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Exploring the Design Context of AI-Powered Services: A Qualitative Investigation of Designers' Experiences with machine learning 3rd

Exploring the Design Context of AI-Powered Services: A Quali...

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3rd international conference on Artificial Intelligence in HCI (AI-HCI) Held as Part of the 24th international conference on Human-computer Interaction (HCII)

作者： Bergstrom, Emil Warnestal, Pontus Halmstad Univ Sch Informat Technol Dept Informat Halmstad Sweden

ISBN: (纸本)9783031056437;9783031056420

Artificial Intelligence (AI) has provided user experience (UX) designers with a richer toolset. To use technologies such as machine learning (ML) that can expand their creative capacity to design intelligent services. ML has the capability to enhance the user experience, for example, by improving efficiency, personalization, and context-aware adaptation. However, research suggests ML as a challenging design material in UX practice, such as difficulties in comprehending data dependencies when prototyping, or the lack of tools and methods for evaluating adaptive user experiences. Previous research indicates that lack of knowledge transfer into the UX design practice may hamper innovative potential. This work aims to provide new insights on how designers think about and experience design for AI-powered services. It is important to make ML-powered services beneficial and sustainable for end-users, organizations, and society. Therefore, we explore UX designers' reflections and experiences of using ML in a design context. We have performed nine deep explorative interviews with professional designers that work with ML. The respondents have different backgrounds, seniority, and work in different sectors. The collected interview material was qualitatively analyzed and resulted in five conceptual themes for how UX designers experience the design context surrounding AI-powered services: 1) Absence of competence, 2) Lack of incentive for competence development, 3) Challenges in articulating design criteria, 4) Mature vs Immature clients, and 5) Lack of support for ethical concerns. We provide implications for how these themes affect the design context and practice.

关键词： machine learning Design material UX design practice Interaction design

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Crime Analysis Using computer Vision Approach with machine learning 3rd

Crime Analysis Using Computer Vision Approach with Machine L...

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3rd international conference on Mobile Radio Communications and 5G Networks, MRCN 2022

作者： William, P. Shrivastava, Anurag Karpagam, N. Shunmuga Mohanaprakash, T.A. Tongkachok, Korakod Kumar, Keshav Department of Information Technology Sanjivani College of Engineering SPPU Pune India S.D. Bansal College of Engineering Madhya Pradesh Indore India Department of CSE Er. Perumal Manimekalai College of Engineering Hosur India Department of CSE Panimalar Institute of Technology Varadarajapuram India Faculty of Law Thaksin University Mueang Songkhla Thailand University Institute of Computing Chandigarh University Punjab India

ISBN: (纸本)9789811979811

Depending on the seriousness of the offence, any deliberate act that causes harm to oneself or another, as well as damage to or loss of property, qualifies as a crime for the purposes of criminal law. The number and diversity of unlawful activities are increasing at an alarming rate, necessitating the creation of efficient enforcement tools by law enforcement agencies. Due to their slowness and inefficiency, traditional crime-solving methods are no longer effective in today’s high-crime climate. As a result, we may be able to reduce the workload of police personnel and contribute to crime prevention if we develop ways for reliably anticipating crime in advance of its occurrence. In order to do this, we suggest using ML and computer vision technologies and methodologies. Throughout this study, we describe the results of several cases in which such strategies were used, which sparked our interest in further study. The fundamental reason for the change in crime detection and prevention strategies is based on the prior and subsequent statistical observations made by the authorities. machine learning and computer vision may help law enforcement and other authorities detect, prevent and solve crimes more quickly and accurately, and this is the primary goal of this research effort. With the use of artificial intelligence and computer vision, law enforcement agencies might be transformed. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

关键词： Crime

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The Effectiveness of Smart Tourism in Malaysia in Covid-19 Post-pandemic Era: A Case Study 3rd

The Effectiveness of Smart Tourism in Malaysia in Covid-19 P...

