2020 IEEE International Conference on Humanized Computing and Communication with Artificial Intelligence (HCCAI)最新文献

Clustering Depressed and Anti-Depressed keywords Based on a Twitter Event of Srilanka Bomb Blasts using text mining methods 基于文本挖掘方法的斯里兰卡爆炸案Twitter事件抑郁与抗抑郁关键词聚类

2020 IEEE International Conference on Humanized Computing and Communication with Artificial Intelligence (HCCAI) Pub Date : 2020-09-01 DOI: 10.1109/HCCAI49649.2020.00014

Sudha Tushara Sadasivuni, Yanqing Zhang

{"title":"Clustering Depressed and Anti-Depressed keywords Based on a Twitter Event of Srilanka Bomb Blasts using text mining methods","authors":"Sudha Tushara Sadasivuni, Yanqing Zhang","doi":"10.1109/HCCAI49649.2020.00014","DOIUrl":"https://doi.org/10.1109/HCCAI49649.2020.00014","url":null,"abstract":"Twitter users' post data on social websites that are casual, critical, emotional, and sharing in real-time. Many keywords related to an event will appear as tweet hashtags during an event and immediately after the event. Twitter allows a length of 140 characters as a hashtag keyword. Algorithms exist for event detection using several scientific methods and express the importance of the event and its features. Many of the earlier studies clustered the events based on the tweets. In this paper, we considered tweets with the bombing, depressed, and anti-depressed related keywords posted from Srilanka during the ‘Bomb’ blasts in April 2019. Similar tweets data also collected and analyzed from a normal period (during May 2019) to compare our results. Our results show that the keywords identified are related to the event. We could further cluster these two keywords sets into similar and dissimilar sets with a Twitter event. We applied Learning Quotient and Text mining methods, and our results support the clustering of keywords.","PeriodicalId":444855,"journal":{"name":"2020 IEEE International Conference on Humanized Computing and Communication with Artificial Intelligence (HCCAI)","volume":"221 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116002314","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: 4

Toward a Framework for Machine Self-Presentation : A survey of self-presentation strategies in human-machine interaction studies 迈向机器自我呈现框架:人机交互研究中的自我呈现策略综述

2020 IEEE International Conference on Humanized Computing and Communication with Artificial Intelligence (HCCAI) Pub Date : 2020-09-01 DOI: 10.1109/HCCAI49649.2020.00007

Jeff Stanley, O. Eris, Monika Lohani

{"title":"Toward a Framework for Machine Self-Presentation : A survey of self-presentation strategies in human-machine interaction studies","authors":"Jeff Stanley, O. Eris, Monika Lohani","doi":"10.1109/HCCAI49649.2020.00007","DOIUrl":"https://doi.org/10.1109/HCCAI49649.2020.00007","url":null,"abstract":"Increasingly, researchers are creating machines with humanlike social behaviors to elicit desired human responses such as trust and engagement, but a systematic characterization and categorization of such behaviors and their demonstrated effects is missing. This paper proposes a taxonomy of machine behavior based on what has been experimented with and documented in the literature to date. We argue that self-presentation theory, a psychosocial model of human interaction, provides a principled framework to structure existing knowledge in this domain and guide future research and development. We leverage a foundational human self-presentation taxonomy (Jones and Pittman, 1982), which associates human verbal behaviors with strategies, to guide the literature review of human-machine interaction studies we present in this paper. In our review, we identified 36 studies that have examined human-machine interactions with behaviors corresponding to strategies from the taxonomy. Of those studies utilizing self-presentation behaviors for machines, the majority have employed a strategy of Ingratiation, while relatively few have employed strategies of Supplication, Self-promotion, Exemplification, and Intimidation. The primary contribution of this research is our analysis of the frequently and infrequently used strategies to identify patterns and gaps, which led to the adaptation of Jones and Pittman's human self-presentation taxonomy to a machine self-presentation taxonomy. The adapted taxonomy identifies strategies and behaviors machines can employ when presenting themselves to humans in order to elicit desired human responses and attitudes. Approved for Public Release; Distribution Unlimited. Public Release Case Number 19-3566.","PeriodicalId":444855,"journal":{"name":"2020 IEEE International Conference on Humanized Computing and Communication with Artificial Intelligence (HCCAI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131252642","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

Hierarchical Evolutionary Heuristic A* Search 层次进化启发式A*搜索

2020 IEEE International Conference on Humanized Computing and Communication with Artificial Intelligence (HCCAI) Pub Date : 2020-09-01 DOI: 10.1109/HCCAI49649.2020.00011

