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Supporting Behaviour Change Techniques with Interaction Design Patterns 1

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8th International Conference on computer-Human Interaction Research and Applications-CHIRA-Annual

作者： Farhat-ul-Ain Popovitz, Olga Amirgaliyeva, Gulassyl Tomberg, Vladimir Tallinn Univ Tallinn Estonia

ISBN: (数字)9783031826337

ISBN: (纸本)9783031826320;9783031826337

Design patterns provide a structured method for addressing common design problems by offering proven solutions that can be reused across different contexts. In the case of designing digital behaviour change interventions, interaction designers may face challenges in translating Behaviour Change Techniques (BCTs) knowledge into digital interventions. Interaction design patterns for BCTs can help overcome this challenge by providing descriptions, examples, rationale for using BCTs, and proven solutions. The current study examines six patterns for commonly used BCTs in digital interventions (reminder, social support, goal setting, self-monitoring, and instruction on performing behaviour and feedback on behaviour). The proposed design patterns were evaluated by four interaction design experts. They found the descriptions and examples provided in the design patterns to be clear and comprehensible. The experts appreciated the balance between concreteness and abstractness. The resulting design patterns can contribute to the informed design of digital behaviour change interventions and are helpful for designers, developers, researchers and product managers. Experts suggested that the language of the design patterns needs to be simplified for industry professionals to ensure that they can understand and apply them. Experts also indicated the need for shorter versions of the patterns, such as plain summaries or mind maps. Future design efforts to refine and simplify the proposed patterns and to develop additional patterns covering a broader range of BCTs are required.

关键词： Design pattern Behaviour change techniques Interaction design

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Multi-Agent Deep Q-Network with Layer-Based Communication Channel for Autonomous Internal Logistics Vehicle Scheduling in Smart Manufacturing 5th

Multi-Agent Deep Q-Network with Layer-Based Communication Ch...

引用

5th International Conference on Innovative Intelligent Industrial Production and Logistics

作者： Feizabadi, Mohammad Hosseini, Arman Yahouni, Zakaria Univ Grenoble Alpes CNRS Grenoble INP G SCOP F-38000 Grenoble France Univ Virginia Syst Engn Charlottesville VA 22903 USA

ISBN: (纸本)9783031807596;9783031807602

In smart manufacturing, scheduling autonomous internal logistic vehicles is crucial for optimizing operational efficiency. This paper proposes a multi-agent deep Q-network (MADQN) with a layer-based communication channel (LBCC) to address this challenge. The main goals are to minimize total job tardiness, reduce the number of tardy jobs, and lower vehicle energy consumption. The method is evaluated against nine well-known scheduling heuristics, demonstrating its effectiveness in handling dynamic job shop behaviors like job arrivals and workstation unavailabilities. The approach also proves scalable, maintaining performance across different layouts and larger problem instances, highlighting the robustness and adaptability of MADQN with LBCC in smart manufacturing.

关键词： Autonomous Internal Logistics AIV Scheduling Dynamic Flexible Job Shop Multi-Agent Deep Q-Network (MADQN) Layer-Based Communication Channel (LBCC)

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computer-Aided Diagnostic System for Alzheimer's Disease Using 3D MRI Images 4th

Computer-Aided Diagnostic System for Alzheimer's Disease Usi...

引用

4th International Conference on Advanced Network Technologies and Intelligent Computing-ANTIC-Annual

作者： Thamizhvani, T. R. Hemalatha, R. J. Vels Inst Sci Technol & Adv Studies Dept Biomed Engn Chennai 600117 Tamil Nadu India

ISBN: (纸本)9783031837890;9783031837906

Alzheimer's disease (AD) is a brain ailment that gradually impairs thinking and memory abilities as well as the capacity to do even the most basic tasks. A proper diagnosis of Alzheimer's disease (AD) is crucial for patient treatment, particularly in the early stages of the illness when patients can take precautions before suffering irreparable brain damage. Many machine detection techniques are constrained by congenital observations, even though several recent research have employed computers to diagnose stages of AD. In the proposed approach, the hippocampus area is identified as a biomarker by segmenting the region using 3D deep learning algorithms and classification of stages of AD performed using 3D network-based transfer learning techniques. Thus, an efficient segmentation and classification technique for the detection of the hippocampus region and identification of the different stages of AD is determined to design a computer aided diagnostic system.

