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2020 31st Irish Signals and Systems Conference (ISSC)最新文献

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Affective Computing as a Service (ACaaS) 情感计算即服务(ACaaS)
Pub Date : 2020-06-01 DOI: 10.1109/ISSC49989.2020.9180158
W. Murphy, Eoghan Furey, Juanita Blue
Affective Computing aims to introduce a higher level of computational intelligence to systems, which enables emulation of human affects and emotions. Today those enhanced computing capabilities are seldom found in IT solutions. This paper reviews both Affective Computing and Cloud Computing, presenting the combined outcome in the form of a Software-as-a-Service solution hosted via a Public Cloud Infrastructure. A framework is proposed for the Affective Computing as a Service (ACaaS) solution with the unique consideration that it uses previously created Public Cloud processing services. The framework is then transformed into a working implementation comprising a PHP front-end and a Python back-end. The system is capable of processing text, image, and voice input files and extracting emotional information from them. The results are then presented and evaluated, demonstrating that in most use cases, the multi-modal inputs will facilitate an Affective Computing as a Service solution which will deliver the necessary information for Affective Computing goals. Exploration of the combination of available cloud computing technologies and Affective Computing goals supports research in the area by removing the need for researchers to build their own models. This solution leverages the best available cutting-edge technologies available from large providers. Thereby, the requirement to train new models and the associated overheads are greatly reduced.
情感计算旨在为系统引入更高水平的计算智能,从而能够模拟人类的情感和情绪。如今,在IT解决方案中很少能找到这些增强的计算能力。本文回顾了情感计算和云计算,以软件即服务解决方案的形式展示了通过公共云基础设施托管的组合结果。提出了一种用于情感计算即服务(ACaaS)解决方案的框架,其独特之处是它使用了先前创建的公共云处理服务。然后将该框架转换为包含PHP前端和Python后端的工作实现。该系统能够处理文本、图像和语音输入文件,并从中提取情感信息。然后对结果进行展示和评估,证明在大多数用例中,多模态输入将促进情感计算即服务解决方案,该解决方案将为情感计算目标提供必要的信息。探索可用的云计算技术和情感计算目标的结合,消除了研究人员建立自己模型的需要,从而支持了该领域的研究。该解决方案利用了大型提供商提供的最佳可用尖端技术。因此,训练新模型的需求和相关的开销都大大减少了。
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引用次数: 1
Towards a Non-Intrusive Context-Aware Speech Quality Model 面向非侵入式上下文感知语音质量模型
Pub Date : 2020-06-01 DOI: 10.1109/ISSC49989.2020.9180171
R. Jaiswal, Andrew Hines
Understanding how humans judge perceived speech quality while interacting through Voice over Internet Protocol (VoIP) applications in real-time is essential to build a robust and accurate speech quality prediction model. Speech quality is degraded in the presence of background noise reducing the Quality of Experience (QoE). Speech Enhancement (SE) algorithms can improve speech quality in noisy environments. The publicly available NOIZEUS speech corpus contains speech in environmental background noise babble, car, street, and train at two Signal-to-noise ratio (SNRs) 5dB and 10dB. Objective Speech Quality Metrics (OSQM) are used to monitor and measure speech quality for VoIP applications. This paper proposes a Context-aware QoE prediction model, CAQoE, which classifies the speech signal context (i.e., noise type and SNR) in order to allow context-specific speech quality prediction. This paper presents experiments conducted to develop the speech context-classification component of the proposed CAQoE model. Speech enhancement algorithms are used in conjunction with an OSQM to estimate Mean Opinion Score (MOS) of noisy and enhanced samples in order to train Machine Learning (ML) classifiers to classify the speech signal context (i.e., noise type and SNR). Results demonstrate that a Decision Tree (DT) classifier has better classification accuracy for the noise classes tested. We present the associated components of the CAQoE model, namely; Voice Activity Detection (VAD) and Speech Quality Model (SQM).
