Vehicular crowdsensing could enhance the traffic accident detection performance on freeways to the existing infrastructures by global task allocation. However, the uncertainty of traffic accidents and unknown of individual users bring difficulties for the task allocation. To address the problem, a global optimization method of task allocation with non-deterministic tasks and unknown users (GOTA-NU) is proposed. In the method, to reduce the influence of accident uncertainty, the accident risk is used to represent the sensing tasks of traffic accidents, and sensing task representation model is constructed by estimating the intrinsic temporal-spatial distribution of accident risk according to the wavelet transform and optimal transport theory. Meanwhile, to determine the users in future, a user estimation model is established according to macro statistical characteristics of traffic flow. Then the task allocation problem is transformed into a coverage problem for accident risk. A task allocation model is constructed by minimizing the user incentive cost with coverage level constraints of accident risk. And a greedy algorithm is proposed to solve it. To validate the proposed method, sensitivity experiments, robustness experiments, and comparison experiments are carried out on an open data source. The results show that the proposed method is efficient and reliable under different traffic conditions. The proposed method provides a reference for the long-term task allocation with non-deterministic tasks and unknown users.

Corner detection is a key step in various computer vision tasks such as image registration and 3D reconstruction. Our research indicates that existing deep learning-based corner detection and description methods have not thoroughly considered the corners' properties, making it impossible for the designed deep learning networks to effectively obtain corner feature representation witch affects corner detection, description of corner and its surrounding information, and related applications. In this work, based on the inherent properties of corners, a new self-supervised scale and affine invariant (S3AI) network for image corner detection and description is proposed which is embedded into a given episodic training mechanism for end-to-end training from scratch. The proposed S3AI network has the capability to extract scale and affine invariant feature representations with automatic scale selection from images and to accurately detect corners from images and describe corners and the surrounding information from images without being affected by threshold selection. Extensive experiments in terms of robustness to affine transformations and noise on image matching, and 3D reconstruction tasks demonstrate the superiority of our proposed method over state-of-the-art benchmarks.

The goal of few-shot fine-grained image classification (FSFGIC) is to distinguish subordinate-level categories with subtle visual differences such as the species of bird and models of car with only a few samples. In this work, we argue that a designed network that has the ability to better distinguish feature descriptors of different categories will effectively improve the performance of FSFGIC. We propose a re-abstraction and perturbing support pair network (RaPSPNet) for FSFGIC. Specifically, we first design a feature re-abstraction embedding (FRaE) module which can not only effectively amplify the difference between the feature information from different categories but also better extract the feature information from images. Furthermore, a novel perturbing support pair (PSP) based similarity measure module is designed which evaluates the relationships of feature information among a query image and two different categories of support images (a support pair) at the same time for guiding the designed FRaE module to find salient feature information from the same category of query and support images and find distinguishable feature information from the different categories of query and support images. Extensive experiments on FSFGIC tasks demonstrate the superiority of the proposed methods over state-of-the-art benchmarks.

Document-level biomedical relation extraction refers to extract relationship facts from unstructured biomedical literature. Due to the fact that many relationship facts span multiple sentences and involve complex interactions between entities, it requires models with strong logical reasoning capabilities. However, previous methods have had limitations in terms of logical reasoning, such as lack logical reasoning and the entities merely based on a single granularity. Although other efforts are based on multiple granularity, including mention pairs and entity pairs, the inference mechanism is weak. In this paper, we propose a module to enhance the model’s logical reasoning capabilities from four kinds of granularity (mention, entity, mention pairs, and entity pairs). Specifically, we encode the entities and mentions separately using BiGRU to capture contextual features. Simultaneously, we employ a U-Net network to model entity pairs and mention pairs, and enhance inter-sentence reasoning abilities. Additionally, to support the model’s reasoning capabilities, we introduce topic nodes into the traditional heterogeneous graph to fully extract document information. Experimental results on CDR and GDA datasets in the biomedical field demonstrate the outstanding performance of the proposed model.

