TEN-GUARD: Tensor Decomposition for Backdoor Attack Detection in Deep Neural Networks

As deep neural networks and the datasets used to train them get larger, the default approach to integrating them into research and commercial projects is to download a pre-trained model and fine tune it. But these models can have uncertain provenance, opening up the possibility that they embed hidden malicious behavior such as trojans or backdoors, where small changes to an input (triggers) can cause the model to produce incorrect outputs (e.g., to misclassify). This paper introduces a novel approach to backdoor detection that uses two tensor decomposition methods applied to network activations. This has a number of advantages relative to existing detection methods, including the ability to analyze multiple models at the same time, working across a wide variety of network architectures, making no assumptions about the nature of triggers used to alter network behavior, and being computationally efficient. We provide a detailed description of the detection pipeline along with results on models trained on the MNIST digit dataset, CIFAR-10 dataset, and two difficult datasets from NIST's TrojAI competition. These results show that our method detects backdoored networks more accurately and efficiently than current state-of-the-art methods

Early identification of kidney allograft dysfunction with in vivo viscoelastic response (VisR) ultrasound

Ten percent of American adults, more than 20 million people, have chronic kidney disease (CKD). The symptoms of CKD start silently, progress through renal dysfunction, and terminate in end-stage renal disease. The most desirable and cost-effective treatment for CKD is renal transplantation. Although transplant surgery techniques and postoperative care have greatly advanced, ten‐year graft survival is 59%, and death‐censored graft survival is 74%. Improving long-term graft survival is one of the major unmet needs in kidney transplantation. Current graft assessment methods include noninvasive, surrogate biomarkers like serum creatinine and proteinuria. However, these biomarkers lack sensitivity and specificity. In the absence of reliable surrogate biomarkers, some transplant programs implement surveillance or ‘protocol’ biopsies. However, biopsies are associated with bleeding complications and, in rare cases, transplant loss. A noninvasive, sensitive, and specific measure of renal allograft dysfunction is needed to enable timely intervention and prolong graft life. The goal of this dissertation is to develop Viscoelastic Response (VisR) ultrasound for noninvasively characterizing the viscoelastic and anisotropic properties of the kidney to delineate early features associated with renal allograft failure. Such features will include those associated with parenchymal fibrosis and inflammation, which are sensitive and specific indications of early allograft dysfunction. Particularly, this dissertation seeks to improve VisR estimation of tissue elasticity, viscosity, and mechanical anisotropy in isotropic and transversely isotropic viscoelastic materials. Association of VisR metrics with renal fibrosis and inflammation in vivo in the swine model is demonstrated. Additionally, this work evaluates VisR metrics relative to kidney allograft status in a cross-sectional pilot clinical study. Finally, it presents the intra-observer reproducibility of VisR parameters and variability in VisR outcomes due to the donor type, sex, race, and BMI of renal transplant patients. Overall, this dissertation represents a large step toward noninvasive and early identification of graft dysfunction.Doctor of Philosoph

Performance evaluation of mobile relays in CDMA system

In this thesis we consider the uplink direction of DS-CDMA (Direct Sequence, Code Division Multiple Access) network with multihop transmission. For the purpose, we discussed simple conditions by which we can understand whether single hop or multihop is better. One promising direction that the current wireless network moves toward is multihopping that allows mobiles to relay packets of other mobiles to their destinations. A major reason for adopting such multihopping is in capacity and range enhancement, which may pay off its increased complexity. Here, we focus on the non-real-time (NRT) services in the uplink of a DS-CDMA cell. Mobiles are moving around the cell, trying to send NRT packets to the base station, possibly by multihopping. Our goal is to derive a per-hop based multihop scheduling algorithm that is easily applicable in a cellular network with high mobility. For the purpose, we utilize the similarity between the basketball game and our multihop uplink packet scheduling problem. By regarding players, the basket and the ball as mobiles, the base station and data packet, respectively, we can mimic passing (multihopping) patterns of the basketball players. A major difference between the two is that in the multihopping problem, there are many packets (balls) while in the basket ball game, there is only one ball to shoot into the basket

Application of Binary Logistic Regression Model for Assessing the Caesarean Risk Factors in Bangladesh: A Case Study of Khulna and Gopalganj District

