An LSH-based offloading method for IoMT services in integrated cloud-edge environment

© 2021 ACM. Benefiting from the massive available data provided by Internet of multimedia things (IoMT), enormous intelligent services requiring information of various types to make decisions are emerging. Generally, the IoMT devices are equipped with limited computing power, interfering with the process of computation-intensive services. Currently, to satisfy a wide range of service requirements, the novel computing paradigms, i.e., cloud computing and edge computing, can potentially be integrated for service accommodation. Nevertheless, the private information (i.e., location, service type, etc.) in the services is prone to spilling out during service offloading in the cloud-edge computing. To avoid privacy leakage while improving service utility, including the service response time and energy consumption for service executions, a Locality-sensitive-hash (LSH)-based offloading method, named LOM, is devised. Specifically, LSH is leveraged to encrypt the feature information for the services offloaded to the edge servers with the intention of privacy preservation. Eventually, comparative experiments are conducted to verify the effectiveness of LOM with respect to promoting service utility

Data Placement for Privacy-Aware Applications over Big Data in Hybrid Clouds

Nowadays, a large number of groups choose to deploy their applications to cloud platforms, especially for the big data era. Currently, the hybrid cloud is one of the most popular computing paradigms for holding the privacy-aware applications driven by the requirements of privacy protection and cost saving. However, it is still a challenge to realize data placement considering both the energy consumption in private cloud and the cost for renting the public cloud services. In view of this challenge, a cost and energy aware data placement method, named CEDP, for privacy-aware applications over big data in hybrid cloud is proposed. Technically, formalized analysis of cost, access time, and energy consumption is conducted in the hybrid cloud environment. Then a corresponding data placement method is designed to accomplish the cost saving for renting the public cloud services and energy savings for task execution within the private cloud platforms. Experimental evaluations validate the efficiency and effectiveness of our proposed method

OptIForest: Optimal Isolation Forest for Anomaly Detection

Anomaly detection plays an increasingly important role in various fields for critical tasks such as intrusion detection in cybersecurity, financial risk detection, and human health monitoring. A variety of anomaly detection methods have been proposed, and a category based on the isolation forest mechanism stands out due to its simplicity, effectiveness, and efficiency, e.g., iForest is often employed as a state-of-the-art detector for real deployment. While the majority of isolation forests use the binary structure, a framework LSHiForest has demonstrated that the multi-fork isolation tree structure can lead to better detection performance. However, there is no theoretical work answering the fundamentally and practically important question on the optimal tree structure for an isolation forest with respect to the branching factor. In this paper, we establish a theory on isolation efficiency to answer the question and determine the optimal branching factor for an isolation tree. Based on the theoretical underpinning, we design a practical optimal isolation forest OptIForest incorporating clustering based learning to hash which enables more information to be learned from data for better isolation quality. The rationale of our approach relies on a better bias-variance trade-off achieved by bias reduction in OptIForest. Extensive experiments on a series of benchmarking datasets for comparative and ablation studies demonstrate that our approach can efficiently and robustly achieve better detection performance in general than the state-of-the-arts including the deep learning based methods.Comment: This paper has been accepted by International Joint Conference on Artificial Intelligence (IJCAI-23

Hypoxia mitigation by manganese-doped carbon dots for synergistic photodynamic therapy of oral squamous cell carcinoma

Photodynamic therapy (PDT) is widely used for cancer treatment due to its non-invasive and precise effectiveness, however, hypoxia in the tumor microenvironment greatly limits the efficacy of photodynamic therapy. Compared with conventional photosensitizers, carbon dots (CDs) have great potential. Therefore, developing a water-soluble, low-toxicity photosensitizer based on CDs is particularly important, especially one that can enhance the photodynamic efficacy using the tumor microenvironment to produce oxygen. Herein, manganese-doped carbon dot (Mn-CDs, ∼2.7 nm) nanoenzymes with excellent biocompatibility were prepared by a solvothermal method using ethylenediaminetetraacetic acid manganese disodium salt hydrate and o-phenylenediamine as precursors. TEM, AFM, HR-TEM, XRD, XPS, FT-IR, ζ potential, DLS, UV-Vis, and PL spectra were used to characterize the Mn-CDs. Cancer resistance was assessed using the CCK-8 kit, calcein AM versus propidium iodide (PI) kit, and the Annexin V-FITC/PI cell apoptosis assay kit. The obtained Mn-CDs have excellent near-infrared emission properties, stability, and efficient 1O2 generation. Notably, the manganese doping renders CDs with catalase (CAT)-like activity, which leads to the decomposition of acidic H2O2in situ to generate O2, enhancing the PDT efficacy against OSCC-9 cells under 635 nm (300 mW·cm−2) irradiation. Thus, this work provides a simple and feasible method for the development of water-soluble photosensitizers with oxygen production, presenting good biosafety for PDT in hypoxic tumors

