Trends in covolutional neural network in 2020 - International Journal of Artificial Intelligence & Applications (IJAIA)

" Trends in Convolutional Neural Network
in 2020"
International Journal of Artificial Intelligence &
Applications (IJAIA)
ISSN: 0975-900X (Online); 0976-2191 (Print)
http://airccse.org/journal/ijaia/ijaia.html

PREDICTING ROAD ACCIDENT RISK USING GOOGLE
MAPS IMAGES AND A CONVOLUTIONAL NEURAL
NETWORK
Aarya Agarwal,
Westwood High School, Austin, USA
ABSTRACT
Location specific characteristics of a road segment such as road geometry as well as surrounding road
features can contribute significantly to road accident risk. A Google Maps image of a road segment
provides a comprehensive visual of its complex geometry and the surrounding features. This paper
proposes a novel machine learning approach using Convolutional Neural Networks (CNN) to accident
risk prediction by unlocking the precise interaction of these many small road features that work in
combination to contribute to a greater accident risk. The model has worldwide applicability and a very
low cost/time effort to implement for a new city since Google Maps are available in most places across
the globe. It also significantly contributes to existing research on accident prevention by allowing for the
inclusion of highly detailed road geometry to weigh in on the prediction as well as the new location based
attributes like proximity to schools and businesses.
KEYWORDS
Deep Learning, Convolutional Neural Networks, Maps Images, Road Accidents.
Full Text: http://aircconline.com/ijaia/V10N6/10619ijaia05.pdf

REFERENCES
[1] Hoekstra, Tamara, and Fred Wegman. “Improving the Effectiveness of Road Safety Campaigns: Current and
New Practices.” IATSS Research 34, no. 2 (2011): 80–86. https://doi.org/10.1016/j.iatssr.2011.01.003.
[2] Polson, Nicholas G., and Vadim O. Sokolov. "Deep learning for short-term traffic flow prediction."
Transportation Research Part C: Emerging Technologies 79 (2017): 1-17.
[3] Zhang, Zhenhua, Qing He, Jing Gao, and Ming Ni. "A deep learning approach for detecting traffic
accidents from social media data." Transportation research part C: emerging technologies 86 (2018): 580-
596.
[4] Chen, Quanjun, Xuan Song, Harutoshi Yamada, and Ryosuke Shibasaki. "Learning deep representation
from big and heterogeneous data for traffic accident inference." In Thirtieth AAAI Conference on
Artificial Intelligence. 2016.
[5] Othman, S., Thomson, R., & Lannér, G. (2009, October). Identifying critical road geometry parameters
affecting crash rate and crash type. In Annals of Advances in Automotive Medicine/Annual Scientific
Conference (Vol. 53, p.155). Association for the Advancement of Automotive Medicine.
[6] Dumbaugh, Eric, Yi Zhang, and Wenhao Li. Community design and the incidence of crashes involving
pedestrians and motorists aged 75 and older. No. UTCM 11-03-67. Texas Transportation Institute. University
Transportation Center for Mobility, 2012.
[7] Geyer, Judy, Elena Lankina, Ching-Yao Chan, David Ragland, Trinh Pham, and Ashkan Sharafsaleh.
“Methods for Identifying High Collision Concentration Locations for Potential Safety Improvements.”
CALIFORNIA PARTNERS FOR ADVANCED TRANSIT AND HIGHWAYS, December 2008.
[8] Rezapour, Mahdi, Shaun S. Wulff, and Khaled Ksaibati. “Effectiveness of Enforcement Resources in the
Highway Patrol in Reducing Fatality Rates.” IATSS Research 42, no. 4 (2018): 259–64.
https://doi.org/10.1016/j.iatssr.2018.04.001.
AUTHORS
Aarya Agarwal is a student at Westwood High school, Austin and he is currently pursuing an
IB diploma. He has completed several machine learning projects and has submitted several of
these projects to prestigious science competitions. His achievements include 1st place at the
Texas State Science Fair and Austin Regional Science Fair, as well as 2nd place at the Texas
Junior Academy of Sciences for the category of Computer Science/Math

An Application of Convolutional Neural Networks
on Human Intention Prediction
Lin Zhang1
, Shengchao Li2
, Hao Xiong2
, Xiumin Diao2
and Ou Ma1
1
Department of Aerospace Engineering and Engineering Mechanics, University of Cincinnati, Cincinnati,
Ohio, USA
2
School of Engineering Technology, Purdue University, West Lafayette, Indiana, USA
ABSTRACT
Due to the rapidly increasing need of human-robot interaction (HRI), more intelligent robots are in
demand. However, the vast majority of robots can only follow strict instructions, which seriously restricts
their flexibility and versatility. A critical fact that strongly negates the experience of HRI is that robots
cannot understand human intentions. This study aims at improving the robotic intelligence by training it
to understand human intentions. Different from previous studies that recognizing human intentions from
distinctive actions, this paper introduces a method to predict human intentions before a single action is
completed. The experiment of throwing a ball towards designated targets are conducted to verify the
effectiveness of the method. The proposed deep learning based method proves the feasibility of applying
convolutional neural networks (CNN) under a novel circumstance. Experiment results show that the
proposed CNN-vote method out competes three traditional machine learning techniques. In current
context, the CNN-vote predictor achieves the highest testing accuracy with relatively less data needed.
KEYWORDS
Human-robot Interaction; Intentions Prediction; Convolutional Neural Networks;

REFERENCES
[1] J. Forlizzi and C. DiSalvo, “Service robots in the domestic environment,” in Proceeding of the 1st ACM
SIGCHI/SIGART conference on Human-robot interaction - HRI ’06, 2006, p. 258.
[2] J. Bodner, H. Wykypiel, G. Wetscher, and T. Schmid, “First experiences with the da VinciTM operating
robot in thoracic surgery☆,” Eur. J. Cardio-Thoracic Surg., vol. 25, no. 5, pp. 844–851, May 2004.
[3] M. J. Micire, “Evolution and field performance of a rescue robot,” J. F. Robot., vol. 25, no. 1–2, pp. 17–30,
Jan. 2008.
[4] F. Mondada et al., “The e-puck , a Robot Designed for Education in Engineering,” in Robotics, 2009, vol. 1,
no. 1, pp. 59–65.
[5] K. Wakita, J. Huang, P. Di, K. Sekiyama, and T. Fukuda, “Human-Walking-Intention-Based Motion Control of
an Omnidirectional-Type Cane Robot,” IEEE/ASME Trans. Mechatronics, vol. 18, no. 1, pp. 285–296, Feb.
2013.
[6] K. Sakita, K. Ogawam, S. Murakami, K. Kawamura, and K. Ikeuchi, “Flexible cooperation between human
and robot by interpreting human intention from gaze information,” in 2004 IEEE/RSJ International Conference
on Intelligent Robots and Systems (IROS) (IEEE Cat. No.04CH37566), 2004, vol. 1, pp. 846–851.
[7] Z. Wang, A. Peer, and M. Buss, “An HMM approach to realistic haptic human-robot interaction,” in
World Haptics 2009 - Third Joint EuroHaptics conference and Symposium on Haptic Interfaces for Virtual
Environment and Teleoperator Systems, 2009, pp. 374–379.
[8] S. Kim, Z. Yu, J. Kim, A. Ojha, and M. Lee, “Human-Robot Interaction Using Intention Recognition,” in
Proceedings of the 3rd International Conference on Human-Agent Interaction, 2015, pp. 299–302.
[9] D. Song et al., “Predicting human intention in visual observations of hand/object interactions,” in
2013 IEEE International Conference on Robotics and Automation, 2013, pp. 1608–1615.
[10] D. Vasquez, T. Fraichard, O. Aycard, and C. Laugier, “Intentional motion on-line learning and prediction,”
Mach. Vis. Appl., vol. 19, no. 5–6, pp. 411–425, Oct. 2008.
[11] B. Ziebart, A. Dey, and J. A. Bagnell, “Probabilistic pointing target prediction via inverse optimal
control,” in Proceedings of the 2012 ACM international conference on Intelligent User Interfaces
- IUI ’12, 2012, p. 1.
[12] Z. Wang et al., “Probabilistic movement modeling for intention inference in human–robot interaction,”
Int. J. Rob. Res., vol. 32, no. 7, pp. 841–858, 2013.
[13] Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nature, vol. 521, no. 7553, p. 436, 2015.
[14] A. Karpathy, G. Toderici, S. Shetty, T. Leung, R. Sukthankar, and L. Fei-Fei, “Large-Scale Video
Classification with Convolutional Neural Networks,” in 2014 IEEE Conference on Computer Vision and
Pattern Recognition, 2014, pp. 1725–1732.
[15] X. Wang, L. Gao, P. Wang, X. Sun, and X. Liu, “Two-Stream 3-D convNet Fusion for Action Recognition in
Videos With Arbitrary Size and Length,” IEEE Trans. Multimed., vol. 20, no. 3, pp. 634–644, Mar. 2018.

