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Large Language Models (LLMs) can perform various natural language processing tasks with suitable instruction prompts. However, designing effective prompts manually is challenging and time-consuming. Existing methods for automatic prompt optimization either lack flexibility or efficiency. In this paper, we propose an effective approach to automatically select the optimal prompt for a given input from a finite set of synthetic candidate prompts. Our approach consists of three steps: (1) clustering the training data and generating candidate prompts for each cluster using an LLM-based prompt generator; (2) synthesizing a dataset of input-prompt-output tuples for training a prompt evaluator to rank the prompts based on their relevance to the input; (3) using the prompt evaluator to select the best prompt for a new input at test time. Our approach balances prompt generality-specificity and eliminates the need for resource-intensive training and inference. It demonstrates competitive performance on zero-shot question-answering datasets: GSM8K, MultiArith, and AQuA., Comment: preprint

Large language models (LLMs) have significantly advanced the field of natural language processing, with GPT models at the forefront. While their remarkable performance spans a range of tasks, adapting LLMs for real-world business scenarios still poses challenges warranting further investigation. This paper presents an empirical analysis aimed at bridging the gap in adapting LLMs to practical use cases. To do that, we select the question answering (QA) task of insurance as a case study due to its challenge of reasoning. Based on the task we design a new model relied on LLMs which are empowered by additional knowledge extracted from insurance policy rulebooks and DBpedia. The additional knowledge helps LLMs to understand new concepts of insurance for domain adaptation. Preliminary results on two QA datasets show that knowledge enhancement significantly improves the reasoning ability of GPT-3.5 (55.80% and 57.83% in terms of accuracy). The analysis also indicates that existing public knowledge bases, e.g., DBPedia is beneficial for knowledge enhancement. Our findings reveal that the inherent complexity of business scenarios often necessitates the incorporation of domain-specific knowledge and external resources for effective problem-solving., Comment: Ongoing work to adapt LLMs for business scenarios

We address catastrophic forgetting issues in graph learning as incoming data transits from one to another graph distribution. Whereas prior studies primarily tackle one setting of graph continual learning such as incremental node classification, we focus on a universal approach wherein each data point in a task can be a node or a graph, and the task varies from node to graph classification. We propose a novel method that enables graph neural networks to excel in this universal setting. Our approach perseveres knowledge about past tasks through a rehearsal mechanism that maintains local and global structure consistency across the graphs. We benchmark our method against various continual learning baselines in real-world graph datasets and achieve significant improvement in average performance and forgetting across tasks., Comment: 16 pages

For summarization, human preference is critical to tame outputs of the summarizer in favor of human interests, as ground-truth summaries are scarce and ambiguous. Practical settings require dynamic exchanges between human and AI agent wherein feedback is provided in an online manner, a few at a time. In this paper, we introduce a new framework to train summarization models with preference feedback interactively. By properly leveraging offline data and a novel reward model, we improve the performance regarding ROUGE scores and sample-efficiency. Our experiments on three various datasets confirm the benefit of the proposed framework in active, few-shot and online settings of preference learning., Comment: The paper is accepted at NAACL 2022

Automatic summarization of legal texts is an important and still a challenging task since legal documents are often long and complicated with unusual structures and styles. Recent advances of deep models trained end-to-end with differentiable losses can well-summarize natural text, yet when applied to legal domain, they show limited results. In this paper, we propose to use reinforcement learning to train current deep summarization models to improve their performance on the legal domain. To this end, we adopt proximal policy optimization methods and introduce novel reward functions that encourage the generation of candidate summaries satisfying both lexical and semantic criteria. We apply our method to training different summarization backbones and observe a consistent and significant performance gain across 3 public legal datasets.

