Pub Date : 2019-10-01DOI: 10.1109/UEMCON47517.2019.8992932
Jackie Leung, Min Chen
Convolutional neural networks (CNNs) have gained global recognition in advancing the field of artificial intelligence and have had great successes in a wide array of applications including computer vision, speech and natural language processing. However, due to the rise of big data and increased complexity of tasks, the efficiency of training CNNs have been severely impacted. To achieve state-of-art results, CNNs require tens to hundreds of millions of parameters that need to be fine-tuned, resulting in extensive training time and high computational cost. To overcome these obstacles, this work takes advantage of distributed frameworks and cloud computing to develop a parallel CNN algorithm. Close examination of the implementation of MapReduce based CNNs as well as how the proposed algorithm accelerates learning are discussed and demonstrated through experiments. Results reveal high accuracy in classification and improvements in speedup, scaleup and sizeup compared to the standard algorithm.
{"title":"Image Recognition with MapReduce Based Convolutional Neural Networks","authors":"Jackie Leung, Min Chen","doi":"10.1109/UEMCON47517.2019.8992932","DOIUrl":"https://doi.org/10.1109/UEMCON47517.2019.8992932","url":null,"abstract":"Convolutional neural networks (CNNs) have gained global recognition in advancing the field of artificial intelligence and have had great successes in a wide array of applications including computer vision, speech and natural language processing. However, due to the rise of big data and increased complexity of tasks, the efficiency of training CNNs have been severely impacted. To achieve state-of-art results, CNNs require tens to hundreds of millions of parameters that need to be fine-tuned, resulting in extensive training time and high computational cost. To overcome these obstacles, this work takes advantage of distributed frameworks and cloud computing to develop a parallel CNN algorithm. Close examination of the implementation of MapReduce based CNNs as well as how the proposed algorithm accelerates learning are discussed and demonstrated through experiments. Results reveal high accuracy in classification and improvements in speedup, scaleup and sizeup compared to the standard algorithm.","PeriodicalId":187022,"journal":{"name":"2019 IEEE 10th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114662933","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}
Pub Date : 2019-10-01DOI: 10.1109/UEMCON47517.2019.8992935
Michael Joseph, K. Elleithy, Mohamed Mohamed
This paper analyzes the current state of the two leading Quantum Computing architectures, namely Ion Trap and Super Conducting, outlines the challenges facing both methods and proposes a new architecture that incorporates the best aspect of both techniques along with a novel method to isolate the Qubits. This new architecture retains the Ion Trapped architecture ability to operate its Qubits at near room temperature while eliminating its scalability issue by utilizing a novel shielding technique and by adopting the surface code layout of the superconducting architecture. By eliminating the extremely low-temperature requirement of the superconducting architecture, this new architecture is poised to be cost-effective, scalable, and practical for mass production.
{"title":"A new Quantum Processor Architecture","authors":"Michael Joseph, K. Elleithy, Mohamed Mohamed","doi":"10.1109/UEMCON47517.2019.8992935","DOIUrl":"https://doi.org/10.1109/UEMCON47517.2019.8992935","url":null,"abstract":"This paper analyzes the current state of the two leading Quantum Computing architectures, namely Ion Trap and Super Conducting, outlines the challenges facing both methods and proposes a new architecture that incorporates the best aspect of both techniques along with a novel method to isolate the Qubits. This new architecture retains the Ion Trapped architecture ability to operate its Qubits at near room temperature while eliminating its scalability issue by utilizing a novel shielding technique and by adopting the surface code layout of the superconducting architecture. By eliminating the extremely low-temperature requirement of the superconducting architecture, this new architecture is poised to be cost-effective, scalable, and practical for mass production.","PeriodicalId":187022,"journal":{"name":"2019 IEEE 10th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122112404","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}
Pub Date : 2019-10-01DOI: 10.1109/UEMCON47517.2019.8993079
Mark Caswell, Yao Liang
Communication links in low-power wireless visual sensor networks (WVSNs) are subject to short-term and long-term noise variations. These variations can cause a WVSN to exhibit prolonged or periodic transitional or bursty transmission performance. In this paper, we present our work on how to generate noise traces that simulate real-world transitional and bursty network behavior in TOSSIM. We develop a toolset called BurstyGen for TOSSIM which can facilitate WVSN protocol designers and application developers to better understand WVSN performance under these conditions. BurstyGen allows users to model both short time-scale and long time-scale variations in WVSN noise environments for the simulation and testing of WVSN system algorithms and protocols.
