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Raising Awareness of the Significance of Awadhi Mother Tongue Among Young Adults via Exhibition Design 1

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15th Indian Conference on Human-computer Interaction Design and Research

作者： Anurag, Apoorv Pal, Swati Indian Inst Technol IDC Sch Design Bombay Maharashtra India

ISBN: (数字)9783031808326

ISBN: (纸本)9783031808319;9783031808326

Many mother tongues worldwide are facing a decline in usage and reputation [1], including Awadhi, spoken in Uttar Pradesh, India. Despite its rich literary heritage, Awadhi is often viewed as unsophisticated and inferior, partly due to a lack of awareness and limited use in education and employment [2]. UNESCO's World Atlas of Languages lists Awadhi as having a 'Potentially Vulnerable' language status situation [3]. This project aims to raise awareness about Awadhi through interactive exhibitions, which may eventually enhance perception about it. For this we are targeting young adults aged 17-25 years in schools and colleges in Uttar Pradesh's Awadh region. However, our project scope does not aim for them to start speaking Awadhi.

关键词： Language Exhibition Design Interactive Installations Linguistic Prejudice

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Using the National Integrated Specimen Referral Network as a Vehicle to Reach the 3rd 95 in Nigeria in FY22 5th

Using the National Integrated Specimen Referral Network as a...

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5th 5th Olympus - International Conference on Supply Chain

作者： Theophilusl, Faruna Nelsons, Ekeh Victor, Obianeri Emmanuel, Omolara Folinas, Dimitris USAID GHSC PSM Chemon Abuja Nigeria USAID Abuja Nigeria NASCP Fed Minist Hlth Abuja Nigeria Int Hellen Univ Thessaloniki Greece

ISBN: (纸本)9783031693434;9783031693441

Laboratory investigations are vital in preventive, diagnostic, therapeutic, and referral services. Efficient delivery of medical laboratory testing services at all levels and locations of healthcare services, especially in rural and poor populations, is essential. Nigeria's diagnostic landscape was plagued with under-utilization of medical laboratory equipment, and a formal structure for referral and transport of diagnostic specimens to facilitate rapid diagnosis and laboratory confirmation needed to be revised. To provide quality medical laboratory testing services equitably and cost-effectively, Nigeria needed an integrated, multi-level specimen referral system. The National Integrated Specimen Referral Network (NiSRN) became operational in March 2018. This study examines the NiSRN operations' impact on attaining the third 95 of UNAIDS 95-95-95 targets. Descriptive methodologies were used to assess the viral load (HIV-VL) testing in Nigeria before NiSRN began. Between 2018 and 2022, annual volumes of VL and EID samples transported across the 36 states and the Federal Capital Territory (FCT) and assayed by the Polymerase Chain Reaction (PCR) laboratories were measured. VL suppression was reviewed. Using NiSRN, specimen backlogs were identified and relocated to laboratories that could promptly be analyzed. Eight hundred ninety-nine thousand nine hundred twelve specimens were transported in 2019, rising to 2,001,184 in 2022. VL coverage was 55% in 2018 and steadily rose to 95% in 2022. VL suppression rate was 78% in 2020 and rose to 96% in 2022, exceeding the UNAIDS' third 95 target. The NiSRN, operated by the Federal Government of Nigeria with support from PEPFAR and the Global Fund, has helped to exceed the UNAIDS' third 95 target. As evidenced by these results, PEPFAR and Global Fund teams in Nigeria should continue to support the NiSRN. Nigeria's Government must devise means of continuous sustainability with innovations. Other countries struggling to meet the UNA

关键词： UNAIDS 95-95-95 viral load suppression third-party logistics optimized specimen referral

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Enhancing the Explainability and Interpretability of Crop Yield Prediction Models Through Precision Agriculture 2nd

Enhancing the Explainability and Interpretability of Crop Yi...