Modern problems in agricultural seek methods to increase the convenience, reliability accuracy and at the same time, cost efficacy in precision farming. To exploit the global research gaps in sustainable agriculture, this thesis organizes three different works committed to provide solutions to three modern agricultural problems: reliable crop modeling, accurate crop counting and cost-effective crop health monitoring. As strategic solutions, this study proposes the use of emerging advancements on recent artificial intelligence technologies, weather forecasting, web-services, aerial imagery and digital image processing to connect the crucial cores of sustainable agriculture visions. The outputs of this work is targeted help decision makers, and specially the farmers themselves, to increase the productivity of farms for better yield, and to draw young people to the farms to meet the global hunger of 21st century with sustainability in life support and food security. The unavailability of seasonal weather data at the simulation time (often beginning of a growth season) forces decision makers in crop growth assessment to consider multiple weather scenarios, which are often generated from longtime observed data. Ironically these scenario immediately become "outdated" as soon as the season begins, because they are always different from newly observed data. In the first research of this thesis, we investigate this dilemma and in particular address three questions: determination of most successful scenario, classification of scenarios into fresh and stale, and generation of a new scenario from fresh scenarios. Algorithms to solve these questions are given as strategies for prediction games of weather generators. We also elaborate on the applications of our results in networking existing weather generation web services. The production of banana - one of the highly consumed fruits - is highly affected due to loss of certain number of banana plants in an early phase of vegetati

Web service composition is an aggregation of two or more related web services to perform a particular task automatically. Though much attention has been paid on composing the new web service or increasing the efficiency as well as quality of such composition, a closely related but distinct problem of composing requests to existing combination services has been relatively unexplored. Users have a big pool of service combinations, however, they have no idea or it is beyond their capabilities to generate a request for using those. This problem could become so complex that demands a supporting system of their own kind. Therefore, we are motivated to design a supporting system that helps with composing web service request for complex systems. Since very less research has been done on this issue, we believe that our work will contribute a very first and important approach for further development. In the scope of this thesis, we first formalize such systems as logic programs, abstracting away from specific implementation languages. We then develop a communication protocol to combine them into a cooperative multi-agent capable of composing requests that cannot be composed by any individual system. We also discuss the potential advantages that future web service systems could take from our approach.

Web service composition is an aggregation of two or more related web services to perform a particular task automatically. Though much attention has been paid on composing the new web service or increasing the efficiency as well as quality of such composition, a closely related but distinct problem of composing requests to existing combination services has been relatively unexplored. Users have a big pool of service combinations, however, they have no idea or it is beyond their capabilities to generate a request for using those. This problem could become so complex that demands a supporting system of their own kind. Therefore, we are motivated to design a supporting system that helps with composing web service request for complex systems. Since very less research has been done on this issue, we believe that our work will contribute a very first and important approach for further development. In the scope of this thesis, we first formalize such systems as logic programs, abstracting away from specific implementation languages. We then develop a communication protocol to combine them into a cooperative multi-agent capable of composing requests that cannot be composed by any individual system. We also discuss the potential advantages that future web service systems could take from our approach.

Modern problems in agricultural seek methods to increase the convenience, reliability accuracy and at the same time, cost efficacy in precision farming. To exploit the global research gaps in sustainable agriculture, this thesis organizes three different works committed to provide solutions to three modern agricultural problems: reliable crop modeling, accurate crop counting and cost-effective crop health monitoring. As strategic solutions, this study proposes the use of emerging advancements on recent artificial intelligence technologies, weather forecasting, web-services, aerial imagery and digital image processing to connect the crucial cores of sustainable agriculture visions. The outputs of this work is targeted help decision makers, and specially the farmers themselves, to increase the productivity of farms for better yield, and to draw young people to the farms to meet the global hunger of 21st century with sustainability in life support and food security. The unavailability of seasonal weather data at the simulation time (often beginning of a growth season) forces decision makers in crop growth assessment to consider multiple weather scenarios, which are often generated from longtime observed data. Ironically these scenario immediately become "outdated" as soon as the season begins, because they are always different from newly observed data. In the first research of this thesis, we investigate this dilemma and in particular address three questions: determination of most successful scenario, classification of scenarios into fresh and stale, and generation of a new scenario from fresh scenarios. Algorithms to solve these questions are given as strategies for prediction games of weather generators. We also elaborate on the applications of our results in networking existing weather generation web services. The production of banana - one of the highly consumed fruits - is highly affected due to loss of certain number of banana plants in an early phase of vegetati

