The Experts below are selected from a list of 306 Experts worldwide ranked by ideXlab platform
John Villasenor - One of the best experts on this subject based on the ideXlab platform.
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A Compressive Sensing Reconstruction Algorithm for Trinary and Binary Sparse Signals Using Pre-mapping
2011 Data Compression Conference, 2011Co-Authors: Xinyu Zhang, Zhuoyuan Chen, John VillasenorAbstract:In this paper, we first analyze impact of the distribution of sparse signals on reconstruction quality in compressive sensing through experimental results and Heuristic Analysis. We suggest that trinary/binary sparse signals are one of the most difficult signals to reconstruct in terms of error bounds. We then show that by incorporating linear or non-linear mapping prior to sensing, significant improvement in the recovery performance can be achieved.
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DCC - A Compressive Sensing Reconstruction Algorithm for Trinary and Binary Sparse Signals Using Pre-mapping
2011 Data Compression Conference, 2011Co-Authors: Xinyu Zhang, Zhuoyuan Chen, Jianwei Ma, John VillasenorAbstract:In this paper, we first analyze impact of the distribution of sparse signals on reconstruction quality in compressive sensing through experimental results and Heuristic Analysis. We suggest that trinary/binary sparse signals are one of the most difficult signals to reconstruct in terms of error bounds. We then show that by incorporating linear or non-linear mapping prior to sensing, significant improvement in the recovery performance can be achieved.
Xinyu Zhang - One of the best experts on this subject based on the ideXlab platform.
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A Compressive Sensing Reconstruction Algorithm for Trinary and Binary Sparse Signals Using Pre-mapping
2011 Data Compression Conference, 2011Co-Authors: Xinyu Zhang, Zhuoyuan Chen, John VillasenorAbstract:In this paper, we first analyze impact of the distribution of sparse signals on reconstruction quality in compressive sensing through experimental results and Heuristic Analysis. We suggest that trinary/binary sparse signals are one of the most difficult signals to reconstruct in terms of error bounds. We then show that by incorporating linear or non-linear mapping prior to sensing, significant improvement in the recovery performance can be achieved.
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DCC - A Compressive Sensing Reconstruction Algorithm for Trinary and Binary Sparse Signals Using Pre-mapping
2011 Data Compression Conference, 2011Co-Authors: Xinyu Zhang, Zhuoyuan Chen, Jianwei Ma, John VillasenorAbstract:In this paper, we first analyze impact of the distribution of sparse signals on reconstruction quality in compressive sensing through experimental results and Heuristic Analysis. We suggest that trinary/binary sparse signals are one of the most difficult signals to reconstruct in terms of error bounds. We then show that by incorporating linear or non-linear mapping prior to sensing, significant improvement in the recovery performance can be achieved.
Ram R. Bishu - One of the best experts on this subject based on the ideXlab platform.
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Which is a Better Method of Web Evaluation? a Comparison of User Testing and Heuristic Evaluation
Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 2020Co-Authors: Ram R. BishuAbstract:Besides recognizing the importance of incorporating usability evaluation techniques in the design and development phase of any user interface (UI), it is also very important that designers recognize the benefits and limitations of the different usability inspection methods. This is because the quality of the usability evaluation is dependent on the method used. Two of the more popular usability evaluation techniques are user testing and Heuristic Analysis. The main objective of this study was to compare the efficiency and effectiveness between user testing and Heuristic Analysis in evaluating four different commercial websites. Comparing the proportion of usability problems and the type of problems addressed by these two methods both in the early and later stage of the design process does this. The results showed that both user testing and Heuristic Analysis addressed very different usability problems and with the exception compatibility and security and privacy problems, where Heuristic Analysis outperfo...
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Web evaluation: Heuristic evaluation vs. user testing.
International Journal of Industrial Ergonomics, 2009Co-Authors: Ram R. BishuAbstract:Abstract It is very important that designers recognize the benefits and limitations of different usability inspection methods. This is because the quality of the usability evaluation is dependent on the method used. Two of the most popular usability evaluation techniques are user testing and Heuristic Analysis. The main objective of this study was to compare the efficiency and effectiveness between user testing and Heuristic Analysis in evaluating four different commercial web sites. The results showed that both user testing and Heuristic Analysis addressed different usability problems. Analysis by severity of problems found and diminishing return Analysis model on the relationship between the number of new problems discovered with users and evaluators used showed that both methods are equally efficient and effective in addressing different categories of usability problems. These significant differences found between these two methods suggested that the two methods are complimentary and should not be competing. In order for better evaluation results, both user testing and Heuristic Analysis are still needed. Relevance to industry The research findings from this study will be of particular value to the web development industry and communities. Knowledge regarding the differences between user testing and Heuristic evaluation will enable appropriate business decisions to be made on when and how to apply these methods to improve the overall efficiency of the design process.