了解人类如何在通过互联网语音协议(VoIP)应用程序进行实时交互时判断感知语音质量,对于构建鲁棒和准确的语音质量预测模型至关重要。在背景噪声的存在下,语音质量会下降,从而降低体验质量(QoE)。语音增强(SE)算法可以提高噪声环境下的语音质量。公开的NOIZEUS语音语料库包含环境背景噪声、汽车、街道和火车中的语音,信噪比分别为5dB和10dB。目的OSQM (Speech Quality Metrics)用于监控和测量VoIP应用的语音质量。本文提出了一种上下文感知的语音质量预测模型CAQoE,该模型对语音信号上下文(即噪声类型和信噪比)进行分类,从而实现特定于上下文的语音质量预测。本文介绍了开发所提出的CAQoE模型的语音上下文分类组件的实验。语音增强算法与OSQM结合使用来估计噪声和增强样本的平均意见分数(MOS),以训练机器学习(ML)分类器对语音信号上下文(即噪声类型和信噪比)进行分类。结果表明,决策树(DT)分类器对测试的噪声类别具有较好的分类精度。我们提出了CAQoE模型的相关组件,即;语音活动检测(VAD)和语音质量模型(SQM)。
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引用次数: 3
Path Kinematics for Combined Discrete and Continuous Event Simulation 离散与连续事件联合仿真的路径运动学
Pub Date : 2020-06-01 DOI: 10.1109/ISSC49989.2020.9180203
John Barry, Joseph Walsh
Combined discrete and continuous event simulations provide a means of investigating the influence of the many factors affecting the productivity of complex electromechanical systems. This paper describes algorithms and methods for establishing the path kinematics of Cartesian axes of motion pick and place systems which must avoid varying obstacle profiles and which have the potential for path intersections with other pick and place systems within a shared working environment. Where intersections arise, one pick and place device must, in accordance with pre-established prioritization, decelerate and wait for another pick and place device to vacate the zone of conflict. Path kinematics represent a continuous event aspect of the simulation under development while awaiting permission to proceed represents a discrete event aspect of the simulation. A requirement of the research is that the kinematics only include periods of constant acceleration and constant velocity and that any deceleration must continue substantially along the original path. The algorithm and methods presented are concise and may be applicable and convenient to apply in the path control of Cartesian axis of motion devices.
离散事件和连续事件相结合的仿真为研究影响复杂机电系统生产率的诸多因素的影响提供了一种手段。本文描述了建立笛卡尔运动轴拾取和放置系统路径运动学的算法和方法,该系统必须避免不同的障碍物轮廓,并且在共享工作环境中有可能与其他拾取和放置系统发生路径交叉。当出现交叉时,一个拾取装置必须按照预先设定的优先级减速,等待另一个拾取装置撤离冲突区域。路径运动学代表正在开发的仿真的连续事件方面,而等待许可进行则代表仿真的离散事件方面。该研究的一个要求是运动学只包括恒定加速度和恒定速度的周期,并且任何减速都必须沿着原始路径持续。所提出的算法和方法简洁,适用于运动装置直角轴的轨迹控制。
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引用次数: 0
IoT Personal Air Quality Monitor 物联网个人空气质量监测器
Pub Date : 2020-06-01 DOI: 10.1109/ISSC49989.2020.9180199
S. M. Grath, C. Flanagan, L. Zeng, Conor O'Leary
With more attention being paid to environmental issues in recent years, air quality monitoring is becoming more important. It has been possible to monitor air quality successfully for many years, but monitoring has traditionally been both expensive and immobile, thus restricted in application. To improve urban environments, air quality monitoring has to be widespread, ubiquitous, cheap, and rapidly responsive. Good, timely data is the key to first identifying, then tackling air pollution issues. This paper develops an alternative, cheap, IoT-based air quality monitor, which can track air pollution in real time, and transmit the relevant data rapidly through a low power wide area network. A large network of such monitors can generate a vast amount of data, which may then be processed and analyzed in the cloud in real time, and correlated with time of day, month, year or weather and other factors.
近年来,随着人们对环境问题的日益关注,空气质量监测变得越来越重要。多年来,人们已经可以成功地监测空气质量,但监测传统上既昂贵又固定,因此在应用上受到限制。为了改善城市环境,空气质量监测必须广泛、无所不在、廉价且反应迅速。良好、及时的数据是首先识别、然后解决空气污染问题的关键。本文开发了一种替代的、廉价的、基于物联网的空气质量监测仪,它可以实时跟踪空气污染,并通过低功率广域网快速传输相关数据。这样一个庞大的监视器网络可以产生大量的数据,然后可以在云中实时处理和分析,并与一天、一个月、一年或天气的时间和其他因素相关联。
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引用次数: 15
Audio Pre-Processing and Neural Network Models for Identification of Orthopedic Reamers in Use 音频预处理与神经网络模型在骨科用铰刀识别中的应用
Pub Date : 2020-06-01 DOI: 10.1109/ISSC49989.2020.9180175
M. Hanlon, B. Jackson, J. Rice, Joseph Walsh, D. Riordan
In order for a successful outcome of Total Hip Arthroplasty (THA) to occur, implant stability is a key concern. A means to achieve this is ensuring that the implant has an optimum seating within the femur cavity. This is achieved during surgery by progressive reaming of the cavity interior. Both under and over reaming have undesirable effects towards implant longevity and post-operative prognosis, and so through education/experience, orthopedic surgeons have learned to anticipate when optimal reaming occurs. The work presented here is part of a larger research project which seeks to use bone resonance as an indicator of good implant seating. Here we present results of initial work using several neural network models on classification of orthopedic reamer type on the basis of sound signature. These results are discussed in the context of an interesting feature found when comparing differing audio- preprocessing methods. Despite identical audio raw data being used for both representations, the models that used the Mel Spectrograms categorically outperformed those which used the STFT Spectrogram.
