SkipcrossNets: Adaptive Skip-cross Fusion for Road Detection

Multi-modal fusion is increasingly being used for autonomous driving tasks, as images from different modalities provide unique information for feature extraction. However, the existing two-stream networks are only fused at a specific network layer, which requires a lot of manual attempts to set up. As the CNN goes deeper, the two modal features become more and more advanced and abstract, and the fusion occurs at the feature level with a large gap, which can easily hurt the performance. In this study, we propose a novel fusion architecture called skip-cross networks (SkipcrossNets), which combines adaptively LiDAR point clouds and camera images without being bound to a certain fusion epoch. Specifically, skip-cross connects each layer to each layer in a feed-forward manner, and for each layer, the feature maps of all previous layers are used as input and its own feature maps are used as input to all subsequent layers for the other modality, enhancing feature propagation and multi-modal features fusion. This strategy facilitates selection of the most similar feature layers from two data pipelines, providing a complementary effect for sparse point cloud features during fusion processes. The network is also divided into several blocks to reduce the complexity of feature fusion and the number of model parameters. The advantages of skip-cross fusion were demonstrated through application to the KITTI and A2D2 datasets, achieving a MaxF score of 96.85% on KITTI and an F1 score of 84.84% on A2D2. The model parameters required only 2.33 MB of memory at a speed of 68.24 FPS, which could be viable for mobile terminals and embedded devices

Biometrics-as-a-Service: A Framework to Promote Innovative Biometric Recognition in the Cloud

Biometric recognition, or simply biometrics, is the use of biological attributes such as face, fingerprints or iris in order to recognize an individual in an automated manner. A key application of biometrics is authentication; i.e., using said biological attributes to provide access by verifying the claimed identity of an individual. This paper presents a framework for Biometrics-as-a-Service (BaaS) that performs biometric matching operations in the cloud, while relying on simple and ubiquitous consumer devices such as smartphones. Further, the framework promotes innovation by providing interfaces for a plurality of software developers to upload their matching algorithms to the cloud. When a biometric authentication request is submitted, the system uses a criteria to automatically select an appropriate matching algorithm. Every time a particular algorithm is selected, the corresponding developer is rendered a micropayment. This creates an innovative and competitive ecosystem that benefits both software developers and the consumers. As a case study, we have implemented the following: (a) an ocular recognition system using a mobile web interface providing user access to a biometric authentication service, and (b) a Linux-based virtual machine environment used by software developers for algorithm development and submission

An Effective Dual Level Flow Optimized AlexNet-BiGRU Model for Intrusion Detection in Cloud Computing

In recent years, several existing techniques have been developed to solve security issues in cloud systems. The proposed study intends to develop an effective deep-learning mechanism for detecting network intrusions. The proposed study involves three stages pre-processing, feature selection and classification. Initially, the available noises in the input data are eliminated by pre-processing via data cleaning, discretization and normalization. The large feature dimensionality of pre-processed data is reduced by selecting optimal features using the wild horse optimization-based feature selection (WHO-FS) model. The selected features are then input into a proposed dual-level flow optimized AlexNet-BiGRU detection model (DLFAB-IDS). Whereas the flow direction algorithm (FDA) approach optimally tunes the hyperparameters and helps to enhance the classification performance. In the proposed model, the intrusions are detected by AlexNet and the multiclass classification is performed through the BiGRU method. The proposed study used the NSL-KDD dataset, and the simulation was done by Python tool. The efficacy of a proposed model is measured by evaluating several performance metrics. The comparison over other existing techniques shows that the proposed model brings higher performance in terms of accuracy 96.81%, recall 95.84%, precision 96.24%, f1-score 96.75%, prediction time 0.43s and training time 152.84s