{"title":"Nonlinear structural equation models for network topology inference","authors":"Yanning Shen, Brian Baingana, G. Giannakis","doi":"10.1109/CISS.2016.7460495","DOIUrl":"https://doi.org/10.1109/CISS.2016.7460495","url":null,"abstract":"Linear structural equation models (SEMs) have been widely adopted for inference of causal interactions in complex networks. Recent examples include unveiling topologies of hidden causal networks over which processes such as spreading diseases, or rumors propagation. However, these approaches are limited because they assume linear dependence among observable variables. The present paper advocates a more general nonlinear structural equation model based on polynomial expansions, which compensates for possible nonlinear dependencies between network nodes. To this end, a group-sparsity regularized estimator is put forth to leverage the inherent edge sparsity that is present in most real-world networks. A novel computationally-efficient proximal gradient algorithm is developed to estimate the polynomial SEM coefficients, and hence infer the edge structure. Preliminary tests on simulated data demonstrate the effectiveness of the novel approach.","PeriodicalId":346776,"journal":{"name":"2016 Annual Conference on Information Science and Systems (CISS)","volume":"169 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133014770","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}
{"title":"Interdependency analysis of communications and control in networked cyber physical systems: An entropy framework","authors":"Husheng Li","doi":"10.1109/CISS.2016.7460493","DOIUrl":"https://doi.org/10.1109/CISS.2016.7460493","url":null,"abstract":"Communications and control are of key importance in the analysis and design in cyber physical systems (CPSs). The interdependency of communications and control is described by studying the entropy evolution law in networked CPSs. Both descriptions in terms of ODEs and PDEs are derived, which integrates both the key quantities of communications and control.","PeriodicalId":346776,"journal":{"name":"2016 Annual Conference on Information Science and Systems (CISS)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133395786","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}
{"title":"Learning with finite memory for machine type communication","authors":"Taehyeun Park, W. Saad","doi":"10.1109/CISS.2016.7460572","DOIUrl":"https://doi.org/10.1109/CISS.2016.7460572","url":null,"abstract":"Machine-type devices (MTDs) will lie at the heart of the Internet of things (IoT) system. A key challenge in such a system is sharing network resources between small MTDs, which have limited memory and computational capabilities. In this paper, a novel learning with finite memory framework is proposed to enable MTDs to effectively learn about each others message state, so as to properly adapt their transmission parameters. In particular, an IoT system in which MTDs can transmit both delay tolerant, periodic messages and critical alarm messages is studied. For this model, the characterization of the exponentially growing delay for critical alarm messages and the convergence of the proposed learning framework in an IoT are analyzed. Simulation results show that the delay of critical alarm messages is significantly reduced up to 94% with very minimal memory requirements. The results also show that the proposed learning with finite memory framework is very effective in mitigating the limiting factors of learning that prevent proper learning procedures.","PeriodicalId":346776,"journal":{"name":"2016 Annual Conference on Information Science and Systems (CISS)","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127272003","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}
{"title":"Joint uplink/downlink and offloading optimization for mobile cloud computing with limited backhaul","authors":"A. Al-Shuwaili, Alireza Bagheri, O. Simeone","doi":"10.1109/CISS.2016.7460540","DOIUrl":"https://doi.org/10.1109/CISS.2016.7460540","url":null,"abstract":"Mobile cloud computing enables the offloading of computationally heavy applications, such as for gaming, object recognition or video processing, from mobile users (MUs) to a cloud server connected to wireless access points. The optimization of the operation of a mobile cloud computing system amounts to the problem of minimizing the energy required for offloading across all MUs under latency constraints at the application layer. In a scenario with multiple MUs transmitting over a shared wireless medium across multiple cells, this problem requires the management of interference for both the uplink, through which MUs offload the data needed for computation in the cloud, and for the downlink, through which the outcome of the cloud computation are fed back to the MUs, as well as the allocation of backhaul resources for communication between wireless edge and cloud and of computing resources at the cloud. In this paper, this problem is formulated for general multi-antenna, or MIMO, channels, and tackled by means of successive convex approximation methods. The numerical results illustrate the advantages of a joint allocation of computing and communication resources.","PeriodicalId":346776,"journal":{"name":"2016 Annual Conference on Information Science and Systems (CISS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129145900","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}
