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24th IEEE International Conference on software Quality, Reliability and Security Companion, QRS-C 2024

作者： Wang, Wen-Li Hussain, Shahid Rondinelli, Nathaniel V. Tang, Mei-Huei Rehman Khan, Saif Ur Penn State University Behrend Department of Computer Science and Software Engineering EriePA United States Gannon University Department of Computer Information Science EriePA United States Shifa Tameer-e-Millat University Department of Computing Islamabad44000 Pakistan

ISBN: (纸本)9798350365658

Context: Grover's algorithm is a powerful tool in the realm of quantum computing because it offers exponential speedup over classical linear searching for unstructured / unsorted search problems. However, it is challenging to construct efficient and scalable circuits for a multi-input control gate in the algorithm, particularly with a huge number of qubit inputs. Problem: Traditional circuit construction methods often require manual composition for the multi-input gate from basic quantum gates, which can be time consuming and error prone, thus hindering practical implementation. Moreover, the number of simple gates in the composition grows exponentially with the addition of each qubit. Method: This research provides an approach to recursively generate the oracle using Toffoli gates as the fundamental units to enhance scalability and reusability while maintaining effectiveness. The methodology leverages IBM Qiskit and Quantum Lab to simulate quantum computation to prove the practicality and feasibility. Result: The findings from the experiments between recursively and non-recursively generated circuits suggest that our recursive approach can offer comparable accuracy and computational complexity compared to the non-recursive approach. However, it tends to require a larger number of basic quantum gates due to a lack of circuit optimization. Conclusion: The results showcase the applicability of utilizing recursion as a speedy and economical alternative to generate circuits when implementing the Grover's algorithm. It not only presents potential scalability benefits, but also creates an avenue for further research on test verification and performance improvement to the computing community when running in actual quantum hardware. © 2024 IEEE.

关键词： Qubits

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Classification of Helpful and Unhelpful Online Customer Reviews Using XLNet and BERT Variants 1

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International Conference on Green Energy, Computing, and Intelligent Technology, GEn-CITy 2023

作者： Bilal, Muhammad Arshad, Muhammad Haseeb Ramzan, Muhammad Department of Computing and Information Systems School of Engineering and Technology Sunway University Petaling Jaya Malaysia Department of Computer Science National University of Computer and Emerging Sciences Chiniot-Faisalabad Campus Chiniot Pakistan Department of Software Engineering University of Sargodha Sargodha Pakistan School of Electronics and Computer Science University of Southampton Malaysia Iskandar Puteri Malaysia

ISBN: (数字)9789819998333

ISBN: (纸本)9789819998326

The majority of businesses have made public appearances on various social media platforms as a result of recent advances in e-commerce and the popularity of social media websites. Customers can share their experiences in the form of online customer reviews, which can assist potential customers in determining the quality of a company and making purchasing decisions. Due to the large volume of published reviews, it becomes difficult for customers to read all of the reviews and assess the quality of the business, resulting in the problem of information overload. Several solutions have been proposed in the literature by researchers using statistical and machine learning techniques to predict the helpfulness of online customer reviews. However, most of the existing solutions are based on the use of various business, review, and reviewer features, which lead to generalizability issues. Moreover, very limited studies have examined the effectiveness of state-of-the-art pre-trained language models for the classification of helpful and unhelpful reviews. Therefore, this study aims to examine the effectiveness of XLNet, Albert, DistilBert, and Roberta for review helpfulness prediction using textual features. The models were fine-tuned using a publicly available dataset of Yelp reviews. The results showed that XLNet achieved the highest F1-score of 0.730 compared to a benchmark of 0.717 achieved by the BERT base model. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Sales

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Empirical Investigation of role of Meta-learning approaches for the Improvement of software Development Process via software Fault Prediction 22

Empirical Investigation of role of Meta-learning approaches ...

