A Cloud-based Mobile Privacy Protection System with Efficient Cache Mechanism

People increasingly rely on their mobile devices and use them to store a lot of data. Some of the data are personal and private, whose leakage leads to users\u27 privacy harm. Meanwhile, mobile apps and services over-collect users\u27 data due to the coarse-grained access control approach utilized by the mobile operating system. We propose a cloud-based approach to provide fine-grained access control toward data requests. We add privacy level, as a new metadata, to data and manage the storage using different policies correspondingly. However, the proposed approach leads to performance decreases because of the extra communication cost. We also introduce a novel cache mechanism to eliminate the extra cost by storing non-private and popular data on the mobile device. As part of our cache mechanism, we design a user-preference-based ordering method along with the principle of locality to determine how popular some data are. We also design a configurable refresh policy to improve the overall performance. Finally, we evaluate our approach using a real phone in a simulated environment. The results show that our approach can keep the response time of all data requests within a reasonable range and the cache mechanism can further improve the performance

Mathematical and Statistical Models of Culex Mosquito Abundance and Transmission Dynamics of West Nile Virus with Weather Impact

West Nile virus (WNV) is a serious public health concern worldwide. Mosquitoes are the key factor in the transmission of the disease. Forecasting mosquito abundance and modeling WNV transmission dynamics with weather conditions are challenging scientific tasks due to the significant weather impact and the magnitude of uncertainty associated with incomplete information. In this dissertation, we employ mathematical and statistical methods to model and forecast the mosquito abundance, the WNV transmission and WNV risk with the weather impact. Compartmental models for WNV transmission usually assume that mosquito population grows with a constant recruiting rate. However in reality, the mosquito abundance is closely related to weather conditions. In the first part, we improve a generalized linear model (GLM) for Culex mosquito abundance with the weather effect. Then we integrate the GLM with a compartmental model for WNV transmission to build a hybrid model. The hybrid model can better capture the reported WNV human infection case pattern in Peel Region, Ontario. As far as we know, this hybrid model is novel and has never been proposed in the literature of modeling WNV transmission. In order to better describe the Culex mosquito behaviors of the whole year, in the second part, we first separate the year into two periods. Then we build a matrix population model for each period respectively. Our simulation results show that our model captures the trends of available mosquito data very well. It is important to model the spatial variation of mosquito population for each region. The classical statistical models are not suitable when some important explanatory factors for each trap are either missing or unobservable. Therefore, in the third part, we study the spatio-temporal distribution of Culex mosquito population by estimating the collective impact of all the unobservable information for each trap. The results demonstrate that the model has a high level of accuracy in comparison with the classical GLM. In the last part, we show our work in forecasting weekly Culex mosquito abundance since 2011 in Peel Region, Ontario. Then we forecast WNV risk using the hybrid model

Shape classification through structured learning of matching measures

Many traditional methods for shape classification involve establishing point correspondences between shapes to produce matching scores, which are in turn used as similarity measures for classification. Learning techniques have been applied only in the second stage of this process, after the matching scores have been obtained. In this paper, instead of simply taking for granted the scores obtained by matching and then learning a classifier, we learn the matching scores themselves so as to produce shape similarity scores that minimize the classification loss. The solution is based on a max-margin formulation in the structured prediction setting. Experiments in shape databases reveal that such an integrated learning algorithm substantially improves on existing methods

C2G2: Controllable Co-speech Gesture Generation with Latent Diffusion Model

Co-speech gesture generation is crucial for automatic digital avatar animation. However, existing methods suffer from issues such as unstable training and temporal inconsistency, particularly in generating high-fidelity and comprehensive gestures. Additionally, these methods lack effective control over speaker identity and temporal editing of the generated gestures. Focusing on capturing temporal latent information and applying practical controlling, we propose a Controllable Co-speech Gesture Generation framework, named C2G2. Specifically, we propose a two-stage temporal dependency enhancement strategy motivated by latent diffusion models. We further introduce two key features to C2G2, namely a speaker-specific decoder to generate speaker-related real-length skeletons and a repainting strategy for flexible gesture generation/editing. Extensive experiments on benchmark gesture datasets verify the effectiveness of our proposed C2G2 compared with several state-of-the-art baselines. The link of the project demo page can be found at https://c2g2-gesture.github.io/c2_gestureComment: 12 pages, 6 figures, 7 table