[16] P. Barros, C. Weber, and S. Wermter, “Emotional expression recognition with a cross-channel convolutional
neural network for human-robot interaction,” in 2015 IEEE-RAS 15th International Conference on Humanoid
Robots (Humanoids), 2015, pp. 582–587.
[17] A. H. Qureshi, Y. Nakamura, Y. Yoshikawa, and H. Ishiguro, “Show, attend and interact: Perceivable human-
robot social interaction through neural attention Q-network,” in 2017 IEEE International Conference on
Robotics and Automation (ICRA), 2017, pp. 1639–1645.
[18] L. Zhang, X. Diao, and O. Ma, “A Preliminary Study on a Robot’s Prediction of Human Intention,” 7th Annu.
IEEE Int. Conf. CYBER Technol. Autom. Control. Intell. Syst., pp. 1446– 1450, 2017.
[19] A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet Classification with Deep Convolutional Neural
Networks,” Adv. Neural Inf. Process. Syst. (NIPS 2012), p. 4, 2012.
[20] J. Shotton et al., “Real-time human pose recognition in parts from single depth images,” in CVPR 2011,
2011, vol. 411, pp. 1297–1304.
[21] F. Pedregosa et al., “Scikit-learn: Machine Learning in Pythons,” J. Mach. Learn. Res., vol. 12, no. 6, pp.
2825–2830, May 2011.
[22] M. Abadi et al., “TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems,” 2016.
[23] S. Li, L. Zhang, and X. Diao, “Improving Human Intention Prediction Using Data Augmentation,” in HRI
2018 WORKSHOP ON SOCIAL HUMAN-ROBOT INTERACTION OF HUMAN-CARE SERVICE
ROBOTS, 2018.
[24] J. Donahue et al., “Long-term recurrent convolutional networks for visual recognition and description,” in
Proceedings of the IEEE conference on computer vision and pattern recognition, 2015, pp. 2625–2634.
[25] K. Simonyan and A. Zisserman, “Very Deep Convolutional Networks for Large-Scale Image Recognition,”
Inf. Softw. Technol., vol. 51, no. 4, pp. 769–784, Sep. 2014.
[26] C. Szegedy, S. Ioffe, V. Vanhoucke, and A. Alemi, “Inception-v4, Inception-ResNet and the Impact of
Residual Connections on Learning,” Pattern Recognit. Lett., vol. 42, pp. 11–24, Feb. 2016.
[27] P. Munya, C. A. Ntuen, E. H. Park, and J. H. Kim, “A BAYESIAN ABDUCTION MODEL FOR
EXTRACTING THE MOST PROBABLE EVIDENCE TO SUPPORT SENSEMAKING,” Int. J. Artif.
Intell. Appl., vol. 6, no. 1, p. 1, 2015.
[28] J. A. Morales and D. Akopian, “Human activity tracking by mobile phones through hebbian learning,” Int.
J. Artif. Intell. Appl., vol. 7, no. 6, pp. 1–16, 2016.
[29] C. Lee and M. Jung, “PREDICTING MOVIE SUCCESS FROM SEARCH QUERY USING SUPPORT
VECTOR REGRESSION METHOD,” Int. J. Artif. Intell. Appl. (IJAIA)., vol. 7, no. 1, 2016.

AUTHORS
Lin Zhang as Sr. Research Associate is currently working in Intelligent Robotics and Autonomous
System Lab at University of Cincinnati. His major research interest is reinforcement learning and
its application in robotics.
Shengchao Li as Ph.D student is doing research in DeePURobotics Lab at Purdue University.
His major research interest is deep neural networks and image processing.
Hao Xiong as Ph.D candidate is doing research in DeePURobotics Lab at Purdue University. His
major research interest is dynamics and control of cable-driven robot and its application in
rehabilitation robots.
Xiumin Diao as Assistant Professor is supervising and directing DeePURobotics Lab at Purdue
University. His research interests are dynamics and control of cable-driven robot and intelligent
robotics.
Ou Ma as Professor is supervising and directing Intelligent Robotics and Autonomous System Lab
at University of Cincinnati. His research interests are multibody dynamics and control, impact-
contact dynamics, intelligent control of robotics, autonomous systems, human-robot
interaction,etc..

Transfer Learning With Convolutional Neural
Networks For Iris Recognition
Maram.G Alaslni1
and Lamiaa A. Elrefaei 1, 2
1
Computer Science Department, Faculty of Computing and Information Technology, King Abdulaziz
University, Jeddah, Saudi Arabia
2
Electrical Engineering Department, Faculty of Engineering at Shoubra
, Benha University, Cairo, Egypt
ABSTRACT
Iris is one of the common biometrics used for identity authentication. It has the potential to recognize
persons with a high degree of assurance. Extracting effective features is the most important stage in the
iris recognition system. Different features have been used to perform iris recognition system. A lot of
them are based on hand-crafted features designed by biometrics experts. According to the achievement of
deep learning in object recognition problems, the features learned by the Convolutional Neural Network
(CNN) have gained great attention to be used in the iris recognition system. In this paper, we proposed
an effective iris recognition system by using transfer learning with Convolutional Neural Networks. The
proposed system is implemented by fine-tuning a pre-trained convolutional neural network (VGG-16) for
features extracting and classification. The performance of the iris recognition system is tested on four
public databases IITD, iris databases CASIA-Iris-V1, CASIA-Iris-thousand and, CASIA-Iris-Interval.
The results show that the proposed system is achieved a very high accuracy rate.
KEYWORDS
Biometrics, iris, recognition, deep learning, convolutional neural network (CNN), transfer learning.

