Department of Computer and Information Science

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https://hdl.handle.net/1805/6744

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Now showing 1 - 10 of 291
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    A neural network approach to multi-biomarker panel discovery by high-throughput plasma proteomics profiling of breast cancer
    (Springer Nature, 2013) Zhang, Fan; Chen, Jake; Wang, Mu; Drabier, Renee; Computer and Information Science, Purdue School of Science
    Background: In the past several years, there has been increasing interest and enthusiasm in molecular biomarkers as tools for early detection of cancer. Liquid chromatography tandem mass spectrometry (LC/MS/MS) based plasma proteomics profiling technique is a promising technology platform to study candidate protein biomarkers for early detection of cancer. Factors such as inherent variability, protein detectability limitation, and peptide discovery biases among LC/MS/MS platforms have made the classification and prediction of proteomics profiles challenging. Developing proteomics data analysis methods to identify multi-protein biomarker panels for breast cancer diagnosis based on neural networks provides hope for improving both the sensitivity and the specificity of candidate cancer biomarkers for early detection. Results: In our previous method, we developed a Feed Forward Neural Network-based method to build the classifier for plasma samples of breast cancer and then applied the classifier to predict blind dataset of breast cancer. However, the optimal combination C* in our previous method was actually determined by applying the trained FFNN on the testing set with the combination. Therefore, in this paper, we applied a three way data split to the Feed Forward Neural Network for training, validation and testing based. We found that the prediction performance of the FFNN model based on the three way data split outperforms our previous method and the prediction performance is improved from (AUC = 0.8706, precision = 82.5%, accuracy = 82.5%, sensitivity = 82.5%, specificity = 82.5% for the testing set) to (AUC = 0.895, precision = 86.84%, accuracy = 85%, sensitivity = 82.5%, specificity = 87.5% for the testing set). Conclusions: Further pathway analysis showed that the top three five-marker panels are associated with complement and coagulation cascades, signaling, activation, and hemostasis, which are consistent with previous findings. We believe the new approach is a better solution for multi-biomarker panel discovery and it can be applied to other clinical proteomics.
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    A trustless architecture of blockchain-enabled metaverse
    (Elsevier, 2023-03) Xu, Minghui; Guo, Yihao; Hu, Qin; Xiong, Zehui; Yu, Dongxiao; Cheng, Xuizhen; Computer and Information Science, School of Science
    Metaverse has rekindled human beings’ desire to further break space-time barriers by fusing the virtual and real worlds. However, security and privacy threats hinder us from building a utopia. A metaverse embraces various techniques, while at the same time inheriting their pitfalls and thus exposing large attack surfaces. Blockchain, proposed in 2008, was regarded as a key building block of metaverses. it enables transparent and trusted computing environments using tamper-resistant decentralized ledgers. Currently, blockchain supports Decentralized Finance (DeFi) and Non-fungible Tokens (NFT) for metaverses. However, the power of a blockchain has not been sufficiently exploited. In this article, we propose a novel trustless architecture of blockchain-enabled metaverse, aiming to provide efficient resource integration and allocation by consolidating hardware and software components. To realize our design objectives, we provide an On-Demand Trusted Computing Environment (OTCE) technique based on local trust evaluation. Specifically, the architecture adopts a hypergraph to represent a metaverse, in which each hyperedge links a group of users with certain relationship. Then the trust level of each user group can be evaluated based on graph analytics techniques. Based on the trust value, each group can determine its security plan on demand, free from interference by irrelevant nodes. Besides, OTCEs enable large-scale and flexible application environments (sandboxes) while preserving a strong security guarantee.
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    Accelerating Experience Replay for Deep Q-Networks with Reduced Target Computation
    (CS & IT, 2023) Zigon, Bob; Song, Fengguang; Computer and Information Science, School of Science
    Mnih’s seminal deep reinforcement learning paper that applied a Deep Q-network to Atari video games demonstrated the importance of a replay buffer and a target network. Though the pair were required for convergence, the use of the replay buffer came at a significant computational cost. With each new sample generated by the system, the targets in the mini batch buffer were continually recomputed. We propose an alternative that eliminates the target recomputation called TAO-DQN (Target Accelerated Optimization-DQN). Our approach focuses on a new replay buffer algorithm that lowers the computational burden. We implemented this new approach on three experiments involving environments from the OpenAI gym. This resulted in convergence to better policies in fewer episodes and less time. Furthermore, we offer a mathematical justification for our improved convergence rate.
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    Advances in Mobile Communications and Computing
    (Hindawi, 2009) Durresi, Arjan; Denko, Mieso; Computer and Information Science, School of Science
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    Advances in Wireless Networks
    (Hindawi, 2009-04-13) Durresi, Arjan; Denko, Mieso; Computer and Information Science, School of Science
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    An Algorithm for Forward Reduction in Sequence-Based Software Specification Read More: http://www.worldscientific.com/doi/abs/10.1142/S0218194016400118
    (World Scientific, 2016-11) Lin, Lan; Xue, Yufeng; Song, Fengguang; Computer and Information Science, School of Science
    Sequence-based software specification is a rigorous method for deriving a formal system model based on informal requirements, through a systematic process called sequence enumeration. Under this process, stimulus (input) sequences are considered in a breadth-first manner, with the expected system response to each sequence given. Not every sequence needs to be further extended by the enumeration rules. The completed specification encodes a Mealy machine and forms a basis for other activities including code development and testing. This paper presents a forward reduction algorithm for sequence-based specification. The need for such an algorithm has been identified by field applications. We used the state machine as an intermediate tool to comprehend and analyze all change impacts resulted from a forward reduction, and used an axiom system for its development. We present the algorithm both mathematically in functional form and procedurally in pseudocode, illustrate it with a symbolic example, and report a larger case study from the published literature in which the algorithm is applied. The algorithm will prove useful and effective in deriving a system-level specification as well as in merging and combining partial work products towards a formal system model in field applications.