Charles Huber, P. Mcdaniel, Scott E. Brown, L. Marvel
{"title":"Cyber Fighter Associate: A Decision Support System for cyber agility","authors":"Charles Huber, P. Mcdaniel, Scott E. Brown, L. Marvel","doi":"10.1109/CISS.2016.7460501","DOIUrl":"https://doi.org/10.1109/CISS.2016.7460501","url":null,"abstract":"In the event of a cyber attack it is important for network defenders to make quick, informed decisions to secure assets. However, the human decision making process is slow and inefficient compared to the speed at which cyber operations can occur. The use of a decision support system (DSS) would help aid agility decisions to shorten the amount of time a network is insecure. In tactical military networks, such a DSS would need to consider constrained resources such as battery life and bandwidth as well as mission goals. In this paper we describe a DSS (Cyber Fighter Associate (CyFiA)) to help select agility maneuvers to contain and eliminate a malicious infection in a mobile ad hoc network (MANET). A variety of scenarios prioritizing factors such as node criticality, health, security and capability are employed. Our results show that CyFiA selects the best sequence of maneuvers for the scenarios and can reduce energy costs when securing a MANET.","PeriodicalId":346776,"journal":{"name":"2016 Annual Conference on Information Science and Systems (CISS)","volume":"8 9","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132899052","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}
H. Imtiaz, Rogers F. Silva, Bradley T. Baker, S. Plis, A. Sarwate, V. Calhoun
{"title":"Privacy-preserving source separation for distributed data using independent component analysis","authors":"H. Imtiaz, Rogers F. Silva, Bradley T. Baker, S. Plis, A. Sarwate, V. Calhoun","doi":"10.1109/CISS.2016.7460488","DOIUrl":"https://doi.org/10.1109/CISS.2016.7460488","url":null,"abstract":"Building good feature representations and learning hidden source models typically requires large sample sizes. In many applications, however, the size of the sample at an individual data holder may not be sufficient. One such application is neuroimaging analyses for mental health disorders - there are many individual research groups, each with a moderate number of subjects. Pooling such data can enable efficient feature learning, but privacy concerns prevent sharing the underlying data. We propose a model for private feature learning in which the data holders share differentially private views of their respective datasets to enable collaborative learning of a joint feature map. We give an example of such an algorithm for independent component analysis (ICA) - a popular blind source separation algorithm used in neuroimaging analyses. Our algorithm is a differentially private version of the recently proposed distributed joint ICA algorithm. We evaluate the performance of this method on simulated functional magnetic resonance imaging (fMRI) data.","PeriodicalId":346776,"journal":{"name":"2016 Annual Conference on Information Science and Systems (CISS)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132142224","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}
Rahul Agarwal, Z. Chen, F. Kloosterman, M. Wilson, S. Sarma
{"title":"Neuronal encoding models of complex receptive fields: A comparison of nonparametric and parametric approaches","authors":"Rahul Agarwal, Z. Chen, F. Kloosterman, M. Wilson, S. Sarma","doi":"10.1109/CISS.2016.7460564","DOIUrl":"https://doi.org/10.1109/CISS.2016.7460564","url":null,"abstract":"Parametric models have been widely used to estimate conditional intensity functions of neuronal spike train point processes and are efficient to construct from experimental data. Furthermore, parametric models are easy to interpret. However, neurons that have more complex receptive fields may not be sufficiently characterized through parametric modeling since it imposes strict structure on the encoding fields. In this paper, we consider a pyramidal neuron recorded from the rat hippocampus, called a “place” cell, that has a diverse apparently multimodal receptive field that encodes information about the spatial position while the rat freely-forages in a circular environment. We construct encoding models for this place cell using two nonparametric modeling approaches, our recently developed band-limited maximum likelihood (BLML) estimator and a kernel density estimator (KDE); and compare them to models constructed using two parametric approaches that have been previously applied to these neurons. We found that the BLML and KDE better capture the complex receptive field of the studied cell as measured by the KS-statistic and log-likelihood.","PeriodicalId":346776,"journal":{"name":"2016 Annual Conference on Information Science and Systems (CISS)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132309436","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}
