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W-triviality of low dimensional manifolds
No full textA space X is W-trivial if for every real vector bundle α over X the total Stiefel-Whitney class w(α) is 1. It follows from a result of Milnor that if X is an orientable closed smooth manifold of dimension 1, 2, 4 or 8, then X is not W-trivial. In this note we completely characterize W-trivial orientable connected closed smooth manifolds in dimensions 3, 5 and 6. In dimension 7, we describe necessary conditions for an orientable connected closed smooth 7-manifold to be W-trivial
Yoyo cryptanalysis on Future
No full textIn ASIACRYPT 2017, Rønjom et al. reported Yoyo tricks on generic rounds of SPNs. Then they applied it to AES and found the most effective way to distinguish AES in several rounds. In FSE 2018, Saha et al. distinguished AES in a known key setting up to 8 rounds. In AFRICACRYPT 2022, Gupta et al. published a block cipher Future, whose design is like AES with some tweaks. In this paper, we analysed Future by Yoyo trick in both secret key settings and known key settings. We show that in the secret key setting, one can distinguish Future upto five and six rounds with data complexity 29.83 and 258.83 respectively. We also demonstrate that with known key settings, one can distinguish Future with data complexity 215 for both six and eight rounds. Our attack is based on an adaptively chosen plaintext/ciphertext attack
A Classification of Attacks in an IDS Using Sparse Convolutional Autoencoder and DNN
No full textThe network security plays an important role in this modern world. After emerging modern technologies like cloud computing, big data, Internet of Things (IOT), Blockchain and so forth, network security set more complex task to firewalls and cyber security department. Network intrusion detection is a software system or a device which helps in monitoring unauthorized access and vulnerabilities in the complex networks. We propose a hybrid model using Sparse Convolution Autoencoder (SCA) along with Deep Neural Network for intrusion detection in the communication network. We applied our model on KDDCup’99 dataset and achieved an accuracy of 99.7%
Can an Image Tell the Tale: Looking beyond the Haze to Determine PM2.5 Concentration
No full textIn the past few decades, due to rapid growth in industrialization, there has been a steady decline of the air quality along with an increase in the concentration of PM2.5. It is well known that a high PM2.5 concentration adversely affects the environment and has hazardous impact on public health. Therefore, it is important to monitor the PM2.5 concentration at geographic locations where air quality monitoring stations are presently unavailable, especially in remote areas. Unfortunately, installation of such monitoring stations requires expensive instruments and constant maintenance. This paper presents a novel, low-cost and portable alternative to such measurement apparatus, where PM2.5 concentration is estimated based on image input obtained from a camera. The novelty of the present work lies in its hitherto unique attempt to capture information regarding PM2.5 content from visibility degradation caused by the pollutant which is further supplemented by important knowledge regarding seasonal and diurnal variation of it. The latter has a crucial role in the prevention of confounding effects arising from the presence of other weather and atmospheric elements. Another important highlight is the use of a full reference image metric as a feature, for which a powerful, dehazing algorithm has been employed. The results obtained are extremely promising, providing a close to accurate estimation of PM2.5 concentration with R2 values far higher than reported in the literature. To summarize, the construction of a unique feature set, together with an appropriate machine learning algorithm, lead to an extremely reliable, stand-alone approach, deployable on a hand-held device such as a mobile and is a very significant contribution indeed of the proposed approach
Criticality-aware Deconfounded Classification of Long-tailed Multi-label 12-lead Electrocardiogram
No full textWe often observe long-tailed distribution in real-world classification problems and consequently, maintaining balanced predictive performance across all the classes is a research challenge. Further, we find, particularly in time series classification tasks like prediction of clinical diseases from physiological signals like Electrocardiogram (ECG), the existence of critically important rare classes and the cost of low sensitivity towards such rare yet critical classes are extremely high not only with higher treatment expenses, but also with higher chances of mortality. We focus on the practical challenge of maximizing the predictive sensitivity of rare yet critically important classes in a long-tailed time series classification with a favourable trade-off towards the aggregate classification performance of the remaining classes. We develop a class criticality-aware inference algorithm by customizing the total direct effect (TDE) in the deconfounded training by incorporating the domain knowledge into the degree of TDE that impacts the decision probabilities by minimizing the confounding variable (momentum in Stochastic Gradient Descent optimizer), which is responsible for the occurrences of high valued classification logits of the head classes. We demonstrate the real-world efficacy through empirical study on practically important Physionet challenge ECG 2020 dataset, which is a multi-label dataset with 27 different cardio-vascular disease classes from 12-lead ECG recordings. From the obtained experimental results with ablation study and state-of-the-art comparison investigation, we clearly observe that our proposed method outperforms the current benchmark algorithm and importantly, it is able to consistently improve the sensitivity metrics while predicting the clinically important yet rare classes
Deep Learning as tool to distinguish words for sharp and round objects in natural languages
