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SSRN 2022年

作者： Xie, Jiayuan Fang, Wenhao Chen, Jiali Cai, Yi Li, Qing School of Software Engineering South China University of Technology Guangzhou China Ministry of Education China Department of Computing Hong Kong Polytechnic University Hong Kong

Visual question generation aims to focus on some target objects in an image to generate questions with certain questioning purposes. Existing studies mainly utilize an answer to extract the target object corresponding to the questioning purpose for questioning. However, answers fail to accurately and completely map to every target object, such as the objects corresponding to the answer are ambiguous or the answers are the relationship between multiple objects. To address this problem, we propose a content-controlled question generation model, which generates questions based on a given target object set specified from an image. Considering that the target objects have different contributions during the generation process, we design a recurrent generative architecture to explicitly control attention to different objects and their corresponding image information at each generative stage. Extensive experiments on the VQA v2.0 dataset and the Visual7w dataset show that the proposed model outperforms the state-of-the-art models and can controllably generate questions with specified content. © 2022, The Authors. All rights reserved.

关键词： Machine learning

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Parallel Global Sequence Alignment with Arbitrary Scoring Scheme: Algorithm Formulation and Evaluation

SSRN

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SSRN 2022年

作者： Sadiq, Muhammad Umair Yousaf, Muhammad Murtaza Department of Computer Science Government College University Lahore54000 Pakistan Department of Software Engineering Faculty of Computing and Information Technology University of the Punjab Lahore54000 Pakistan Faculty of Computing and Information Technology University of the Punjab Lahore54000 Pakistan

Alignment of the sequences is useful in many biological and non-biological domains. Needleman-Wunsch algorithm is deemed as the basic algorithm for finding the optimal alignment between two sequences. It is a dynamic programming based algorithm which has quadratic time and space requirement. Nonetheless, as a result of increase in the capacity of sequence databases in recent years, sizes of sequences may contain billions of characters for computationally intensive problems. Comparison of such large sequences using sequential methods is avoided as it takes unacceptable amount of time for producing the results. In this paper, we proposed a parallel variation of the Needleman-Wunsch algorithm by reformulating the dependencies in the prevalent dynamic programming solution. Proof of correctness of the proposed algorithm is established. It evinces certain characteristics such as it allows arbitrary linear scoring scheme when comparing the sequences which is important to obtain peerless alignment. Moreover, it ensures load balancing among the computational units and maximizes the utilization of resources. It is compared with various state-of-the-art approaches. Experimental results using the real DNA and user trajectories sequences over graphic processing units (GPUs) suggest that the proposed algorithm is notably superior to the existing approaches that allow arbitrary linear scoring schemes. It achieved the GCUPs up to 14.06 and maximum speedup of 48.18x compared to the sequential implementation. © 2022, The Authors. All rights reserved.

关键词： Dynamic programming

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Smartformer: Graph-Based Transformer Model for Energy Load Forecasting

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SSRN 2024年

作者： Saeed, Faisal Rehman, Abdul Shah, Hasnain Ali Diyan, Muhammad Jie, Chen Kang, Jae-Mo College of Computer Science and Software Engineering Shenzhen University Guangdong Shenzhen518060 China School of Computer Science and Engineering Kyungpook National University Buk-gu Daegu41566 Korea Republic of Department of Artificial Intelligence Kyungpook National University Buk-gu Daegu41566 Korea Republic of Department of Computing and Games Teesside University Middlesbrough United Kingdom

Electric load forecasting is a pivotal component in the power industry, providing essential intelligence for optimizing smart grid operations. Energy load data, inherently characterized as a multivariate time series, is influenced by various interdependent factors such as weather conditions, economic activity, and seasonal variations, all of which significantly impact the overall load dynamics. Though deep learning techniques, particularly with transformer-based models, have achieved significant progress in forecasting time series data, a gap exists in adequately acknowledging the importance of inter-series dependencies in multi-series load data. This paper proposes a novel graph-nested transformer model to effectively capture inter-series dependencies and forecast the load using a graph structure. The proposed Transformer model addresses two primary challenges: efficiently representing various temporal patterns and reducing redundant information within the series. In the proposed model, the graph neural network components are seamlessly integrated into the Transformer layers, allowing for the fusion of sequence encoding and graph aggregation in an iterative workflow. Evaluations across four distinct datasets demonstrate the superiority of the proposed model over state-of-the-art techniques in power load forecasting. © 2024, The Authors. All rights reserved.