The level effect and volatility effect of uncertainty shocks in China

Previous studies have assumed that the volatility of exogenous shocks is constant, which can only measure the level effects of uncertain shocks. This article introduces the time-varying volatility model into a Dynamic Stochastic General Equilibrium (D.S.G.E.) model and uses the third-order perturbation method to identify and decompose the level and volatility effects of uncertainty shocks. Based on the results of empirical research in China, the effect of volatility shocks is different from that of level shocks: the effect of level shocks is direct and positive, and its impact is larger, while the effect of volatility shocks is indirect and negative, and its impact is smaller. This article also finds that the impact of uncertainty shocks will lead to economic stagnation, inflation, and the stagflation effect

The projection of climate change impact on the fatigue damage of offshore floating photovoltaic structures

In marine environment, floating photovoltaic (FPV) plants are subjected to wind, wave and current loadings. Waves are the primary source of fatigue damage for FPVs. The climate change may accumulatively affect the wave conditions, which may result in the overestimation or underestimation of fatigue damage. This paper aims to present a projection method to evaluate the climate change impact on fatigue damage of offshore FPVs in the future. Firstly, climate scenarios are selected to project the global radiative forcing level over decadal or century time scales. Secondly, global climate models are coupled to wind driven wave models to project the long-term sea states in the future. At last, fatigue assessment is conducted to evaluate the impact of climate change on fatigue damage of FPVs. A case study is demonstrated in the North Sea. A global-local method of fatigue calculation is utilized to calculate the annual fatigue damage on the FPVs’ joints. The conclusions indicate that there are decreasing trends of significant wave height and annual fatigue damage in the North Sea with the high emission of greenhouse gases. The fatigue design of FPVs based on the current wave scatter diagrams may be conservative in the future. The manufacture cost of FPVs can be reduced to some extent, which is beneficial to the FPV manufacturers

A Muscle Teleoperation System of a Robotic Rollator Based on Bilateral Shared Control

The approach that achieves the teleoperation between human muscle signals and the mobile robot is increasingly applied to transfer human muscle stiffness to enhance robotic performance. In this paper, we develop a mobile rollator control system applying a muscle teleoperation interface and a shared control method to enhance the obstacle avoidance in an effective way. In order to control intuitively, haptic feedback is utilized in the teleoperation interface and is integrated with EMG stiffness to provide a large composition force. Then the composition force is implemented with an artificial potential field method to keep the robotic rollator away from the obstacle in advance. This algorithm is superior to the traditional APF algorithm regardless of the required time and trajectory length. The experimental results demonstrate the effectiveness of the proposed muscle teleoperation system

Novel online data allocation for hybrid memories on tele-health systems

[EN] The developments of wearable devices such as Body Sensor Networks (BSNs) have greatly improved the capability of tele-health industry. Large amount of data will be collected from every local BSN in real-time. These data is processed by embedded systems including smart phones and tablets. After that, the data will be transferred to distributed storage systems for further processing. Traditional on-chip SRAMs cause critical power leakage issues and occupy relatively large chip areas. Therefore, hybrid memories, which combine volatile memories with non-volatile memories, are widely adopted in reducing the latency and energy cost on multi-core systems. However, most of the current works are about static data allocation for hybrid memories. Those mechanisms cannot achieve better data placement in real-time. Hence, we propose online data allocation for hybrid memories on embedded tele-health systems. In this paper, we present dynamic programming and heuristic approaches. Considering the difference between profiled data access and actual data access, the proposed algorithms use a feedback mechanism to improve the accuracy of data allocation during runtime. Experimental results demonstrate that, compared to greedy approaches, the proposed algorithms achieve 20%-40% performance improvement based on different benchmarks. (C) 2016 Elsevier B.V. All rights reserved.This work is supported by NSF CNS-1457506 and NSF CNS-1359557.Chen, L.; Qiu, M.; Dai, W.; Hassan Mohamed, H. (2017). Novel online data allocation for hybrid memories on tele-health systems. Microprocessors and Microsystems. 52:391-400. https://doi.org/10.1016/j.micpro.2016.08.003S3914005