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3rd international conference on Design, Operation and Evaluation of Mobile Communications (MOBILE) Held as Part of 24th international conference on Human-computer Interaction (HCII)

作者： Thong, Chee Ling Chaw, Lee Yen Cherukuri, Aswani Kumar Jalil, Abdulrahman Chit, Su Mon Lee, Chiw Yi UCSI Univ Kuala Lumpur 56000 Malaysia Vellore Inst Technol Vellore 632014 Tamil Nadu India Univ Comp Sci & Engn Cyberjaya 63000 Malaysia UCSI Coll Kuala Lumpur 56000 Malaysia

ISBN: (纸本)9783031050145;9783031050138

The use of technology in supporting business processes has become an integral part of business in the post-pandemic era. This study focuses on two aspects of technology which are digitization and digitalization of business processes in tourism industry in Malaysia. Due to movement constraints in Covid-19 pandemic, most of the related studies are conducted based on media report, peered review articles and research letters or empirical articles. There is a lack of case study to examine the effectiveness of various factors and how these factors impact the recovery of the post- pandemic tourism industry. A research case in earlier study implies that digitization and digitalization have an impact in recovery of tourism industry and insight gained motivates this study on effectiveness of smart tourism in Malaysia. Hence, this study aims to investigate effectiveness of smart tourism by using a self-developed mobile application which enables multiple trips organize in Kuala Lumpur, Malaysia. Test cases are conducted among eleven managers in travel agencies and positive feedback are collected on system quality, perceived mobility, mobile usefulness, mobile ease of use and usage intention. Findings show that ICT has a major role in reviving business processes when tourism enters recovery phase. The study also discovered the importance of understanding the new patterns emerging which may include new tour packages or travel destinations that are emerging in future. In future work, artificial intelligence or machine learning based smart tourism models are introduced to analyse the new pattern emerging.

关键词： Smart tourism Travel agency Post-pandemic Mobile application

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An Architecture for a Smart Fourth Party in an Online Dispute Resolution Platform 3

An Architecture for a Smart Fourth Party in an Online Disput...

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3rd international conference on Next Generation Computing applications, NextComp 2022

作者： Butgereit, Laurie Nelson Mandela University School of Ict Gqeberha South Africa

ISBN: (数字)9781665469548

ISBN: (纸本)9781665469548

Disputes involve two different people or parties. Because of cost or time constraints, the two parties may wish to avoid a lengthy court case and, instead, involve a mediator or arbitrator to help them resolve the dispute. This mediator or arbitrator is often called the third party with the two original participants being called the first and second parties. As this mediation or arbitration process becomes an online process (as in the case of Online Depute Resolution - ODR), the platform used for the ODR has been called the Fourth Party. This paper looks at a technology architecture for creating such a Fourth Party. This paper looks at different types of Alternate Dispute Resolution systems. The paper then progresses to ODR systems. Different facilities of various ODR systems are itemised and a few examples of ODR systems which have already been used or developed are described. A brief disussion of a pilot program is provided including comments about the data which was collected during this pilot. Finally, a proposed IT architecture is provided for an ODR system including a fully fledged Fourth Party as defined by Katsh and Rifkin - including algorithmic support. © 2022 IEEE.

关键词： machine learning algorithms Costs computer architecture Time factors Mediation Next generation networking

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An improved Harris Hawk optimization algorithm and its application to Extreme learning machine

An improved Harris Hawk optimization algorithm and its appli...

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Consumer Electronics and computer Engineering (ICCECE), 2021 IEEE international conference on

作者： Ziliang Liu Hongwei Chen School of Computer Science Hubei University of Technology Wuhan China

The Harris Hawk optimization (HHO) algorithm is an excellent swarm intelligence optimization algorithm which has the advantages of high efficiency in finding the best, ease of implementation and wide application. It also has some disadvantages such as the possibility of convergence too fast and the tendency to fall into local optima. This paper combines an improved escape energy update approach and the leader update operator of the Salp Swarm Algorithm to improve the HHO, named IMHHO. The experiments show that the improvements have improved the algorithm's ability to find the best. IMHHO was also used in the parameter optimization of the Extreme learning machine, which also enables the ELM to find the right weights and bias values and to regress the data more accurately.

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3rd international conference on Technology and Innovation in learning, Teaching and Education, TECH-EDU 2022

3rd International Conference on Technology and Innovation in...

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3rd international conference on Technology and Innovation in learning, Teaching and Education, TECH-EDU 2022

