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Mitigating Noise in Quantum Software Testing Using Machine Learning

IEEE TRANSACTIONS ON SOFTWARE ENGINEERING 2024年第11期50卷 2947-2961页

作者： Muqeet, Asmar Yue, Tao Ali, Shaukat Arcaini, Paolo Simula Res Lab N-0164 Oslo Norway Univ Oslo N-0313 Oslo Norway Oslo Metropolitan Univ N-0130 Oslo Norway Natl Inst Informat Tokyo 1018430 Japan

Quantum computing (QC) promises computational speedup over classic computing. However, noise exists in near-term quantum computers. Quantum software testing (for gaining confidence in quantum software's correctness) is inevitably impacted by noise, i.e., it is impossible to know if a test case failed due to noise or real faults. Existing testing techniques test quantum programs without considering noise, i.e., by executing tests on ideal quantum computer simulators. Consequently, they are not directly applicable to test quantum software on real quantum computers or noisy simulators. Thus, we propose a noise-aware approach (named $\mathit{QOIN}$QOIN) to alleviate the noise effect on test results of quantum programs. $\mathit{QOIN}$QOIN employs machine learning techniques (e.g., transfer learning) to learn the noise effect of a quantum computer and filter it from a program's outputs. Such filtered outputs are then used as the input to perform test case assessments (determining the passing or failing of a test case execution against a test oracle). We evaluated $\mathit{QOIN}$QOIN on IBM's 23 noise models, Google's two available noise models, and Rigetti's Quantum Virtual Machine, with six real-world and 800 artificial programs. We also generated faulty versions of these programs to check if a failing test case execution can be determined under noise. Results show that $\mathit{QOIN}$QOIN can reduce the noise effect by more than $80\%$80% on most noise models. We used an existing test oracle to evaluate $\mathit{QOIN}$QOIN's effectiveness in quantum software testing. The results showed that $\mathit{QOIN}$QOIN attained scores of $99\%$99%, $75\%$75%, and $86\%$86% for precision, recall, and F1-score, respectively, for the test oracle across six real-world programs. For artificial programs, $\mathit{QOIN}$QOIN achieved scores of $93\%$93%, $79\%$79%, and $86\%$86% for precision, recall, and F1-score respectively. This highlights $\mathit{QOIN}$QOIN's effectiveness in le

关键词： Noise Quantum computing Qubit Computers Software testing Logic gates Computational modeling Software testing and debugging computing methodologies quantum computing and machine learning

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An improved calculation system for phase-functioned neural network and implementation in unreal engine

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CLUSTER computing-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2019年第6-Sup期22卷 15505-15516页

作者： Kuang, Ping Luo, Dingli Wang, Haoshuang Zhang, Lixue Univ Elect Sci & Technol China Informat & Software Engn Chengdu Sichuan Peoples R China Chengdu Prod Promot Ctr Chengdu Sichuan Peoples R China

The problem attempting to be solved in this paper is optimizing phase-functioned neural network to support generated animation for game engine. The approach adopted is using CUDA and parallel programming to improve large prediction of matrices calculation. The results of this research included a 4-layer architecture of PFNN prediction framework, a CUDA calculation solution and a showcase binding in unreal engine. As for the effects of the results obtained, PFNN calculation has been sped up from 1.8 ms to 1.0, 1.1 ms. And according to the result of performance test of the utility of PFNN in real game development, its optimization has been proven.

