Heuristic Algorithms for Energy and Performance Dynamic Optimization in Cloud Computing

Cloud computing becomes increasingly popular for hosting all kinds of applications not only due to their ability to support dynamic provisioning of virtualized resources to handle workload fluctuations but also because of the usage based on pricing. This results in the adoption of data centers which store, process and present the data in a seamless, efficient and easy way. Furthermore, it also consumes an enormous amount of electrical energy, then leads to high using cost and carbon dioxide emission. Therefore, we need a Green computing solution that can not only minimize the using costs and reduce the environment impact but also improve the performance. Dynamic consolidation of Virtual Machines (VMs), using live migration of the VMs and switching idle servers to sleep mode or shutdown, optimizes the energy consumption. We propose an adaptive underloading detection method of hosts, VMs migration selecting method and heuristic algorithm for dynamic consolidation of VMs based on the analysis of the historical data. Through extensive simulation based on random data and real workload data, we show that our method and algorithm observably reduce energy consumption and allow the system to meet the Service Level Agreements (SLAs)

Towards Privacy-Preserving Person Re-identification via Person Identify Shift

Recently privacy concerns of person re-identification (ReID) raise more and more attention and preserving the privacy of the pedestrian images used by ReID methods become essential. De-identification (DeID) methods alleviate privacy issues by removing the identity-related of the ReID data. However, most of the existing DeID methods tend to remove all personal identity-related information and compromise the usability of de-identified data on the ReID task. In this paper, we aim to develop a technique that can achieve a good trade-off between privacy protection and data usability for person ReID. To achieve this, we propose a novel de-identification method designed explicitly for person ReID, named Person Identify Shift (PIS). PIS removes the absolute identity in a pedestrian image while preserving the identity relationship between image pairs. By exploiting the interpolation property of variational auto-encoder, PIS shifts each pedestrian image from the current identity to another with a new identity, resulting in images still preserving the relative identities. Experimental results show that our method has a better trade-off between privacy-preserving and model performance than existing de-identification methods and can defend against human and model attacks for data privacy

Invisible Backdoor Attack with Dynamic Triggers against Person Re-identification

In recent years, person Re-identification (ReID) has rapidly progressed with wide real-world applications, but also poses significant risks of adversarial attacks. In this paper, we focus on the backdoor attack on deep ReID models. Existing backdoor attack methods follow an all-to-one/all attack scenario, where all the target classes in the test set have already been seen in the training set. However, ReID is a much more complex fine-grained open-set recognition problem, where the identities in the test set are not contained in the training set. Thus, previous backdoor attack methods for classification are not applicable for ReID. To ameliorate this issue, we propose a novel backdoor attack on deep ReID under a new all-to-unknown scenario, called Dynamic Triggers Invisible Backdoor Attack (DT-IBA). Instead of learning fixed triggers for the target classes from the training set, DT-IBA can dynamically generate new triggers for any unknown identities. Specifically, an identity hashing network is proposed to first extract target identity information from a reference image, which is then injected into the benign images by image steganography. We extensively validate the effectiveness and stealthiness of the proposed attack on benchmark datasets, and evaluate the effectiveness of several defense methods against our attack

Chromosomal DNA deletion confers phage resistance to Pseudomonas aeruginosa.

Bacteria develop a broad range of phage resistance mechanisms, such as prevention of phage adsorption and CRISPR/Cas system, to survive phage predation. In this study, Pseudomonas aeruginosa PA1 strain was infected with lytic phage PaP1, and phage-resistant mutants were selected. A high percentage (~30%) of these mutants displayed red pigmentation phenotype (Red mutant). Through comparative genomic analysis, one Red mutant PA1r was found to have a 219.6 kb genomic fragment deletion, which contains two key genes hmgA and galU related to the observed phenotypes. Deletion of hmgA resulted in the accumulation of a red compound homogentisic acid; while A galU mutant is devoid of O-antigen, which is required for phage adsorption. Intriguingly, while the loss of galU conferred phage resistance, it significantly attenuated PA1r in a mouse infection experiment. Our study revealed a novel phage resistance mechanism via chromosomal DNA deletion in P. aeruginosa

Warranty service contracts design for deteriorating products with maintenance duration commitments

With the increasing diversification of customers’ demand and purchasing behaviors, more and more manufacturers have focused their attention on the warranty service contracts design. The maintenance duration of the sold product, which plays an important role in the normal production and operation process of the user, is frequently taken into consideration in warranty contracts. In this study, we design different warranty contracts with various combinations of maintenance duration and availability requirements. The manufacturer commits to compensate for each overdue repair or failing to satisfy the availability target. The customers’ choice behavior is described by the multinomial logit (MNL) model, and customers often form their own minimum acceptable levels (also referred to as reference points) of maintenance duration and availability when making purchasing decisions, which have an impact on the contract choice. The expected warranty servicing profit is maximized to determine the optimal price, maintenance duration and availability. Finally, the proposed warranty contracts are demonstrated by numerical examples. We find that the maintenance duration affects not only the warranty cost but also the customer choice, which further affects the optimal contract pricing and profits

Advanced Geological Prediction

Due to the particularity of the tunnel project, it is difficult to find out the exact geological conditions of the tunnel body during the survey stage. Once it encounters unfavorable geological bodies such as faults, fracture zones, and karst, it will bring great challenges to the construction and will easily cause major problems, economic losses, and casualties. Therefore, it is necessary to carry out geological forecast work in the tunnel construction process, which is of great significance for tunnel safety construction and avoiding major disaster accident losses. This lecture mainly introduces the commonly used methods of geological forecast in tunnel construction, the design principles, and contents of geological forecast and combines typical cases to show the implementation process of comprehensive geological forecast. Finally, the development direction of geological forecast theory, method, and technology is carried out. Prospects provide a useful reference for promoting the development of geological forecast of tunnels

Learning Semantic Representation from Restaurant Reviews: A Study of Yelp Dataset

Users' preference such as rating only provides uni-dimension information, but reasons behind users' preference may be related to various aspects of an item, such as the types, certain attributes. By observing user-generated review always provides such rich information, we proposed an item representation based on review data. This approach supports semantic operation, which could potentially enables more recommendation scenarios. Our experiments further demonstrated that this approach gained much better performance than classical item representation methods