OmniAvatar: Geometry-Guided Controllable 3D Head Synthesis

We present OmniAvatar, a novel geometry-guided 3D head synthesis model trained from in-the-wild unstructured images that is capable of synthesizing diverse identity-preserved 3D heads with compelling dynamic details under full disentangled control over camera poses, facial expressions, head shapes, articulated neck and jaw poses. To achieve such high level of disentangled control, we first explicitly define a novel semantic signed distance function (SDF) around a head geometry (FLAME) conditioned on the control parameters. This semantic SDF allows us to build a differentiable volumetric correspondence map from the observation space to a disentangled canonical space from all the control parameters. We then leverage the 3D-aware GAN framework (EG3D) to synthesize detailed shape and appearance of 3D full heads in the canonical space, followed by a volume rendering step guided by the volumetric correspondence map to output into the observation space. To ensure the control accuracy on the synthesized head shapes and expressions, we introduce a geometry prior loss to conform to head SDF and a control loss to conform to the expression code. Further, we enhance the temporal realism with dynamic details conditioned upon varying expressions and joint poses. Our model can synthesize more preferable identity-preserved 3D heads with compelling dynamic details compared to the state-of-the-art methods both qualitatively and quantitatively. We also provide an ablation study to justify many of our system design choices

Simultaneous lattice engineering and defect control via cadmium incorporation for high‐performance inorganic perovskite solar cells

Doping of all‐inorganic lead halide perovskites to enhance their photovoltaic performance and stability has been reported to be effective. Up to now most studies have focused on the doping of elements in to the perovskite lattice. However, most of them cannot be doped into the perovskite lattice and the roles of these dopants are still controversial. Herein,the authors introduce CdI2 as an additive into CsPbI3−xBr x and use it as active layer to fabricate high‐performance inorganic perovskite solar cells (PSCs). Cd with a smaller radius than Pb can partially substitute Pb in the perovskite lattice by up to 2 mol%. Meanwhile, the remaining Cd stays on the surface and grain boundaries (GB) of the perovskite film in the form of Cs2CdI4−xBr−x, which is found to reduce non‐radiative recombination. These effects result in prolonged charge carrier lifetime, suppressed defect formation, decreased GBs, and an upward shift of energybands in the Cd‐containing film. A champion efficiency of 20.8% is achieved for Cd‐incorporated PSCs, together with improved device ambient stability. This work highlights the importance of simultaneous lattice engineering, defectcontrol and atomic‐level characterization in achieving high‐performance inorganic PSCs with well‐defined structure‐property relationships

Privacy Preservation for Federated Learning with Robust Aggregation in Edge Computing

Benefiting from the powerful data analysis and prediction capabilities of artificial intelligence (AI), the data on the edge is often transferred to the cloud center for centralized training to obtain an accurate model. To resist the risk of privacy leakage due to frequent data transmission between the edge and the cloud, federated learning (FL) is engaged in the edge paradigm, uploading the model updated on the edge server (ES) to the central server for aggregation, instead of transferring data directly. However, the adversarial ES can infer the update of other ESs from the aggregated model and the update may still expose some characteristics of data of other ESs. Besides, there is a certain probability that the entire aggregation is disrupted by the adversarial ESs through uploading a malicious update. In this paper, a privacy-preserving FL scheme with robust aggregation in edge computing is proposed, named FL-RAEC. First, the hybrid privacy-preserving mechanism is constructed to preserve the integrity and privacy of the data uploaded by the ESs. For the robust model aggregation, a phased aggregation strategy is proposed. Specifically, anomaly detection based on autoencoder is performed while some ESs are selected for anonymous trust verification at the beginning. In the next stage, via multiple rounds of random verification, the trust score of each ES is assessed to identify the malicious participants. Eventually, FL-RAEC is evaluated in detail, depicting that FL-RAEC has strong robustness and high accuracy under different attacks

Potential Game Based Distributed IoV Service Offloading With Graph Attention Networks in Mobile Edge Computing

Vehicular services aim to provide smart and timely services (e.g., collision warning) by taking the advantage of recent advances in artificial intelligence and employing task offloading techniques in mobile edge computing. In practice, the volume of vehicles in the Internet of Vehicles (IoV) often surges at a single location and renders the edge servers (ESs) severely overloaded, resulting in a very high delay in delivering the services. Therefore, it is of practical importance and urgency to coordinate the resources of ESs with bandwidth allocation for mitigating the occurrence of a spike traffic flow. For this challenge, existing work sought the periodicities of traffic flow by analyzing historical traffic data. However, the changes in traffic flow caused by sudden traffic conditions cannot be obtained from these periodicities. In this paper, we propose a distributed traffic flow forecasting and task offloading approach named TFFTO to optimize the execution time and power consumption in service processing. Specifically, graph attention networks (GATs) are leveraged to forecast future traffic flow in short-term and the traffic volume is utilized to estimate the number of services offloaded to the ESs in the subsequent period. With the estimate, the current load of the ESs is adjusted to ensure that the services can be handled in a timely manner. Potential game theory is adopted to determine the optimal service offloading strategy. Extensive experiments are conducted to evaluate our approach and the results validate our robust performance