2021 IEEE 20th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC)最新文献

{"title":"Evaluating the Cognitively-Related Productivity of a Universal Dependency Parser","authors":"Sagar Indurkhya, R. Berwick","doi":"10.1109/ICCICC53683.2021.9811322","DOIUrl":"https://doi.org/10.1109/ICCICC53683.2021.9811322","url":null,"abstract":"A key goal of cognitive computing is to correctly model human language. Recently, much has been made of the ability of deep neural nets trained on huge datasets to precisely parse sentences. But do these systems truly incorporate human knowledge of language? In this paper we apply a standard linguistic methodology, transformational analysis, to determine whether this claim is accurate. On this view, if a deep net parser operates properly on one kind of sentence, it should also work correctly on its transformed counterpart. Applying this to a standard set of statement-question transformed sentence pairs, we find that a state of the art neural network system does not replicate human behavior and makes numerous errors. We suggest that this kind of test is more relevant for highlighting what deep neural networks can and cannot do with respect to human language.","PeriodicalId":101653,"journal":{"name":"2021 IEEE 20th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC)","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115159318","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}

{"title":"Estimation of Circadian Rhythms Using Complexity Analysis with Temporal Scale Dependency in Electroencephalogram Signals","authors":"Yuta Iinuma, S. Nobukawa, Sho Takagi, Haruhiko Nishimura","doi":"10.1109/iccicc53683.2021.9811332","DOIUrl":"https://doi.org/10.1109/iccicc53683.2021.9811332","url":null,"abstract":"Disturbances in circadian rhythms have been recently associated with a variety of healthy states and psychiatric pathologies. Therefore, estimating the degree of circadian rhythm disturbance is important for discriminating psychiatric disorders from healthy conditions. Electroencephalogram (EEG) allows to detect brain activity directly, but the recorded signal combines neural activity across multiple time scales. The complexity of brain activity across multiple time scales has been previously quantified using multiscale entropy (MSE) analysis. In this study, we investigated whether MSE analysis of EEG data can detect circadian rhythms. Our results show that in the daytime, the complexity of brain activity is increased at larger temporal scale, and that MSE analysis detects these changes more accurately than conventional power analysis. Because complexity at large temporal scales arises from the long-range connectivity in brain networks, we suggest that the decrease in this EEG pattern complexity by night is mediated by melatonin, which suppresses neural firing and reduces wide-range interactions between brain regions. Our method can be applied for the EEG-based analysis of circadian rhythms in longitudinal studies and may help to diagnose healthy states and psychiatric conditions.","PeriodicalId":101653,"journal":{"name":"2021 IEEE 20th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122927791","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}

{"title":"A Robust Polyscale Length Complexity Measure for Stochastic Self-Affine Processes","authors":"W. Kinsner","doi":"10.1109/ICCICC53683.2021.9811308","DOIUrl":"https://doi.org/10.1109/ICCICC53683.2021.9811308","url":null,"abstract":"Cognitive and other nonlinear systems often involve deterministic differentiable processes and stochastic non-differentiable processes. Measuring the complexity of such processes is important when extracting objective features from the processes for their classification in either reactive, or adaptive, or predictive control. This applies to classifiers based not only to the traditional neural networks, but also to deep learning systems, and particularly in cognitive systems. This paper describes a robust algorithm to measure the length complexity of a self-affine time series using multiscale and polyscale analyses, and provides new insight in the theoretical and practical aspects of extracting the measure.","PeriodicalId":101653,"journal":{"name":"2021 IEEE 20th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC)","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122250929","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}

{"title":"Improving Preference Detection with Eye Movement Gaze and Cognitive Diversity","authors":"Christina Schweikert, S. Shimojo, Zihan Zhang, Jonida Tato, Rebecca Hendsey, D. Hsu","doi":"10.1109/ICCICC53683.2021.9811298","DOIUrl":"https://doi.org/10.1109/ICCICC53683.2021.9811298","url":null,"abstract":"Preference choices of a subject when given two human face images is a complex cognitive process. One way to detect a subject’s preference is by recording and examining the subject’s eye movement gaze sequence before his/her decision. Combinatorial fusion analysis / algorithm (CFA) is a new approach for combining multiple scoring systems using rank-score characteristic (RSC) function and cognitive diversity (CD) measure. In this paper, we apply CFA to the study of the eye movement gaze sequences for preference detection. In particular, we use the RSC function to characterize each of the attributes and the CD to measure the diversity between attributes. Our results demonstrate that weighted combination of attributes using diversity strength, computed using average CD’s, improves the preference detection.","PeriodicalId":101653,"journal":{"name":"2021 IEEE 20th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC)","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130595556","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}