关键词： Smart power grids

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An Empathetic AI Coach for Self-Attachment Therapy

arXiv

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arXiv 2022年

作者： Alazraki, Lisa Ghachem, Ali Polydorou, Neophytos Khosmood, Foaad Edalat, Abbas Department of Computing Imperial College London United Kingdom Computer Science and Software Engineering California Polytechnic State University San Luis Obispo United States

In this work, we present a new dataset and a computational strategy for a digital coach that aims to guide users in practicing the protocols of self-attachment therapy. Our framework augments a rule-based conversational agent with a deep-learning classifier for identifying the underlying emotion in a user’s text response, as well as a deep-learning assisted retrieval method for producing novel, fluent and empathetic utterances. We also craft a set of human-like personas that users can choose to interact with. Our goal is to achieve a high level of engagement during virtual therapy sessions. We evaluate the effectiveness of our framework in a non-clinical trial with N=16 participants, all of whom have had at least four interactions with the agent over the course of five days. We find that our platform is consistently rated higher for empathy, user engagement and usefulness than the simple rule-based framework. Finally, we provide guidelines to further improve the design and performance of the application, in accordance with the feedback received. Copyright © 2022, The Authors. All rights reserved.

关键词： Deep learning

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Bayesian Formal Synthesis of Unknown Systems via Robust Simulation Relations

arXiv

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arXiv 2023年

作者： Schön, Oliver van Huijgevoort, Birgit Haesaert, Sofie Soudjani, Sadegh The School of Computing Newcastle University NewcastleNE4 5TG United Kingdom The Electrical Engineering Department TU Eindhoven Netherlands The Max Planck Institute for Software Systems KaiserslauternD-67663 Germany

This paper addresses the problem of data-driven computation of controllers that are correct by design for safety-critical systems and can provably satisfy (complex) functional requirements. With a focus on continuous-space stochastic systems with parametric uncertainty, we propose a two-stage approach that decomposes the problem into a learning stage and a robust formal controller synthesis stage. The first stage utilizes available Bayesian regression results to compute robust credible sets for the true parameters of the system. For the second stage, we introduce methods for systems subject to both stochastic and parametric uncertainties. We provide simulation relations for enabling correct-by-design control refinement that are founded on coupling uncertainties of stochastic systems via sub-probability measures. The presented relations are essential for constructing abstract models that are related to not only one model but to a set of parameterized models. The results are demonstrated on three case studies, including a nonlinear and a high-dimensional system. © 2023, CC BY-NC-ND.

关键词： Stochastic systems

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Transparent AI for Adaptive Fraud Detection

Transparent AI for Adaptive Fraud Detection

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International Conference on Ubiquitous Information Management and Communication (IMCOM)

作者： Okechukwu Clement Agomuo Agomuo Kingsley Uzoma Zohaib Khan Agomuo Ijeoma Otuomasirichi Junaid Hussain Muzamal Department of Operations and Information Systems University of Utah Salt Lake City USA Center for Environmental Management and Green Energy University of Nigeria Enugu Nigeria dept. of Software Engineering FAST - NUCES Lahore Pakistan Department of Agricultural Economics University of Nigeria Enugu Nigeria Fast School of computing Fast - NUCES Lahore Pakistan

ISBN: (数字)9798331507817

ISBN: (纸本)9798331507824

The rapid evolution of digital financial services has significantly increased the vulnerability of financial systems to fraudulent activities. Traditional machine learning models have offered substantial progress in detecting fraudulent transactions; however, they often lack adaptability and transparency, critical for dynamic financial environments and regulatory compliance. This paper introduces a novel approach that integrates Deep Reinforcement Learning (DRL) with Explainable Artificial Intelligence (XAI) to enhance fraud detection capabilities in cloud-based financial platforms. Our model, tested on the synthetic PaySim mobile money transaction dataset, leverages the adaptive nature of DRL to continuously learn and update its fraud detection strategies based on evolving transaction patterns. Concurrently, the integration of XAI provides clear, interpretable insights into the decision-making process, thus ensuring transparency and building trust. The proposed model demonstrates superior performance with an achieved accuracy of 99.5% and an Fl-score of 99.0%, significantly outperforming traditional fraud detection systems. These results not only underscore the efficacy of our model in improving the accuracy of fraud detection but also highlight its capability in offering significant improvements in understanding the underlying reasons for each decision, thereby aiding compliance with financial regulations. The practical implications of this research highlight its potential to provide a robust, scalable, and transparent solution for realtime fraud detection, which is essential for safeguarding the integrity of modern financial systems.

