Data-driven Approximation of Distributionally Robust Chance Constraints using Bayesian Credible Intervals

The non-convexity and intractability of distributionally robust chance constraints make them challenging to cope with. From a data-driven perspective, we propose formulating it as a robust optimization problem to ensure that the distributionally robust chance constraint is satisfied with high probability. To incorporate available data and prior distribution knowledge, we construct ambiguity sets for the distributionally robust chance constraint using Bayesian credible intervals. We establish the congruent relationship between the ambiguity set in Bayesian distributionally robust chance constraints and the uncertainty set in a specific robust optimization. In contrast to most existent uncertainty set construction methods which are only applicable for particular settings, our approach provides a unified framework for constructing uncertainty sets under different marginal distribution assumptions, thus making it more flexible and widely applicable. Additionally, under the concavity assumption, our method provides strong finite sample probability guarantees for optimal solutions. The practicality and effectiveness of our approach are illustrated with numerical experiments on portfolio management and queuing system problems. Overall, our approach offers a promising solution to distributionally robust chance constrained problems and has potential applications in other fields

Improving Matsui\u27s Search Algorithm for the Best Differential/Linear Trails and its Applications for DES, DESL and GIFT

Automatic search methods have been widely used for cryptanalysis of block ciphers, especially for the most classic cryptanalysis methods -- differential and linear cryptanalysis. However, the automatic search methods, no matter based on MILP, SMT/SAT or CP techniques, can be inefficient when the search space is too large. In this paper, we improve Matsui\u27s branch-and-bound search algorithm which is known as the first generic algorithm for finding the best differential and linear trails by proposing three new methods. The three methods, named Reconstructing DDT and LAT According to Weight, Executing Linear Layer Operations in Minimal Cost and Merging Two 4-bit S-boxes into One 8-bit S-box respectively, can efficiently speed up the search process by reducing the search space as much as possible and reducing the cost of executing linear layer operations. We apply our improved algorithm to DESL and GIFT, which are still the hard instances for the automatic search methods. As a result, we find the best differential trails for DESL (up to 14 rounds) and GIFT-128 (up to 19 rounds). The best linear trails for DESL (up to 16 rounds), GIFT-128 (up to 10 rounds) and GIFT-64 (up to 15 rounds) are also found. To the best of our knowledge, these security bounds for DESL and GIFT under single-key scenario are given for the first time. Meanwhile, it is the longest exploitable (differential or linear) trails for DESL and GIFT. Furthermore, benefiting from the efficiency of the improved algorithm, we do experiments to demonstrate that the clustering effect of differential trails for 13-round DES and DESL are both weak

Target recognitions in multiple camera CCTV using colour constancy

People tracking using colour feature in crowded scene through CCTV network have been a popular and at the same time a very difficult topic in computer vision. It is mainly because of the difficulty for the acquisition of intrinsic signatures of targets from a single view of the scene. Many factors, such as variable illumination conditions and viewing angles, will induce illusive modification of intrinsic signatures of targets. The objective of this paper is to verify if colour constancy (CC) approach really helps people tracking in CCTV network system. We have testified a number of CC algorithms together with various colour descriptors, to assess the efficiencies of people recognitions from real multi-camera i-LIDS data set via Receiver Operating Characteristics (ROC). It is found that when CC is applied together with some form of colour restoration mechanisms such as colour transfer, the recognition performance can be improved by at least a factor of two. An elementary luminance based CC coupled with a pixel based colour transfer algorithm, together with experimental results are reported in the present paper

Social Commonsense-Guided Search Query Generation for Open-Domain Knowledge-Powered Conversations

Open-domain dialog involves generating search queries that help obtain relevant knowledge for holding informative conversations. However, it can be challenging to determine what information to retrieve when the user is passive and does not express a clear need or request. To tackle this issue, we present a novel approach that focuses on generating internet search queries that are guided by social commonsense. Specifically, we leverage a commonsense dialog system to establish connections related to the conversation topic, which subsequently guides our query generation. Our proposed framework addresses passive user interactions by integrating topic tracking, commonsense response generation and instruction-driven query generation. Through extensive evaluations, we show that our approach overcomes limitations of existing query generation techniques that rely solely on explicit dialog information, and produces search queries that are more relevant, specific, and compelling, ultimately resulting in more engaging responses.Comment: Accepted in EMNLP 2023 Finding