REFERENCES
[1] Haghighat, M., S. Zonouz, and M. Abdel-Mottaleb, CloudID: Trustworthy cloud-based and
crossenterprise biometric identification. Expert Systems with Applications, 2015. 42(21): p. 7905-7916.
[2] Kesavaraja, D., D. Sasireka, and D. Jeyabharathi, Cloud software as a service with iris authentication.
Journal of Global Research in Computer Science, 2010. 1(2): p. 16-22.
[3] Bowyer, K.W., K. Hollingsworth, and P.J. Flynn, Image understanding for iris biometrics: A
survey.Computer vision and image understanding, 2008. 110(2): p. 281-307.
[4] Dehkordi, A.B. and S.A. Abu-Bakar, A review of iris recognition system. Jurnal Teknologi, 2015.77(1).
[5] Shah, N. and P. Shrinath, Iris Recognition System–A Review. International Journal of Computer and
Information Technology, 2014. 3(02).
[6] Minaee, S., A. Abdolrashidiy, and Y. Wang. An experimental study of deep convolutional features for iris
recognition. in Signal Processing in Medicine and Biology Symposium (SPMB), 2016 IEEE.2016. IEEE.
[7] Minaee, S., A. Abdolrashidi, and Y. Wang. Iris recognition using scattering transform and textural features.
in Signal Processing and Signal Processing Education Workshop (SP/SPE), 2015 IEEE.2015. IEEE.
[8] Minaee, S., A. Abdolrashidi, and Y. Wang, Face Recognition Using Scattering Convolutional Network.
arXiv preprint arXiv:1608.00059, 2016.
[9] Sharif Razavian, A., et al. CNN features off-the-shelf: an astounding baseline for recognition. in Proceedings
of the IEEE conference on computer vision and pattern recognition workshops. 2014.
[10] Lucena, O., et al. Transfer Learning Using Convolutional Neural Networks for Face Anti-spoofing.in
International Conference Image Analysis and Recognition. 2017. Springer.
[11] Torrey, L. and J. Shavlik, Transfer learning. Handbook of Research on Machine Learning
Applications and Trends: Algorithms, Methods, and Techniques, 2009. 1: p. 242.
[12] Masood, S., et al. Identification of diabetic retinopathy in eye images using transfer learning. in
Computing, Communication and Automation (ICCCA), 2017 International Conference on. 2017.IEEE.
[13] IIT Delhi Database. Available from:http://www4.comp.polyu.edu.hk/~csajaykr/IITD/Database_Iris.htm.
[14] Casia Iris Database. Available from:http://www.idealtest.org/findDownloadDbByMode.do?mode=Iris.
[15] CASIA Iris Image Database Version 4.0 (CASIA-Iris-Thousand). Available
from:http://biometrics.idealtest.org/dbDetailForUser.do?id=4.
[16] CASIA Iris Image Database Version 3.0 (CASIA-Iris-Interval). Available
from:http://biometrics.idealtest.org/dbDetailForUser.do?id=3.
[17] Nguyen, K., et al., Iris Recognition with Off-the-Shelf CNN Features: A Deep Learning Perspective.IEEE
Access, 2017.
[18] Romero, A., C. Gatta, and G. Camps-Valls, Unsupervised deep feature extraction for remote sensing image
classification. IEEE Transactions on Geoscience and Remote Sensing, 2016. 54(3): p. 1349-1362.

[19] Oyedotun, O. and A. Khashman, Iris nevus diagnosis: convolutional neural network and deep belief
network. Turkish Journal of Electrical Engineering & Computer Sciences, 2017. 25(2): p. 1106-1115.
[20] Nagi, J., et al. Max-pooling convolutional neural networks for vision-based hand gesture recognition. in
Signal and Image Processing Applications (ICSIPA), 2011 IEEE International Conference on. 2011. IEEE.
[21] Al-Waisy, A.S., et al., A multi-biometric iris recognition system based on a deep learning
approach.Pattern Analysis and Applications, 2017: p. 1-20.
[22] Ng, R.Y.F., N.Y.H. Tay, and K.M. Mok. A review of iris recognition algorithms. in Information
Technology, 2008. ITSim 2008. International Symposium on. 2008. IEEE.
[23] Sangwan, S. and R. Rani, A Review on: Iris Recognition. (IJCSIT) International Journal of Computer
Scienceand Information Technologies, 2015. 6(4): p. 3871-3873.
[24] Daugman, J.G., High confidence visual recognition of persons by a test of statistical independence.Pattern
Analysis and Machine Intelligence, IEEE Transactions on, 1993. 15(11): p. 1148-1161.
[25] Daugman, J., How iris recognition works. Circuits and Systems for Video Technology, IEEE Transactions
on, 2004. 14(1): p. 21-30.
[26] Wildes, R.P., Iris recognition: an emerging biometric technology. Proceedings of the IEEE, 1997.85(9): p.
1348-1363.
[27] Jayachandra, C. and H.V. Reddy, Iris Recognition based on Pupil using Canny edge detection and K-Means
Algorithm. Int. J. Eng. Comput. Sci., 2013. 2: p. 221-225.
[28] Daouk, C., et al. Iris recognition. in IEEE ISSPIT. 2002.
[29] Raghava, N. Iris recognition on hadoop: A biometrics system implementation on cloud computing.in Cloud
Computing and Intelligence Systems (CCIS), 2011 IEEE International Conference on. 2011. IEEE.
[30] Darve, N.R. and D.P. Theng, Biometric User Authentication for Mobile Cloud Computing (MCC) Servises
Through Eye Images. 2015.
[31] Elrefaei, L.A., et al., Developing Iris Recognition System for Smartphone Security. Multimedia Tools and
Applications, 2017: p. 1-25.
[32] Saini, R.G.H., Generation of Iris Template for recognition of Iris in Efficient Manner. International Journal
of Computer Science and Information Technologies (IJCSIT), 2011. 2(4): p. 1753-1755.
[33] Nguyen, K., et al., Long range iris recognition: A survey. Pattern Recognition, 2017. 72: p. 123-143.
[34] Pirale, D., M. Nirgude, and S. Gengje, Iris Recognition using Wavelet Transform and Neural Networks.
International Journal of Science and Research (IJSR), 2016. 5(5): p. 1055-1060.
[35] Elgamal, M. and N. Al-Biqami, An efficient feature extraction method for iris recognition based on wavelet
transformation. Int. J. Comput. Inf. Technol, 2013. 2(03): p. 521-527.
[36] Bharath, B., et al. Iris recognition using radon transform thresholding based feature extraction with
Gradient-based Isolation as a pre-processing technique. in Industrial and Information Systems (ICIIS), 2014
9th International Conference on. 2014. IEEE.