{"title":"Unsupervised adversarial domain adaptation abnormal sound detection for machine condition monitoring under domain shift conditions","authors":"Xiaohua Gu, Renjie Li, Ming Kang, Fei Lu, Dedong Tang, Jun Peng","doi":"10.1109/ICCICC53683.2021.9811305","DOIUrl":"https://doi.org/10.1109/ICCICC53683.2021.9811305","url":null,"abstract":"Relying on mechanical sound signals to carry out anomaly detection is a challenging task. Due to the stability of the production process of complex industrial mechanical systems, there are very few or no abnormalities, and the types of mechanical failures are also difficult to describe in detail. In addition, the sound characteristics of the machine itself will change with the change of production operating conditions, and traditional anomaly detection models are prone to misjudge normal sounds as abnormal. We recommend that the change of mechanical conditions in similar situations be regarded as a domain shift between the source domain and the target domain. For unsupervised anomaly detection under the premise of domain shift, we propose an unsupervised adversarial domain adaptation method (UADA-OCSVM) based on Adversarial Domain Adaptation and One-Class SVM. Through adversarial learning strategy, the source domain and target domain data are aligned in an unsupervised method. Meanwhile, a special loss is introduced for the feature extraction layer. Finally, the anomaly detection based only on normal data is regarded as the one class classification problem, and the anomaly detection task after feature extraction is performed by OCSVM.We applied the proposed method to the MIMII DUE dataset for verification, and compared it with the autoencoder-based anomaly detection method. Experiments show that the AUC of our method is better than the method based on the autoencoder in different mechanical types, especially on the Value data set, the average AUC is increased by 15.31%, indicating that the method we proposed is better than the method based on AE Significant improvement.","PeriodicalId":101653,"journal":{"name":"2021 IEEE 20th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124149632","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}

{"title":"On the Emergence of Autonomous Systems towards Deep Thinking Machines and General AI Driven by Abstract Intelligence Theories and Intelligent Mathematics","authors":"Guoyin Wang","doi":"10.1109/ICCICC53683.2021.9811299","DOIUrl":"https://doi.org/10.1109/ICCICC53683.2021.9811299","url":null,"abstract":"Thinking and inference are key essences of human intelligence as René Descartes stated that \"I think therefore I am (1637).\" The ultimate goal of General AI (GAI) [1], [2], [3], [4] is to enable machine thinking and inference [5], [6], [7], [8], [9] beyond data-based learning [10], [11] towards run-time intelligence generation [9], [12] driven by Autonomous Systems (AS) [13], [14] and cognitive robots [15]. Basic research on machine thinking is powered by Abstract Intelligence theories [16], [17], [18], [19], [20], [21] and Intelligent Mathematics (IM) [22], [23], [24], [25], [26], [27], [28], [29], [30], [31], [32] towards enabling symbiotic and collective intelligence [13], [33] and cognitive systems [34], [35], [36], [37], [38], [39].This keynote lecture presents theoretical foundations of and latest advances in machine thinking and inference driven by AS, GAI, and cognitive robots. Latest breakthroughs in IMs and run-time behavior generation by cognitive systems and AS are elaborated. Emerging paradigms of machine thinking autonomies are introduced to demonstrate the next generation of general AI and symbiotic intelligent systems. The advances in AS are expected to lead to a wide range of novel applications in deep machine thinking, machine knowledge learning, cognitive robots, inference computers, brain-inspired systems, and mission-critical intelligent systems.","PeriodicalId":101653,"journal":{"name":"2021 IEEE 20th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127972267","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}

{"title":"Cognitive Driving Data Visualization and Driving Style Transfer","authors":"H. Hiraishi","doi":"10.1109/ICCICC53683.2021.9811337","DOIUrl":"https://doi.org/10.1109/ICCICC53683.2021.9811337","url":null,"abstract":"This study proposes a visualization method for imaging cognitive driving data, which records data related to driving operations and drivers’ cognitive states. The visualization yielded an easy understanding of the characteristics of the driving operation and cognitive states. This allows intuitive comparison using images. Some differences between novice and experienced drivers can be visually embossed. Furthermore, the image style transfer algorithm using deep learning can be adopted for driving style transfer by representing the driving data as an image. Therefore, the image of an experienced driver can be used as a style image, and that of a novice driver can be modified by the style of an experienced driver. In this study, some trials of driving style transfer were attempted. Consequently, the driving style of a novice driver can be modified to clear and smooth operations like those of an experienced driver. As for the cognitive state, although a novice driver has always felt stressed up, the style of relaxed driving based on road conditions can be applied.","PeriodicalId":101653,"journal":{"name":"2021 IEEE 20th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC)","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115039919","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}