ISBN: (纸本)9783031229176

The proceedings contain 39 papers. The special focus in this conference is on Technology and Innovation in learning, Teaching and Education. The topics include: The Impact of Artificial Intelligence on Chatbot Design;an Initial Framework for Adaptive Serious Games Based on a Systematic Literature Review;student Engagement Detection Using Emotion Analysis, Eye Tracking and Head Movement with machine learning;teacher Readiness to Adopt the Flipped learning Model: Exploring Greek Teachers’ Views and Perceptions;designing, Deploying and Evaluating an Undergraduate Course on the "Didactics of Informatics";on Enhancing the Conversation Skills: The Dealogos Prototype;learning Analytics Framework Applied to Training Context;preface;structuring Collaborative Setting in the Learner Centered learning Environment;towards an Accessibility Evaluation of elearning Tools in Emerging 3D Virtual Environments Like Metaverse: Taking Advantage of Acquired Knowledge in Moodle and Second Life;On Modeling LMS Users’ Quality of Interaction Using Temporal Convolutional Neural Networks;from Stories to Science: An Exploration Guide to Promote Epistemic Practices in Primary School;Teacher Training in the Fields of STEAM: From Physical to Digital Tools;virtual Laboratory in Electromagnetism: A Study of Instrumental Orchestration;Instrumental Orchestration in the Primary School and the Use of Digital Resources to Link STEM and Art: Systematic Literature Review;know Me: Promoting Gender Equality in Education Through an Interactive Digital Narrative;Enabling Educators to Self-assess their STEAM Readiness;a Systematic Literature Review on the learning Technologies Implemented in Organizations;integrating Video Production in Early Ages to Promote Motivation for Mathematics and Transversal Competences: Examples from ViduKids Project.

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3rd international Workshop on New Approaches for Multidimensional Signal Processing, NAMSP 2022

3rd International Workshop on New Approaches for Multidimens...

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3rd international Workshop on New Approaches for Multidimensional Signal Processing, NAMSP 2022

ISBN: (纸本)9789811978418

The proceedings contain 23 papers. The special focus in this conference is on New Approaches for Multidimensional Signal Processing. The topics include: Development of IoT Indoor Monitoring System for Independent Elderly;improving the Process of Evaluating User Stories Using the Paraconsistent Annotated Evidential Logic Eτ;neural Network Algorithm Applied in Electrical Engineering Automation;Truss Structure Optimization Design Based on FE-PSO-SQP Algorithm;practice System of Ant Colony Optimization Algorithm in Business Administration;literature Review of Smart Contracts Using Blockchain Technology;a Comprehensive Study of 5th Generation Scheduling Algorithms;a Comparative Analysis of Homogeneous and Heterogeneous Protocols to Maximize the Lifetime of Wireless Sensor Network for Precision Agriculture;the Integration Development and Upgrading Path of Industry 4.0 Architecture Industrial Engineering Network Driven by Big Data;video Tracing of Moving Objects by Fusing Three-Term Decompositions;SAAS application Prospect Analysis in Hrm and Methods to Upgrade the Contemporary System;All Digital Phase Locked Loop (ADPLL) and Its Blocks—A Comprehensive Knowledge;on Realization of Smart Logistic Warehouse Management with Internet of Things;development and Design of Intelligent Traction System for Bulk Cargo Terminal;deep learning Approaches for Classroom Audio Classification Using Mel Spectrograms;Tensor Spectral Pyramid for Color Video Sequences Representation, Based on 3D FO-AHKLT;electromyography Signal Acquisition, Processing, Optimization and Its applications;research on the Radar Signal Classification Method Based on the Deep Faith Network Model;measuring machine Intelligence Using Black-Box-Based Universal Intelligence Metrics;COVID Detection Using ECG Image Reports: A Survey;disease Detection Techniques in Plants: Transition from Manual to Automation.

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Cauliflower Disease Recognition Using machine learning and Transfer learning 3rd

Cauliflower Disease Recognition Using Machine Learning and T...

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3rd international conference on Smart Systems - Innovations in Computing (SSIC)

作者： Maria, Syeda Khadizatul Taki, Shahrun Siddique Mia, Md Jueal Biswas, Al Amin Majumder, Anup Hasan, Firoz Daffodil Int Univ Dept CSE Dhaka Bangladesh Jahangirnagar Univ Dept CSE Dhaka Bangladesh

ISBN: (纸本)9789811628771;9789811628764

In terms of overall winter cropping area and production in Bangladesh, cauliflower dominates a large share. It has many health benefits like decrease the risk of obesity, diabetes, and heart disease. It is a cultivated and winter crop which has huge demand in the country. But if proper care is not taken, many serious diseases will affect plants and will reduce productivity, quantity, and quality of cauliflower. Manual monitoring of plant disease is very difficult as it requires a tremendous amount of work and excessive time. Automatic recognition of disease through computer vision approach is becoming more popular day by day. So, in this paper, we introduced several techniques to recognize diseases that occur on plants in cauliflower. Our proposed solution would support the agriculture field of Bangladesh to grow cauliflower more effectively and will increase its production by taking the proper step after automated recognition of diseases. In our work, we have compared traditional machine learning and transfer learning. In machine learning, for image segmentation, k-means clustering is used after the image preprocessing method is applied, and then, ten relevant features are extracted. For classification, we compared various classification techniques. Random forest algorithm achieves overall 81.68% accuracy. Different CNN-based architectures with transfer learning, namely InceptionV3, MobileNetV2, ResNet50, and VGG16, are also applied. InceptionV3 achieves 90.08% accuracy which is the highest accuracy among these two approaches.