关键词： computing methodologies Procedural animation Artificial intelligence

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Optimizing R with SparkR on a commodity cluster for biomedical research

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COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2016年 137卷 321-328页

作者： Sedlmayr, Martin Wuerfl, Tobias Maier, Christian Haeberle, Lothar Fasching, Peter Prokosch, Hans-Ulrich Christoph, Jan Friedrich Alexander Univ Erlangen Nurnberg Wetterkreuz 13 D-91058 Erlangen Germany Erlangen Univ Hosp Dept Gynecol & Obstet Univ Str 21-23 D-91054 Erlangen Germany

Background and Objectives: Medical researchers are challenged today by the enormous amount of data collected in healthcare. Analysis methods such as genome-wide association studies (GWAS) are often computationally intensive and thus require enormous resources to be performed in a reasonable amount of time. While dedicated clusters and public clouds may deliver the desired performance, their use requires upfront financial efforts or anonymous data, which is often not possible for preliminary or occasional tasks. We explored the possibilities to build a private, flexible cluster for processing scripts in R based on commodity, non-dedicated hardware of our department. Methods: For this, a GWAS-calculation in R on a single desktop computer, a Message Passing Interface (MPI)-cluster, and a SparkR-cluster were compared with regards to the performance, scalability, quality, and simplicity. Results: The original script had a projected runtime of three years on a single desktop computer. Optimizing the script in R already yielded a significant reduction in computing time (2 weeks). By using R-MPI and SparkR, we were able to parallelize the computation and reduce the time to less than three hours (2.6 h) on already available, standard office computers. While MPI is a proven approach in high-performance clusters, it requires rather static, dedicated nodes. SparkR and its Hadoop siblings allow for a dynamic, elastic environment with automated failure handling. SparkR also scales better with the number of nodes in the cluster than MPI due to optimized data communication. Conclusion: R is a popular environment for clinical data analysis. The new SparkR solution offers elastic resources and allows supporting big data analysis using R even on non-dedicated resources with minimal change to the original code. To unleash the full potential, additional efforts should be invested to customize and improve the algorithms, especially with regards to data distribution. (C) 2016 The Authors.

关键词： computing methodologies Genome-wide association study Big data Cluster computing SparkR

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Understanding and measuring the urban pervasive infrastructure

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PERSONAL AND UBIQUITOUS computing 2009年第5期13卷 355-364页

作者： Kostakos, Vassilis Nicolai, Tom Yoneki, Eiko O'Neill, Eamonn Kenn, Holger Crowcroft, Jon Univ Bath Dept Comp Sci Bath BA2 7AY Avon England Univ Bremen Wearable Comp Lab TZI Bremen Germany Univ Cambridge Comp Lab Cambridge CB2 3QG England

The increasing popularity of mobile computing devices has allowed for new research and application areas. Specifically, urban areas exhibit an elevated concentration of such devices enabling potential ad-hoc co-operation and sharing of resources among citizens. Here, we argue that people, architecture and technology together provide the infrastructure for these applications and an understanding of this infrastructure is important for effective design and development. We focus on describing the metrics for describing this infrastructure and elaborate on a set of observation, analysis and simulation methods for capturing, deriving and utilising those metrics.

关键词： Mobile applications Pervasive computing computing methodologies Simulation and modelling Mobility

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You or Me? Personality Traits Predict Sacrificial Decisions in an Accident Situation

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IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS 2019年第5期25卷 1898-1907页

作者： Uijong, Ju Kang, June Wallraven, Christian Korea Univ Cognit Syst Lab Seoul South Korea

Emergency situations during car driving sometimes force the driver to make a sudden decision. Predicting these decisions will have important applications in updating risk analyses in insurance applications, but also can give insights for drafting autonomous vehicle guidelines. Studying such behavior in experimental settings, however, is limited by ethical issues as it would endanger peoples' lives. Here, we employed the potential of virtual reality (VR) to investigate decision-making in an extreme situation in which participants would have to sacrifice others in order to save themselves. In a VR driving simulation, participants first trained to complete a difficult course with multiple crossroads in which the wrong turn would lead the car to fall down a cliff. In the testing phase, obstacles suddenly appeared on the "safe" turn of a crossroad: for the control group, obstacles consisted of trees, whereas for the experimental group, they were pedestrians. In both groups, drivers had to decide between falling down the cliff or colliding with the obstacles. Results showed that differences in personality traits were able to predict this decision: in the experimental group, drivers who collided with the pedestrians had significantly higher psychopathy and impulsivity traits, whereas impulsivity alone was to some degree predictive in the control group. Other factors like heart rate differences, gender, video game expertise, and driving experience were not predictive of the emergency decision in either group. Our results show that self-interest related personality traits affect decision-making when choosing between preservation of self or others in extreme situations and showcase the potential of virtual reality in studying and modeling human decision-making.