Voxel-based methods have achieved state-of-the-art performance for 3D object detection in autonomous driving. However, their significant computational and memory costs pose a challenge for their application to resource-constrained vehicles. One reason for this high resource consumption is the presence of a large number of redundant background points in Lidar point clouds, resulting in spatial redundancy in both 3D voxel and BEV map representations. To address this issue, we propose an adaptive inference framework called Ada3D, which focuses on reducing the spatial redundancy to compress the model’s computational and memory cost. Ada3D adaptively filters the redundant input, guided by a lightweight importance predictor and the unique properties of the Lidar point cloud. Additionally, we maintain the BEV features’ intrinsic sparsity by introducing the Sparsity Preserving Batch Normalization. With Ada3D, we achieve 40% reduction for 3D voxels and decrease the density of 2D BEV feature maps from 100% to 20% without sacrificing accuracy. Ada3D reduces the model computational and memory cost by 5×, and achieves 1.52× / 1.45× end-to-end GPU latency and 1.5× / 4.5× GPU peak memory optimization for the 3D and 2D backbone respectively.

The value that can be extracted from big data greatly motivates users to explore data analytics technologies for better decision making and problem solving in various application domains. Analytical solutions can be expensive due to the demand for large-scale and high-performance computing resources. To provision online big data Analytics-as-a-Service (AaaS) to users in various domains, a general purpose AaaS platform is required to deliver on-demand services at low cost and in an easy to use manner. Our research focuses on proposing efficient and automatic admission control and resource scheduling algorithms for AaaS platforms in cloud environments. In this paper, we propose scalable and automatic admission control and profit optimization resource scheduling algorithms, which effectively admit data analytics requests, dynamically provision resources, and maximize profit for AaaS providers, while satisfying QoS requirements of queries with Service Level Agreement (SLA) guarantees. Moreover, the proposed algorithms enable users to trade-off accuracy for faster response times and less resource costs for query processing on large datasets. We evaluate the algorithm performance by adopting a data splitting method to process smaller data samples as representatives of the original big datasets. We conduct extensive experiments to evaluate the proposed admission control and profit optimization scheduling algorithms. Experimental evaluation shows the algorithms perform significantly better compared to the state-of-the-art algorithms in enhancing profits, reducing resource costs, increasing query admission rates, and decreasing query response times.

The surge of medical and e-commerce applications has generated tremendous amount of data, which brings people to a so-called “Big Data” era. Different from traditional large data sets, the term “Big Data” not only means the large size of data volume but also indicates the high velocity of data generation. However, current data mining and analytical techniques are facing the challenge of dealing with large volume data in a short period of time. This paper explores the efficiency of utilizing the Normal Distribution (ND) method for splitting and processing large volume medical data in cloud environment, which can provide representative information in the split data sets. The ND-based new model consists of two stages. The first stage adopts the ND method for large data sets splitting and processing, which can reduce the volume of data sets. The second stage implements the ND-based model in a cloud computing infrastructure for allocating the split data sets. The experimental results show substantial efficiency gains of the proposed method over the conventional methods without splitting data into small partitions. The ND-based method can generate representative data sets, which can offer efficient solution for large data processing. The split data sets can be processed in parallel in Cloud computing environment.

Discovering optimal data analytics solutions to extract value from data for better and faster decision making is essential for many application domains, especially in the big data era. Big data analytics typically requires a tremendous amount of computational resources to process large data volumes that can be very expensive and time consuming. Our research focuses on providing optimization solutions for Analytics-as-a-Service (AaaS) platforms that automatically and elastically provision cloud resources to execute queries guaranteeing Service Level Agreements (SLAs) across a range of Quality of Service (QoS) requirements. We propose admission control and resource scheduling algorithms for AaaS platforms to maximize profits while providing time-minimized query execution plans to meet user demands and expectations. To enable timely responses as required for many domains, the algorithms utilize data splitting-based query admission and resource scheduling offering parallel processing on the split datasets. Extensive experiments are conducted to evaluate the algorithm performance compared to state-of-the-art optimization algorithms. Experimental results show that our algorithms perform significantly better from a range of perspectives, including increasing query admission rates and creating higher profits, whilst supporting efficient resource configurations that are able to support big data processing demands under tight deadlines.