关键词： Adaptation models Accuracy Explainable AI Scalability Decision making Predictive models Regulation Fraud Security Standards

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Automated Processing of Pipelines Managing Now- and Forecasting of Infectious Diseases 47

Automated Processing of Pipelines Managing Now- and Forecast...

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47th ICT and Electronics Convention, MIPRO 2024

作者： Memon, Shahbaz Jadebeck, Johann F. Osthege, Michael Wendler, Anna Kerkmann, David Zunker, Henrik Wiechert, Wolfgang Nöh, Katharina Göbbert, Jens Henrik Hagemeier, Björn Riedel, Morris Kühn, Martin J. Forschungszentrum Jülich GmbH Jülich Supercomputing Centre Jülich Germany Institute For Bio- And Geosciences IBG-1: Biotechnology Forschungszentrum Jülich GmbH Jülich Germany Aachen Germany Institute For Software Technology German Aerospace Center Department For High-Performance Computing Cologne Germany Helmholtz Centre For Infection Research Brunswick Germany University Of Iceland School Of Engineering And Natural Sciences Reykjavik Iceland Life And Medical Sciences Institute University Of Bonn Bonn Germany

ISBN: (纸本)9798350382495

When faced with the challenge of now- and forecasting infectious diseases, multiple data sources and state-of-the-art models have to be considered. Automatic aggregation, processing, and publishing to relevant data sinks is paramount to achieving consistent, reproducible, and timely results given daily-reported data. To facilitate scientific collaboration and reproducibility of workflows, open and extensible architectures for compute pipelines are *** this research, we devise an architecture realizing the seamless management and processing of reproducible pipelines. Our case-study is a daily pipeline for nowcasting the state of SARS-CoV-2 in Germany based on public data and state-of-the-art models implemented in the simulation software MEmilio. The results of our pipeline are pushed to ESID (Epidemiological Scenarios for Infectious Diseases), a user interface to epidemiological *** realize the given pipeline, a workflow management system is required to ensure pipeline processing and secure access to multiple heterogeneous data storages. For this purpose, we based our work on an open-source workflow management system - Apache Airflow, which provides the orchestration, coordination and management of complex connected tasks. S3 is utilized as an intermediate data storage service for sharing data between workflow steps and persisting experiment output. We provide a comprehensive view on our work on automated, end-to-end and reproducible pipelines, with detailed commentary on use case, and its realization. © 2024 IEEE.

关键词： Pipelines

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Data-Driven Decision Management: Multi-Modal Analytics in a Health Case Study

Data-Driven Decision Management: Multi-Modal Analytics in a ...

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International Conference on Ubiquitous Information Management and Communication (IMCOM)

作者： Okechukwu Clement Agomuo Zohaib Khan Agomuo Ijeoma Otuomasirichi Agomuo Kingsley Uzoma Junaid Hussain Muzamal Department of Operations and Information Systems University of Utah Salt Lake City USA dept. of Software Engineering FAST - NUCES Lahore Pakistan Department of Agricultural Economics University of Nigeria Enugu Nigeria Center for Environmental Management and Green Energy University of Nigeria Enugu Nigeria Fast - NUCES Fast School of computing Lahore Pakistan

ISBN: (数字)9798331507817

ISBN: (纸本)9798331507824

In an era where data is abundant yet underutilized, organizations across various sectors face challenges in extracting actionable insights from complex, multi-modal datasets. To address these challenges, this study proposes a versatile data analytics framework designed to enhance predictive accuracy and support informed decision-making. The framework integrates structured and unstructured data, leveraging advanced Transformer architectures to capture temporal dynamics and complex interactions within the data. In a healthcare use case, the framework demonstrated its effectiveness by achieving an Accuracy of 0.854, an F1-Score of 0.895, and an AUC-ROC of 0.930, significantly outperforming traditional models. By incorporating SHAP for interpretability, the framework ensures that its predictions are both accurate and transparent, making it a valuable tool for decision-makers. While healthcare is one application, the framework's flexibility allows it to be adapted to a wide range of industries. Future work will focus on validating the framework across different sectors and integrating it into operational workflows to optimize decision-making and outcomes.