{"title":"Simulation of Transitional and Bursty Wireless Visual Sensor Networks","authors":"Mark Caswell, Yao Liang","doi":"10.1109/UEMCON47517.2019.8993079","DOIUrl":"https://doi.org/10.1109/UEMCON47517.2019.8993079","url":null,"abstract":"Communication links in low-power wireless visual sensor networks (WVSNs) are subject to short-term and long-term noise variations. These variations can cause a WVSN to exhibit prolonged or periodic transitional or bursty transmission performance. In this paper, we present our work on how to generate noise traces that simulate real-world transitional and bursty network behavior in TOSSIM. We develop a toolset called BurstyGen for TOSSIM which can facilitate WVSN protocol designers and application developers to better understand WVSN performance under these conditions. BurstyGen allows users to model both short time-scale and long time-scale variations in WVSN noise environments for the simulation and testing of WVSN system algorithms and protocols.","PeriodicalId":187022,"journal":{"name":"2019 IEEE 10th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125791937","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}
Pub Date : 2019-10-01DOI: 10.1109/UEMCON47517.2019.8992967
Brian Luu, Bradley Hansberger, T. Tothong, K. George
This paper presents evidence for the possibility of a neuroadaptive system, based on electroencephalography (EEG) readings from the motor cortex region, to predict an individual's actions before the onset of motion. Testing for the neuroadaptive system utilized a G.nautilus headset, MATLAB with the EEGLAB toolbox, and a computer with the Processing IDE. Code from the Processing IDE provides an image slideshow which displays faces of various individuals so that the subject presses a keyboard key on a certain image. Three subjects were tested for 60 trials each, 30 trials where the keyboard key was pressed, and 30 trials where they were not, to gather enough data to train and test a classifier by using a machine learning algorithm. Machine learning assessed classification accuracy initially using 10 training trials and increased the training set by 10 trials each time to reassess accuracy until a total of 40 training trials were used. A set of 20 trials were used to assess accuracy without and with machine learning. Additionally, theoretical accuracy was computed by removing unfeasible machine learning features to assess the potential accuracy in a real-time system. The results provided an average accuracy of 52% without machine learning and an average accuracy ranging from 91.66% to 96.66% using the K-Nearest Neighbors(KNN) algorithm. The average theoretical accuracy ranged from to be 60% to 68.33%.
{"title":"Conceptual Neuroadaptive Brain-Computer Interface utilizing Event-related Desynchronization","authors":"Brian Luu, Bradley Hansberger, T. Tothong, K. George","doi":"10.1109/UEMCON47517.2019.8992967","DOIUrl":"https://doi.org/10.1109/UEMCON47517.2019.8992967","url":null,"abstract":"This paper presents evidence for the possibility of a neuroadaptive system, based on electroencephalography (EEG) readings from the motor cortex region, to predict an individual's actions before the onset of motion. Testing for the neuroadaptive system utilized a G.nautilus headset, MATLAB with the EEGLAB toolbox, and a computer with the Processing IDE. Code from the Processing IDE provides an image slideshow which displays faces of various individuals so that the subject presses a keyboard key on a certain image. Three subjects were tested for 60 trials each, 30 trials where the keyboard key was pressed, and 30 trials where they were not, to gather enough data to train and test a classifier by using a machine learning algorithm. Machine learning assessed classification accuracy initially using 10 training trials and increased the training set by 10 trials each time to reassess accuracy until a total of 40 training trials were used. A set of 20 trials were used to assess accuracy without and with machine learning. Additionally, theoretical accuracy was computed by removing unfeasible machine learning features to assess the potential accuracy in a real-time system. The results provided an average accuracy of 52% without machine learning and an average accuracy ranging from 91.66% to 96.66% using the K-Nearest Neighbors(KNN) algorithm. The average theoretical accuracy ranged from to be 60% to 68.33%.","PeriodicalId":187022,"journal":{"name":"2019 IEEE 10th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON)","volume":"94 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121833078","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}
Pub Date : 2019-10-01DOI: 10.1109/UEMCON47517.2019.8992929
Heekyung Kim, K. Choi