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26th ACM International Conference on Evaluation and Assessment in software engineering, EASE 2022

作者： Hussain, Shahid Ibrahim, Naseem Department of Computer Science and Software Engineering Penn State University United States

ISBN: (纸本)9781450396134

Context: software engineering (SE) community has empirically investigated software defect prediction as a proxy to benchmark it as a process improvement activity to assure software quality. In the domain of software fault prediction, the performance of classification algorithms is highly provoked with the residual effects attributed to feature irrelevance and data redundancy issues. Problem: The meta-learning-based ensemble methods are usually carried out to mitigate these noise effects and boost the software fault prediction performance. However, there is a need to benchmark the performance of meta-learning ensemble methods (as fault predictor) to assure software quality control and aid developers in their decision making. Method: We conduct an empirical and comparative study to evaluate and benchmark the improvement in the fault prediction performance via meta-learning ensemble methods as compared to their component base-level fault predictors. In this study, we perform a series of experiments with four well-known meta-level ensemble methods Vote, StackingC (i.e., Stacking), MultiScheme, and Grading. We also use five high-performance fault predictors Logistic (i.e., Logistic Regression), J48 (i.e., Decision Tree), IBK (i.e. k-nearest neighbor), NaiveBayes, and Decision Table (DT). Subsequently, we performed these experiments on public defect datasets with k-fold (k=10) cross-validation. We used F-measure and ROC-AUC (Receiver Operating Characteristic-Area Under Curve) performance measures and applied the four non-parametric tests to benchmark the fault prediction performance results of meta-learning ensemble methods. Results and Conclusion: we conclude that meta-learning ensemble methods, especially Vote could outperform the base-level fault predictors to tackle the feature irrelevance and redundancy issues in the domain of software fault prediction. Having said that, their performance is highly related to the number of base-level classifiers and the set of softwa

关键词： Metrics Ensemble method Performance Fault Prediction Classification

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Parallel intelligent education with ChatGPT

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Frontiers of Information Technology & Electronic engineering 2024年第1期25卷 12-18页

作者： Jiacun WANG Ying TANG Ryan HARE Fei-Yue WANG Department of Computer Science and Software Engineering Monmouth UniversityNew Jersey 07764USA Department of Electrical and Computer Engineering Rowan UniversityNew Jersey 08028USA Institute of Automation Chinese Academy of SciencesBeijing 100190China

This paper presents a framework for parallel intelligent education that involves physical and virtual learning for a personalized learning *** especially focus on Chat Generative Pre-trained Transformer(ChatGPT)owing to its considerable potential to supplement regular class *** address the strengths and weaknesses of learning with ***,we discuss the challenges and solutions of the proposed parallel intelligent education with ChatGPT.

关键词： education GPT intelligent

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Modif-SegUnet: Innovatively Advancing Liver Cancer Diagnosis and Treatment through Efficient and Meaningful Segmentation of 3D Medical Images

Modif-SegUnet: Innovatively Advancing Liver Cancer Diagnosis...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Wang, Chenhang Jin, Yujian Liang, Jiayi Yang, YuHan Nie, Shanling Hai, Yang East China University of Science and Technology Department of Computer Science and Engineering Shanghai China Shanghai Key Laboratory of Computer Software Evaluating and Testing Shanghai China The University of Sydney Faculty of Engineering Sydney Australia

ISBN: (纸本)9798350337488

Hepatocellular Carcinoma (HCC) holds a record of high incidence and severe global harm. In tasks of liver cancer segmentation based on 3D medical images, the majority of methods have endeavored to enhance the 3D U-net by integrating the latest modules from the field of computer Vision (such as the transformer), often overlooking the distinct characteristics of liver components. We introduced a novel deep learning architecture, Modif-SegUnet, to circumvent this limitation. This architecture extends the U-net model by incorporating a 3D-modifiable attention module, thereby fostering a heightened focus on distinguishing between normal liver sections and lesions. Furthermore, Modif-SegUnet ingeniously amalgamates the 3D-modifiable attention module with the Modif-transformer block, enabling efficient capture of relevant and valuable full-text information in CT images that contain liver or tumor regions. We subjected the proposed Modif-SegUnet to evaluation on the Liver Tumor Segmentation benchmark dataset. Experimental outcomes indicate that our methodology surpasses state-of-the-art approaches in liver tumor segmentation, suggesting potential pathways for advancing the diagnosis and treatment of liver cancer. © 2023 IEEE.

