Unsupervised image retrieval aims to learn the important visual characteristics without any given level to retrieve the similar images for a given query image. The Convolutional Neural Network (CNN)-based approaches have been extensively exploited with self-supervised contrastive learning for image hashing. However, the existing approaches suffer due to lack of effective utilization of global features by CNNs and biased-ness created by false negative pairs in the contrastive learning. In this paper, we propose a TransClippedCLR model by encoding the global context of an image using Transformer having local context through patch based processing, by generating the hash codes through product quantization and by avoiding the potential false negative pairs through clipped contrastive learning. The proposed model is tested with superior performance for unsupervised image retrieval on benchmark datasets, including CIFAR10, NUS-Wide and Flickr25K, as compared to the recent state-of-the-art deep models. The results using the proposed clipped contrastive learning are greatly improved on all datasets as compared to same backbone network with vanilla contrastive learning.

Graph databases have become a common infrastructure component. Yet existing systems either operate on offline snapshots, provide weak consistency guarantees, or use expensive concurrency control techniques that limit performance. In this paper, we introduce a new distributed graph database, called Weaver, which enables efficient, transactional graph analyses as well as strictly serializable ACID transactions on dynamic graphs. The key insight that allows Weaver to combine strict serializability with horizontal scalability and high performance is a novel request ordering mechanism called refinable timestamps. This technique couples coarse-grained vector timestamps with a fine-grained timeline oracle to pay the overhead of strong consistency only when needed. Experiments show that Weaver enables a Bitcoin blockchain explorer that is 8x faster than Blockchain.info, and achieves 10.9x higher throughput than the Titan graph database on social network workloads and 4x lower latency than GraphLab on offline graph traversal workloads.

This paper proposes a new approach to determining the order of interdependent operations in a distributed system. The key idea behind our approach is to factor the task of tracking happens-before relationships out of components that comprise the system, and to centralize them in a separate event ordering service. This not only simplifies implementation of individual components by freeing them from having to propagate dependence information, but also enables dependence relationships to be maintained across multiple independent systems. A novel API enables the system to detect and take advantage of concurrency whenever possible by maintaining fine-grained information and binding events to a time order as late as possible. We demonstrate the benefits of this approach through several example applications, including a transactional key-value store, and an online graph store. Experiments show that our event ordering service scales well and has low overhead in practice.

Task starvation leads to huge variation in the completion times of the tasks posted on to the crowd. The price offered to a given task together with the dynamics of the crowd at the time of posting affect its completion time. Large organizations/requesters who frequent the crowd at regular intervals in order to get their tasks done desire predictability in completion times of the tasks. Thus, such requesters have to take into account the crowd dynamics at the time of posting the tasks and price them accordingly. In this work, we study an instance of the pricing problem and propose a solution based on the framework of Markov Decision Processes (MDPs).

Mobile browsing habits are characteristically different from browsing on traditional devices. Mobile users often look for information snippets instead of complete web pages. Also, mobile devices are often constrained in terms of resource availability, such as battery, data plan limits and network bandwidth. Under such constraints, partial-loading of a web page, by loading the most relevant content snippets early, can satisfy the user and consume less resources. Mobile content adaptation middleware has traditionally focused on user factors, such as user feedbacks and user context. We believe that the content creator, with complete knowledge of the importance of each item in the web page, is well-suited to guide the adaptation process. Combining user choice, and ratings assigned by the content creator to different web page elements (items), enables delivering the most relevant items in an ordered manner in response to a page request. We present ScoDA, a cooperative content adaptation middleware framework, under resource constraints. We evaluate the effectiveness of page loading using ScoDA, on simulated complex web pages as well as real web pages.