为了使全髋关节置换术(THA)成功,植入物的稳定性是一个关键问题。实现这一目标的一种方法是确保植入物在股骨腔内具有最佳的坐位。这是在手术中通过逐步扩孔腔内部来实现的。扩孔不足和过大都会对种植体寿命和术后预后产生不良影响,因此通过教育/经验,骨科医生已经学会了预测最佳扩孔时间。这里展示的工作是一个更大的研究项目的一部分,该项目旨在利用骨共振作为良好植入物坐位的指标。本文介绍了基于声音特征的几种神经网络模型在骨科铰刀类型分类上的初步工作结果。这些结果是在比较不同音频预处理方法时发现的一个有趣特征的背景下讨论的。尽管两种表示都使用了相同的音频原始数据,但使用Mel谱图的模型明显优于使用STFT谱图的模型。
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引用次数: 0
A Statistically Significant Test to Evaluate the Order or Disorder of a Binary String 评估二进制字符串的有序或无序的统计显著性检验
Pub Date : 2020-06-01 DOI: 10.1109/ISSC49989.2020.9180178
J. Blackledge, N. Mosola
This paper addresses a basic problem in regard to the analysis of a finite binary string or bit stream (of compact support), namely, how to tell whether the string is representative of non-random or intelligible information (involving some form of periodicity, for example), whether it is the product of an entirely random process or whether it is something in between the two. This problem has applications that include cryptanalysis, quantitative finance, machine learning, artificial intelligence and other forms of signal and image processing involving the general problem of how to distinguishing real noise from information embedded in noise, for example. After providing a short introduction to the problem, we focus on the application of information entropy for solving the problem given that this fundamental metric is an intrinsic measure on information in regard to some measurable system. A brief overview on the concept of entropy is given followed by examples of how algorithms can be design to compute the binary entropy of a finite binary string including important variations on a theme such as the BiEntropy. The problem with computing a single metric of this type is that it can be representative of similar binary strings and lacks robustness in terms of its statistically significance. For this reasons, the paper presents a solution to the problem that is based on the Kullback-Leibler Divergence (or Relative Entropy) which yields a measure of how one probability distribution is different from another reference probability distribution. By repeatedly computing this metric for different reference (simulated or otherwise) random finite binary strings, it is shown how the distribution of the resulting signal changes for intelligible and random binary strings of a finite extent. This allows a number of standard statistical metrics to be computed from which the foundations for a machine learning system can be developed. A limited number of results are present for different natural languages to illustrate the approach, a prototype MATLAB function being provide for interested readers to reproduce the results given as required, investigate different data sets and further develop the method considered.