Ying Fung Yiu, R. Mahapatra

{"title":"Hierarchical Evolutionary Heuristic A* Search","authors":"Ying Fung Yiu, R. Mahapatra","doi":"10.1109/HCCAI49649.2020.00011","DOIUrl":"https://doi.org/10.1109/HCCAI49649.2020.00011","url":null,"abstract":"A* is an informed pathfinding algorithm that uses a heuristic function to determine the best action to take based on the given information. The performance of A* is heavily dependent on the quality of the heuristic function. The heuristic function determines the search speed, accuracy, and memory consumption. Hence, designing good heuristic functions for specific domains becomes the primary research focus on pathfinding algorithms optimization. However, designing new heuristic functions is a difficult task, especially when they are used to solve complex problems. Moreover, a single heuristic function might not be enough to digest all the provided information and return the best guidance during the search. Previous works suggest that multiple heuristics for complex problems can dramatically speed up the search. However, choosing the appropriate combination of heuristic functions is tricky. Current optimization approaches rely on hand-tuning the parameters via trial and error by the engineers over many iterations. There is a need to reduce the difficulty of designing heuristic functions for search performance maximization. In this paper, we develop a novel heuristic search called Hierarchical Evolutionary Heuristic A* (HEHA*) where multiple heuristics are chosen and evolved using Genetic Algorithm. HEHA* combines the techniques of map abstraction, pattern database, and heuristic improvement. The advantage of HEHA* is twofold: 1) it partitions and reduces the search space based on local features to speed-up the search, and 2) it automatically designs and optimizes heuristics for different local regions to maximize the search performance. We test our algorithm on a widely used grid-based map benchmark to compare with A* variants. Our results show that HEHA* outperforms compared with other pathfinding algorithms in terms of execution time and memory consumption.","PeriodicalId":444855,"journal":{"name":"2020 IEEE International Conference on Humanized Computing and Communication with Artificial Intelligence (HCCAI)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117114958","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 New Method for Discovering Daily Depression from Tweets to Monitor Peoples Depression Status 一种从推特中发现每日抑郁状态以监测人们抑郁状态的新方法

2020 IEEE International Conference on Humanized Computing and Communication with Artificial Intelligence (HCCAI) Pub Date : 2020-09-01 DOI: 10.1109/HCCAI49649.2020.00013

Sudha Tushara Sadasivuni, Yanqing Zhang

{"title":"A New Method for Discovering Daily Depression from Tweets to Monitor Peoples Depression Status","authors":"Sudha Tushara Sadasivuni, Yanqing Zhang","doi":"10.1109/HCCAI49649.2020.00013","DOIUrl":"https://doi.org/10.1109/HCCAI49649.2020.00013","url":null,"abstract":"Many countries are actively involved in Mental Health Illness prevention programs as at present, this affects more than 300 million (>4%) people across the world, and this number is increasing every day. Predictions assume that Mental Health Illness will become the second leading cause for disease burden to stakeholders and rulers in the coming years. Identification of a mental health illness patient is complicated, as many do not agree that they have this stigma. Social Networks is one media that is involved in every ones' life to share/exhibit his emotions and feelings. More people share emotion-related tweets indicate that a predominant feature occurred on that day or in that location. We attempted to study the tweets related to depression and anti-depression and computed a new parameter, which indicates the depressive level of that day. While comparing with past data, this parameter will help the social scientists in the study of psychotherapy (afterburn) and ‘agitated depression’ levels to promote mental health and psychosocial interventions and sustainable development goals.","PeriodicalId":444855,"journal":{"name":"2020 IEEE International Conference on Humanized Computing and Communication with Artificial Intelligence (HCCAI)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126827793","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

Utility of Deep Learning Features for Facial Attributes Manipulation Detection 深度学习特征在面部属性操作检测中的应用

2020 IEEE International Conference on Humanized Computing and Communication with Artificial Intelligence (HCCAI) Pub Date : 2020-09-01 DOI: 10.1109/HCCAI49649.2020.00015