Effects of dietary supplementation with Lactobacillus acidophilus on the performance, intestinal physical barrier function, and the expression of NOD-like receptors in weaned piglets

Lactobacillus supplementation is beneficial to the barrier function of the intestinal physical barrier in piglets. However, the mechanisms underlying this beneficial function remain largely unknown. Here, we investigated the effects of dietary supplementation with Lactobacillus acidophilus on the performance, intestinal physical barrier functioning, and NOD-like receptors (NLRs) expression in weaned piglets. Sixteen weaned piglets were randomly allocated to two groups. The control group received a corn-soybean basal diet, while the treatment group received the same diet adding 0.1% L. acidophilus, for 14 days. As a result, dietary L. acidophilus supplementation was found to increase the average daily gain (ADG) (P < 0.05), reduced serum diamine oxidase (DAO) activity (P < 0.05), increased the mRNA expression and protein abundance of occludin in the jejunum and ileum (P < 0.01), reduced the mRNA levels of NOD1 (P < 0.01), receptor interacting serine/threonine kinase 2 (RIPK2) (P < 0.05), nuclear factor κB (NF-κB) (P < 0.01), NLR family pyrin domain containing 3 (NLRP3) (P < 0.01), caspase-1 (P < 0.01), interleukin 1β (IL-1β) (P < 0.05) and IL-18 (P < 0.01) in the jejunum tissues of the weaned pigs. The expression of NLRP3 (P < 0.05), caspase-1 (P < 0.01), IL-1β (P < 0.05) and IL-18 (P < 0.05) was also reduced in the ileum tissues of the weaned pigs. These results showed that L. acidophilus supplementation improves the growth performance, enhances the intestinal physical barrier function, and inhibits the expression of NOD1 and NLRP3 signaling-pathway-related genes in jejunum and ileum tissues. They also suggest that L. acidophilus enhances the intestinal physical barrier functioning by inhibiting IL-1β and IL-18 pro-inflammatory cytokines via the NOD1/NLRP3 signaling pathway in weaned piglets

Digital Twin Brain: a simulation and assimilation platform for whole human brain

In this work, we present a computing platform named digital twin brain (DTB) that can simulate spiking neuronal networks of the whole human brain scale and more importantly, a personalized biological brain structure. In comparison to most brain simulations with a homogeneous global structure, we highlight that the sparseness, couplingness and heterogeneity in the sMRI, DTI and PET data of the brain has an essential impact on the efficiency of brain simulation, which is proved from the scaling experiments that the DTB of human brain simulation is communication-intensive and memory-access intensive computing systems rather than computation-intensive. We utilize a number of optimization techniques to balance and integrate the computation loads and communication traffics from the heterogeneous biological structure to the general GPU-based HPC and achieve leading simulation performance for the whole human brain-scaled spiking neuronal networks. On the other hand, the biological structure, equipped with a mesoscopic data assimilation, enables the DTB to investigate brain cognitive function by a reverse-engineering method, which is demonstrated by a digital experiment of visual evaluation on the DTB. Furthermore, we believe that the developing DTB will be a promising powerful platform for a large of research orients including brain-inspiredintelligence, rain disease medicine and brain-machine interface.Comment: 12 pages, 11 figure