Exploring optimal big data analytics solutions to benefit various domains in decision making and problem solving becomes an ever-important research area. Big data Analytics-as-a-Service (AaaS) platforms offer online AaaS to various domains in a pay-as-you-go model. Big data analytics incurs expensive costs and takes lengthy processing times due to large-scale computing requirements. To tackle the cost and time challenges for big data analytics, we focus on proposing efficient and automatic resource management algorithms to maximize profits and minimize query times while guaranteeing Service Level Agreements (SLAs) on Quality of Service (QoS) requirements of queries. For query processing constrained by tight deadlines and limited budgets, our proposed algorithms enable data splitting and sampling based resource scheduling for parallel and approximate processing that significantly reduce data processing times and resource costs. We formulate the multi-objective resource scheduling problem to optimize profits for AaaS platforms while guaranteeing SLAs of queries with minimized response times. We design extensive experiments for algorithm performance evaluation, results show our proposed algorithms outperform state-of-the-art algorithms that maximize profits for AaaS platforms while improving admission rates and minimizing response times for queries. The scheduling algorithms support elastic and automatic large-scale resource configurations to minimize resource costs, and deliver timely, cost-effective, and reliable AaaS with SLA guarantees.

Most existing person re-identification (re-id) methods are designed based on the artificial closed-set assumption that the probe and gallery identities are exactly overlapped with a small search pool. This leads to poor scalability in real-world applications where the task is often to re-id a small set of target people (i.e., watch-list) among a large search pool with unknown ID overlap, namely, an open-set deployment setting. In this paper, we firstly propose a new person re-id setting called Watch-List based Open-Set (WLOS) person re-id, which is characterised by the above open-set deployment and a watch-list available at the training stage. Then, we address such a under-studied WLOS problem by formulating a novel Task Dedicated Deep Hashing (TDDH) approach which learning a purpose-specific deep hash model particularly for the given target people in an efficient end-to-end manner. Extensive experiments on three large-scale re-id benchmarks are conducted to demonstrate the advantages and superiority of the TDDH over a wide range of the state-of-the-art hashing and re-id methods under the more realistic open-set setting.

The value that can be extracted from big data greatly motivates organizations to explore data analytics technologies for better decision making and problem solving in a wide range of application domains. Cloud computing greatly eases and benefits big data analytics by offering on-demand and scalable computing infrastructures, platforms, and applications as services. Big data Analytics-as-a-Service (AaaS) platforms aim to deliver data analytics as consumable services in cloud computing environments in a pay as you go model with Service Level Agreement (SLA) guarantees. Resource scheduling for AaaS platforms is significant as big data analytics requires large-scale computing, which can consume huge amounts of resources and incur high resource costs. Our research focuses on proposing automatic and scalable resource scheduling algorithms to maximize the profits for AaaS platforms while delivering AaaS services to users with SLA guarantees on budgets and deadlines to allow timely responses with controllable costs. In this paper, we model and formulate the profit optimization resource scheduling problem and propose an optimization scheduling algorithm that maximizes profits for AaaS platforms and guarantees SLAs for query requests. Experimental evaluations show that the profit optimization scheduling algorithm performs significantly better in cost saving and profit enhancement compared to the state-of-the-art scheduling algorithms.

Data analytics plays a significant role in gaining insight of big data that can benefit in decision making and problem solving for various application domains such as science, engineering, and commerce. Cloud computing is a suitable platform for Big Data Analytic Applications (BDAAs) that can greatly reduce application cost by elastically provisioning resources based on user requirements and in a pay as you go model. BDAAs are typically catered for specific domains and are usually expensive. Moreover, it is difficult to provision resources for BDAAs with fluctuating resource requirements and reduce the resource cost. As a result, BDAAs are mostly used by large enterprises. Therefore, it is necessary to have a general Analytics as a Service (AaaS) platform that can provision BDAAs to users in various domains as consumable services in an easy to use way and at lower price. To support the AaaS platform, our research focuses on efficiently scheduling Cloud resources for BDAAs to satisfy Quality of Service (QoS) requirements of budget and deadline for data analytic requests and maximize profit for the AaaS platform. We propose an admission control and resource scheduling algorithm, which not only satisfies QoS requirements of requests as guaranteed in Service Level Agreements (SLAs), but also increases the profit for AaaS providers by offering a cost-effective resource scheduling solution. We propose the architecture and models for the AaaS platform and conduct experiments to evaluate the proposed algorithm. Results show the efficiency of the algorithm in SLA guarantee, profit enhancement, and cost saving.