关键词： Cauliflower disease CNN computer vision machine learning Random forest Transfer learning

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Siamese network tracking algorithm based on reinforcement learning 3

Siamese network tracking algorithm based on reinforcement le...

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3rd international conference on Artificial Intelligence and computer Engineering, ICAICE 2022

作者： Ji, Qingbo Qi, Yufei Liu, Hang Hou, Changbo Gong, Jianyang School of Information and Communication Engineering Harbin Engineering University Heilongjiang Harbin150001 China Key Laboratory of Advanced Ship Communication and Information Technology Ministry of Industry and Information Technology Harbin Engineering University Heilongjiang Harbin150001 China China Telecom Heilongjiang Branch Heilongjiang Harbin150001 China

ISBN: (纸本)9781510663473

In the field of computer vision, object tracking is a hot research topic. At present, the coordinate box of most algorithms is vertical box. However, in practical application, the frame selection area of the object is expanded due to the rotation of the object, which makes it impossible to obtain the real motion angle of the object. To solve this problem, this paper proposes a Siamese network tracking algorithm (SiamRotate) based on rotation box reinforcement learning. In order to predict the rotation Angle of the object, firstly, we processes the VOT2013 and VOT2014 datasets to increase the angle richness, and used the processed datasets to train the angle decision-making network through the combination of supervised training and reinforcement learning. Based on the SiamRPN network, the tracking network obtains the feature response through the deep cross-correlation operation of the feature map of the two branches through SiamRPN, and then processes the feature response map through the angle decision network to predict the object rotation angle. The performance indexes of the proposed method reached 0.62(A), 0.21(R), 0.425(EAO) on VOT2016, and 0.61(A), 0.27(R), 0.399(EAO) on VOT2018, and the tracking speed reaches 138fps. Finally, comparative experiments show that the algorithm has good performance in speed and accuracy. © 2023 SPIE.

关键词： Forecasting

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An Ensemble of CapsNet and KSVM Using Feature Fusion: application to COVID-19 Detection 3

An Ensemble of CapsNet and KSVM Using Feature Fusion: Applic...

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3rd international conference on Pattern Recognition and machine learning, PRML 2022

作者： Aggrey, Esther Stacy E. B. Zhen, Qin Kodjiku, Seth Larweh Aidoo, Evans Fiasam, Linda Delali Mensah, Acheampong Edward University of Electronic Science & Technology of China School of Information & Software Engineering Chengdu China University of Electronic Science & Technology of China School of Information & Software Engineering Network and Data Security Key Lab of Sichuan Province Chengdu China Zhejiang Gongshang University School of Computer & Information Engineering Hangzhou China

ISBN: (数字)9781665499507

ISBN: (纸本)9781665499507

COVID-19 virus is a major worldwide pandemic that is growing at a fast pace throughout the world. The usual approach for diagnosing COVID-19 is the use of a real-time polymerase chain reaction (RT-PCR) based nucleic acid test. However, RT-PCR has lower sensitivity in the early phases of COVID-19 detection. Recent studies have indicated that X-ray images may be useful throughout the early detection of the virus. Human screening has been shown to be cost-effective, susceptible to mistakes, and time-demanding, which has sparked an interest in using Convolutional Neural Networks (CNNs) to automate the process. CNNs, on the other hand, fail to view the exact placement of features as advantageous in medical imaging. Furthermore, for successful training and prediction, CNNs need a huge quantity of datasets. CNNs are rapidly reducing picture resolution, resulting in worsening accuracy in classification. We used newly created capsule networks (CapsNets) in our study to circumvent these disadvantages. The primary contribution is to improve the identification of SARS-CoV-2 with images obtained from X-ray by coupling capsule network with a kernel support vector machine (KSVM). The technique was evaluated using a publicly available dataset, and the proposed model shows that the accuracy of the CapsNet-KSVM based model is improved by 94.6% accuracy, 95% sensitivity, and 98% specificity, which outperforms the traditional CNN and other existing ensemble models. The proposed CapsNet-KSVM based system can be employed to identify the presence of COVID-19 in the human body using X-ray images. © 2022 IEEE.

关键词： COVID-19

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