This paper shows the possibility of the existing low power register transfer level (RTL) techniques can be effective as a low power design scheme for CNN-based object recognition system acceleration in contrast to conventional techniques. Most power efficient design techniques regarding CNN acceleration are focused on the High-level Synthesis (HLS) aspect, such as memory bandwidth optimization, network architecture reconfiguration, data reuse, and batch normalization. However, these attempts have reached the limits of the effectiveness of itself. Using the post-synthesis RTL code generated by field-programmable gate arrays (FPGA) manufacturers, the proposed RTL low power design technique was applied to the original FIFO part for reducing the power consumption during data transformation. We compared the HLS optimized result with the RTL optimized result in the aspect of power consumption. We configured the testbench for the modified FIFO module and analyzed the estimated power dissipation result. These power effectiveness factors, such as a look-up table (LUT), a lookup table RAM (LUTRAM), can reduce the power dissipation by 54%, 49% respectively, even though increased block RAM (BRAM) leads to the elevated power dissipation by 154%. Thus, the total power consumption was able to be decreased by 10%. This paper discusses two factors of FPGA with system-on-chip (FPGA-SoC) design for CNN-based hardware implementation in power consumption aspect, such as RTL architecture, memory design architecture, and the model architecture-based hardware implementation methods. The virtual additional memory can support the high throughput at full speed. Our simulated low power schemes applied to Processing System (PS) and Programmable Logic (PL) architecture effectively reduced the power consumption by 25.9% in the FIFO data transformation. We established that the increased LUT blocks affect the power-efficient rate and reduce the power consumption of the PL design up to 49%.
{"title":"Low Power FPGA-SoC Design Techniques for CNN-based Object Detection Accelerator","authors":"Heekyung Kim, K. Choi","doi":"10.1109/UEMCON47517.2019.8992929","DOIUrl":"https://doi.org/10.1109/UEMCON47517.2019.8992929","url":null,"abstract":"This paper shows the possibility of the existing low power register transfer level (RTL) techniques can be effective as a low power design scheme for CNN-based object recognition system acceleration in contrast to conventional techniques. Most power efficient design techniques regarding CNN acceleration are focused on the High-level Synthesis (HLS) aspect, such as memory bandwidth optimization, network architecture reconfiguration, data reuse, and batch normalization. However, these attempts have reached the limits of the effectiveness of itself. Using the post-synthesis RTL code generated by field-programmable gate arrays (FPGA) manufacturers, the proposed RTL low power design technique was applied to the original FIFO part for reducing the power consumption during data transformation. We compared the HLS optimized result with the RTL optimized result in the aspect of power consumption. We configured the testbench for the modified FIFO module and analyzed the estimated power dissipation result. These power effectiveness factors, such as a look-up table (LUT), a lookup table RAM (LUTRAM), can reduce the power dissipation by 54%, 49% respectively, even though increased block RAM (BRAM) leads to the elevated power dissipation by 154%. Thus, the total power consumption was able to be decreased by 10%. This paper discusses two factors of FPGA with system-on-chip (FPGA-SoC) design for CNN-based hardware implementation in power consumption aspect, such as RTL architecture, memory design architecture, and the model architecture-based hardware implementation methods. The virtual additional memory can support the high throughput at full speed. Our simulated low power schemes applied to Processing System (PS) and Programmable Logic (PL) architecture effectively reduced the power consumption by 25.9% in the FIFO data transformation. We established that the increased LUT blocks affect the power-efficient rate and reduce the power consumption of the PL design up to 49%.","PeriodicalId":187022,"journal":{"name":"2019 IEEE 10th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON)","volume":"166 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127200032","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}
Pub Date : 2019-10-01DOI: 10.1109/UEMCON47517.2019.8993018
A. Shah, H. Ilhan, U. Tureli
Unmanned aerial vehicles (UAVs) are popular because they are low cost and do not put in danger human pilot lives. In this study, performance of IEEE 802.11 medium access control (MAC) for UAVs ad hoc networks is studied using an analytical model based on Markov chain model. Three dimensional (3D) nature and high mobility are main characteristics of UAVs ad hoc networks which are considered. The relationship among parameters and performance metrics such as throughput, packet dropping rate (PDR), and delay expressions are derived. Complexity analysis and numerical results are presented. Moreover, to ensure safe and efficient communication in UAVs ad hoc networks, IEEE 802.11 MAC can satisfy the performance requirements or not is investigated.