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2nd International Conference on Agriculture-Centric Computation

作者： Kumar, Ajay Gill, Anmol Singh Sharma, Ananya Kumar, Vivek Manipal Univ Jaipur Dept Comp Sci & Engn Jaipur 303007 Rajasthan India

ISBN: (纸本)9783031744396;9783031744402

Indian agriculture stands at a critical juncture, poised for transformative change. The research endeavors to explore the intricate landscape of Indian agriculture, addressing the multifaceted decisions surrounding crop selection intertwined with biological and non-biological factors. It explores precision agriculture, a burgeoning field leveraging machine learning and data-driven models to revolutionize crop yield predictions. However, a significant challenge arises regarding the transparency and interpretability of machine learning algorithms essential for crop yield forecasts. Also, the study undertakes a profound exploration, focusing on building trust within India's diverse farming community to harness the benefits of machine learning-based models effectively. It conducts a comprehensive review of existing literature, emphasizing the urgent need for clear, understandable models. These models empower farmers to make informed decisions, enhancing yields and resource management in Indian agriculture. The analysis extends beyond precision agriculture, addressing economic implications, climate-resilient practices, gender inclusivity, and ethical considerations surrounding data utilization. It underscores the imperative to bridge the gap between technological advancements and traditional agricultural practices. This research gathers adaptive strategies, interdisciplinary collaborations, and a holistic vision for a sustainable and technology-enhanced future for Indian agriculture. It advocates for long-term solutions that resonate through time, echoing the essence of a harmonious coexistence between technology and tradition.

关键词： Artificial Intelligence Machine Learning Agriculture Crop Data analytics

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Online Structure Learning with Dirichlet Processes Through Message Passing 5th

Online Structure Learning with Dirichlet Processes Through M...

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5th International Workshop on Active Inference

作者： van Erp, Bart Nuijten, Wouter W. L. de Vries, Bert Eindhoven Univ Technol NL-5612 AP Eindhoven Netherlands Lazy Dynam NL-5611 XD Eindhoven Netherlands GN Hearing NL-5612 AB Eindhoven Netherlands

ISBN: (纸本)9783031771378;9783031771385

Generative or probabilistic modeling is crucial for developing intelligent agents that can reason about their environment. However, designing these models manually for complex tasks is often infeasible. Structure learning addresses this challenge by automating model creation based on sensory observations, balancing accuracy with complexity. Central to structure learning is Bayesian model comparison, which provides a principled framework for evaluating models based on their evidence. This paper focuses on model expansion and introduces an online message passing procedure using Dirichlet processes, a prominent prior in non-parametric Bayesian methods. Our approach builds on previous work by automating Bayesian model comparison using message passing based on variational free energy minimization. We derive novel message passing update rules to emulate Dirichlet processes, offering a flexible and scalable method for online structure learning. Our method generalizes to arbitrary models and treats structure learning identically to state estimation and parameter learning. The experimental results validate the effectiveness of our approach on an infinite mixture model.

关键词： Dirichlet processes Factor graphs Infinite mixture model Message passing Probabilistic inference Scale factors Structure learning

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Augmenting Knowledge Tracing: Personalized Modeling by Considering Forgetting Behavior in Learning Process 7th

Augmenting Knowledge Tracing: Personalized Modeling by Consi...

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8th Asia Pacific Web and Web-Age information Management Joint International Conference on Web and Big Data

作者： Yang, Hongxin Du, Yuefeng Liu, Tingting Ding, Linlin Liaoning Univ Shenyang 110036 Liaoning Peoples R China

ISBN: (纸本)9789819600540;9789819600557

With the development of internet online education system, knowledge tracking (KT) is becoming more and more widely used. This technology can accurately model students' learning process, so as to provide students with a personalized knowledge push. However, most previous KT methods don't take into account the learning ability (LA) and forgetting behavior (FB) of student when assessing the state of knowledge. In fact, each student has their own LA and FB, which plays an important role in the KT process. However, at present, the personalized LA and FB of different students are not given in advance, which makes it more challenging to predict the learning status of each student. To address students' challenges in modeling KT, we design augmenting knowledge tracing (AKT), which first uses concept-wised percent correct (CPC) to describe students' overall mastery of knowledge, and builds an individualized forgetting rate (IFR) to describe the degree of forgetting during student learning process. The relationship between student history learning situation and time is considered, moreover, the knowledge mastery and FBare measured from the interaction with the topic during student learning process. The final experimental results show that the performance of proposed model is better than that of traditional methods.