[37] Dhage, S.S., et al., DWT-based feature extraction and radon transform based contrast enhancement for
improved iris recognition. Procedia Computer Science, 2015. 45: p. 256-265
[38] Xu, G., Z. Zhang, and Y. Ma, A novel method for iris feature extraction based on intersecting cortical
model network. Journal of Applied Mathematics and Computing, 2008. 26(1-2): p. 341-352.
[39] Abhiram, M., et al. Novel DCT based feature extraction for enhanced iris recognition. in
Communication, Information & Computing Technology (ICCICT), 2012 International Conference on. 2012.
IEEE.
[40] Ribeiro, E. and A. Uhl. Exploring texture transfer learning via convolutional neural networks for iris super
resolution. in 2017 International Conference of the Biometrics Special Interest Group (BIOSIG). 2017.
IEEE.
[41] Maram G. Alaslani, L.a.A.E., Convolutional Neural Network based Feature Extraction for Iris Recognition.
International Journal of Computer Science & Information Technology (IJCSIT), 2018.10(April): p. 65-87.
[42] Hsieh, L., W.-S. Chen, and T.-H. Li. Personal Authentication Using Human Iris Recognition Based on
Embedded Zerotree Wavelet Coding. in Computing in the Global Information Technology (ICCGI), 2010
Fifth International Multi-Conference on. 2010. IEEE.
[43] Zhu, Y., T. Tan, and Y. Wang. Biometric personal identification based on iris patterns. in icpr. 2000.IEEE.
[44] Kotsiantis, S.B., I. Zaharakis, and P. Pintelas, Supervised machine learning: A review of classification
techniques. Emerging artificial intelligence applications in computer engineering, 2007. 160: p. 3-24.
[45] Roy, K., P. Bhattacharya, and C.Y. Suen, Iris recognition using shape-guided approach and game
theory. Pattern Analysis and Applications, 2011. 14(4): p. 329-348.
[46] Farzam, S. and H. Mirvaziri, IRIS RECOGNITION BASED ON WAVELET TRANSFORM USING
SUPPORT VECTOR MACHINE AND DECISION TREE COMBINATION CLASSIFIER. Indian
Journal of Fundamental and Applied Life Sciences, 2016. 6: p. 1-9.
[47] Krizhevsky, A., I. Sutskever, and G.E. Hinton. Imagenet classification with deep convolutional neural
networks. in Advances in neural information processing systems. 2012.
[48] Yu, W., et al. Visualizing and comparing AlexNet and VGG using deconvolutional layers. in
Proceedings of the 33 rd International Conference on Machine Learning. 2016.
[49] Ozbulak, G., Y. Aytar, and H.K. Ekenel. How transferable are CNN-based features for age and gender
classification? in Biometrics Special Interest Group (BIOSIG), 2016 International Conference of the. 2016.
IEEE.
[50] Syafeeza, A., et al., Convolutional neural networks with fused layers applied to face recognition.
International Journal of Computational Intelligence and Applications, 2015. 14(03): p. 1550014

AUTHORS
Maram G. Alaslani, female, received her B.Sc. degree in Computer Science with honors from King Abdulaziz
University in 2010. her M.Sc. in 2018 at King Abdulaziz University, Jeddah, Saudi Arabia. She works as
Teaching Assistant from 2011 to date at Faculty of Computers and Information Technology at King Abdulaziz
University, Rabigh, Saudi Arabia. She has a research interest in image processing, pattern recognition, and
neural network.
Lamiaa A. Elrefaei, female, received her B.Sc. degree with honors in Electrical Engineering (Electronics and
Telecommunications) in 1997, her M.Sc. in 2003 and Ph.D. in 2008 in Electrical Engineering (Electronics) from
faculty of Engineering at Shoubra, Benha University, Egypt. She held a number of faculty positions at Benha
University, as Teaching Assistant from 1998 to 2003, as an Assistant Lecturer from 2003 to 2008, and has been
a lecturer from 2008 to date. She is currently an Associate Professor at the faculty of Computing and
Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia. Her research interests include
computational intelligence, biometrics, multimedia security, wireless networks, and Nano networks. She is a
senior member of IEEE.

Motion Prediction Using Depth Information of
Human Arm Based on Alexnet
JingYuan Zhu1
, ShuoJin Li1
, RuoNan Ma2
, Jing Cheng1
1
School of Aerospace of Engineering, Tsinghua University, Beijing, China
2
School of Economics and Management, Tsinghua University, Beijing, China
ABSTRACT
The development of convolutional neural networks(CNN) has provided a new tool to make classification
and prediction of human's body motion. This project tends to predict the drop point of a ball thrown out
by experimenters by classifying the motion of their body in the process of throwing. Kinect sensor v2 is
used to record depth maps and the drop points are recorded by a square infrared induction module. Firstly,
convolutional neural networks are made use of to put the data obtained from depth maps in and get the
prediction of drop point according to experimenters' motion. Secondly, huge amount of data is used to
train the networks of different structure, and a network structure that could provide high enough accuracy
for drop point prediction is established. The network model and parameters are modified to improve the
accuracy of the prediction algorithm. Finally, the experimental data is divided into a training group and a
test group. The prediction results of test group reflect that the prediction algorithm effectively improves
the accuracy of human motion perception.
KEYWORDS
Human Motion, Prediction, Convolutional Neural Network, Depth Information
Full Text: http:/aircconline.com/ijaia/V10N4/10419ijaia02.pdf

REFERENCES
[1] Sherif, R. and Jonathan, B., 2018. “Behavior-based security for mobile devices using machine learning
techniques”. International Journal of Artificial and Applications(IJAIA), 9(4).
[2] Dilanam, H., Lin, Z., and Harald C. 2018. “Performance evaluation of various emotion classification
approaches from physiological signals”. International Journal of Artificial and Applications(IJAIA), 9(4).
[3] Abdelkarim Mars and Georges Antoniadis. 2016. “Arabic online handwriting recognition using neural
network”. International Journal of Artificial and Applications(IJAIA), 7(5).
[4] Alayna Kennedy and Rory Lewis. 2016. “Optimization of neural network architecture for
biomechanicclassdfication tasks with electromyogram inputs”. International Journal of Artificial and
Applications(IJAIA), 7(5).
[5] Johansson, G., 1973. “Visual perception of biological motion and a model for its analysis”. Perception
Psychophysics, 14(2), pp. 201–211.
[6] IEEE, 2002. “Proceedings of ieee conference on computer vision and pattern recognition”. IEEE Computer
Society Conference on Computer Vision Pattern Recognition, Cvpr.
[7] CiptadiA.,Goodwin, M.S.,andRehg,J.M.,2014.“Movement pattern histogram for action recognition and
retrieval”. European Conference on Computer Vision.
[8] Adeli, M. E., Raahemifar, K., and Fathy, M., 2013. “Multi-view human activity recognition in distributed
camera sensor networks”. Sensors, 13(7), pp. 8750–8770.
[9] Nicola, B. and Huosheng, H., 2009. “Multisensor-based human detection and tracking for mobile service
robots”. IEEE Transactions on Systems Man Cybernetics Part B Cybernetics A Publication of the IEEE
Systems Man Cybernetics Society, 39(1), pp. 167–81.
[10] Shotton, J., Fitzgibbon, A., Cook, M., Sharp, T., Finocchio, M., Moore, R., Kipman, A., and Blake, A.,
2013. Real-Time Human Pose Recognition in Parts from Single Depth Images.
[11] Ellis, Chris, Masood, Zain, S., Tappen, Marshall, F., LaViola, Joseph, J., Jr, and Sukthankar,
2013.“Exploring the trade-off between accuracy and observational latency in;action recognition”.
International Journal of Computer Vision, 101(3), pp. 420–436.
[12] Lu, X. L. X., 2011. “Human detection using depth information by kinect”. IEEE Computer Society
Conference on Computer Vision Pattern Recognition, Cvpr.
[13] Foix, S., Alenya, G., and Torras, C., 2011. “Lock-in time-of-flight (tof) cameras: A survey”. IEEE Sensors
Journal, 11(9), pp. 1917–1926.
[14] Stokoe, W. C., 1980. “Sign language structure”. Annual Review of Anthropology, 9(9), pp. 365–390.
[15] Imagawa, K., Lu, S., and Igi, S., 1998. “Color-based hands tracking system for sign language recognition”
IEEE International Conference on Automatic Face & Gesture Recognition.
[16] Triesch, J., and Malsburg, C. V. D., 1999. “A gesture interface for human-robot-interaction”. IEEE
International Conference on Automatic Face & Gesture Recognition