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    All weather road edge identification based on driving video mining
    (IEEE, 2017) Wang, Zheyuan; Cheng, Guo; Zheng, Jiang Yu; Computer and Information Science, School of Science
    To avoid vehicle running off road, road edge detection is a fundamental function. Current work on road edge detection has not exhaustively tackled all weather and illumination conditions. We first sort the visual appearance of roads based on physical and optical properties under various illuminations. Then, data mining approach is applied to a large driving video set that contains the full spectrum of seasons and weathers to learn the statistical distribution of road edge appearances. The obtained parameters of road environment in color on road structure are used to classify weather in video briefly, and the corresponding algorithm and features are applied for robust road edge detection. To visualize the road appearance as well as evaluate the accuracy of detected road, a compact road profile image is generated to reduce the data to a small fraction of video. Through the exhaustive examination of all weather and illuminations, our road detection methods can locate road edges in good weather, reduce errors in dark illuminations, and report road invisibility in poor illuminations.
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    Alliance Makes Difference? Maximizing Social Welfare in Cross-Silo Federated Learning
    (IEEE, 2024-02) Chen, Jianan; Hu, Qin; Jiang, Honglu; Computer and Information Science, Purdue School of Science
    As one of the typical settings of Federated Learning (FL), cross-silo FL allows organizations to jointly train an optimal Machine Learning (ML) model. In this case, some organizations may try to obtain the global model without contributing their local training power, lowering the social welfare. In this article, we model the interactions among organizations in cross-silo FL as a public goods game and theoretically prove that there exists a social dilemma where the maximum social welfare is not achieved in Nash equilibrium. To overcome this dilemma, we employ the Multi-player Multi-action Zero-Determinant (MMZD) strategy to maximize the social welfare. With the help of the MMZD, an individual organization can unilaterally control the social welfare without extra cost. Since the MMZD strategy can be adopted by all organizations, we further study the case of multiple organizations jointly adopting the MMZD strategy to form an MMZD Alliance (MMZDA). We prove that the MMZDA strategy can strengthen the control of the maximum social welfare. Experimental results validate that the MMZD strategy is effective in obtaining the maximum social welfare and the MMZDA can achieve a larger maximum value.
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    An Uncertainty- and Collusion-Proof Voting Consensus Mechanism in Blockchain
    (IEEE, 2023-10) Wang, Shengling; Qu, Xidi; Hu, Qin; Wang, Xia; Cheng, Xiuzhen; Computer and Information Science, Purdue School of Science
    Though voting-based consensus algorithms in blockchain outperform proof-based ones in energy- and transaction-efficiency, they are prone to incur wrong elections and bribery elections. The former originates from the uncertainties of candidates’ capability and availability, and the latter comes from the egoism of voters and candidates. Hence, in this paper, we propose an uncertainty- and collusion-proof voting consensus mechanism, including the selection pressure-based voting algorithm and the trustworthiness evaluation algorithm. The first algorithm can decrease the side effects of candidates’ uncertainties, lowering wrong elections while trading off the balance between efficiency and fairness in voting miners. The second algorithm adopts an incentive-compatible scoring rule to evaluate the trustworthiness of voting, motivating voters to report true beliefs on candidates by making egoism consistent with altruism so as to avoid bribery elections. A salient feature of our work is theoretically analyzing the proposed voting consensus mechanism by the large deviation theory. Our analysis provides not only the voting failure rate of a candidate but also its decay speed. The voting failure rate measures the incompetence of any candidate from a personal perspective by voting, based on which the concepts of the effective selection valve and the effective expectation of merit are introduced to help the system designer determine the optimal voting standard and guide a candidate to behave in an optimal way for lowering the voting failure rate.
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    Analysis of AI Models for Student Admissions: A Case Study
    (ACM, 2023-03) Van Basum, Kelly; Fang, Shaiofen; Computer and Information Science, School of Science
    This research uses machine learning-based AI models to predict admissions decisions at a large urban research university. Admissions data spanning five years was used to create an AI model to determine whether a given student would be directly admitted into the School of Science under various scenarios. During this time, submission of standardized test scores as part of a student's application became optional which led to interesting questions about the impact of standardized test scores on admission decisions. We first developed AI models and analyzed these models to understand which variables are important in admissions decisions, and how the decision to exclude test scores affects the demographics of the students who are admitted. We then evaluated the predictive models to detect and analyze biases these models may carry with respect to three variables chosen to represent sensitive populations: gender, race, and whether a student was the first in his family to attend college.