关键词： knowledge tracking augmenting knowledge tracing concept-wised percent correct individualized forgetting rate

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Applying Machine Learning Approaches with Integrated Internet of Things for Water Management System 4th

Applying Machine Learning Approaches with Integrated Interne...

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

作者： Belmir, Meroua Difallah, Wafa Ghazli, Abdelkader Tahri Mohammed Univ Bechar Fac Exact Sci Dept Math & Comp Sci Bechar Algeria Coding & Informat Secur Lab LACOSI Oran Algeria Lab Energet Arid Zones ENERGARID Bechar Algeria

ISBN: (纸本)9783031821523;9783031821530

The challenges of water loss and inadequate scheduling in traditional irrigation methods hinder the ability to meet the increasing demand for food production while also conserving valuable water resources. In order to overcome these issues, this study introduces an advanced smart irrigation system that harnesses the capabilities of the Internet of Things (IoT), data analysis, and machine learning. The primary objective is to determine the most effective method for applying and scheduling irrigation, thus optimizing resource usage. This innovative system relies on a meticulously curated dataset, "INTELLIGENT IRRIGATION SYSTEM" obtained from Kaggle. The dataset encompasses four essential attributes, extensively detailed in the metadata, providing foundational information crucial for precise irrigation management. This paper proposes an intelligent irrigation approach that can be deployed in different contexts. This approach is based on machine learning algorithms. Multiple models, including K-Nearest Neighbors (KNN), Logistic Regression (LR), and Gaussian process (GP) classifier, were employed in the development of this approach. The results showed that LR and GP are better with an accuracy of 0.9833, precision of 0.9782, F1 score of 0.9890, specificity of 0.9333, and sensitivity of 1.0 compared to KNN model.

关键词： Smart irrigation system machine learning smart agriculture Internet of Things

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Highly Adaptive Dual-Robot Collaborative Embodied Intelligence Grasping System 5th

Highly Adaptive Dual-Robot Collaborative Embodied Intelligen...

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5th China Intelligent Robotics Academic Conference

作者： Song, Honghao Ma, Jitong Yuan, Xuchang Liu, Yingqi Yao, Xiaonan Chen, Xiao Liu, Xiaokai Wang, Jie Dalian Maritime Univ Informat Sci & Technol Coll Dalian 116026 Peoples R China Dalian Maritime Univ Marine Engn Coll Dalian 116026 Peoples R China

ISBN: (纸本)9789819616138;9789819616145

With the development of artificial intelligence technology, the application prospect of Embodied AI robots has been widely recognized by the industry and has a vital role in industrial production, scientific research, and other scenarios. Usually, the perception of the environment by an Embodied AI robot is based on the hand-eye calibration system;however, in practice, the vision system is often affected by the attitude of the robot's end-effector and the limitation of the gripper's range of motion, to deal with this problem, we propose a dual-mechanicalarm collaborative grasping system for an intelligent robot, which consists of a six-degree-of-freedom grasping arm and a six-degree-of-freedom searching arm. We construct a kinematic model for the grasping arm based on the DH method, and at the same time, we configure the Fast-RCNN-based part visual recognition algorithm in the search arm to realize the localization of the position of the end apparatus of the grasping arm and construct a smooth grasping arm motion trajectory scheme. We validated the proposed kinematic planning and image recognition methods in both the Matlab and PyTorch frameworks. Additionally, we conducted practical experiments on cooperative grasping with dual arms. The results indicate that the dual-arm cooperative system can achieve high-precision localization of target objects and execute smooth joint motion trajectories for grasping under specific orientations. This effectively enhances the operational accuracy and capability of the robot in grasping scenarios.

关键词： Embodied AI 6-DOF Mechanical Arm Visual Recognition

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The Effect of a Weighted Mean Pooling on the Performance of Convolution Neural Network 4th

The Effect of a Weighted Mean Pooling on the Performance of ...