[17] Nlker, C., Ritter, H., Fakultt, T., and Bielefeld, U., 1998. “Illumination independent recognition of deictic
arm pos- tures”. In Conference of the IEEE Industrial Electronics Society.
[18] Bobick,A.F.1997.“Movement, activity and action:the role of knowledge in the perception of
motion”.Philosophical Transactions of the Royal Society of London, 352(1358), pp. 1257–1265.
[19] Wang, Y., Jiang, H., Drew, M. S., Li, Z. N., and Mori, G., 2006. “Unsupervised discovery of action
classes”. IEEE Computer Society Conference on Computer Vision Pattern Recognition.
[20] Shuiwang, J., Ming, Y., and Kai, Y., 2013. “3d convolutional neural networks for human action
recognition”. IEEE Transactions on Pattern Analysis Machine Intelligence, 35(1), pp. 221–231.
[21] Krizhevsky A, Sutskever I , Hinton G. 2012. “ImageNet Classification with Deep Convolutional
NeuralNetworks”. Advances in neural information processing systems, 25(2).
[22] Yuan, Z. W., and Zhang, J., 2016. “Feature extraction and image retrieval based on
alexnet”.EighthInternational Conference on Digital Image Processing.
[23] Gu, S., Lu, D., Yue, Y., and Chen, X., 2017. “A new deep learning method based on alexnet model and ssd
model for tennis ball recognition”. IEEE International Workshop on Computational Intelligence
Applications.
[24] Jing, S., Cai, X., Sun, F., and Zhang, J., 2016. “Scene image classification method based on alex-net
model”. International Conference on Informative Cybernetics for Computational Social Systems.
[25] Pal, R., and Saraswat, M., 2018. [IEEE 2018 Eleventh International Conference on Contemporary
Computing (IC3) - Noida, India (2018.8.2-2018.8.4)] 2018 Eleventh International Conference on
Contemporary Computing (IC3) - Enhanced Bag of Features Using Alexnet and Improved Biogeography-
Based Optimization for Histopathological Image Analysis. pp. 1–6.
[26] Dang, Y., Zhang, J., Deng, K., Zhao, Y., and Fan, Y. U., 2017. “Study on the evaluation of land cover
classification using remote sensing images based on alexnet”. Journal of Geo-Information Science, 19(11).

Artificial Intelligence and its Impact on the
Fourth Industrial Revolution: A Review
Gissel Velarde
ABSTRACT
Artificial Intelligence may revolutionize everything during the so-called fourth industrial revolution,
which carries several emerging technologies and could progress without precedents in human history due
to its speed and scope. Government, academia, industry, and civil society show interest in understanding
the multidimensional impact of the emerging industrial revolution; however, its development is hard to
predict. Experts consider emerging technologies could bring tremendous benefits to humanity; at the same
time, they could pose an existential risk. This paper reviews the development and trends in AI, as well as
the benefits, risks, and strategies in the field. During the course of the emerging industrial revolution, the
common good may be achieved in a collaborative environment of shared interests and the hardest work
will be the implementation and monitoring of projects at a global scale.
KEYWORDS
Artificial Intelligence, Fourth Industrial Revolution, Deep Machine Learning, Emerging Technology,
Human Computer Interaction, Common Good.

REFERENCES
[1] K. Schwab, The Fourth Industrial Revolution. New York: Crown Business, 2016.
[2] S. Makridakis, “The forthcoming Artificial Intelligence (AI) revolution: Its impact on society and
firms,” Futures, vol. 90, pp. 46–60, 2017.
[3] I. J. Goodfellow, Y. Bengio, and A. Courville, Deep Learning. London, England: MIT Press, 2016.
[4] I. Arel, D. C. Rose, and T. P. Karnowski, “Deep Machine Learning - A New Frontier in Artificial Intelligence
Research [Research Frontier],” IEEE Computational Intelligence Magazine, vol. 5, no. 4, pp.13–18, Nov
2010.
[5] D. Silver, J. Schrittwieser, K. Simonyan, I. Antonoglou, A. Huang, A. Guez, T. Hubert, L. Baker, M. Lai, A.
Bolton et al., “Mastering the game of Go without human knowledge,” Nature, vol. 550, no. 7676, p.354,
2017.
[6] N. J. Nilsson, The Quest for Artificial Intelligence: A History of Ideas and Achievements. New York, USA:
Cambridge University Press, 2010.
[7] J. Schmidhuber, “Deep learning in neural networks: An overview,” Neural networks, vol. 61, pp. 85–117,
2015.
[8] N. Bostrom, Superintelligence. Oxford University Press, 2016.
[9] A. Agrawal, J. Gans, and A. Goldfarb, Prediction Machines: The Simple Economics of Artificial Intelligence.
Boston, Massachusetts: Business School Publishing, 2018.
[10] D. Amodei, C. Olah, J. Steinhardt, P. Christiano, J. Schulman, and D. Mané, “Concrete problems in AI
safety,” arXiv preprint arXiv:1606.06565, 2016.
[11] P. H.Winston, Artificial Intelligence. United States of America: Addison-Weley, 1992.
[12] L. F. Menabrea and A. Lovelace, Sketch of the analytical engine in- vented by Charles Babbage, Esq. with
notes by the Translator. London: Richard and John E. Taylor, 1843, extracted from the’ SCIENTIFIC
MEMOIRS’, vol. iii.
[13] A. L.Samuel, “Some studies in machine learning using the game of checkers,” IBM Journal of research
and development, vol. 3, no. 3, pp. 210–229, 1959
[14] A. Dominguez, “A history of the convolution operation,” Pulse IEEE, 2015. [Online].
Available:http://pulse.embs.org/january-2015/ history-convolution-operation/
[15] A.Ng, “The state of artificial intelligence,” https://www.youtube.com/ watch?v=NKpuX yzdYs, 2017,
accessed September 12, 2019.
[16] V. C. Müller and N. Bostrom, “Future progress in artificial intelligence: A survey of expert opinion,” in
Fundamental issues of artificial intelligence. Springer, 2016, pp. 555–572.
[17] R. Lambiotte and M. Kosinski, “Tracking the digital footprints of personality.” Proceedings of the IEEE, vol.
102, no. 12, pp. 1934–1939, 2014.