Improved (Related-key) Differential Cryptanalysis on GIFT

In this paper, we reevaluate the security of GIFT against differential cryptanalysis under both single-key scenario and related-key scenario. Firstly, we apply Matsui\u27s algorithm to search related-key differential trails of GIFT. We add three constraints to limit the search space and search the optimal related-key differential trails on the limited search space. We obtain related-key differential trails of GIFT-64/128 for up to 15/14 rounds, which are the best results on related-key differential trails of GIFT so far. Secondly, we propose an automatic algorithm to increase the probability of the related-key boomerang distinguisher of GIFT by searching the clustering of the related-key differential trails utilized in the boomerang distinguisher. We find a 20-round related-key boomerang distinguisher of GIFT-64 with probability 2^-58.557. The 25-round related-key rectangle attack on GIFT-64 is constructed based on it. This is the longest attack on GIFT-64. We also find a 19-round related-key boomerang distinguisher of GIFT-128 with probability 2^-109.626. We propose a 23-round related-key rectangle attack on GIFT-128 utilizing the 19-round distinguisher, which is the longest related-key attack on GIFT-128. The 24-round related-key rectangle attack on GIFT-64 and 22-round related-key boomerang attack on GIFT-128 are also presented. Thirdly, we search the clustering of the single-key differential trails. We increase the probability of a 20-round single-key differential distinguisher of GIFT-128 from 2^-121.415 to 2^-120.245. The time complexity of the 26-round differential attack on GIFT-128 is improved from 2^124:415 to 2^123:245

An Automatic Search Tool for Iterative Trails and its Application to estimation of differentials and linear hulls

The design and cryptanalysis are the both sides from which we look at symmetric-key primitives. If a symmetric-key primitive is broken by a kind of cryptanalysis, it\u27s definitely insecure. If a designer claims a symmetric-key primitive to be secure, one should demonstrate that the primitive resists against all known attacks. Differential and linear cryptanalysis are two of the most important kinds of cryptanalysis. To conduct a successful differential (linear) cryptanalysis, a differential (linear) distinguisher with significant differential probability (linear correlation) is needed. We observe that, for some lightweight symmetric-key primitives, their significant trails usually contain iterative trails. In this work, We propose an automatic tool for searching iterative trails. We model the problem of searching itrative trails as a problem of finding elementry ciucuits in a graph. Based on the iterative trails found, we further propose a method to estimate the probability (correlation) of a differential (linear hull). We apply our methods to the 256-bit KNOT permutation, PRESENT, GIFT-64 and RECTANGLE. Iterative trails are found and visualized. If iterative trails are found, we show our method can efficiently find good differentials and linear hulls. What\u27s more, the results imply that for the primitives we test with bit permutations as their linear layers, the good differentials and linear hulls are dominated by iterative trails

AutoShot: A Short Video Dataset and State-of-the-Art Shot Boundary Detection

The short-form videos have explosive popularity and have dominated the new social media trends. Prevailing short-video platforms,~\textit{e.g.}, Kuaishou (Kwai), TikTok, Instagram Reels, and YouTube Shorts, have changed the way we consume and create content. For video content creation and understanding, the shot boundary detection (SBD) is one of the most essential components in various scenarios. In this work, we release a new public Short video sHot bOundary deTection dataset, named SHOT, consisting of 853 complete short videos and 11,606 shot annotations, with 2,716 high quality shot boundary annotations in 200 test videos. Leveraging this new data wealth, we propose to optimize the model design for video SBD, by conducting neural architecture search in a search space encapsulating various advanced 3D ConvNets and Transformers. Our proposed approach, named AutoShot, achieves higher F1 scores than previous state-of-the-art approaches, e.g., outperforming TransNetV2 by 4.2%, when being derived and evaluated on our newly constructed SHOT dataset. Moreover, to validate the generalizability of the AutoShot architecture, we directly evaluate it on another three public datasets: ClipShots, BBC and RAI, and the F1 scores of AutoShot outperform previous state-of-the-art approaches by 1.1%, 0.9% and 1.2%, respectively. The SHOT dataset and code can be found in https://github.com/wentaozhu/AutoShot.git .Comment: 10 pages, 5 figures, 3 tables, in CVPR 2023; Top-1 solution for scene / shot boundary detection https://paperswithcode.com/paper/autoshot-a-short-video-dataset-and-state-o