无人驾驶飞行器(uav)之所以受欢迎，是因为它们成本低，而且不会危及飞行员的生命。本文采用基于马尔可夫链模型的分析模型，对无人机自组织网络的IEEE 802.11介质访问控制(MAC)性能进行了研究。三维特性和高机动性是无人机自组织网络的主要特点。导出了参数与吞吐量、丢包率(packet drop rate, PDR)和时延表达式等性能指标之间的关系。给出了复杂度分析和数值结果。此外，为了保证无人机自组织网络的安全高效通信，研究了IEEE 802.11 MAC是否能满足性能要求。
{"title":"Designing and Analysis of IEEE 802.11 MAC for UAVs Ad Hoc Networks","authors":"A. Shah, H. Ilhan, U. Tureli","doi":"10.1109/UEMCON47517.2019.8993018","DOIUrl":"https://doi.org/10.1109/UEMCON47517.2019.8993018","url":null,"abstract":"Unmanned aerial vehicles (UAVs) are popular because they are low cost and do not put in danger human pilot lives. In this study, performance of IEEE 802.11 medium access control (MAC) for UAVs ad hoc networks is studied using an analytical model based on Markov chain model. Three dimensional (3D) nature and high mobility are main characteristics of UAVs ad hoc networks which are considered. The relationship among parameters and performance metrics such as throughput, packet dropping rate (PDR), and delay expressions are derived. Complexity analysis and numerical results are presented. Moreover, to ensure safe and efficient communication in UAVs ad hoc networks, IEEE 802.11 MAC can satisfy the performance requirements or not is investigated.","PeriodicalId":187022,"journal":{"name":"2019 IEEE 10th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON)","volume":"73 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129126991","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}
Pub Date : 2019-10-01DOI: 10.1109/UEMCON47517.2019.8992936
C. Berke, E. Fassman-Beck, Jack Li, Gregg Vesonder
In New York and New Jersey, and many older areas in the United States, stormwater runoff is cleared from the streets and is dispensed into combined sewers. These combined sewers transport both runoff and domestic waste through pipes that lead to wastewater treatment plants. The problem is that when it rains a couple of tenths of an inch, the runoff enters the sewer and dramatically increases the amount of water and domestic waste in the sewer, exceeding the maximum capacity. This causes the water-waste mixture to bypass the treatment plant and dispense into the nearest waterway (e.g. the Hudson River). This pollutes the waterway it enters and makes the environment uninhabitable for wildlife it used to support, and unsafe for the people that use it. The introduction of rain barrels - barrels that collect stormwater runoff from the roofs of buildings - can delay the runofffrom going into the sewer, allowing it to be dispensed in a manner that the sewer systems can manage. [1]
{"title":"Towards Better Management of Combined Sewage Systems","authors":"C. Berke, E. Fassman-Beck, Jack Li, Gregg Vesonder","doi":"10.1109/UEMCON47517.2019.8992936","DOIUrl":"https://doi.org/10.1109/UEMCON47517.2019.8992936","url":null,"abstract":"In New York and New Jersey, and many older areas in the United States, stormwater runoff is cleared from the streets and is dispensed into combined sewers. These combined sewers transport both runoff and domestic waste through pipes that lead to wastewater treatment plants. The problem is that when it rains a couple of tenths of an inch, the runoff enters the sewer and dramatically increases the amount of water and domestic waste in the sewer, exceeding the maximum capacity. This causes the water-waste mixture to bypass the treatment plant and dispense into the nearest waterway (e.g. the Hudson River). This pollutes the waterway it enters and makes the environment uninhabitable for wildlife it used to support, and unsafe for the people that use it. The introduction of rain barrels - barrels that collect stormwater runoff from the roofs of buildings - can delay the runofffrom going into the sewer, allowing it to be dispensed in a manner that the sewer systems can manage. [1]","PeriodicalId":187022,"journal":{"name":"2019 IEEE 10th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129131835","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}
Pub Date : 2019-10-01DOI: 10.1109/UEMCON47517.2019.8992992
Shajina Anand, G. Raja, Aishwarya Ganapathisubramaniyan