[18] M. I. Jordan and T. M. Mitchell, “Machine learning: Trends, perspectives, and prospects,” Science,
vol.349, no. 6245, pp. 255–260, 2015. [Online]. Available:
https://science.sciencemag.org/content/349/6245/255
[19] D. Hassabis and D. Silver, “AlphaGo Zero: Learning from scratch.” https://deepmind.com/blog/alphagozero-
learning-scratch/, 2017, accessed September 12, 2019.
[20] Y. Bengio, A. Courville, and P. Vincent, “Representation learning: A review and new perspectives,”IEEE
Transactions on Pattern Analysis and Machine Intelligence, vol. 35, no. 8, pp. 1798–1828, 2013.
[21] S. Dieleman and B. Schrauwen, “End-to-end learning for music audio,” in 2014 IEEE International
Conference on Acoustics, Speech and Signal Processing(ICASSP).Florence,Italy:IEEE,May2014,pp.6964–
6968.
[22] E. S. Paul and S. B. Kaufman, The philosophy of creativity: New essays. New York, USA: Oxford
University Press, 2014.
[23] R. J. Haier, The neuroscience of intelligence. New York, NY: Cam- bridge University Press, 2016.
[24] M. A. Boden, “Creativity and artificial intelligence,” Artificial Intelligence, vol. 103, no. 1-2, pp. 347–356,
1998.
[25] A. Jordanous, “A standardised procedure for evaluating creative systems: Computational creativity
evaluation based on what it is to be creative,” Cognitive Computation, vol. 4, no. 3, pp. 246–279, 2012.
[26] M. Ackerman, A. Goel, C. G. Johnson, A. Jordanous, C. Le ́on, R. Perez y Perez, H. Toivonen, and
D.Ventura, “Teaching computational creativity,” in Proceedings of the Eighth International Conference on
Computational Creativity, ICCC 2017, Atlanta, 19 - 23 June, A. Goel, A. Jordanous, and A. Pease,
Eds.,ICCC 2017. Atlanta, Georgia, US: Georgia Institute of Technology, 2017, pp. 9–16.
[27] P. J. Liu, M. Saleh, E. Pot, B. Goodrich, R. Sepassi, L. Kaiser, and N. Shazeer, “Generating Wikipedia by
summarizing long sequences,” arXiv preprint arXiv:1801.10198, 2018.
[28] D. Muoio, “These 29 gorgeous images created by Google’s AI raised almost $100,000 at
auction,”https://www.businessinsider.com/google-ai-images-raise-100000-at-auction-2016-2?
r=US&IR=T&IR=T, 2016, accessed September 12, 2019.
[29] F. Liang, M. Gotham, M. Johnson, and J. Shotton, “Automatic stylistic composition of Bach chorales with
deep LSTM,” in Proceedings of the 18th International Society for Music Information Retrieval Conference
(ISMIR-17), Suzhou, China. Suzhou, China: International Society for Music Information Retrieval, 2017, pp.
449–456.
[30] S. Carter and M. Nielsen, “Using Artificial Intelligence to Augment Human
Intelligence,”https://distill.pub/2017/aia/, 2017, dOI 10.23915/distill.00009. Accessed August 20, 2019.
[31] V.Krishnamoorthy, W. Luo, M. Lewis, and K. Sycara, “A computational framework for integrating task
planning and norm aware reasoning for social robots,” in 2018 27th IEEE International Symposium on Robot
and Human Interactive Communication (RO-MAN). Nanjing, China: IEEE, Aug 2018, pp. 282–287. [32] O.
A. Osoba and W. Welser IV, An intelligence in our image: The risks of bias and errors in artificial
intelligence. Santa Monica, California: Rand Corporation, 2017.

[33] “ETHICS GUIDELINES FOR TRUSTWORTHY AI, High-Level Expert Group on Artificial Intelligence,”
https://ec.europa.eu/ digital-single-market/en/news/ethics-guidelines-trustworthyai, 2019.
[34] “Ethically Aligned Design, First Edition Overview, A Vision for Prioritizing Human Well-being with
Autonomous and Intelligent Systems,” https://ethicsinaction.ieee.org/, 2019.
[35] Association for Computing Machinery, “ACM code of ethics and professional conduct,”
https://www.acm.org/binaries/content/assets/about/acm-code-of-ethics-booklet.pdf, 2018.
[36] S. Dehaene, H. Lau, and S. Kouider, “What is consciousness, and could machines have it?” Science, vol.358,
no. 6362, pp. 486–492, 2017.
[37] V. Flasbeck, C. Gonzalez-Liencres, and M. Brüne, “The brain that feels into others: Toward a neuroscience
of empathy,” in The Neuroscience of Empathy, Compassion, and Self-Compassion. London, United King-
dom: Elsevier, 2018, pp. 23–51.
[38] Future of Life Institute, “Benefits and risks of artificial intelligence,” https://futureoflife.org/
background/benefits-risks-of-artificial-intelligence/, 2016, accessed March
[39] “Autonomous Weapons: an Open Letter from AI & Robotics Researchers,” https://futureoflife.org/openletter-
autonomous-weapons/, 2015, accessed September 12, 2019.
[40] “National and International AI Strategies,” https://futureoflife. org/national-internationalai- strategies/,
accessed September 12, 2019.
[41] A. Cortina and J. Conill, “La responsabilidad ética de la sociedad civil,” Mediterráneo Económico, vol.26,
pp. 13–29, 2014.
[42] W.Hussain, “The common good,” in The Stanford Encyclopedia of Philosophy, spring 2018 ed.,E. N. Zalta,
Ed. California, USA: Metaphysics Research Lab, Stanford University, 2018.
[43] T. Piketty, “About capital in the twenty-first century,” American Economic Review, vol. 105, no. 5,
pp.48–53, 2015.
[44] R.B.Reich, The Common Good. New York: Knopf, 2018.
[45] S. Zanganehpour, “Ai-supported global governance through bottom-up deliberation,” https://
globalchallenges.org/new-shape-library/ 592fc37b764c301cad4e30a8/details, 2018.
[46] L. N. Gerston, Public policy making: Process and principles. New York: M.E. Sharpe, Inc., 2010.
[47] T. Ruppert, J. Dambruch, M. Krämer, T. Balke, M. Gavanelli, S. Bragaglia, F. Chesani, M. Milano, and J.
Kohlhammer, “Visual decision support for policy making: advancing policy analysis with visualization,” in
Policy Practice and Digital Science. Switzerland: Springer, Cham, 2015, pp. 321–353.

AUTHOR
Gissel Velarde holds a PhD degree in Computer Science and Engineering from
Aalborg University. She participated as a research member of the European project,
“Learning to Create” (Lrn2Cre8) of the European Commission. She has developed
machine learning models for classification, structural analysis, pattern discovery,
representation learning and recommendation systems.

An Obnoxious Lacuna on Discourses and Counter
Discourses Over Artificial Intelligence
Dr. Atindra Dahal
Associate Professor, Kathmandu School of Law, Bhaktapur, Nepal
ABSTRACT
Artificial intelligence is the highest form of human development and sound outcome of human conscience
till the date. But the very development seems to be devastating to human future ahead and has been
heavily projected accordingly. More than it may be to decay and destroy the world, the negative and
chilling views on the prospective damages of AI that scholars are percolating to public are costing many
times on humans; and that is plunging human mindset into irreparable pessimism and negativity. This
article explores the way that AI is being depressingly explored and investigated to browbeat public. In
addition, this write-up highlights the serious lacuna, which the advanced academic engagement has still
grossly failed to fill up, of a great deal in course of mainstreaming views and discussions for noble cause
of human development and societal well-belling . Further, it unmasks the dire need in making
constructive, encouraging and optimistic mind-set building academic pursuits and writings then makes an
alarming call to the all prominent scholars to engage with due compliance of it. As a doctrinal qualitative
research based on extensive survey of secondary data and literature, methodologically, with adoption of
paradigm of descriptive interpretation, this research hypothesizes that the discussions and discourses over
AI are biased, hold a serious lacuna thus need to be reconstructed to make it balanced and build better
world than to browbeat people.
KEYWORDS
Artificial Intelligence, Human Future, Economic Development, Job Market