Z. Akhtar, Murshida Rahman Mouree, D. Dasgupta

{"title":"Utility of Deep Learning Features for Facial Attributes Manipulation Detection","authors":"Z. Akhtar, Murshida Rahman Mouree, D. Dasgupta","doi":"10.1109/HCCAI49649.2020.00015","DOIUrl":"https://doi.org/10.1109/HCCAI49649.2020.00015","url":null,"abstract":"ML-synthesized face samples, frequently called DeepFakes, is a serious issue menacing the integrity of information on the Internet and face recognition systems. One of the main defenses against face manipulations is DeepFakes detection. In this paper, we first created a new DeepFakes dataset using a publicly available MUCT database, which contains diverse set of facial manipulations. In particular, we employed smartphone FaceApp with eleven different filters (i.e., every filter concurs with a different facial manipulation) such as gender conversion, face swapping, tattoo and hair style changes. Deep learning features have recently demonstrated magnificent performances in various real-world applications. Therefore, with collected dataset, we study the efficiency of deep features for identifying the DeepFakes under different scenarios. We performed a rigorous and comparative analysis of a convolutional neural networks (CNNs) model and immensely utilized deep architectures such as VGG16, SqueezNet, DenseNet, ResaNet, and GoogleNet via transfer learning for face manipulation detection. Empirical results show that deep features based DeepFakes detection systems attain notable accuracies when trained and tested on same kind of manipulation. But their performances drop drastically when they encounter with novel manipulation type that was not used during the training stage, thereby having low generalization capability.","PeriodicalId":444855,"journal":{"name":"2020 IEEE International Conference on Humanized Computing and Communication with Artificial Intelligence (HCCAI)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123124598","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: 9

Using Gradient Methods to Predict Twitter Users' Mental Health with Both COVID-19 Growth Patterns and Tweets 使用梯度方法预测推特用户的心理健康，包括COVID-19的增长模式和推文

2020 IEEE International Conference on Humanized Computing and Communication with Artificial Intelligence (HCCAI) Pub Date : 2020-09-01 DOI: 10.1109/HCCAI49649.2020.00017

Sudha Tushara Sadasivuni, Yanqing Zhang

引用次数: 4

Intelligent Assistance for End-Users in creating Point and Click Games Storylines 智能辅助终端用户创建点和点击游戏故事情节

2020 IEEE International Conference on Humanized Computing and Communication with Artificial Intelligence (HCCAI) Pub Date : 2020-09-01 DOI: 10.1109/HCCAI49649.2020.00018

Federico Maria Cau, Angelo Mereu, Lucio Davide Spano

引用次数: 0

2020 IEEE International Conference on Humanized Computing and Communication with Artificial Intelligence 2020 IEEE与人工智能的人性化计算和通信国际会议

2020 IEEE International Conference on Humanized Computing and Communication with Artificial Intelligence (HCCAI) Pub Date : 2020-09-01 DOI: 10.1109/hccai49649.2020.00002

Hccai

引用次数: 0

Desirable Features of a Chatbot-building Platform 聊天机器人构建平台的理想功能

2020 IEEE International Conference on Humanized Computing and Communication with Artificial Intelligence (HCCAI) Pub Date : 2020-09-01 DOI: 10.1109/HCCAI49649.2020.00016

S. Srivastava, T. Prabhakar

引用次数: 3

Extending the learning shout-ahead architecture with user-defined exception rules – a case study for traffic light controls 使用用户定义的例外规则扩展学习大喊-交通灯控制的案例研究

2020 IEEE International Conference on Humanized Computing and Communication with Artificial Intelligence (HCCAI) Pub Date : 2020-09-01 DOI: 10.1109/HCCAI49649.2020.00008

Christian Roatis, J. Denzinger

{"title":"Extending the learning shout-ahead architecture with user-defined exception rules – a case study for traffic light controls","authors":"Christian Roatis, J. Denzinger","doi":"10.1109/HCCAI49649.2020.00008","DOIUrl":"https://doi.org/10.1109/HCCAI49649.2020.00008","url":null,"abstract":"We present an extension of the shout-ahead agent architecture that allows for adding human user-defined exception rules to the rules created by the hybrid learning approach for this architecture. The user-defined rules can be added after learning as reaction to weaknesses of the learned rules or learning can be performed with the user-defined rules already in place. We applied the extended shout-ahead architecture and the associated learning to a new application area, cooperating controllers for the traffic lights of intersections. In our experimental evaluations, adding user-defined exception rules to the learned rules for several traffic flow instances increased the efficiency of the resulting controllers substantially compared to just using the learned rules. Performing learning with user-defined exception rules already in place decreased the learning time substantially for all flows, but had mixed results with respect to efficiency. But both variants of adding user-defined exception rules create controllers that are much more flexible than what using the original shout-ahead architecture with its learning is able to create as experiments with variations of flows show.","PeriodicalId":444855,"journal":{"name":"2020 IEEE International Conference on Humanized Computing and Communication with Artificial Intelligence (HCCAI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130933838","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