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

作者： Shadoul, Inas Hossen, Abdulnasir Mesbah, Mostefa Al-Hmouz, Rami Sultan Qaboos Univ Dept Elect Engn Coll Engn Al Khoud Oman

ISBN: (纸本)9783031821493;9783031821509

Convolutional Neural Networks (CNNs), a deep learning application, are powerful tools particularly suited for image processing and classification applications. Pooling is a major component of CNNs and significantly influences learning. In this step, data is reduced in size through a specific algorithm or technique, resulting in the reduction of the computational load on the layers. The various pooling techniques in CNNs have specific uses, features, and effects, some desirable and others counterproductive to the training goal. Using one pooling technique can produce results that other techniques cannot. Some cases can benefit differently from different pooling techniques. This raises the question of whether combining these pooling techniques could achieve a collective positive impact, potentially leading to performance gains beyond those achievable by individual techniques used separately. A control parameter is added to optimize the selection of the pooling method or could be a weighted combination of more than one method. The results show that the presented method guarantees the same performance as a single pooling layer at least and could be improved when weighted pooling layers are involved in some datasets.

关键词： Machine learning Convolution Neural Network (CNN) Pooling Mixed pooling Image classification

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Addressing WSN Deployments Over Obstacle Clad- Irregular Terrains 4th

Addressing WSN Deployments Over Obstacle Clad- Irregular Ter...

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24th International Conference Web Engineering (ICWE)

作者： Tyagi, Shekhar Srivastava, Abhishek Indian Inst Technol Indore Indore India

ISBN: (纸本)9783031751097;9783031751103

Wireless Sensor Networks (WSNs) are majorly employed for real time data collection and monitoring, particularly in scenarios characterized by environments that are harsh for direct human involvement. Optimised deployment of WSN nodes is a long standing issue and several ideas have been proposed to address this. Existing deployment strategies are mostly based on the assumption that the terrain for deployment of nodes is perfectly regular. This is an impractical assumption and in this paper we address this gap by proposing a deployment strategy for WSN nodes over irregular terrains. Such terrains comprise uneven elevations, morphology and vegetation based obstacles, rocky obstacles and so on. Our approach comprises extraction of satellite images of the Region of Interest (RoI) from Google Earth and generating a KML file (Keyhole Markup File) for the RoI containing the latitude, longitude, and elevation values of each and every point in the RoI. These points are used to generate a contour map of the RoI containing detailed terrain morphology. A radio frequency path loss model in combination with an advanced Inverse Distance Weighted (IDW)-Interpolation technique is proposed that ensures connectivity and coverage in such irregular terrains with varying nature of obstacles. The technique effectively detects occlusions and enables effective deployment. The approach is compared with existing deployment techniques over simulated irregular settings and the results validate its efficacy.

关键词： Wireless Sensor Network Irregular obstacle ridden-Terrain Sensor deployment

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Data and Knowledge Dual-Driven Traffic Sign Recognition Algorithm 4th

Data and Knowledge Dual-Driven Traffic Sign Recognition Algo...

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4th International Conference on Artificial Intelligence Logic and Applications

作者： Huang, Guopeng Chen, Xinyu Chen, Yixiang East China Normal Univ Natl Engn Res Ctr Trustworthy Embedded Software Shanghai 200062 Peoples R China

ISBN: (纸本)9789819603534;9789819603541

Traffic sign recognition can quickly extract and identify road perception information, making it one of the key technologies in the field of autonomous driving. The reliable recognition of traffic signs is the primary task in achieving safe autonomous driving. However, current machine learning-based traffic sign recognition technologies still suffer from low interpretability and an excessive dependence on the sample space. Therefore, this paper proposes a Data and Knowledge Dual-Driven Traffic Sign Recognition Algorithm that leverages the convenience of data-driven methods and the reliability of knowledge-driven approaches. The algorithm integrates data-driven methods such as color recognition, shape recognition, and convolutional neural networks, along with knowledge-driven methods that involve reasoning based on traffic sign knowledge base, to accomplish the task of traffic sign recognition. The experimental results demonstrate that compared to other methods, the dual-drive approach proposed in this paper can identify traffic signs with greater accuracy and reliability.

关键词： Traffic Sign Recognition Convolutional Neural Networks Siamese Neural Networks Anomaly Recognition

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