Due to enormous growth of devices and its interconnected communication, there are lot of possibilities for attacks and vulnerabilities in the network. From recent studies, we come to know about various security related techniques and their usage. A lot of cryptographic algorithms are used efficiently and effectively in plenty of applications. Thus it is notable to classify the attacks as two types Active and Passive Attack. Unauthorized access to the information implies passive attack. For example, in the communication channel doing operations like intercepting and eavesdropping are considered as passive attack. If the attacker tries to access the information and modify the information in an unauthorized manner, then it is considered as active attack. Man in the Middle attack is the best example of active attack. Here the attacker sends its public key as the hosts public and tries to hack the message. To overcome the issues in tradional authentication model, we proposed an Advanced Elliptical Curve Cryptographic (AECC) Algorithm which uses a code bit with a random function in both the sender and receiver side for encoding and decoding functions. This code bit is used to identify whether the public key belongs to attacker or the sender. By identifying the sender, the receiver can communicate with it without any restrictions. The results analysis section shows that our proposed AECC algorithm enhances security for user defined input messages like text, image, video and also for binary data.
{"title":"AECC: An Enhanced Public Key Cryptosystem for User Defined Messages","authors":"Shajina Anand, G. Raja, Aishwarya Ganapathisubramaniyan","doi":"10.1109/UEMCON47517.2019.8992992","DOIUrl":"https://doi.org/10.1109/UEMCON47517.2019.8992992","url":null,"abstract":"Due to enormous growth of devices and its interconnected communication, there are lot of possibilities for attacks and vulnerabilities in the network. From recent studies, we come to know about various security related techniques and their usage. A lot of cryptographic algorithms are used efficiently and effectively in plenty of applications. Thus it is notable to classify the attacks as two types Active and Passive Attack. Unauthorized access to the information implies passive attack. For example, in the communication channel doing operations like intercepting and eavesdropping are considered as passive attack. If the attacker tries to access the information and modify the information in an unauthorized manner, then it is considered as active attack. Man in the Middle attack is the best example of active attack. Here the attacker sends its public key as the hosts public and tries to hack the message. To overcome the issues in tradional authentication model, we proposed an Advanced Elliptical Curve Cryptographic (AECC) Algorithm which uses a code bit with a random function in both the sender and receiver side for encoding and decoding functions. This code bit is used to identify whether the public key belongs to attacker or the sender. By identifying the sender, the receiver can communicate with it without any restrictions. The results analysis section shows that our proposed AECC algorithm enhances security for user defined input messages like text, image, video and also for binary data.","PeriodicalId":187022,"journal":{"name":"2019 IEEE 10th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133588017","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}
Pub Date : 2019-10-01DOI: 10.1109/UEMCON47517.2019.8993010
M. Royer, S. Chawathe
We present an approach for scientific data management systems to apply certificates to scientific objects, which are typically unformatted datasets, to facilitate analysis by climate scientists. Typically, for a program to process data, the program requires cleansed data in a form that supports automatic manipulation. Most systems require that data must adhere to a specific format to achieve that goal. The technique described in this paper takes the opposite approach; instead, any dataset may be imported and manipulated in the system. But upon initial import, however, only a subset of system functions may work with any given dataset. As the data is refined and transformed by system functions, more functions may become compatible. Certificates are associated with objects that pass constraint validation within the system to ensure that they conform to function requirements. The attached object constraints represent invariant properties of the object, which may be used by functions in the system as function preconditions. Furthermore, the functions defined in the system may associate certificates with the newly generated results. Certificates related to function results are effectively function postconditions, which in turn are used to associate certificates with the objects generated in the system. Additionally, attached object certificates reflect the refinement of data into a more pristine version. This paper describes the technique for modeling and enforcing the constraints for data scientists that have similar requirements.
