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A New Algorithm for Computing Branch Number of Non-Singular Matrices Over Finite Fields
No full textThe notion of branch number of a linear transformation is crucial for both linear and differential cryptanalysis. The number of non-zero elements in a state difference or linear mask directly correlates with the active S-Boxes. The differential or linear branch number indicates the minimum number of active S-Boxes in two consecutive rounds of an SPN cipher, specifically for differential or linear cryptanalysis, respectively. This paper presents a new algorithm for computing the branch number of non-singular matrices over finite fields. The algorithm is based on the existing classical method but demonstrates improved computational complexity compared to its predecessor. We conduct a comparative study of the proposed algorithm and the classical approach, providing an analytical estimation of the algorithm’s complexity. Our analysis reveals that the computational complexity of our algorithm is the square root of that of the classical approach
Be Informed of the Known to Catch the Unknown
No full textMany real-world applications are perturbed by the misprediction of the unknown instances into the known or seen domain. The issue is more compounded when we have to recognize the unknowns as well as correctly classify the knowns in a mixed bag of known and unknown instances. In this article, we present a scheme that can efficiently classify instances from the seen classes and can also detect instances coming from unseen (unknown) classes. We have integrated the principles of reverse nearest neighborhood and the principles of intuitionistic fuzzy sets for this purpose. Reverse nearest neighborhood provides a natural and elegant way of tackling the issue of unknown class without incommoding the known class classifications. Further, we incorporate intuitionistic fuzzy sets to infer the unknown class memberships of the instances from the reverse nearest neighbor information of the known classes. Empirical evidence on five real-world datasets indicates the improved efficaciousness of the proposed method over six state-of-the-art competing methods
BRI3L: A BRIGHTNESS ILLUSION IMAGE DATASET FOR IDENTIFICATION AND LOCALIZATION OF REGIONS OF ILLUSORY PERCEPTION
No full textVisual illusions play a significant role in understanding visual perception. Current methods in understanding and evaluating visual illusions are mostly deterministic filtering based approach and they evaluate on a handful of visual illusions, and the conclusions therefore, are not generic. To this end, we generate a large-scale dataset of 22,366 images (BRI3L: BRightness Illusion Image dataset for Identification and Localization of illusory perception) of the five types of brightness illusions and benchmark the dataset using data-driven neural network based approaches. The dataset contains label information - (1) whether a particular image is illusory/non-illusory, (2) the segmentation mask of the illusory region of the image. Hence, both the classification and segmentation task can be evaluated using this dataset. We follow the standard psychophysical experiments involving human subjects to validate the dataset. To the best of our knowledge, this is the first attempt to develop a dataset of visual illusions and benchmark using data-driven approach for illusion classification and localization. We consider five well-studied types of brightness illusions: 1) Hermann grid, 2) Simultaneous Brightness Contrast, 3) White illusion, 4) Grid illusion, and 5) Induced Grating illusion. Benchmarking on the dataset achieves 99.56% accuracy in illusion identification and 84.37% pixel accuracy in illusion localization. The application of deep learning model, it is shown, also generalizes over unseen brightness illusions like brightness assimilation to contrast transitions. We also test the ability of state-of-the-art diffusion models to generate brightness illusions. We have provided all the code, dataset, instructions etc in the github repo: https://github.com/aniket004/BRI3L
Concrete Time/Memory Trade-Offs in Generalised Stern’s ISD Algorithm
No full textThe first contribution of this work is a generalisation of Stern’s information set decoding (ISD) algorithm. Stern’s algorithm, a variant of Stern’s algorithm due to Dumer, as well as a recent generalisation of Stern’s algorithm due to Bernstein and Chou are obtained as special cases of our generalisation. Our second contribution is to introduce the notion of a set of effective time/memory trade-off (TMTO) points for any ISD algorithm for given ranges of values of parameters of the algorithm. Such a set succinctly and uniquely captures the entire landscape of TMTO points with only a minor loss in precision. We further describe a method to compute a set of effective TMTO points. As an application, we compute sets of effective TMTO points for the five variants of the Classic McEliece cryptosystem corresponding to the new algorithm as well as for Stern’s, Dumer’s and Bernstein and Chou’s algorithms. The results show that while Dumer’s and Bernstein and Chou’s algorithms do not provide any interesting TMTO points beyond what is achieved by Stern’s algorithm, the new generalisation that we propose provide about twice the number of effective TMTO points that is obtained from Stern’s algorithm. Consequences of the obtained TMTO points to the classification of the variants of Classic McEliece in appropriate NIST categories are discussed