The main focus of this study is to investigate the caesarean risk factors in a particular area of Bangladesh. The caesarean delivery rate is increasing day by day in most developing countries like Bangladesh and number of caesarean births has almost doubled in the last eight years in Bangladesh largely due to maternal, socio-economic and demographic factors. Instead of many disadvantages, caesarean deliveries are most common among women but it is not clinically justified. For improving the maternal health status, it is essential to determine the risk factors of caesarean delivery. For this study some hospitals have selected from Khulna and Gopalganj district. Our population is the total number of pregnant women admitted for delivery in the hospitals and 600 respondents were taken as sample. After collecting data, information were arranged in tables and analyzed. For the analysis, chi-square test and fisher’s exact test were performed to identify the significant association between delivery type (caesarean/non-caesarean) and maternal, socio-demographic and socio-economic factor’s respectively. A stepwise binary logistic regression analysis was carried out to identify the most impact factors on caesarean delivery. We found that 14 risk factors were statistically associated with delivery type out of 21 risk factors. From this study, it is clear to us that above influential factors may affects the mother’s health status in Bangladesh as well as Khulna and Gopalganj district

Biosynthesis of ZnO Nano-particle and its quality evaluation on the shelf life extension of fruit

Consumers around the world want fruits with high quality, without chemical preservatives, and with an extended shelf life. Edible films and coating received a considerable amount of attention in recent years because they are useful and beneficial over synthetic packaging. Prolonging of shelf life of food is an important goal to be attained. Many storage techniques have been adapted to extend the marketing distance and holding periods for commodities after harvest. Edible coatings are thin layers of edible material applied to the product surface to provide a barrier to moisture, oxygen, and solute movement for food. The purpose of this study was to produce bio-synthesized ZnO Nano-particles from spinach. The coating solution is prepared by mixing Nano-particles with chitosan-acetic acid solution and evaluates the shelf-life after treatment as a coating. The study showed that coated fruit maintained its quality up to 28 days of the study period. Thus, it can be concluded that ZnO Nano-particles can be used as a coating for increasing shelf-life

On the Quantitative Potential of Viscoelastic Response (VisR) Ultrasound Using the One-Dimensional Mass-Spring-Damper Model

Viscoelastic Response (VisR) ultrasound is an acoustic radiation force (ARF)-based imaging method that fits induced displacements to a one-dimensional (1D) mass-spring-damper (MSD) model to estimate the ratio of viscous to elastic moduli, τ, in viscoelastic materials. Error in VisR τ estimation arises from inertia and acoustic displacement underestimation. These error sources are herein evaluated using finite element method (FEM) simulations, error correction methods are developed, and corrected VisR τ estimates are compared to true simulated τ values to assess VisR’s relevance to quantifying viscoelasticity. In regards to inertia, adding a mass term in series with the Voigt model, to achieve the MSD model, accounts for inertia due to tissue mass when ideal point force excitations are used. However, when volumetric ARF excitations are applied, the induced complex system inertia is not described by the single-degree-of-freedom MSD model, causing VisR to overestimate τ. Regarding acoustic displacement underestimation, associated deformation of ARF-induced displacement profiles further distorts VisR τ estimates. However, median error in VisR τ is reduced to approximately −10% using empirically derived error correction functions applied to simulated viscoelastic materials with viscous and elastic properties representative of tissue. The feasibility of corrected VisR imaging is then demonstrated in vivo in the rectus femoris muscle of an adult with no known neuromuscular disorders. These results suggest VisR’s potential relevance to quantifying viscoelastic properties clinically

Status of deep learning for EEG-based brain–computer interface applications

In the previous decade, breakthroughs in the central nervous system bioinformatics and computational innovation have prompted significant developments in brain–computer interface (BCI), elevating it to the forefront of applied science and research. BCI revitalization enables neurorehabilitation strategies for physically disabled patients (e.g., disabled patients and hemiplegia) and patients with brain injury (e.g., patients with stroke). Different methods have been developed for electroencephalogram (EEG)-based BCI applications. Due to the lack of a large set of EEG data, methods using matrix factorization and machine learning were the most popular. However, things have changed recently because a number of large, high-quality EEG datasets are now being made public and used in deep learning-based BCI applications. On the other hand, deep learning is demonstrating great prospects for solving complex relevant tasks such as motor imagery classification, epileptic seizure detection, and driver attention recognition using EEG data. Researchers are doing a lot of work on deep learning-based approaches in the BCI field right now. Moreover, there is a great demand for a study that emphasizes only deep learning models for EEG-based BCI applications. Therefore, we introduce this study to the recent proposed deep learning-based approaches in BCI using EEG data (from 2017 to 2022). The main differences, such as merits, drawbacks, and applications are introduced. Furthermore, we point out current challenges and the directions for future studies. We argue that this review study will help the EEG research community in their future research