Modern problems in agricultural seek methods to increase the convenience, reliability accuracy and at the same time, cost efficacy in precision farming. To exploit the global research gaps in sustainable agriculture, this thesis organizes three different works committed to provide solutions to three modern agricultural problems: reliable crop modeling, accurate crop counting and cost-effective crop health monitoring. As strategic solutions, this study proposes the use of emerging advancements on recent artificial intelligence technologies, weather forecasting, web-services, aerial imagery and digital image processing to connect the crucial cores of sustainable agriculture visions. The outputs of this work is targeted help decision makers, and specially the farmers themselves, to increase the productivity of farms for better yield, and to draw young people to the farms to meet the global hunger of 21st century with sustainability in life support and food security. The unavailability of seasonal weather data at the simulation time (often beginning of a growth season) forces decision makers in crop growth assessment to consider multiple weather scenarios, which are often generated from longtime observed data. Ironically these scenario immediately become "outdated" as soon as the season begins, because they are always different from newly observed data. In the first research of this thesis, we investigate this dilemma and in particular address three questions: determination of most successful scenario, classification of scenarios into fresh and stale, and generation of a new scenario from fresh scenarios. Algorithms to solve these questions are given as strategies for prediction games of weather generators. We also elaborate on the applications of our results in networking existing weather generation web services. The production of banana - one of the highly consumed fruits - is highly affected due to loss of certain number of banana plants in an early phase of vegetati

Web service composition is an aggregation of two or more related web services to perform a particular task automatically. Though much attention has been paid on composing the new web service or increasing the efficiency as well as quality of such composition, a closely related but distinct problem of composing requests to existing combination services has been relatively unexplored. Users have a big pool of service combinations, however, they have no idea or it is beyond their capabilities to generate a request for using those. This problem could become so complex that demands a supporting system of their own kind. Therefore, we are motivated to design a supporting system that helps with composing web service request for complex systems. Since very less research has been done on this issue, we believe that our work will contribute a very first and important approach for further development. In the scope of this thesis, we first formalize such systems as logic programs, abstracting away from specific implementation languages. We then develop a communication protocol to combine them into a cooperative multi-agent capable of composing requests that cannot be composed by any individual system. We also discuss the potential advantages that future web service systems could take from our approach.

Modern problems in agricultural seek methods to increase the convenience, reliability accuracy and at the same time, cost efficacy in precision farming. To exploit the global research gaps in sustainable agriculture, this thesis organizes three different works committed to provide solutions to three modern agricultural problems: reliable crop modeling, accurate crop counting and cost-effective crop health monitoring. As strategic solutions, this study proposes the use of emerging advancements on recent artificial intelligence technologies, weather forecasting, web-services, aerial imagery and digital image processing to connect the crucial cores of sustainable agriculture visions. The outputs of this work is targeted help decision makers, and specially the farmers themselves, to increase the productivity of farms for better yield, and to draw young people to the farms to meet the global hunger of 21st century with sustainability in life support and food security. The unavailability of seasonal weather data at the simulation time (often beginning of a growth season) forces decision makers in crop growth assessment to consider multiple weather scenarios, which are often generated from longtime observed data. Ironically these scenario immediately become "outdated" as soon as the season begins, because they are always different from newly observed data. In the first research of this thesis, we investigate this dilemma and in particular address three questions: determination of most successful scenario, classification of scenarios into fresh and stale, and generation of a new scenario from fresh scenarios. Algorithms to solve these questions are given as strategies for prediction games of weather generators. We also elaborate on the applications of our results in networking existing weather generation web services. The production of banana - one of the highly consumed fruits - is highly affected due to loss of certain number of banana plants in an early phase of vegetati