关键词： Segmentation 3D Deep Learning networks Hippocampus region computer-aided 3D transfer learning MRI images

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Boosting CRM Chatbot Solutions with Flash Attention and Probabilistic Inference 4th

Boosting CRM Chatbot Solutions with Flash Attention and Prob...

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4th International Conference on Intelligent Systems and Pattern Recognition-ISPR

作者： Gunay, Ahmet Can Ozara, Muhammet Furkan Celik, Ahmet Erkan Akbulut, Akhan Next4biz R&D Ctr Pakpen Plaza 40-4 TR-34734 Istanbul Turkiye Istanbul Kultur Univ Dept Comp Engn TR-34536 Istanbul Turkiye

ISBN: (纸本)9783031821493;9783031821509

In an effort to deliver exceptional customer service, organizations are increasingly recognising the critical role that advanced language models (LLMs) play in the integration of CRM systems. This article explores the forefront of chatbot technology, with a particular focus on the essential function they play in contemporary consumer relationship management. The ability of chatbots to provide personalized and contextually pertinent responses to customer inquiries has been significantly enhanced by the rapid development of LLMs, specifically in the areas of sentiment, intent, and context comprehension. Organizations have the opportunity to enhance customer contentment and optimize operations by leveraging the capabilities of these advanced AI systems. In addition, offline capabilities guarantee continuous support, thereby enhancing customer confidence and loyalty in a time when connectivity fluctuations continue to be a challenge. The present study introduces an innovative offline chatbot system that aims to overcome the limitations of traditional cloud-based counterparts. By efficiently integrating data from various sources, such as project documentation and social media, this chatbot is capable of operating independently and delivering assistance to users, even in locations with sporadic internet connectivity. The experimental outcomes illustrate that our newly developed chatbot model outperforms established benchmarks, as evidenced by its 4.1-second inference time, 90.2-point BLEU score, and 9.7-point WER score. These metrics underscore the model's effectiveness, precision, and timeliness in handling user inquiries and producing responses of exceptional quality. The experiments we conduct are designed to validate the effectiveness of our model in improving customer service experiences within CRM systems through comprehensive performance testing.

关键词： Natural Language Processing NLP Chatbot Flash Attention Probabilistic Inference Data ingestion

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Mitigating Dimensional Collapse and Model Drift in Non-IID Data of Federated Learning 5th

Mitigating Dimensional Collapse and Model Drift in Non-IID D...

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5th International Conference on Neural Computing for Advanced Applications (NCAA)

作者： Jiang, Ming Li, Yun Lu, Yao Guo, Biao Zhang, Feng Guilin Univ Elect Technol Sch Comp Sci & Informat Secur Guilin 541004 Guangxi Peoples R China Metaverse Applicat Engn Ctr Nanning 530000 Guangxi Peoples R China Guangxi Inst Digital Technol Nanning 530000 Guangxi Peoples R China

ISBN: (纸本)9789819770069;9789819770076

One of the key challenges in federated learning is addressing the non-independent and identically distributed (Non-IID) data among parties, which can lead to divergence of local model parameters and decreased convergence accuracy of the global model, along with serious dimension collapse issues. In this paper, we introduce VICON (Variance-Invariance-Covariance model Contrastive Learning), a method to prevent dimension collapse issues. In local training, particularly, a regularization technique is employed to foster orthogonal feature representations across dimensions and to sustain the variance of individual embedded dimensions above a predefined level. Complementary to this, contrastive learning methodologies are utilized to cluster-like instances while dissociating divergent ones, further enhancing discriminative capabilities. It helps control the model's parameter norm and adapt it to high-dimensional data, reducing information loss and aligning local models with the global optimization objective in federated learning to minimize bias and collapse. Extensive experiments show that VICON performs better than other algorithms such as federated averaging (FedAvg), federated proximal optimization (FedProx), and model-agnostic federated learning (Moon). Compared to the Moon algorithm, it not only improves accuracy by 2.2% to 3.7%, but also enables efficient communication, achieves higher accuracy, and remains robust when dealing with imbalanced data and uncertain local updates.

关键词： Federated Learning Non-IID Dimension Collapse Contrastive Learning

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Ensemble Methods for Predicting Cyanobacteria's Potential Toxicity in Water Dams 4th

Ensemble Methods for Predicting Cyanobacteria's Potential To...