本文解决了关于有限二进制字符串或位流(紧支持)分析的一个基本问题,即如何判断字符串是否代表非随机或可理解的信息(例如,涉及某种形式的周期性),它是完全随机过程的产物还是介于两者之间的东西。这个问题的应用包括密码分析、定量金融、机器学习、人工智能和其他形式的信号和图像处理,例如,涉及如何区分真实噪声和嵌入在噪声中的信息的一般问题。在对这个问题做了简短的介绍之后,我们重点讨论了信息熵在解决这个问题中的应用,因为这个基本度量是关于某些可测量系统的信息的内在度量。简要概述了熵的概念,然后给出了如何设计算法来计算有限二进制字符串的二进制熵的示例,其中包括BiEntropy等主题的重要变化。计算这种类型的单个度量的问题是,它可以代表相似的二进制字符串,并且在统计显著性方面缺乏鲁棒性。由于这个原因,本文提出了一个基于Kullback-Leibler散度(或相对熵)的问题解决方案,它产生了一个概率分布与另一个参考概率分布如何不同的度量。通过对不同参考(模拟或其他)随机有限二进制字符串重复计算该度量,显示了有限范围的可理解和随机二进制字符串的结果信号分布如何变化。这允许计算一些标准的统计指标,从中可以开发机器学习系统的基础。对于不同的自然语言,提供了有限数量的结果来说明该方法,为感兴趣的读者提供了一个原型MATLAB函数,以根据需要重现给出的结果,研究不同的数据集并进一步发展所考虑的方法。
{"title":"A Statistically Significant Test to Evaluate the Order or Disorder of a Binary String","authors":"J. Blackledge, N. Mosola","doi":"10.1109/ISSC49989.2020.9180178","DOIUrl":"https://doi.org/10.1109/ISSC49989.2020.9180178","url":null,"abstract":"This paper addresses a basic problem in regard to the analysis of a finite binary string or bit stream (of compact support), namely, how to tell whether the string is representative of non-random or intelligible information (involving some form of periodicity, for example), whether it is the product of an entirely random process or whether it is something in between the two. This problem has applications that include cryptanalysis, quantitative finance, machine learning, artificial intelligence and other forms of signal and image processing involving the general problem of how to distinguishing real noise from information embedded in noise, for example. After providing a short introduction to the problem, we focus on the application of information entropy for solving the problem given that this fundamental metric is an intrinsic measure on information in regard to some measurable system. A brief overview on the concept of entropy is given followed by examples of how algorithms can be design to compute the binary entropy of a finite binary string including important variations on a theme such as the BiEntropy. The problem with computing a single metric of this type is that it can be representative of similar binary strings and lacks robustness in terms of its statistically significance. For this reasons, the paper presents a solution to the problem that is based on the Kullback-Leibler Divergence (or Relative Entropy) which yields a measure of how one probability distribution is different from another reference probability distribution. By repeatedly computing this metric for different reference (simulated or otherwise) random finite binary strings, it is shown how the distribution of the resulting signal changes for intelligible and random binary strings of a finite extent. This allows a number of standard statistical metrics to be computed from which the foundations for a machine learning system can be developed. A limited number of results are present for different natural languages to illustrate the approach, a prototype MATLAB function being provide for interested readers to reproduce the results given as required, investigate different data sets and further develop the method considered.","PeriodicalId":351013,"journal":{"name":"2020 31st Irish Signals and Systems Conference (ISSC)","volume":"55 5","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120857567","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
Comparison of Mathematical and Physical Phase Noise Performance in Fractional-N Synthesizers 分数n合成器中数学与物理相位噪声性能的比较
Pub Date : 2020-06-01 DOI: 10.1109/ISSC49989.2020.9180168
Kyle Jansen, Michael Peter Kennedy
Fractional-N frequency synthesizers are widely employed in wireless communications to produce sinusoidal carrier signals. Traditionally, analog synthesizers have offered the best phase noise performance whilst digital synthesizers are more flexible and have also demonstrated excellent phase noise performance. In addition, hybrid architectures have looked to combine the benefits of both analog and digital. This paper provides a qualitative analysis of the performances for each architecture with an eye towards identifying their respective performance limits.
分数n频率合成器广泛应用于无线通信中产生正弦载波信号。传统上,模拟合成器提供了最好的相位噪声性能,而数字合成器更灵活,也展示了出色的相位噪声性能。此外,混合架构已经将模拟和数字的优势结合起来。本文对每种体系结构的性能进行了定性分析,着眼于确定它们各自的性能限制。
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引用次数: 1
Improving Academic Performance Amongst First Years Computer Science Students Through Goal-Setting 通过设定目标来提高计算机科学一年级学生的学习成绩
Pub Date : 2020-06-01 DOI: 10.1109/ISSC49989.2020.9180154
R. Donovan, Jamie Cotter, Ruairi O'Reilly
Academic performance across Computer Science (CS) courses in the Republic of Ireland is underwhelming. CS undergraduates are statistically the most likely cohort in the country not to progress past year one of their studies. Insufficient motivation to pursue CS studies has been demonstrated to be a significant cause of poor CS academic performance. Goal-setting programs are an efficient, cost-effective, and student empowering way to boost motivation. Goal-setting is the formulation of a set of activities intended to motivate an individual to the desired goal state. This paper provides an experimental design for assessing the effectiveness of a written goal-setting program on academic performance concerning individual differences. Participants are randomly assigned either to the written goal-setting program or an active control task via an online platform. The goal-setting program requires participants to articulate both a desired future life and a feared future life. The program also requires participants to: identify goals and sub-goals across several domains (e.g. family, health, study); the benefits that achieving their goals would have for themselves for their connected group; the daily habits they could develop to make their ideal future more likely to occur. This study also investigates how personality (via a high-resolution personality model) and cognitive differences influence goal-setting effectiveness. Differences in both Semester 1 performance and the number of students who progress to Semester 2 are assessed between experimental groups. An ANCOVA analysis will assess whether the effectiveness of an experimental task varied based on individual differences in personality and/or cognitive ability.