{"title":"Mobile AI Stroke Health App: A Novel Mobile Intelligent Edge Computing Engine based on Deep Learning models for Stroke Prediction – Research and Industry Perspective","authors":"B. Elbagoury, Marwa Zaghow, A. Salem, T. Schrader","doi":"10.1109/ICCICC53683.2021.9811307","DOIUrl":"https://doi.org/10.1109/ICCICC53683.2021.9811307","url":null,"abstract":"Artificial Intelligence (AI) techniques for mobile Health (mHealth) in remote medical systems has opened up new opportunities in healthcare systems. Combining AI techniques to the existing Internet of Medical things(IoMT) will enhance the quality of care that patients receive at home remotely or The success establishment of Smart living environments while still having access to the resources within reach to respond to any medical diagnostic crisis. Mobile Health is a steadily growing field in telemedicine. However, building a real AI for Mobile Edge computing is a challenging problem due to the complexities of receiving IoT medical sensors data, data analysis and Deep Learning algorithm complexity programming for Mobile Edge Computing Complexities, especially when we tackle real-time environments of wearable technologies. In this paper, we introduce a New Real-Time Artificial Intelligence and IoMT Engine for Mobile Health Edge Computing technology. Its main goal to is to Predict stroke diseases as an urgent case that may cause that may cause problems like weakness, numbness, vision problems, confusion, trouble walking or moving or talking. It may also cause sudden death. However, today ’s Mobile Health research still missing an intelligent remote diagnosis engine for Stroke Prediction and Diagnosis for patient emergency cases This research work proposes a Hybrid Intelligent remote diagnosis technique for Mobile Health Application for Stroke Prediction and diagnosis. The hybrid techniques are Sparse Auto-Encoders Deep Learning (DL) technique and Group Handling method (GMDH) neural networks. Both techniques depend on dataset of Electromyography (EMG) signals, which provides significant source of information for identification of stroke normal and abnormal motions. The State of the art of the presented Artificial Intelligence mHealth App is new and the proposed techniques achieves high accuracies as Sparse Auto-Encoders reaches almost 98% for Stroke Diagnosis and GMDH Neural Networks proves to be a good technique for monitoring the EMG signal of the same patient case with average accuracies 98.60% to average 96.68% of the signal prediction. This paper also presents conclusion and future works for the proposed overall new system architecture.","PeriodicalId":101653,"journal":{"name":"2021 IEEE 20th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115395539","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}

{"title":"Measurement System of the Interpersonal Proximity using a Wearable Eye-Tracker","authors":"Airi Tsuji, Satoru Sekine","doi":"10.1109/ICCICC53683.2021.9811302","DOIUrl":"https://doi.org/10.1109/ICCICC53683.2021.9811302","url":null,"abstract":"In this study, we propose the measurement system of the interpersonal proximity using a wearable eye tracker. The proposed system uses the area occupied by the other person in the visual field, head tilt and gazing at the face as distance indicators. Measuring the interpersonal proximity compares with the measuring the physical distance between the two persons for the development of the quantitatively measurement. In this paper, we conducted a preliminary experiment using the proposed system. Results shows that the ratio of face gazing decreases in the approached participant. The face gazing does not occur when the approached participant in sitting.","PeriodicalId":101653,"journal":{"name":"2021 IEEE 20th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC)","volume":"69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127076373","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}

{"title":"Artificial intelligence against COVID-19 Pandemic using Chest X-ray Images","authors":"P. Soda","doi":"10.1109/iccicc53683.2021.9811313","DOIUrl":"https://doi.org/10.1109/iccicc53683.2021.9811313","url":null,"abstract":"This talk will dive into the AI for COVID initiative, a multicentre research project aimed at supporting the development and promoting the use of innovative AI-based methods to predict clinical outcomes of SARS-CoV-2-related disease. In the talk we will first discuss three AI-based approaches that use features extracted from CXR images, either handcrafted or automatically learnt by convolutional neuronal networks (CNNs), which are then integrated with the clinical data in a multimodal fashion. Furthermore, the talk will also present another application of the same repository, which is used to test a new late fusion approach combining the outputs of several state-of-the-art CNNs. It is driven by a two-objective function that constructs an optimum ensemble determining which and how many base learners should be aggregated, by maximizing the accuracy and the diversity of the ensemble itself.","PeriodicalId":101653,"journal":{"name":"2021 IEEE 20th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128643276","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}