关键词： Accuracy Decision making Mortality Medical services Organizations Predictive models Transformers Data models Information management X-ray imaging

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A Data-Driven Approach Towards software Regression Testing Quality Optimization

A Data-Driven Approach Towards Software Regression Testing Q...

引用

Information Technology (ACIT)

作者： Abdallah Moubayed Nouh Alhindawi Jamal Alsakran MohammadNoor Injadat Mohammad Kanan Computer Engineering Department and Interdisciplinary Research Center for Intelligent Secure Systems King Fahd University of Petroleum & Minerals Dhahran Saudi Arabia School of Computing and Augmented Intelligence Arizona State University Gilbert Arizona USA Department of Software Engineering Jadara University Irbid Jordan Fairleigh Dickinson University Canada Data Science & Artificial Intelligence Department Zarqa University Zarqa Jordan Department of Industrial Engineering University of Business and Technology Jeddah Saudi Arabia

ISBN: (数字)9798331540012

ISBN: (纸本)9798331540029

software testing is very important in software development to ensure its quality and reliability. As software systems have become more complex, the number of test cases has increased, which presents the challenge of executing all the tests in a limited time frame. Various test case prioritization techniques have been introduced to solve this problem. These methods aim to identify and implement the most critical tests first. In this paper, we propose an implementation of a dynamic test case prioritization strategy to improve software quality by increasing code coverage with special attention to edge case handling. Edge case test prioritization is a technique that improves test efficiency by selecting extreme case scenarios that can reveal critical bugs or unexpected behavior early in development, improving overall software reliability and dependability. In order to prioritize test cases, this paper presents a regression-based method that makes use of machine learning algorithms. The approach leverages previous performance data to optimize regression testing efficiency by examining variables like test time and execution status. Performance evaluations, when compared against industry standards and cutting-edge techniques, show how effective these algorithms are at correctly prioritizing test cases and identifying faults. This study offers simplified yet reliable solutions for regression testing optimization by shedding light on the efficacy of regression algorithms, such as Random Forest and decision trees.

关键词： software testing Machine learning algorithms software algorithms software quality software systems software reliability Regression tree analysis Optimization Standards software development management

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Optimizing Mobile-Friendly Viewport Prediction for Live 360-Degree Video Streaming

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IEEE Transactions on Mobile computing 2025年

作者： Zhang, Lei Chen, Peng Zhang, Cong Pan, Cheng Long, Tao Xu, Weizhen Cui, Laizhong Liu, Jiangchuan Shenzhen University College of Computer Science and Software Engineering Guangdong Shenzhen518060 China Guangdong Shenzhen518000 China University of Hong Kong Department of Electrical and Electronic Engineering Pok Fu Lam Hong Kong Simon Fraser University School of Computing Science BurnabyBCV5A 1S6 Canada

Viewport prediction is the crucial task for adaptive 360-degree video streaming, as the bitrate control algorithms usually require the knowledge of the user's viewing portions of the frames. Various methods are studied and adopted for viewport prediction from less accurate statistic tools to highly calibrated deep neural networks. Conventionally, it is difficult to implement sophisticated deep learning methods on mobile devices, which have limited computation capability. In this work, we propose an advanced learning-based viewport prediction approach and carefully design it to minimize transmission and computation overhead for mobile terminals. To improve viewport prediction accuracy, we utilize both spatial information through a saliency prediction model and temporal information through a modified LSTM model. Different computations introduced by the neural network models are distributed across the network to keep the computation light on mobile devices. To better adapt to the content dynamics in live streaming, we employ the model-agnostic meta-learning (MAML) method for video saliency prediction. The learned saliency prediction model with optimized initialization via offline meta-training can be fast fine-tuned online using a few samples. We further discuss how to integrate this mobile-friendly viewport prediction (MFVP) approach into a typical 360-degree video live streaming system by formulating and solving the bitrate adaptation problem. Extensive experiment results demonstrate that our approach achieves real-time prediction for live video streaming and surpasses existing methods in prediction accuracy on mobile terminals, which, together with our bitrate adaptation algorithm, significantly improves the streaming QoE from various aspects. Compared to baseline methods, MFVP achieves a 4.7–28.7% improvement in accuracy and demonstrates faster adaptability to dynamic content changes, enabling rapid fine-tuning and adjustment. When integrated into a streaming syste

关键词： Deep neural networks

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