419147

We present the Time Series Event Annotator (TSEA), a graphical user interface annotation tool for time series data that enables rapid visualization, labeling, and annotation of signals, including individual points and ranges. Time series data are common to a variety of applications. Oftentimes there is a need to label segments and/or points of the signals, highlighting important elements that are later used for feature extraction or for signal analysis. A number of illustrative applications of the developed tool are discussed, particularly for the detection of "R" peaks from electrocardiogram signals. While algorithms for detection of "R" peaks can achieve good results when applied to an electrocardiogram signal with a high signal-to-noise ratio, they often lead to incorrect detections in the presence of noise or motion artifact commonly found in clinical setups. In such cases, the Time Series Event Annotator (TSEA) enables efficient imputing of missed or incorrect "R" peak detections, leading to increased data integrity for downstream analysis, at minimum cost. Considering that data cleaning often represents the majority of effort when developing a new machine learning pipeline, our annotation tool will accelerate the development of a wide range of new machine learning applications.

关键词： Time series annotation label graphical user interface open source

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Sentiment analysis of online product reviews using Lexical Semantic Corpus-Based technique 11

Sentiment analysis of online product reviews using Lexical S...

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11th ieee symposium on Computer Applications and Industrial Electronics (ISCAIE)

作者： Aminuddin, Raihah Zulkefli, Aina Zuliana Moketar, Nor Aiza Abu Samah, Khyrina Airin Fariza Univ Teknol MARA Fac Comp & Math Sci Malacca Branch Jasin Campus Malacca Malaysia Celcom Axiata Berhad Petaling Jaya Selangor Darul Malaysia

ISBN: (纸本)9781665403382

large number of product reviews may help the product looks more interesting, but it also may cause difficulties for customers in deciding to buy the product. The seller will also be facing difficulties such as to improve the quality of their product due to the huge number of reviews. The aim of this project is to develop a web-based system for customers and sellers to get an analysis of the online product reviews. The customer and seller will get the visualization for the positive, neutral, and negative distributions of the product reviews. The system uses MySQL as a database and Python as a programming language for development of the system. For the future work, the system could be able to compare the analysis of product reviews from other brands.

关键词： product review sentiment analysis lexicon semantic classification

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TOWARDS VISUAL EXPLORATION OF SEMANTICALLY ENRICHED REMOTE SENSING SCENE KNOWLEDGE GRAPHS (RSS-KGS)

TOWARDS VISUAL EXPLORATION OF SEMANTICALLY ENRICHED REMOTE S...

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ieee International Geoscience and Remote Sensing symposium (IGARSS)

作者： Potnis, Abhishek, V Durbha, Surya S. Shinde, Rajat C. Talreja, Pratyush, V Indian Inst Technol Ctr Studies Resources Engn Mumbai Maharashtra India

ISBN: (纸本)9781665403696

There has been an increase in the adoption of Linked data and subsequently representing data in the form of knowledge graphs across a wide spectrum of domains. There has also been significant interest in the remote sensing community to publish Earth Observation data in the form of Linked data. As the geospatial Linked data cloud on the internet grows, there arises a need for efficient methods of exploratory analysis of such information-rich geospatial knowledge graphs. Knowledge graph representation of remote sensing scenes has proved to add significant value for effective mining of implicit information in addition to seamless integration with other data sources. This work is geared towards visual exploration of semantically enriched Remote Sensing Scene Knowledge Graphs (RSS-KGs). In this paper, we propose and implement an interactive web-based interface to visually explore and interact with RSS-KGs using Cesium. The proposed interface seeks to visualize the knowledge graph in the form of nodes and edges, mapped over the remote sensing scene consisting of different land use land cover regions and their inferred characteristics in addition to their spatial relationships with one another. It is envisaged that visualization in the form of nodes and edges would aid in visually validating the spatial relations in the knowledge graph, thus enhancing the understanding of the geospatial knowledge graph from the end user perspective. We demonstrate the efficacy of the interface through the visual exploration of an enriched geospatial knowledge graph of a remote sensing scene captured during an urban flood event.

关键词： visualization exploration knowledge graphs remote sensing scenes earth observation linked data

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analysis and research on the policy performance of college students returning home to start a business based on data visualization 'artificial intelligence + agriculture' 2

Analysis and research on the policy performance of college s...

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2nd International symposium on Artificial Intelligence and its Application on Media, ISAIAM 2022

作者： Peng, Zhaoyu Cao, Sheng School of Management Wuhan Donghu University Hubei Wuhan China

ISBN: (纸本)9781665485418

As a new generation of scientific and technological forces, artificial intelligence is promoting high-quality economic development, accelerating social innovation and intelligent transformation of industries. After the issuance of the sub-'New Generation Artificial Intelligence Development Plan', the introduction of artificial intelligence into the agricultural field can not only promote the economic growth of rural areas, but also provide new opportunities for college students to return to their hometowns to start a business. Taking 'artificial intelligence + agriculture' as the research background, taking the returned entrepreneurial college students in Jingzhou City as the survey object, the policy performance is objectively analyzed by the analytic hierarchy method (AHP analysis method), and the fuzzy comprehensive method (FCE) is used to quantitatively evaluate the scores of various indicators, and constructive suggestions and optimization measures are put forward to help the returned college students better carry out entrepreneurial activities. © 2022 ieee.