{"title":"Sequential aggregate authentication codes with information theoretic security","authors":"Shinichiro Tomita, Yohei Watanabe, Junji Shikata","doi":"10.1109/CISS.2016.7460500","DOIUrl":"https://doi.org/10.1109/CISS.2016.7460500","url":null,"abstract":"Sequential aggregate signature (SAS) schemes provide a single, compact signature, which is generated from a number of signatures, that simultaneously ensures that each signature is legally generated from the corresponding message with a defined order. Although SAS schemes have various applications such as a secure border gateway protocol, all existing schemes are computationally secure (i.e., assuming computationally bounded adversaries). In this paper, we first propose sequential aggregate authentication codes (SAA-codes), which has similar functionality of SAS in the information theoretic security setting. Specifically, we give a model and security formalization of SAA-codes, derive lower bounds on sizes of secret keys and authenticators required in secure SAA-codes, and present two kinds of optimal constructions in the sense that each construction meets the lower bounds with equalities.","PeriodicalId":346776,"journal":{"name":"2016 Annual Conference on Information Science and Systems (CISS)","volume":"90 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130912683","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}
{"title":"On the LLR criterion based shortening design for LDPC codes","authors":"Hui Wang, Qingchun Chen, Yong Zhang","doi":"10.1109/CISS.2016.7460482","DOIUrl":"https://doi.org/10.1109/CISS.2016.7460482","url":null,"abstract":"In shortening based coding design, a certain amount of known information bits (for instance, all 0 bits) will be inserted into each information packet for encoding and be removed before transmission. Since they are known, the decoder can assume a complete codeword in decoding, even those known bits are not delivered at all. Basically, shortening provides a framework to generate a set of more powerful codewords with shorter size and lower rate from one mother LDPC code. In this paper, a novel log-likelihood ratio (LLR) criterion is proposed to identify those shortening information indices for LDPC codes. Firstly, the LLR criterion is introduced to show how to determine the LLR reliability metric for each individual information bit via a simple noise-free decoding step. Then it is proposed to determine the shortened information indices based on the LLR ordering. The relationship between the proposed shortening scheme and the known approaches are highlighted as well. Finally, the irregular QC-LDPC codes in IEEE 802.11ac standard is utilized as an example to verify the proposed LLR criterion based shortening design. And our analysis unveils that the proposed shortening scheme provides an efficient shortening scheme for LDPC codes with lower computational complexity but better reliability performance.","PeriodicalId":346776,"journal":{"name":"2016 Annual Conference on Information Science and Systems (CISS)","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127898672","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}
{"title":"Fidelity loss in distribution-preserving anonymization and histogram equalization","authors":"L. Varshney, Kush R. Varshney","doi":"10.1109/CISS.2016.7460471","DOIUrl":"https://doi.org/10.1109/CISS.2016.7460471","url":null,"abstract":"In this paper, we show a formal equivalence between histogram equalization and distribution-preserving quantization. We use this equivalence to connect histogram equalization to quantization for preserving anonymity under the k-anonymity metric, while maintaining distributional properties for data analytics applications. Finally, we make connections to mismatched quantization. These relationships allow us to characterize the loss in mean-squared error (MSE) performance of privacy-preserving quantizers that must meet distribution-preservation constraints as compared to MSE-optimal quantizers in the high-rate regime. Thus, we obtain a formal characterization of the cost of anonymity.","PeriodicalId":346776,"journal":{"name":"2016 Annual Conference on Information Science and Systems (CISS)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125395377","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}