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4th International Conference on Intelligent Systems and Pattern Recognition-ISPR

作者： Berrezzek, Amira Dendani, Nadjette Amara, Nour Djihane Azizi, Nabiha Saoudi, Amel Badji Mokhtar Univ Dept Comp Sci Annaba Algeria Badji Mokhtar Univ Dept Biochem Annaba Algeria

ISBN: (纸本)9783031821554;9783031821561

Cyanobacteria are microorganisms that thrive in terrestrial and aquatic environments. Flowers of cyanobacteria can produce blue toxins. These toxins pose a significant threat to the overall health of humans and animals alike, and their presence in dams affects the quality of drinking water for consumers, which is of concern to the authorities. When it comes to forecasting and monitoring, artificial intelligence and machine learning techniques are used to monitor potential toxic cyanobacteria concentrations in dams. Advance data processing is an essential step in machine learning, as it helps to improve model accuracy and the efficiency of the learning process. Techniques used in this study include multi linear regression (MLR), support vector regression (SVR), artificial neural network (ANN), and random forest regression (RFR). Enhancement technologies such as Boosting, bagging and Stacking improving predictive continuity and leveraging the collective power of weak models to achieve better accuracy, stronger generalization and reduced error ratio across different transformation scenarios. To assess the performance of these technologies, metrics are used to understand the accuracy of machine learning models predictions to concentrate cyanobacteria helping to improve strategies for controlling the spread of cyanobacteria. Combining these models can provide more accurate predictions and insights into the different factors affecting focus levels. These are smart strategies to address the complexity of environmental monitoring [1].

关键词： Cyanobacteria cyanobacterial toxin Mexa machine learning prediction Boosting Bagging Stacking Squared-R

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Facilitating the Propagation of Oscillatory Signals in Cortical Networks Through Mixed Resonance 4th

Facilitating the Propagation of Oscillatory Signals in Corti...

引用

4th International Conference on Artificial Intelligence Logic and Applications

作者： Hu, Yun Liu, Dazhi Chen, Yanhui Liu, Jinhui Xian Univ Elect Sci & Technol Xian 710068 Peoples R China

ISBN: (纸本)9789819603534;9789819603541

This article proposes the Mixed Resonance Pair (MRP) as a method for efficient transmission of oscillatory signals between different brain regions to enhance overall cognitive function and coordination. Through simulation analysis in a seven-layer excitatory-inhibitory (E-I) neural network, it reveals the impact of feedback connections on interregional information transmission in the brain. The results indicate that feedback connections to excitatory clusters significantly enhance the network's ability to transmit neural information, while feedback connections to inhibitory clusters suppress sustained oscillations, maintaining system stability. By adjusting feedback connection parameters, the network's transmission capability can be significantly improved. Furthermore, the network shows a preference for transmitting periodic oscillatory signals based on the overall loop delay, suggesting that different delays have varying effects on the transmission capability of different frequency oscillatory signals. In essence, MRP connections can regulate the dynamic pathway selection of oscillatory information of different frequencies.

关键词： E-I network MRP connection information transmission

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Hybrid Neuro-Fuzzy Modeling for Electricity Consumption Prediction in a Middle-Income Household in Gauteng, South Africa: Utilizing Fuzzy C-means Method 5th

Hybrid Neuro-Fuzzy Modeling for Electricity Consumption Pred...

引用

5th International Conference on Neural Computing for Advanced Applications (NCAA)

作者： Oladipo, Stephen Sun, Yanxia Adegoke, Samson Ademola Univ Johannesburg Dept Elect & Elect Engn Sci Johannesburg South Africa

ISBN: (纸本)9789819770038;9789819770045

Machine learning (ML) models, renowned for their precision, are increasingly utilized in forecasting electricity consumption, a crucial aspect for empowering utilities with insights to optimize system performance in terms of productivity and efficiency. The limitations of conventional methods have fueled the adoption of ML-driven approaches for modeling energy consumption. This study introduces a fusion of fuzzy c-means (FCM)-clustered adaptive neuro-fuzzy inference system (ANFIS) with evolutionary algorithms (EAs) to predict energy consumption in middle-income households, focusing on Gauteng province, South Africa. FCM clustering was selected for its ability to enhance computational efficiency and speed. The model's simulation involved fine-tuning ANFIS structure through particle swarm optimization (PSO) and genetic algorithm (GA), with the optimal model selected through performance evaluation utilizing relevant statistical metrics such as root mean square error (RMSE), mean absolute error (MAE), coefficient of root mean square (CVRMSE), mean absolute deviation (MAD), and coefficient of variation (RCoV). The best-performing model obtained in the initial scenario underwent further enhancement by integrating a scaling factor (SF) into the GA. Subsequently, the modified GA-ANFIS-FCM (with 2 clusters) yielded the best performance, with values of RMSE, MAD, MAE, RCoV, and CVRMSE at 0.1715, 0.1272, 0.1251, 0.3619, and 73.5171, respectively. This study highlights the potential of the optimal model to serve as a dependable tool for accurately predicting energy consumption and forecasting.