No full textSound Symbolism is a well studied psychological phenomena of the relation between sound and meaning in natural language. Though the phenomena has been studied by psychologists and linguists, the phenomena has not been put to use natural language processing or modeled by machine learning. In this work we select words for round and sharp objects from various natural languages. We attempted to see if a machine learning algorithm could perform better than Chance in distinguishing words for round and sharp objects in natural languages. We performed a psychophysics experiment to see if human subjects will associate words for sharp objects with a round object and round object with sharp figure. We show that human subjects are more likely than chance to associate words for sharp objects with sharp figure and vice versa. We propose that the algorithms can be improved by using training sets consisting of words whose sound symbolic properties are labelled by psychophysics experiments
Designing Full-Rate Sponge Based AEAD Modes
No full textSponge based constructions have gained significant popularity for designing lightweight authenticated encryption modes. Most of the authenticated ciphers following the Sponge paradigm can be viewed as variations of the Transform-then-permute construction. It is known that a construction following the Transform-then-permute paradigm provides security against any adversary having data complexity D and time complexity T as long as DT≪2b-r. Here, b represents the size of the underlying permutation, while r pertains to the rate at which the message is injected. The above result demonstrates that an increase in the rate leads to a degradation in the security of the constructions, with no security guaranteed to constructions operating at the full rate, where r=b. This present study delves into the exploration of whether adding some auxiliary states could potentially improve the security of the Transform-then-permute construction. Our investigation yields an affirmative response, demonstrating that a special class of full rate Transform-then-permute with additional states, dubbed frTtP+, can indeed attain security when operated under a suitable feedback function and properly initialized additional state. To be precise, we prove that frTtP+ provides security as long as D≪2s/2 and T≪2s, where s denotes the size of the auxiliary state in terms of bits. To demonstrate the applicability of this result, we show that the construction ORANGE-ZESTmod belongs to this class, thereby obtaining the desired security. In addition, we propose a family of full rate Transform-then-permute construction with Beetle like feedback function, dubbed fr-Beetle, which also achieves the same level of security
Enhancing Martian Mineral Identification Using an Artificial Neural Network With Extracted Spectral Features In CRISM MTRDR Data
No full textCreating a supervised learning model for mineral identification is challenging due to the lack of ground-truth data. This study utilizes a method from existing literature that generates a training dataset by augmenting available spectra in the MICA spectral library. However, rather than using entire spectra for identification, this study extracts spectral features from each spectrum for model training. It employs the apparent continuum removal method, Segmented Curve Fitting, to identify the most informative or distinguishable parts in the spectral domain. Spectral features are then extracted based on band-centers and band-areas for each selected part. The model is evaluated against a Targeted Reduced Data Record (TRDR) dataset obtained using a hierarchical Bayesian model, demonstrating improved identification performance than the existing supervised models. Finally, using this model, dominant minerals are identified in MTRDR data from the Nilli Fossae region of Mars, and a corresponding mineral map is presented
Fault Testing in AI-Accelerators: A Review
No full textWith the emergence of all-inclusive AI/ML applications, hardware solutions, commonly known as AI-Accelerators (AIA), are now being widely adopted to emulate deep neural networks (DNN) to facilitate faster and large-scale data analytics. An AIA-chip comprises a 2D systolic array of identical processing units (PEs), registers, and glue logic. These arrays may be implemented with traditional digital logic or with analog primitives such as memristors. As the packing density of AIA-chips increases, they become vulnerable to various manufacturing defects thereby compromising yield and the accuracy of prediction. In this review article, we summarize various methods that have been recently proposed for expediting Automatic Test-Pattern Generation (ATPG) for stuck-at and transition faults in AIA-arrays. Other relevant issues such as fault-criticality, self-test, fault-recovery, and the asymmetry of fault behavior, are also discussed
Grover on Chosen IV Related Key Attack Against GRAIN-128a
No full textIn this paper, we present a chosen IV related key attack on Grain-128a, that exploits Grover’s algorithm as a tool. Earlier a classical version of such a chosen IV related key attack was considered by Banik et al. in ACISP 2013. They showed that using around γ·232 related keys (where γ is an experimentally determined constant and is estimated as 28), and γ·264 chosen IVs one can mount the attack in the classical domain. This is because for each related key on an average 232 chosen IVs need to be examined. Thus, the query complexity becomes O(232·232), i.e., O(264). Contrary to this, thanks to the quantum paradigm, we use the superposition of all these 264 queries at a time and feed them to the oracle. As a result, we could manage to decrease the complexity of the related key search to the order of 216, consequently reducing the number of required IVs to 232 through the exploitation of the Grover search algorithm. Simulation of the attack against a reduced version of Grain-128a like cipher in the IBMQ simulator has also been presented as proof of the concept. Resource estimation for hardware implementation of the attack is presented and analyzed under NIST MAXDEPTH limit