Web service composition is an aggregation of two or more related web services to perform a particular task automatically. Though much attention has been paid on composing the new web service or increasing the efficiency as well as quality of such composition, a closely related but distinct problem of composing requests to existing combination services has been relatively unexplored. Users have a big pool of service combinations, however, they have no idea or it is beyond their capabilities to generate a request for using those. This problem could become so complex that demands a supporting system of their own kind. Therefore, we are motivated to design a supporting system that helps with composing web service request for complex systems. Since very less research has been done on this issue, we believe that our work will contribute a very first and important approach for further development. In the scope of this thesis, we first formalize such systems as logic programs, abstracting away from specific implementation languages. We then develop a communication protocol to combine them into a cooperative multi-agent capable of composing requests that cannot be composed by any individual system. We also discuss the potential advantages that future web service systems could take from our approach.

This paper addresses the problem of lane detection which is fundamental for self-driving vehicles. Our approach exploits both colour and depth information recorded by a single RGB-D camera to better deal with negative factors such as lighting conditions and lane-like objects. In the approach, colour and depth images are first converted to a half-binary format and a 2D matrix of 3D points. They are then used as the inputs of template matching and geometric feature extraction processes to form a response map so that its values represent the probability of pixels being lane markers. To further improve the results, the template and lane surfaces are finally refined by principal component analysis and lane model fitting techniques. A number of experiments have been conducted on both synthetic and real datasets. The result shows that the proposed approach can effectively eliminate unwanted noise to accurately detect lane markers in various scenarios. Moreover, the processing speed of 20 frames per second under hardware configuration of a popular laptop computer allows the proposed algorithm to be implemented for real-time autonomous driving applications., Comment: 2018 2nd International Conference on Recent Advances in Signal Processing, Telecommunications & Computing (SigTelCom)

Modern problems in agricultural seek methods to increase the convenience, reliability accuracy and at the same time, cost efficacy in precision farming. To exploit the global research gaps in sustainable agriculture, this thesis organizes three different works committed to provide solutions to three modern agricultural problems: reliable crop modeling, accurate crop counting and cost-effective crop health monitoring. As strategic solutions, this study proposes the use of emerging advancements on recent artificial intelligence technologies, weather forecasting, web-services, aerial imagery and digital image processing to connect the crucial cores of sustainable agriculture visions. The outputs of this work is targeted help decision makers, and specially the farmers themselves, to increase the productivity of farms for better yield, and to draw young people to the farms to meet the global hunger of 21st century with sustainability in life support and food security. The unavailability of seasonal weather data at the simulation time (often beginning of a growth season) forces decision makers in crop growth assessment to consider multiple weather scenarios, which are often generated from longtime observed data. Ironically these scenario immediately become "outdated" as soon as the season begins, because they are always different from newly observed data. In the first research of this thesis, we investigate this dilemma and in particular address three questions: determination of most successful scenario, classification of scenarios into fresh and stale, and generation of a new scenario from fresh scenarios. Algorithms to solve these questions are given as strategies for prediction games of weather generators. We also elaborate on the applications of our results in networking existing weather generation web services. The production of banana - one of the highly consumed fruits - is highly affected due to loss of certain number of banana plants in an early phase of vegetati

Web service composition is an aggregation of two or more related web services to perform a particular task automatically. Though much attention has been paid on composing the new web service or increasing the efficiency as well as quality of such composition, a closely related but distinct problem of composing requests to existing combination services has been relatively unexplored. Users have a big pool of service combinations, however, they have no idea or it is beyond their capabilities to generate a request for using those. This problem could become so complex that demands a supporting system of their own kind. Therefore, we are motivated to design a supporting system that helps with composing web service request for complex systems. Since very less research has been done on this issue, we believe that our work will contribute a very first and important approach for further development. In the scope of this thesis, we first formalize such systems as logic programs, abstracting away from specific implementation languages. We then develop a communication protocol to combine them into a cooperative multi-agent capable of composing requests that cannot be composed by any individual system. We also discuss the potential advantages that future web service systems could take from our approach.