Unsupervised Triplet Hashing for Fast Image Retrieval

Hashing has played a pivotal role in large-scale image retrieval. With the development of Convolutional Neural Network (CNN), hashing learning has shown great promise. But existing methods are mostly tuned for classification, which are not optimized for retrieval tasks, especially for instance-level retrieval. In this study, we propose a novel hashing method for large-scale image retrieval. Considering the difficulty in obtaining labeled datasets for image retrieval task in large scale, we propose a novel CNN-based unsupervised hashing method, namely Unsupervised Triplet Hashing (UTH). The unsupervised hashing network is designed under the following three principles: 1) more discriminative representations for image retrieval; 2) minimum quantization loss between the original real-valued feature descriptors and the learned hash codes; 3) maximum information entropy for the learned hash codes. Extensive experiments on CIFAR-10, MNIST and In-shop datasets have shown that UTH outperforms several state-of-the-art unsupervised hashing methods in terms of retrieval accuracy

Towards Provenance Cloud Security Auditing Based on Association Rule Mining

Cloud storage provides external data storage services by combining and coordinating different types of devices in a network to work collectively. However, there is always a trust relationship between users and service providers, therefore, an effective security auditing of cloud data and operational processes is necessary. We propose a trusted cloud framework based on a Cloud Accountability Life Cycle (CALC). We suggest that auditing provenance data in cloud servers is a practical and efficient method to log data, being relatively stable and easy to collect type of provenance data. Furthermore, we suggest a scheme based on user behaviour (UB) by analysing the log data from cloud servers. We present a description of rules for a UB operating system log, and put forward an association rule mining algorithm based on the Long Sequence Frequent Pattern (LSFP) to extract the UB. Finally, the results of our experiment prove that our solution can be implemented to track and forensically inspect the data leakage in an efficient manner for cloud security auditing

Upcoming Transformations in Integrated Energy/Chemicals Sectors: Some Challenges and Several Opportunities

The sociopolitical events over the past few years led to transformative changes in both the energy and chemical sectors. One of the most evident consequences of these events is the significant focus on sustainability. In fact, rather than an engaging discussion within elite social circles, the search for sustainability is now one of the hard requirements investors impose on companies. The concept of sustainability itself has developed since its inception, and now it encompasses environmental as well as socioeconomic aspects. The major players in the energy and chemical sectors seem to embrace these changes and the related challenges; in most cases, tangible ambitious goals have been proposed. For example, bp aims “to become a net zero company by 2050 or sooner, and to help the world get to net zero”. Although tragic events such as the war in Ukraine directly affect global supply chains, leading to some reconsiderations in medium-term industrial and political strategies, trends and public demands seem determined to pursue ambitious sustainable goals, as tangible as the European Union’s “Fit for 55” climate package, approved on May 12, 2022, which effectively bans internal combustion engines for new passenger cars and light commercial vehicles from 2035. These trends will likely lead to profound changes in both the chemical and energy sectors. While some predictions may miss the target, speculating about upcoming challenges and opportunities could help us prepare for the future. This is the purpose of this brief Perspective