关键词： computing methodologies Virtual reality Applied computing Psychology Software and its engineering Interactive games Human-centered computing Laboratory experiments

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TERMTrial - terminology-based documentation systems for cooperative clinical trials

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COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2005年第1期78卷 11-24页

作者： Merzweiler, A Weber, R Garde, S Haux, R Knaup-Gregori, P Heidelberg Univ Inst Med Biometry & Informat Dept Med Informat D-69120 Heidelberg Germany Heidelberg Univ Coordinat Ctr Clin Trials D-69120 Heidelberg Germany Univ Cent Queensland Hlth Informat Res Grp Fac Informat & Commun Rockhampton Qld 4702 Australia Tech Univ Braunschweig Inst Med Informat D-38106 Braunschweig Germany

Within cooperative groups of multi-center clinical trials a standardized documentation is a prerequisite for communication and sharing of data. Standardizing documentation systems means standardizing the underlying terminology. The management and consistent application of terminology systems is a difficult and fault-prone task, which should be supported by appropriate software tools. Today, documentation systems for clinical trials are often implemented as so-called Remote-Data-Entry-Systems (RDE-systems). Although there are many commercial systems, which support the development of RDE-systems there is none offering a comprehensive terminological support. Therefore, we developed the software system TERMTriat which consists of a component for the definition and management of terminology systems for cooperative groups of clinical trials and two components for the terminology-based automatic generation of trial databases and terminology-based interactive design of electronic case report forms (eCRFs). TERMTriat combines the advantages of remote data entry with a comprehensive terminological control. (c) 2004 Elsevier Ireland Ltd. All rights reserved.

关键词： medical informatics terminology clinical trial data collection computing methodologies

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Adaptive movement behavior for real-time crowd simulation

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VISUAL COMPUTER 2024年第7期40卷 4789-4803页

作者： Ruprecht, Irena Michelic, Florian Eggeling, Eva Preiner, Reinhold Fraunhofer Austria Res GmbH Inffeldgasse 16C A-8010 Graz Austria Graz Univ Technol Inffeldgasse 16C A-8010 Graz Austria

We present a novel method for modeling adaptive movement behavior in force-based crowd simulations tailored to the requirements of interactive real-time applications. We aim for an efficient, controllable and situation-dependent synthesis of global path planning and local collision avoidance. To achieve this, we address two typical problems that can cause severe performance impacts and undesired motion: local neighbor search and the combination of goal directions with collision avoidance. We employ a direction-aware neighbor set to efficiently select agents for collision avoidance computation and crowd density estimation. We adaptively blend between goal and avoidance directions to utilize the entire spectrum of navigation strategies for goal attainment, ranging from completely goal-oriented to strongly avoidance-centric behaviors. Our method can be combined with established force-based simulation techniques and easily integrates into existing real-time simulation pipelines. Experiments show that our method can improve control over run-time performance as well as the movement behavior of agents compared to plain force-based methods.

关键词： Real-time crowd simulation Agent-based simulation Physically based modeling computing methodologies Real-time simulation Agent/discrete models

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Exploring the Correlation between Emojis and Mood Expression in Thai Twitter Discourse

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ACM TRANSACTIONS ON ASIAN AND LOW-RESOURCE LANGUAGE INFORMATION PROCESSING 2024年第11期23卷 1-14页

作者： Rutherford, Attapol Akarajaradwong, Pawitsapak Chulalongkorn Univ Linguist Bangkok Thailand

