APSIPA Transactions on Signal and Information Processing最新文献_第8页

Integrating Human Decisions in the Presence of Byzantines: An Evolutionary Game Theoretical Approach 在拜占庭人的存在下整合人类决策:一种进化博弈论方法

IF 3.2

APSIPA Transactions on Signal and Information Processing Pub Date : 2022-01-01 DOI: 10.1561/116.00000035

Yiqing Lin, Hong Hu, H. V. Zhao, Yan Chen

引用次数: 0

RGGID: A Robust and Green GAN-Fake Image Detector RGGID:一种鲁棒的绿色GAN-Fake图像检测器

IF 3.2

APSIPA Transactions on Signal and Information Processing Pub Date : 2022-01-01 DOI: 10.1561/116.00000005

Yao Zhu, Xinyu Wang, Ronald Salloum, Hong-Shuo Chen, C. J. Kuo

引用次数: 1

Predicting Pair Success in a Pair Programming Eye Tracking Experiment Using Cross-Recurrence Quantification Analysis 用交叉递归量化分析预测结对编程眼动追踪实验的成功

IF 3.2

APSIPA Transactions on Signal and Information Processing Pub Date : 2022-01-01 DOI: 10.1561/116.00000031

Maureen M. Villamor, M. M. Rodrigo

{"title":"Predicting Pair Success in a Pair Programming Eye Tracking Experiment Using Cross-Recurrence Quantification Analysis","authors":"Maureen M. Villamor, M. M. Rodrigo","doi":"10.1561/116.00000031","DOIUrl":"https://doi.org/10.1561/116.00000031","url":null,"abstract":"Pair programming is a model of collaborative learning. It has become a well-known pedagogical practice in teaching introductory programming courses because of its potential benefits to students. This study aims to investigate pair patterns in the context of pair program tracing and debugging to determine what characterizes collaboration and how these patterns relate to success, where success is measured in terms of performance task scores. This research used eye-tracking methodologies and techniques such as cross-recurrence quantification analysis. The potential indicators for pair success were used to create a model for predicting pair success. Findings suggest that it is possible to create a model capable of predicting pair success in the context of pair programming. The predictors for the pair success model that can obtain the best performance are the pairs’ proficiency level and degree of acquaintanceship. This was achieved using an ensemble algorithm such as Gradient Boosted Trees. The performance of the pairs is largely determined by the proficiency level of the individuals in the pairs; hence, it is recommended that the struggling students be paired with someone who is considered proficient in programming and with whom the struggling student is comfortable working with.","PeriodicalId":44812,"journal":{"name":"APSIPA Transactions on Signal and Information Processing","volume":null,"pages":null},"PeriodicalIF":3.2,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67081331","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: 2

Detecting Deepfake Videos in Data Scarcity Conditions by Means of Video Coding Features 基于视频编码特征的数据稀缺条件下深度假视频检测

IF 3.2

APSIPA Transactions on Signal and Information Processing Pub Date : 2022-01-01 DOI: 10.1561/116.00000032

Jun Wang, Omran Alamayreh, B. Tondi, Andrea Costanzo, M. Barni

引用次数: 0

Bayesian Multi-Temporal-Difference Learning 贝叶斯多时差学习

IF 3.2

APSIPA Transactions on Signal and Information Processing Pub Date : 2022-01-01 DOI: 10.1561/116.00000037

Jen-Tzung Chien, Y. Chiu

引用次数: 4

Is Self-Rated Confidence a Predictor for Performance in Programming Comprehension Tasks? 自评自信是编程理解任务表现的预测指标吗?

IF 3.2

APSIPA Transactions on Signal and Information Processing Pub Date : 2022-01-01 DOI: 10.1561/116.00000041

Zubair Ahsan, U. Obaidellah, M. Danaee

引用次数: 1

Cross-layer knowledge distillation with KL divergence and offline ensemble for compressing deep neural network 基于KL发散和离线集成的跨层知识提取压缩深度神经网络

IF 3.2

APSIPA Transactions on Signal and Information Processing Pub Date : 2021-11-17 DOI: 10.1017/ATSIP.2021.16