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HCI (10) - Web usability and evaluation: issues and concerns
Usability and Internationalization. HCI and Culture, 2007Co-Authors: S. Batra, Ram R. BishuAbstract:This paper presents a summary of usability work done at the Usability Laboratory at University of Nebraska in the last few years. The main objective of the first study was to compare the efficiency and effectiveness between user testing and Heuristic Analysis in evaluating four different commercial websites. The results showed that both user testing and Heuristic Analysis addressed very different usability problems and both methods are equally efficient and effective. In the second study. the primary purpose was to compare the performance between remote usability testing and traditional usability testing. The results indicate that remote usability testing is no different from traditional usability testing. The third study attempted to look at cultural differences in web usability. The results indicated that cultural dimensions have significant effects on user's web preferences. The primary objective of final study was to determine if user's surfing behavior could be predicted through their cognitive style. Results show that cognitive span scores are not strong enough to form association rule with individual difference clusters of web surfing behavior. The results are discussed with respect to all perspectives of Web.
Zhuoyuan Chen - One of the best experts on this subject based on the ideXlab platform.
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A Compressive Sensing Reconstruction Algorithm for Trinary and Binary Sparse Signals Using Pre-mapping
2011 Data Compression Conference, 2011Co-Authors: Xinyu Zhang, Zhuoyuan Chen, John VillasenorAbstract:In this paper, we first analyze impact of the distribution of sparse signals on reconstruction quality in compressive sensing through experimental results and Heuristic Analysis. We suggest that trinary/binary sparse signals are one of the most difficult signals to reconstruct in terms of error bounds. We then show that by incorporating linear or non-linear mapping prior to sensing, significant improvement in the recovery performance can be achieved.
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DCC - A Compressive Sensing Reconstruction Algorithm for Trinary and Binary Sparse Signals Using Pre-mapping
2011 Data Compression Conference, 2011Co-Authors: Xinyu Zhang, Zhuoyuan Chen, Jianwei Ma, John VillasenorAbstract:In this paper, we first analyze impact of the distribution of sparse signals on reconstruction quality in compressive sensing through experimental results and Heuristic Analysis. We suggest that trinary/binary sparse signals are one of the most difficult signals to reconstruct in terms of error bounds. We then show that by incorporating linear or non-linear mapping prior to sensing, significant improvement in the recovery performance can be achieved.
Satoshi Matsuura - One of the best experts on this subject based on the ideXlab platform.
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BigData - Detection of Hijacked Authoritative DNS Servers by Name Resolution Traffic Classification
2019 IEEE International Conference on Big Data (Big Data), 2019Co-Authors: Masahiko Tomoishi, Satoshi MatsuuraAbstract:Authoritative DNS server hijacking has been a critical threat which can be hardly prevented by DNSSEC. In this work, we propose a machine learning based detection method against DNS responses replied from hijacked external authoritative DNS servers by DNS traffic data classification and Heuristic Analysis. The proposed method consists of header, answer, authority and additional section Analysis each of which is combined with the corresponding question section. The decision maker decides if a DNS query-response pair has been related to a hijacked external authoritative DNS server by conducting Heuristic Analysis on the classified DNS traffic data with comparing with old cached DNS data. We have setup a local experimental network and achieved DNS traffic data (A records) of the top 500 FQDNs listed on Alexa web site for about one month and confirmed that the required features can be attracted from the DNS traffic data. Accordingly, we confirmed that it was expectable to conduct the DNS traffic classification and Heuristic Analysis in order to detect DNS responses replied from hijacked external authoritative DNS servers. The future work includes the DNS traffic data training and the evaluations on a local experimental network as well as in a large scale real network environment.
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Detection of Hijacked Authoritative DNS Servers by Name Resolution Traffic Classification
2019 IEEE International Conference on Big Data (Big Data), 2019Co-Authors: Masahiko Tomoishi, Satoshi MatsuuraAbstract:Authoritative DNS server hijacking has been a critical threat which can be hardly prevented by DNSSEC. In this work, we propose a machine learning based detection method against DNS responses replied from hijacked external authoritative DNS servers by DNS traffic data classification and Heuristic Analysis. The proposed method consists of header, answer, authority and additional section Analysis each of which is combined with the corresponding question section. The decision maker decides if a DNS query-response pair has been related to a hijacked external authoritative DNS server by conducting Heuristic Analysis on the classified DNS traffic data with comparing with old cached DNS data. We have setup a local experimental network and achieved DNS traffic data (A records) of the top 500 FQDNs listed on Alexa web site for about one month and confirmed that the required features can be attracted from the DNS traffic data. Accordingly, we confirmed that it was expectable to conduct the DNS traffic classification and Heuristic Analysis in order to detect DNS responses replied from hijacked external authoritative DNS servers. The future work includes the DNS traffic data training and the evaluations on a local experimental network as well as in a large scale real network environment.