Harnessing the Power of Multi-Lingual Datasets for Pre-training: Towards Enhancing Text Spotting Performance
No full textThe adaptation capability to a wide range of domains is crucial for scene text spotting models when deployed to real-world conditions. However, existing SOTA approaches usually incorporate scene text detection and recognition simply by pretraining on natural scene text datasets, which do not directly exploit the intermediate feature representations between multiple domains. Here, we investigate the problem of domain-adaptive scene text spotting, i.e., training a model on multi-domain source data such that it can directly adapt to target domains rather than being specialized for a specific domain or scenario. Further, we investigate a transformer baseline called Swin-TESTR to focus on solving scene-text spotting for both regular and arbitraryshaped text along with an exhaustive evaluation. The results demonstrate the potential of intermediate representations to gain significant performance on text spotting benchmarks across multiple domains (e.g. language, synth-to-real, and documents). both in terms of accuracy and efficiency
Image as a Language: Revisiting Scene Text Recognition via Balanced, Unified and Synchronized Vision-Language Reasoning Network
No full textScene text recognition is inherently a vision-language task. However, previous works have predominantly focused either on extracting more robust visual features or designing better language modeling. How to effectively and jointly model vision and language to mitigate heavy reliance on a single modality remains a problem. In this paper, aiming to enhance vision-language reasoning in scene text recognition, we present a balanced, unified and synchronized vision-language reasoning network (BUSNet). Firstly, revisiting the image as a language by balanced concatenation along length dimension alleviates the issue of over-reliance on vision or language. Secondly, BUSNet learns an ensemble of unified external and internal vision-language model with shared weight by masked modality modeling (MMM). Thirdly, a novel vision-language reasoning module (VLRM) with synchronized vision-language decoding capacity is proposed. Additionally, BUSNet achieves improved performance through iterative reasoning, which utilizes the vision-language prediction as a new language input. Extensive experiments indicate that BUSNet achieves state-of-the-art performance on several mainstream benchmark datasets and more challenge datasets for both synthetic and real training data compared to recent outstanding methods. Code and dataset will be available at https://github.com/jjwei66/BUSNet
Current Aspects of Additive Manufacturing in the Aerospace Industry
No full textAdditive manufacturing (AM) is a layer-by-layer process of manufacturing that enables a product to be generated using a simple 3D model or 3D scanner. Additive manufacturing technology is utilized in automotive, aerospace, medical, and manufacturing industries due to its diversity in part size, material composition, and production time. The aircraft industry was one of the early users of AM prototyping technology, which is now utilized to make both final and replacement parts. Furthermore, thin-walled aircraft engine components, complicated geometries, and material processing problems are propelling the aviation industry’s use of AM. It is poised to revolutionize the aerospace industry’s production of intricate, lightweight, and virtually waste-free components. This chapter examines current aspects and development efforts in the aerospace industries in the field of AM and provides a literature review. From the literature, AM technology has shown enhanced printing speed due to multiple nozzle applications. Significant output characteristics of products such as ultimate tensile strength, surface finish, and skewness has improved for different alloys in liquid- and powder-based AM. Titanium powder-based alloys AM has shown difficulties due to high temperature characteristics. Researchers are currently working on the possibilities of AM-attached subtractive manufacturing process
Fuzzy Clustering for Streaming Environment with Explainable Parameter Determination
No full textThe importance of real-time analysis is growing, and the explainability of learning about the environment derived from data is also an important trait for understanding and application. Due to the dynamic, continuous, and drifting nature of streaming data, every learning model should be capable of discerning the properties of the environment in real-time with adaptability. Due to the concept drift and the emergence of new concepts that are very common in a streaming environment, fuzzy clustering will be advantageous for consideration, as it provides the feature where each data point will have a dedicated member for each cluster, and due to the drifting nature of the environment, this membership will play a critical role in making the clustering algorithm adaptive. The proposed algorithm determines the number of clusters and the fuzzifier value depending on the data entropy. The membership of each data point changes as concepts drift, and the algorithm detects this; similarly, as new clusters emerge, it detects new clusters and determines the membership of each data point. The algorithm’s novelty is the detection of several clusters through learning with data and adaptability to the environment. The experimental analysis demonstrates the proposed method’s efficacy on real-world and benchmark synthetic data