Hsing-Hung Chou, Ching-Te Chiu, Yi-Ping Liao

{"title":"Cross-layer knowledge distillation with KL divergence and offline ensemble for compressing deep neural network","authors":"Hsing-Hung Chou, Ching-Te Chiu, Yi-Ping Liao","doi":"10.1017/ATSIP.2021.16","DOIUrl":"https://doi.org/10.1017/ATSIP.2021.16","url":null,"abstract":"Deep neural networks (DNN) have solved many tasks, including image classification, object detection, and semantic segmentation. However, when there are huge parameters and high level of computation associated with a DNN model, it becomes difficult to deploy on mobile devices. To address this difficulty, we propose an efficient compression method that can be split into three parts. First, we propose a cross-layer matrix to extract more features from the teacher's model. Second, we adopt Kullback Leibler (KL) Divergence in an offline environment to make the student model find a wider robust minimum. Finally, we propose the offline ensemble pre-trained teachers to teach a student model. To address dimension mismatch between teacher and student models, we adopt a $1times 1$ convolution and two-stage knowledge distillation to release this constraint. We conducted experiments with VGG and ResNet models, using the CIFAR-100 dataset. With VGG-11 as the teacher's model and VGG-6 as the student's model, experimental results showed that the Top-1 accuracy increased by 3.57% with a $2.08times$ compression rate and 3.5x computation rate. With ResNet-32 as the teacher's model and ResNet-8 as the student's model, experimental results showed that Top-1 accuracy increased by 4.38% with a $6.11times$ compression rate and $5.27times$ computation rate. In addition, we conducted experiments using the ImageNet$64times 64$ dataset. With MobileNet-16 as the teacher's model and MobileNet-9 as the student's model, experimental results showed that the Top-1 accuracy increased by 3.98% with a $1.59times$ compression rate and $2.05times$ computation rate.","PeriodicalId":44812,"journal":{"name":"APSIPA Transactions on Signal and Information Processing","volume":null,"pages":null},"PeriodicalIF":3.2,"publicationDate":"2021-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46579651","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

A Network-Based Approach to QAnon User Dynamics and Topic Diversity During the COVID-19 Infodemic 新冠肺炎疫情期间基于网络的QAnon用户动态和主题多样性方法

IF 3.2

APSIPA Transactions on Signal and Information Processing Pub Date : 2021-10-31 DOI: 10.1561/116.00000055

Wentao Xu, K. Sasahara

引用次数: 6

Social rhythms measured via social media use for predicting psychiatric symptoms 通过使用社交媒体来预测精神症状的社会节律

IF 3.2

APSIPA Transactions on Signal and Information Processing Pub Date : 2021-10-28 DOI: 10.1017/ATSIP.2021.17

K. Yokotani, Masanori Takano

{"title":"Social rhythms measured via social media use for predicting psychiatric symptoms","authors":"K. Yokotani, Masanori Takano","doi":"10.1017/ATSIP.2021.17","DOIUrl":"https://doi.org/10.1017/ATSIP.2021.17","url":null,"abstract":"Social rhythms have been considered as relevant to mood disorders, but detailed analysis of social rhythms has been limited. Hence, we aim to assess social rhythms via social media use and predict users' psychiatric symptoms through their social rhythms. A two-wave survey was conducted in the Pigg Party, a popular Japanese avatar application. First and second waves of data were collected from 3504 and 658 Pigg Party users, respectively. The time stamps of their communication were sampled. Furthermore, the participants answered the General Health Questionnaire and perceived emotional support in the Pigg Party. The results indicated that social rhythms of users with many social supports were stable in a 24-h cycle. However, the rhythms of users with few social supports were disrupted. To predict psychiatric symptoms via social rhythms in the second-wave data, the first-wave data were used for training. We determined that fast Chirplet transformation was the optimal transformation for social rhythms, and the best accuracy scores on psychiatric symptoms and perceived emotional support in the second-wave data corresponded to 0.9231 and 0.7462, respectively. Hence, measurement of social rhythms via social media use enabled detailed understanding of emotional disturbance from the perspective of time-varying frequencies.","PeriodicalId":44812,"journal":{"name":"APSIPA Transactions on Signal and Information Processing","volume":null,"pages":null},"PeriodicalIF":3.2,"publicationDate":"2021-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"57024136","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

Robust deep convolutional neural network against image distortions 抗图像失真的鲁棒深度卷积神经网络

IF 3.2

APSIPA Transactions on Signal and Information Processing Pub Date : 2021-10-11 DOI: 10.1017/ATSIP.2021.14

Liang Wang, Sau-Gee Chen, Feng-Tsun Chien

{"title":"Robust deep convolutional neural network against image distortions","authors":"Liang Wang, Sau-Gee Chen, Feng-Tsun Chien","doi":"10.1017/ATSIP.2021.14","DOIUrl":"https://doi.org/10.1017/ATSIP.2021.14","url":null,"abstract":"Many approaches have been proposed in the literature to enhance the robustness of Convolutional Neural Network (CNN)-based architectures against image distortions. Attempts to combat various types of distortions can be made by combining multiple expert networks, each trained by a certain type of distorted images, which however lead to a large model with high complexity. In this paper, we propose a CNN-based architecture with a pre-processing unit in which only undistorted data are used for training. The pre-processing unit employs discrete cosine transform (DCT) and discrete wavelets transform (DWT) to remove high-frequency components while capturing prominent high-frequency features in the undistorted data by means of random selection. We further utilize the singular value decomposition (SVD) to extract features before feeding the preprocessed data into the CNN for training. During testing, distorted images directly enter the CNN for classification without having to go through the hybrid module. Five different types of distortions are produced in the SVHN dataset and the CIFAR-10/100 datasets. Experimental results show that the proposed DCT-DWT-SVD module built upon the CNN architecture provides a classifier robust to input image distortions, outperforming the state-of-the-art approaches in terms of accuracy under different types of distortions.","PeriodicalId":44812,"journal":{"name":"APSIPA Transactions on Signal and Information Processing","volume":null,"pages":null},"PeriodicalIF":3.2,"publicationDate":"2021-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48955187","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: 0