REFERENCES
.
[1] Acemoglu, Daron. (2003). “Directed Technological Change.” Review of Economic Studies 69(4). P+781-
809.
[2] Acemoglu, Daron and Autor, David. (2011). “Skills, Tasks and Technologies: Implications for Employment
and Earnings.” Handbook of Labor Economics, Vol. 4. Edited by Ashenfelter and D.Card. Elsevier, P+ 1043–
1171.
[3] Agrawal, Ajay, John McHale and Alex Oettl. (2017). “Artificial Intelligence and Recombinant Growth.”
Manuscript. Canada: University of Toronto.
[4] Andrew, Ng. (2016). “What AI Can and Can’t Do.” Harvard Business Review. November, 2016.
[5] Arntz, M., T. Gregory and U. Zierahn. (2016). “The Risk of Automation for Jobs in OECD Countries:A
Comparative Analysis.” OECD Social, Employment and Migration Working Papers, No. 189,December.
[6] Autor, David H. (2015). “Why are There Still So Many Jobs? The History and Future of Workplace
Automation.” Journal of Economic Perspectives, Vol. 29, No. 3. P+ 3-30.
[7] Autor, David H. and Dorn, David. (2013). “The Growth of Low-Skill Service Jobs and the Polarization of
the US Labor Market.” American Economic Review 103(5): 1553-97.
[8] Autor, David H., Frank Levy and Richard J. Murnane. (2003). “The Skill Content of Recent Technological
Change: An Empirical Exploration.” The Quarterly Journal of Economics. November 2003, 118 (4), P+ 1279–
1333.
[9] Bamberger, Kenneth A. and Mulligan, Deirdre K. (2015). Privacy on the Ground: Driving Corporate
Behaviour in the United States and Europe. Cambridge, Massachusetts: MIT Press.
[10] Barocas, S. and Selbst, A. D. (2016). “Big Data’s Disparate Impact.” California Law Review, Vol.104.
[11] Baslandze, Salome. (2016). “The Role of the IT Revolution in Knowledge Diffusion, Innovation and
Reallocation.” EIEF manuscript.
[12] Baudrillard, Jean. (1981). Simulacra and Simulation. Translated by Sheila Faria Galser. USA: Michigan.
[13] Bloom, P. (2013). Just babies: the Origins of Good and Evil. New York: Crow.
[14] Bloom, Nick, Richard Blundell, Rachel Griffith and Peter Howitt. (2005). “Competition and Innovation: An
Inverted-U Relationship.” The Quarterly Journal of Economics. USA: MIT Press, May, 120 (2), P+ 701–728.

[15] Bostrom, N. (2004). “The Future of Human Evolution.” Death and Anti‐Death: Two Hundred Years after
Kant, Fifty Years after Turing. Edited by Charles Tandy & Palo Alto. California: Ria University Press.
[16] Bostrom, N. (2014). Superintelligence: Paths, Dangers, Strategies. New York: Oxford University Press.
[17] Bostrom, N. (2016). Superintelligence. New York: Oxford University Press.
[18] Bostrom, Nick and Ćirković, Milan M. (2008). Global Catastrophic Risks. New York: Oxford University
press.
[19] Bovee, Courtland and Thrill, John V. (2013). Business Communciation Today. USA: Pearson.
[20] Bresnahan, Timothy F., Erik Brynjolfsson and Lorin M. Hitt. (2002). “Information Technology, Workplace
Organization, and the Demand for Skilled Labour: Firm-Level Evidence.” Quarterly Journal of
Economics, Vol. 117, No. 1. P+ 339-376.
[21] Brynjolfsson, Erik and McAfee, Andrew. (2014). The Second Machine Age: Work, Progress, and Prosperity
in a Time of Brilliant Technologies. USA: WW Norton & Company.
[22] Calo, M. Ryan. (2010). “People Can Be So Fake: A New Dimension to Privacy and Technology Scholarship.”
Penn State Law Review 114, no. 3. P+ 809–855
[23] Cherry, M. A. (2015). “Beyond Misclassification: The Digital Transformation of Work.” Comparative Labour
Law Journal & Policy Journal, Vol. 37, 2015. P+ 577.
[24] Corduck, Pamela Mc. (2004). Machines Who Think: A Personal Inquiry into the History and Prospects of
Artificial Intelligence. San Francisco: Natick, MA: A. K. Peters.
[25] Crawford, Kate. (2016). “Artificial Intelligence’s White Guy Problem.” New York Times, June 25.
[26] Davis, E. (2015). “Ethical Guidelines for a Super-intelligence.” Artificial Intelligence, 220. P+ 121–124.
[27] Davis, John S. and Osoba, Osonde A. (2017). Privacy Preservation in the Age of Big Data: A Survey.Santa
Monica, Calif.: RAND Corporation, WR-1161.
[28] Frey, Carl Benedikt and Osborne, Michael A. (2013). The Future of Employment: How Susceptible Are Jobs
to Computerization? USA: Oxford Martin School.
[29] Goertzel, B. and Pennachin, C. (2006). Artificial General Intelligence. New York, NY: Springer‐Verlag.
[30] Graetz, Georg and Michaels, Guy. (2015). “Robots at Work.” CEPR Discussion Paper, No. 10477.
[31] Hall, J. S. (2008). Engineering Utopia.” Frontiers in Artificial Intelligence and Applications, 171,460.
[32] Hayes, John R. (2013). The Complete Problem Solver. USA: Routledge.

[33] Jaimovich, Nir and Siu, Henry E. (2012). The Trend is the Cycle: Job Polarization and Jobless Recoveries
(Working Paper 18334). Cambridge, Mass: National Bureau of Economic Research.
[34] Jaynes, E. T. (2003). Probability Theory: The Logic of Science. Edited by G. Larry Bretthorst. NewYork:
Cambridge University Press. doi:10.2277/0521592712.
[35] Joshi, Aparna and Roh, Hyntak. (2009). “The Role of Context in Work Team Diversity Research: A Meta-
Analytic Review.” Academy of Management Journal (52). P+ 599-627.
[36] Kamm, F. (2007). Intricate Ethics: Rights, Responsibilities, and Permissible Harm. Oxford: Oxford University
Press.
[37] Kaplan, J. (2017). “Artificial Intelligence: Think Again.” Communications of the ACM (60). P+ 36-38.
[38] Kurzweil, R. (2005). The Singularity is Near: When Humans Transcend Biology. USA: Penguin.
[39] Lewis, R. L., A. Howes & S. Singh. (2014). “Computational rationality: Linking Mechanism and
Behaviour through Bounded Utility Maximization.” Topics in Cognitive Science, 6(2). P+ 279-311.
[40] Lyng, S (2004). Edgework: The Sociology of Risk-Taking. London, UK; New York: Routledge, Taylor &
Francis Group.
[41] Michael, G. (2015). “The Future of Artificial Intelligence: Benevolent or Malevolent?” Skeptic,Altadena, CA.
P+1: 57
[42] Minsky, Marvin. (1986). The Society of Mind. New York: Simon & Schuster.
[43] Nilsson, Nils J. (2010). The Quest for Artificial Intelligence: A History of Ideas and Achievements. Cambridge.
UK: Cambridge University Press.
[44] Omohundro, S. (2008). “The Basic AI Drives.” Proceedings of the AGI‐08 Workshop. Amsterdam:IOS Press.
P+ 483‐492.
[45] Paul, Ohm. (2010). “Broken Promises of Privacy: Responding to the Surprising Failure of Anonymization”.
UCLA Law Review, Vol. 57, P+ 1701.
[46] Pavaloiu, A. (2016). “The Impact of Artificial Intelligence on Global Trends.” Journal of Multidisciplinary
Developments, 1(1). P+ 21-37.
[47] Peretto, Pietro F. and Seater, John J. (2013). “Factor-eliminating Technical Change.” Journal of Monetary
Economics, 60 (4). P+ 459–473.
[48] Russell, S., & Norvig, P. (2010). Artificial Intelligence: A Modern Approach. New Jersey: Pearson.
[49] Russell, S. J., P. Norvig, J.F. Canny, J. M. Malik, & D. D. Edwards. (2003). Artificial Intelligence: A Modern
Approach (Vol. 2). Prentice Hall: Upper Saddle River.