关键词： 3D medical image attention CT Liver cancer Segmentation transformer U-Net

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Addressing Different Goal Selection Strategies In Hindsight Experience Replay With Actor-Critic Methods For Robotic Hand Manipulation 2

Addressing Different Goal Selection Strategies In Hindsight ...

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2nd International Conference on Robotics, Automation and Artificial Intelligence, RAAI 2022

作者： Shams, Ayman Fevens, Thomas Concordia University Department of Computer Science and Software Engineering Montréal Canada

ISBN: (纸本)9781665459440

One of the most challenging problems in reinforcement learning is dealing with minimal rewards obtained from an environment. We present a combined technique of Twin Delayed Deep Deterministic Policy Gradient known as TD3, an off-policy Reinforcement Learning algorithm with Hindsight Experience Replay (HER). This combined technique allows for sampleefficient learning from sparse and binary rewards and avoids the need for complicated reward engineering. We use the challenge of moving things with a robotic arm to illustrate our methodology. We specifically tested six different tasks: pushing, sliding, picking up and placing in the Fetch environment, as well as manipulating a block, an egg, or a pen with our hands. We solely use binary rewards every time to indicate whether or not a task has been performed. In a comparative study, we primarily concentrate on the impact of various goal selection strategies of HER replay butter on both DDPG and TD3. We discovered that HER was crucial in enabling training in these demanding situations. © 2022 IEEE.

关键词： Reinforcement learning

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Intelligent System to Maximize Customer Experience in Restaurants 5

Intelligent System to Maximize Customer Experience in Restau...

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5th International Conference on Advancements in Computing, ICAC 2023

作者： Jayalal, R.U.A. Nimeshika, Y.E. Samarasekara, G.N.N. Wijerathna, G.R.C.I. Gamage, Narmada Gunathilake, Poojani Sri Lanka Institute of Information Technology Department of Computer Systems Engineering Malabe Sri Lanka Sri Lanka Institute of Information Technology Department of Computer Science and Software Engineering Malabe Sri Lanka

ISBN: (纸本)9798350358131

With technological improvements in the restaurant industry, competition is fierce, and a restaurant's success depends on providing excellent and luxury customer satisfaction. The food industry has had to continuously advance with technology in order to adequately satisfy the wants of people leading increasingly luxurious lives. While some restaurants still rely on manpower, the majority are eager to advance with technology in order to improve the standard of their offerings and enhance their customers' experiences. In order to optimize customer satisfaction and experience, this research project intends to investigate the potential of intelligence in the restaurant business. This research has explored intelligent systems such as digital self-service menus for ordering food, augmented reality (AR) visualization systems for customers, the observation of chefs in the kitchen, and the usage of robots to serve meals. This research paper analyzes the advantages of automating restaurant operations and how to do so to enhance customer satisfaction. Overall, this study has the potential to transform the restaurant industry and increase customer satisfaction. © 2023 IEEE.

关键词： Augmented reality

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Medium-Term AQI Prediction in Selected Areas of Bangladesh Based on Bidirectional GRU Network Model

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SN computer science 2024年第5期5卷 627页

作者： Mandal, Ashis Kumar Sen, Rikta Department of Computer Science and Engineering Hajee Mohammad Danesh Science and Technoloy University Rangpur Dinajpur 5200 Bangladesh Department of Computer Science University of Saskatchewan Saskatoon S7N5C9 SK Canada Graduate School of Software and Information Science Iwate Prefectural University Sugo Iwate Takizawa 020-0693 Japan