Mood, a long-lasting affective state detached from specific stimuli, plays an important role in behavior. Although sentiment analysis and emotion classification have garnered attention, research on mood classification remains in its early stages. This study adopts a two-dimensional structure of affect, comprising "pleasantness" and "activation," to classify mood patterns. Emojis, graphic symbols representing emotions and concepts, are widely used in computer-mediated communication. Unlike previous studies that consider emojis as direct labels for emotion or sentiment, this work uses a pre-trained large language model which integrates both text and emojis to develop a mood classification model. Our contributions are three-fold. First, we annotate 10,000 Thai tweets with mood to train the models and release the dataset to the public. Second, we show that emojis contribute to determining mood to a lesser extent than text, far from mapping directly to mood. Third, through the application of the trained model, we observe the correlation of moods during the Thai political turmoil of 2019-2020 on Thai X (formerly known as Twitter) and find a significant correlation. These moods closely reflect the news events and reveal one side of Thai public opinion during the turmoil.

关键词： computing methodologies Natural language processing

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Minimizing Fatigue Damage in Aircraft Structures

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IEEE INTELLIGENT SYSTEMS 2016年第4期31卷 22-29页

作者： Ruotsalainen, Marja Jylha, Juha Visa, Ari Tampere Univ Technol Ind Projects FIN-33101 Tampere Finland Tampere Univ Technol PhD Comp & Elect Engn FIN-33101 Tampere Finland Tampere Univ Technol Dept Signal Proc FIN-33101 Tampere Finland

Aircraft structural health monitoring refers to a process in which sensors assess structural state in terms of aging and deterioration. A knowledge discovery approach based on dynamic time warping and genetic programm... 详细信息

关键词： Aircraft Military aircraft Structural engineering Artificial intelligence Monitoring Aerospace electronics Wireless sensor networks INSPEC: Controlled Indexing socio-economic effects aerospace safety aircraft condition monitoring data mining fatigue genetic algorithms mechanical engineering computing INSPEC: Non-Controlled Indexing aircraft fleet fatigue damage minimization aircraft structures aircraft structural health monitoring SHM flight safety economic considerations knowledge discovery approach dynamic time warping genetic programming artificial intelligence Author Keywords evolutionary computing and genetic algorithms computing methodologies artificial intelligence intelligent systems applications and expert knowledge-intensive systems decision support machine learning pattern recognition engineering

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Transformer-Based Topic Modeling for Urdu Translations of the Holy Quran

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ACM TRANSACTIONS ON ASIAN AND LOW-RESOURCE LANGUAGE INFORMATION PROCESSING 2024年第10期23卷 1-21页

作者： Zafar, Amna Wasim, Muhammad Zulfiqar, Shaista Waheed, Talha Siddique, Abubakar Univ Engn & Technol Dept Comp Sci Lahore Pakistan Univ Management & Technol Dept Comp Sci Sialkot Campus Sialkot Pakistan

Topic modeling enables the discovery of concealed themes and patterns in extensive text collections. It facilitates a thorough examination of the messages present in religious texts. Topic modeling for Quranic verses is a trending study area, with various translations already explored including Bahasa, English, and Arabic. Yet, there is a need for further research, particularly in Urdu translations of the Quran. In this study, we propose applying the BERTopic framework to Urdu translations of the Holy Quran. By leveraging the BERTopic approach, which incorporates a fine-tuned BERT model, we aim to capture the contextual nuances and linguistic complexities unique to the Quran. In this study, we utilized existing Urdu translations of the Quran from eight different translators sourced from Tanzil, a renowned resource for Quranic text and translations. We assessed the performance of our proposed BERTopic model compared to traditional techniques like LDA and NMF, using coherence and diversity metrics. The results indicate that our BERT-based approach outperforms these conventional methods, achieving an average coherence improvement of 0.03 and a diversity score of 0.83. These findings highlight the effectiveness of BERTopic in extracting meaningful topics from Urdu translations of the Holy Quran and contribute to the computational analysis of religious texts, supporting scholarly endeavors in comparative studies of Quranic translations in Urdu.

关键词： computing methodologies Language resources

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