在爱尔兰共和国,计算机科学(CS)课程的学术表现平平。从统计数据来看,计算机科学本科生是美国最有可能在第一学年没有取得进展的群体。学习计算机科学的动机不足已被证明是计算机科学学习成绩不佳的重要原因。目标设定计划是一种有效的、成本效益高的、能激励学生的方法。目标设定是一套旨在激励个人达到预期目标状态的活动的制定。本文提供了一个实验设计,以评估一个书面目标设定计划对个体差异的学业成绩的有效性。参与者被随机分配到书面目标设定计划或通过在线平台进行主动控制任务。目标设定计划要求参与者清楚地表达对未来生活的期望和对未来生活的恐惧。该方案还要求参与者:确定多个领域(如家庭、健康、学习)的目标和子目标;实现他们的目标将给他们自己和他们的关联群体带来的好处;他们可以养成的日常习惯,使他们理想的未来更有可能发生。本研究还探讨了人格(通过高分辨率人格模型)和认知差异如何影响目标设定的有效性。在实验组之间评估第一学期的表现和进入第二学期的学生人数的差异。ANCOVA分析将评估实验任务的有效性是否根据个性和/或认知能力的个体差异而变化。
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引用次数: 2
Copyright 版权
Pub Date : 2020-06-01 DOI: 10.1109/issc49989.2020.9180177
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引用次数: 0
Effects of Intra-Subject Variation in Gait Analysis on ASD Classification Performance in Machine Learning Models 步态分析中受试者内部差异对机器学习模型中ASD分类性能的影响
Pub Date : 2020-06-01 DOI: 10.1109/ISSC49989.2020.9180201
Benn Henderson, P. Yogarajah, B. Gardiner, M. McGinnity, Kitty Forster, B. Nicholas, D. Wimpory, J. Wanigasinghe
Autism Spectrum Disorder (ASD) is a developmental disorder that is prevalent globally. Research into detecting autism traditionally focused on behavioural aspects of the condition, however, more recently, focus has shifted to more objective alternatives using techniques such as machine learning and gait analysis. Gait measurements, having been used for person identification, varies from person to person, introducing a lot of intra-subject variance. This applies to the 8 spatial-temporal features used in this study, representing the time that an individual spends in each phase of a gait cycle, collected using a Vicon motion tracking system. The features were averaged across each gait trial that the subjects performed, producing a second set of features with reduced intra-subject variance. Four common classifiers, a Support Vector Machine (SVM), K-Nearest Neighbour (KNN), Random Forests (RF) and a Decision Tree (DT) classifier, were all trained using the two feature sets and their classification rates were compared. The results show that for the RF classifier, reducing the intra-subject variance, was able to successfully increase the classification power. The KNN and DT classifiers experienced a minimal decrease in accuracy, where the SVM suffered the greatest loss when intra-subject variance was reduced. Results overall show that the effect intra-subject variance has on classification power depends heavily on the suitability of the classifier to the initial problem as well as size and class balance of the data.
自闭症谱系障碍(ASD)是一种全球普遍存在的发育障碍。传统上,检测自闭症的研究主要集中在症状的行为方面,然而,最近,焦点已经转移到使用机器学习和步态分析等技术的更客观的替代方案上。步态测量,已经被用于人的识别,因人而异,引入了很多受试者内部方差。这适用于本研究中使用的8个时空特征,代表个体在步态周期的每个阶段花费的时间,使用Vicon运动跟踪系统收集。这些特征在受试者进行的每次步态试验中平均,产生第二组特征,受试者内部方差减少。四种常用分类器,支持向量机(SVM), k近邻(KNN),随机森林(RF)和决策树(DT)分类器,都使用两个特征集进行训练,并比较它们的分类率。结果表明,对于射频分类器来说,通过减小主题内方差,可以成功地提高分类能力。KNN和DT分类器的准确性下降最小,其中SVM在受试者内方差降低时损失最大。结果表明,主体内方差对分类能力的影响在很大程度上取决于分类器对初始问题的适用性以及数据的大小和类别平衡。
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引用次数: 0
期刊
2020 31st Irish Signals and Systems Conference (ISSC)
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