Currently, Bangladesh is among the countries with the highest levels of air pollution in the world. Major cities of Bangladesh experience severe air pollution, significantly impacting public health and the environment. The Air Quality Index (AQI) serves as a convenient measure to assess air quality in specific locations. Accurate AQI forecasting is crucial as it allows policymakers to prepare and implement strategies to mitigate air pollution, thereby safeguarding human health and the ecosystem. The primary aim of this paper is to develop an effective model capable of forecasting AQI on a medium-term basis for four selected locations in Bangladesh. To achieve this, we propose a Bidirectional Gated Recurrent Unit (BiGRU)-based model to forecast the AQI for each location up to seven days in advance (i.e., medium-term AQI prediction). We collected daily AQI data from four distinct locations in Bangladesh from March 1, 2014, to December 31, 2020. To validate the performance of the model, we used various statistical measures, including Root Mean Square Error (RMSE), Normalized Root Mean Square Error (nRMSE), Mean Absolute Percentage Error (MAPE), and the Coefficient of Determination (R²) score. We also compared the performance of the proposed model with three related models: Gated Recurrent Unit (GRU), Long Short-term Memory (LSTM), and Convolutional Neural Network (CNN). Simulation results demonstrate that the BiGRU model outperforms other approaches, showcasing an RMSE ranging from 27.42 to 43.27 for medium-term AQI prediction. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Air pollution Air Quality Index (AQI) Deep learning Gated Recurrent Unit (GRU) Time series analysis

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Accurate 3D Grapevine Structure Extraction from High-Resolution Point Clouds

Accurate 3D Grapevine Structure Extraction from High-Resolut...

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International Conference on Image and Vision Computing New Zealand, IVCNZ

作者： Harry Dobbs Casey Peat Oliver Batchelor James Atlas Richard Green Department of Computer Science and Software Engineering University of Canterbury

ISBN: (数字)9798331518776

ISBN: (纸本)9798331518783

Accurate 3D modelling of grapevines is crucial for precision viticulture, particularly for informed pruning decisions and automated management techniques. However, the intricate structure of grapevines poses significant challenges for traditional skeletonization algorithms. This paper presents an adaptation of the Smart-Tree algorithm for 3D grapevine modelling, addressing the unique characteristics of grapevine structures. We introduce a graph-based method for disambiguating skeletonization. Our method delineates individual cane skeletons, which are crucial for precise analysis and management. We validate our approach using annotated real-world grapevine point clouds, demonstrating an improvement of 15.8% in the F1 score compared to the original Smart-Tree algorithm. This research contributes to advancing 3D grapevine modelling techniques, potentially enhancing both the sustainability and profitability of grape production through more precise and automated viticulture practices.

关键词： Point cloud compression Adaptation models Solid modeling Three-dimensional displays Accuracy Profitability Pipelines Skeleton Yield estimation Sustainable development

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Design of Flexure Robotic Hand for Teleoperation 6th

Design of Flexure Robotic Hand for Teleoperation

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6th Joint International Conference on Multibody System Dynamics and 10th Asian Conference on Multibody Systems, IMSD-ACMD 2020

作者： Zubair, Mohammad Bhandari, Shubham Chatterjee, Indranath Department of Mechanical Engineering Adamas University Kolkata India Software Development Engineer-I HashedIn by Deloitte Bengaluru India Department of Computer Science Manchester Metropolitan University Manchester United Kingdom

ISBN: (纸本)9789819775248

Robots are getting deployed at home and in industries, becoming an integral part of our daily lives. Sophisticated mechanisms are required to make the joints of the robots compact and simple. In this paper, we have developed a flexure joint for the fingers of the robotic hand. The flexure joint is arranged in series to make a three-DOF underactuated finger. Five fingers are arranged to make a robotic hand. Castigliano’s theorem is used to develop the deformation related to the applied loads, using the equation the flexure mechanism was designed. Pseudo-rigid model is developed to determine the stiffness of the flexure mechanism. A virtual model of the flexure mechanism is developed in a CAD environment. Finite Element (FE) simulation is conducted to the different loading conditions to evaluate the performance of the flexure mechanism. It was seen that the flexure mechanism is flexible about the rotational axis and stiff about the other axes. The stiffness model developed using the pseudo-rigid model has a good correlation with the FE simulation model. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Robotic arms

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