关键词： Informatization

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analysis of Information Security in a Corporate Environment – a Human Perspective

Analysis of Information Security in a Corporate Environment ...

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International symposium on Applied Machine Intelligence and Informatics (SAMI)

作者： Andrea Tick Nikolett Szabó-Harka Keleti Károly Faculty of Business and Management Óbuda University Budapest Hungary

Information security (InfoSec) as becoming one of the most important fields of information systems at corporate level accounts for a large proportion of data losses, attacks and breaches. One of the possible proactive and preventive measures is to train employees and raise their information security awareness. The aim of the research to reveal with the help of a quantitative analysis how companies can effectively interfere in the process and prepare their employees to be alert and aware in this specific field during regular business operations. The results show that large enterprises and small and medium-sized enterprises (SMEs) are differently prepared for employee training and to build their information security shield.

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Crop Detection Method using Relative Positional Relationships for Small Weeding Robots

Crop Detection Method using Relative Positional Relationship...

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ieee/SICE International symposium on System Integration

作者： Yusuke Iuchi Atsuki Koshigoe Soki Nishiwaki Takanori Emaru Graduate School of Engineering Hokkaido University Hokkaido Japan Faculty of Engineering Hokkaido University Hokkaido Japan

ISBN: (数字)9798331531614

ISBN: (纸本)9798331531621

Automation in agriculture is increasingly critical for addressing global food security and sustainability challenges. This paper presents a novel crop detection method using relative positional relationships, specifically designed for small weeding robots. Unlike traditional approaches that rely heavily on visual characteristics and large annotated datasets, our method leverages the spatial arrangement of plants to distinguish crops from weeds, thereby reducing the dependency on extensive data collection and annotation efforts. We implemented a multi-stage detection system that first identifies all plants using an object detection algorithm and then classifies them based on their positional and size information. Experimental results on soybean datasets demonstrate that our approach achieves AP of 71.3% for soy crops and 27.2% for weeds in environments not included in the training dataset, showing comparable effectiveness to traditional visual-based detection methods in scenarios with limited data. This advancement offers potential for enhancing the adaptability and efficiency of agricultural automation technologies.

关键词： Training visualization Automation Crops Object detection System integration Feature extraction Agriculture Sustainable development Robots

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A Prototypical Knowledge Oriented Adaptation Framework for Semantic Segmentation

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ieee TRANSACTIONS ON IMAGE PROCESSING 2022年 31卷 149-163页

作者： Tian, Haitao Qu, Shiru Payeur, Pierre Northwestern Polytech Univ Sch Automat Xian 710129 Peoples R China Univ Ottawa Sch Elect Engn & Comp Sci Ottawa ON K1N 6N5 Canada Univ Ottawa Sensing & Machine Vis Automat & Robot Intelligenc Ottawa ON K1N 6N5 Canada

A prevalent family of fully convolutional networks are capable of learning discriminative representations and producing structural prediction in semantic segmentation tasks. However, such supervised learning methods require a large amount of labeled data and show inability of learning cross-domain invariant representations, giving rise to overfitting performance on the source dataset. Domain adaptation, a transfer learning technique that demonstrates strength on aligning feature distributions, can improve the performance of learning methods by providing inter-domain discrepancy alleviation. Recently introduced output-space based adaptation methods provide significant advances on cross-domain semantic segmentation tasks, however, a lack of consideration for intra-domain divergence of domain discrepancy remains prone to over-adaptation results on the target domain. To address the problem, we first leverage prototypical knowledge on the target domain to relax its hard domain label to a continuous domain space, where pixel-wise domain adaptation is developed upon a soft adversarial loss. The development of prototypical knowledge allows to elaborate specific adaptation strategies on under-aligned regions and well-aligned regions of the target domain. Furthermore, aiming to achieve better adaptation performance, we employ a unilateral discriminator to alleviate implicit uncertainty on prototypical knowledge. At last, we theoretically and experimentally demonstrate that the proposed prototypical knowledge oriented adaptation approach provides effective guidance on distribution alignment and alleviation on over-adaptation. The proposed approach shows competitive performance with state-of-the-art methods on two cross-domain segmentation tasks.

关键词： Image segmentation Semantics Task analysis Adaptation models Training visualization Image classification Domain adaptation semantic segmentation prototypical knowledge unsupervised learning transfer learning

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