关键词： fuzzy c-means adaptive neuro-fuzzy inference system evolutionary algorithms clustering

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Detection of Scoliosis in X-Ray Images Using a Convolutional Neural Network 10th

Detection of Scoliosis in X-Ray Images Using a Convolutional...

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10th International Conference on Technologies and Innovation

作者： Salazar-Fierro, Fausto Cumbal, Carlos Trejo-Espana, Diego Leon-Fernandez, Cayo Pusda-Chulde, Marco Garcia-Santilllan, Ivan Univ Tecn Norte Fac Ingn Ciencias Aplicadas Ibarra Ecuador Univ Nacl Mayor San Marcos Dept Acad Ciencias Computac Lima Peru

ISBN: (纸本)9783031757013;9783031757020

Scoliosis is the deviation or curvature of the spine in both the lumbar and thoracic spine in a 'C' or 'S' shape, causing health problems. Scoliosis is measured in degrees and is performed manually by orthopedic radiologists. This diagnosis consumes time and human effort to determine the severity of the spinal deformity based on the angle of deviation or curvature (Cobb Angle). This research aims to automatically calculate the spine curvature angle in X-ray images using a convolutions neural network based on Mask R-CNN architecture for semantic segmentation of the backbone. The Knowledge Discovery in Databases (KDD) development methodology was used to acquire and construct the image dataset, train the process, and validate the generated modelings in Python and the TensorFlow framework with the Keras library. The statistical results obtained by applying the t-Student statistical test show that there is no statistically significant difference (p value > 0.05) between the degree of manual deviation obtained by 3 specialists (average) and the degree of automatic deviation obtained using the proposed model. Furthermore, Pearson's correlation coefficient of r = 0.99 indicates a high linear association between these evaluations and a relatively low error MAE = 2.34 (degrees). Thus, the model detects scoliosis with similar accuracy to the specialists in significantly less time (15-25 s vs. 2-5 min). Therefore, the model helps physicians understand the severity of scoliosis better and prescribe the most appropriate clinical treatment.

关键词： Medical image analysis Scoliosis detection Deep learning CNN Mask R-CNN Semantic segmentation Cobb's Angle Spine classification

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Enhancing Generative AI Chatbot Accuracy Using Knowledge Graph 33rd

Enhancing Generative AI Chatbot Accuracy Using Knowledge Gra...

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33rd International Conference on Software and Data Engineering

作者： Bandi, Ajay Babu, Jameer Zeng, Ruida Muthyala, Sai Ram Northwest Missouri State Univ Sch Comp Sci & Informat Syst 800 Univ Dr Maryville MO 64468 USA Brown Univ Dept Comp Sci Providence RI 02912 USA

ISBN: (纸本)9783031752001;9783031752018

In recent years, generative AI chatbots have significantly improved in their ability to simulate human-like conversations. However, ensuring the accuracy and contextual relevance of their responses remains a challenge. This paper presents an innovative approach to enhancing the accuracy of generative AI chatbots by integrating knowledge graphs using Neo4j. We demonstrate how combining structured data from Knowledge Graphs with advanced large language models can result in more accurate and context-aware chatbot interactions. By implementing this approach, we aim to provide a robust framework for developing intelligent chatbots that can deliver precise and contextually appropriate responses. We created three categories of test cases: Data-Relevant Inquiries, Non-Contextual Queries, and Contextually Relevant but Data-Irrelevant Questions. The accuracy obtained for the data-relevant test cases was 91.44%.

关键词： Retrieval and Augmented Generation (RAG) Large Language Model (LLM) Knowledge graphs Chatbot Generative AI Word embeddings Vector index Cypher Similarity search Neo4j

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