{"title":"Unformatted, Certified Scientific Objects","authors":"M. Royer, S. Chawathe","doi":"10.1109/UEMCON47517.2019.8993010","DOIUrl":"https://doi.org/10.1109/UEMCON47517.2019.8993010","url":null,"abstract":"We present an approach for scientific data management systems to apply certificates to scientific objects, which are typically unformatted datasets, to facilitate analysis by climate scientists. Typically, for a program to process data, the program requires cleansed data in a form that supports automatic manipulation. Most systems require that data must adhere to a specific format to achieve that goal. The technique described in this paper takes the opposite approach; instead, any dataset may be imported and manipulated in the system. But upon initial import, however, only a subset of system functions may work with any given dataset. As the data is refined and transformed by system functions, more functions may become compatible. Certificates are associated with objects that pass constraint validation within the system to ensure that they conform to function requirements. The attached object constraints represent invariant properties of the object, which may be used by functions in the system as function preconditions. Furthermore, the functions defined in the system may associate certificates with the newly generated results. Certificates related to function results are effectively function postconditions, which in turn are used to associate certificates with the objects generated in the system. Additionally, attached object certificates reflect the refinement of data into a more pristine version. This paper describes the technique for modeling and enforcing the constraints for data scientists that have similar requirements.","PeriodicalId":187022,"journal":{"name":"2019 IEEE 10th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON)","volume":"80 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130381139","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}
Pub Date : 2019-10-01DOI: 10.1109/UEMCON47517.2019.8992993
Resham Jhangiani, Doina Bein, Abhishek Verma
Fraud has become a major problem in e-commerce and a lot of resources are being invested to recognize and prevent it. Present fraud detection and prevention systems are designed to prevent only a small fraction of fraudulent transactions processed, which still costs billions of dollars in loss. There is an urgent need for better fraud detection and prevention as the online transactions are estimated to increase substantially in the coming year. We propose a data driven model using machine learning algorithms on big data to predict the probability of a transaction being fraudulent or legitimate. The model was trained on historical e-commerce credit card transaction data to predict the probability of any future transaction by the customer being fraudulent. Supervised machine learning algorithms like Random Forest, Support Vector Machine, Gradient Boost and combinations of these are implemented and their performance are compared. While at the same time the problem of class imbalance is taken into consideration and techniques of oversampling and data pre-processing are performed before the model is trained on a classifier.
{"title":"Machine Learning Pipeline for Fraud Detection and Prevention in E-Commerce Transactions","authors":"Resham Jhangiani, Doina Bein, Abhishek Verma","doi":"10.1109/UEMCON47517.2019.8992993","DOIUrl":"https://doi.org/10.1109/UEMCON47517.2019.8992993","url":null,"abstract":"Fraud has become a major problem in e-commerce and a lot of resources are being invested to recognize and prevent it. Present fraud detection and prevention systems are designed to prevent only a small fraction of fraudulent transactions processed, which still costs billions of dollars in loss. There is an urgent need for better fraud detection and prevention as the online transactions are estimated to increase substantially in the coming year. We propose a data driven model using machine learning algorithms on big data to predict the probability of a transaction being fraudulent or legitimate. The model was trained on historical e-commerce credit card transaction data to predict the probability of any future transaction by the customer being fraudulent. Supervised machine learning algorithms like Random Forest, Support Vector Machine, Gradient Boost and combinations of these are implemented and their performance are compared. While at the same time the problem of class imbalance is taken into consideration and techniques of oversampling and data pre-processing are performed before the model is trained on a classifier.","PeriodicalId":187022,"journal":{"name":"2019 IEEE 10th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130741342","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}
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