Population Dynamics of Gamma Proteobacteria: Critical Analysis during Different Phases of Composting
No full textThe fourth chapter presents analytical data regarding the population dynamics of ?- Proteobacteria during different phases of composting using flow cytometry. Studies have shown that members of phylum Proteobacteria predominate the process of composting. Composting refers to the process of converting organic wastes into nutrient-rich soil through natural decomposition. Waste valorization is the process of reusing, recycling or composting waste materials and converting them into more useful products including materials, chemicals, fuels or other sources of energy. In the present study, a composting pile was set up with a mixture of vegetable waste, rice straw and cow dung (ratio-5:1:02). Samples were collected from three different phases of composting: initial phase, thermophilic phase and cooling or maturation phase. Total cell count and ?- Proteobacteria population were enumerated by flow cytometry. Results showed the population of ?- Proteobacteria decreased during the thermophilic stage. A strong negative correlation was observed between temperature and percentage of ?- Proteobacteria population, indicating that members of this subgroup are essentially mesophiles. The case study portrays the data through detailed confocal micrographs and cytograms. The chapter focuses on addressing SDG 2 (Zero Hunger), SDG 3 (Good Health and Wellbeing), SDG 7 (Affordable and Clean Energy), SDG 9 (Industry, Innovation, and Infrastructure), SDG 12 (Responsible Consumption and Production), and SDG 15 (Life on Land)
Design and Analysis of Some Symmetric Key Schemes for Encryption and Authentication
No full textThis thesis mainly focuses on the design and analysis of tweakable enciphering schemes (TESs) and message authentication codes (MACs). Tweakable enciphering schemes are length preserving encryption schemes that provide security of a strong tweakable pseudorandom permutation. There are several constructions of TES using block ciphers as the main cryptographic primitive. Recently, public random permutations have been widely considered as a replacement for block ciphers in several cryptographic schemes, including Authenticated Encryption (AE) schemes, MACs, etc. However, to the best of our knowledge, a systematic study of constructing TESs using public random permutations is missing. We fill this gap by constructing TES using public permutations. We propose two main constructions with several variants. The basic construction, which we call ppTES is generically constructed using a public random permutation, a length expanding pseudorandom function (PRF) based on public random permutations and an almost xor-universal and almost-regular (AXUAR) hash function. We show a concrete instantiation of ppTES and prove its security using the H-Coefficient technique. ppTES requires both forward and inverse calls to the public random permutation. Most public random permutations are designed with the goal of making the forward calls extremely fast. Thus, a TES construction that does not need computing the inverse of a permutation will have better efficiency. This fact leads us to design a TES that uses a public permutation but does not require the inverse calls to the permutation. We call this construction as IpTES. In addition to a public permutation, IpTES uses an AXUAR hash function. To ensure the inverse free property, we suitably use a two-round Feistel structure. We prove that IpTES is a birthday bound secure public permutation based TES. The rest of the work is on MACs. TrCBC is a variant of the famous CBC MAC which was proposed by Zhang et al. in 2012. It was claimed that TrCBC is a secure MAC with significant efficiency advantages over other secure variants of CBC. The authors also mentioned the only disadvantage of TrCBC to be the fact that it produces shorter tags; in particular, it was claimed that TrCBC can only produce secure tags of length less than n=2, where n is the block length of the underlying block cipher. We mount a concrete practical attack on TrCBC. We show that with high probability, an adversary can forge TrCBC with tag length n=2 �� 1 with just three queries. We discuss some general scenarios of our concrete attack and also do a detailed analysis of the authors’ security claims of TrCBC. Next, we study variable output length pseudorandom functions and their use in constructing secure MACs, which can produce tags of varying lengths using the same key. In this regard, we propose a generic construction of converting a fixed output length PRF to a variable output length PRF and discuss its utility in constructing MACs. We also propose some modifications to the famous block cipher based MAC called PMAC to equip it to produce tags of varying lengths. Finally, we do an extensive study of a newly proposed MAC, 2k-LightMAC_Plus. 2k-LightMAC_Plus was proposed by Datta et al. in FSE 2018, where the author proved that the scheme provides 2n=3 bits of security. We improve this bound and show that 2k-LightMAC_Plus provably achieves 3n=4 bit security. We also exhibit a matching attack on the construction and hence establish that our bound is tight. Our proof uses several components of Mirror Theory