[50] Sorokina, O. (2015). Artificial Intelligence in Social Media: What AI Knows About You, and What You Need
to Know? Hootsuite Social Media Management
[51] Sotala, K. (2012). “Advantages of Artificial Intelligences, Uploads and Digital Minds.”International Journal
of Machine Consciousness, 04(01), P+ 275–291. doi: 10 .1142/S1793843012400161.
[52] Tirole, Jean. (2017). Economics for the Common Good. USA: Princeton University Press.
[53] Tzafestas, S.G. (2016). Roboethics: A Navigating Overview. Berlin: Springer.
[54] Van-Reenen, John. (2011). “Wage Inequality, Technology and Trade: 21st Century Evidence.”Labour
Economics, Vol. 18, No. 6. P+ 730-741.
[55] Wallach, W., & Allen, C. (2008). Moral Machines: Teaching Robots Right from Wrong. USA: Oxford
University Press.
[56] Wystrach, A. (2013). “We’ve been Looking at Ant Intelligence the Wrong Way.” Scientific American, 30
August.
[57] Yudkowsky, Eliezer. (2008). “Artificial Intelligence as a Positive and Negative Factor in Global Risk.” Global
Catastrophic Risks. Edited by Nick Bostrom and Milan M. Ćirković. New York:Oxford University Press, P+
308–345.
[58] Zeira, Joseph. (1998). “Workers, Machines, and Economic Growth.” Quarterly Journal of Economics,
November 113 (4). P+ 1091–1117.

An IOT-Based Crowd Sourcing System for Object
Tracking and Information Sharing
Mike Qu1
, Yu Sun2
1
Northwood High School, Irvine, CA 92602
2
California State Polytechnic University, Pomona, CA 91768
ABSTRACT
Technological advancements has offered many solutions to the important current issues such as the
growing numbers of runaway children, wandering Alzheimer’s patients and lost pets in the society, yet
most branches of current technologies are not capable of encompassing all of these key problems. My
research proposes a solution that is practical, durable and reliable -- a proximity sensor device powered by
other users in the area with a process known as “crowd sourcing”, by using their mobile devices as
receiving stations of the service, extensively increasing the effectiveness of this service in especially
urban and suburban areas where there is a high population density
KEYWORDS
Beacon, Device Network, Crowd sourcing, Double-blind, Artificial Intelligence.

REFRENCES
[1] ASPCA. “Shelter Intake and Surrender Pet Statistics.” The American Society for the Prevention of Cruelty to
Animals
[2] Glenner, George G., and Caine W. Wong. "Alzheimer's disease: initial report of the purification and
characterization of a novel cerebrovascular amyloid protein." Biochemical and biophysical research
communications 120, no. 3 (1984): 885-890.
[3] NCIC Missing Persons. "Unidentified Person Statistics for 2009." National Crime Information Center, Federal
Bureau of Investigation (2014).
[4] Newman, Nic. "Apple iBeacon technology briefing." Journal of Direct, Data and Digital Marketing Practice
15, no. 3 (2014): 222-225.
[5] Koühne, Markus, and Jürgen Sieck. "Location-based services with iBeacon technology." In Artificial
Intelligence, Modelling and Simulation (AIMS), 2014 2nd International Conference on, pp. 315-321.IEEE,
2014.
[6] Lin, Xin-Yu, Te-Wei Ho, Cheng-Chung Fang, Zui-Shen Yen, Bey-Jing Yang, and Feipei Lai. "A mobile
indoor positioning system based on iBeacon technology." In Engineering in Medicine and Biology Society
(EMBC), 2015 37th Annual International Conference of the IEEE, pp. 4970-4973.IEEE, 2015.
[7] Ji, Myungin, Jooyoung Kim, Juil Jeon, and Youngsu Cho. "Analysis of positioning accuracy corresponding to
the number of BLE beacons in indoor positioning system." In Advanced Communication Technology
(ICACT), 2015 17th International Conference on, pp. 92-95. IEEE,2015.
[8] Yang, Jingjing, Zhihui Wang, and Xiao Zhang. "An ibeacon-based indoor positioning systems for hospitals."
International Journal of Smart Home 9, no. 7 (2015): 161-168.
[9] Corna, Andrea, L. Fontana, A. A. Nacci, and Donatella Sciuto. "Occupancy detection via iBeacon on
Android devices for smart building management." In Proceedings of the 2015 Design, Automation & Test
in Europe Conference & Exhibition, pp. 629-632. EDA Consortium, 2015.
[10] Burzacca, P., M. Mircoli, S. Mitolo, and A. Polzonetti. "“iBeacon” technology that will make possible Internet
of Things." (2014): 159-165.
[11] Conte, Giorgio, Massimo De Marchi, Alessandro Antonio Nacci, Vincenzo Rana, and Donatella Sciuto.
"BlueSentinel: a first approach using iBeacon for an energy efficient occupancy detection system." In
BuildSys@ SenSys, pp. 11-19. 2014.
[12] Akinsiku, Adegboyega, and Divyesh Jadav. "BeaSmart: A beacon enabled smarter workplace." In Network
Operations and Management Symposium (NOMS), 2016 IEEE/IFIP, pp. 1269-1272. IEEE, 2016.

[13] Fard, Hadis Kakanejadi, Yuanzhu Chen, and Kyung Kook Son. "Indoor positioning of mobile devices with
agile iBeacon deployment." In Electrical and Computer Engineering (CCECE), 2015 IEEE 28th
Canadian
Conference on, pp. 275-279. IEEE, 2015.
[14] Bhagwat, Pravin. "Bluetooth: technology for short-range wireless apps." IEEE Internet Computing 5, no. 3
(2001): 96-103.
[15] McCurdy, Roger A. "Emergency assistance system using bluetooth technology." U.S. Patent 6,340,928, issued
January 22, 2002.
[16] Erasala, Naveen, and David C. Yen. "Bluetooth technology: a strategic analysis of its role in global 3G
wireless communication era." Computer Standards & Interfaces 24, no. 3 (2002): 193-206.
[17] Tajika, Yosuke, Takeshi Saito, Keiichi Teramoto, Naohisa Oosaka, and Masao Isshiki. "Networked home
appliance system using Bluetooth technology integrating appliance control/monitoring with Internet service."
IEEE Transactions on Consumer Electronics 49, no. 4 (2003): 1043-1048.

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