Reviewerly/exHarmony huggingface dataset:
(Short Paper) Analysis of a Strong Fault Attack on Static/Ephemeral CSIDH

CSIDH is an isogeny-based post-quantum key establishment protocol proposed in 2018. In this work we analyze attacking implementations of CSIDH which use dummy isogeny operations using fault injections from a mathematical perspective. We detail an attack (implicit in prior works on implementations of CSIDH) by which a static private key can be learned (up to sign) by the attacker with certainty using \(\sum \lceil \log _2(b_i) + 1 \rceil \) faults using a binary search approach, where \(\boldsymbol{\mathbf {b}}\) is the bound vector defining the keyspace. A natural idea for a countermeasure to this attack is to randomly mix the real degree \(\ell _j\) isogenies together with the dummy ones, so that binary search becomes ineffective. In this work we evaluate the efficacy of this idea as a fault attack countermeasure; in particular, we give bounds (as a function of the bound vector entries) on the number of fault injections (of a particular relatively strong, hypothetical type) required for an attacker to have a given success probability for guessing an unknown key, and present the results of simulated attacks on keys sampled from 6 keyspaces found in the literature. We find that the number of faults required to reach any constant success probability in guessing a static key is quadratic in the bound vector entries, rather than logarithmic as in the “real-then-dummy” setting—concretely, the number of faults required increases from a few hundred to tens of thousands. Broadly, this behaviour is reflected in our simulations.
The 'Prime Factors' of Quantum Cryptography Regulation

Quantum computing exhibits promise to be a revolutionary technology. While the use cases may not replace all tasks performed by classical computers, certain areas of “quantum advantage,” a concept theorized by researchers, will impact everyone, even if in more nuanced ways. As the pace of quantum research accelerates, analysis and regulation of these complicated impacts on society, and a realistic timeline for their application, have become necessary.Likely due to a combination of the esoteric nature of quantum technology and the relatively nascent stage of development, political and legal governance mechanisms have been slow to keep up with innovation and application. However, this policy lag time is not new. Many recent tides of technological change have been met with phases of straggling governance. But in the case of quantum computing, how soon should the United States and the rest of the world begin thinking about the regulatory framework necessary for facilitating innovation and minimizing deleterious consequences? And where should this process begin? This Comment asserts cryptography to be a critical starting point, as it could be a task for limited scope quantum computers with severe consequences. This Comment surveys the risks of quantum computers to encryption and the applicable policy and legal levers to address concerns. As this Comment shows, lawmakers must reassess regulations for the exportation of cryptographic products for the quantum regime. For example, current export regulations focus on bit length–which quantum cryptography directly undermines. Further, current controls prevent dispersion of physical products, while a rapidly growing amount of classical and, particularly quantum, computing now takes place “on the cloud.” Finally, in the context of protective governance, policies will need to be put in place to facilitate post-quantum encryption deployment to critical industries. Beyond the realm of cryptography, many of these recommendations will also apply to other emerging application areas for quantum computing, or even to non-quantum emerging technologies.
CNN-TDNN-Based Architecture for Speech Recognition Using Grapheme Models in Bilingual Czech-Slovak Task

Czech and Slovak languages are very similar, not only in writing but also in phonetic form. This work aims to find a suitable combination of these two languages concerning better recognition results. We would like to show such a contribution on the Malach project. The Malach speech of Holocaust survivors is highly emotional, filled with many disfluencies, heavy accents, age-related coarticulation, and many non-speech events. Due to the nature of the corpus, it is very difficult to find other appropriate data for acoustic modeling, so such a combination can significantly improve the amount of training data. We will discuss the differences between the phoneme and grapheme way of combining Czech with Slovak. We will also compare different architectures of deep neural networks (TDNN, TDNNF, CNN-TDNNF) and tune the optimal topology. The proposed bilingual ASR approach provides a slight improvement over monolingual ASR systems, not only at the phoneme level but also at the grapheme.
Spy the Lie: Fraudulent Jobs Detection in Recruitment Domain using Knowledge Graphs

Fraudulent jobs are an emerging threat over online recruitment platforms such as LinkedIn, Glassdoor. Fraudulent job postings affect the platform's trustworthiness and have a negative impact on user experience. Therefore, these platforms need to detect and remove these fraudulent jobs. Generally, fraudulent job postings contain untenable facts about domain-specific entities such as mismatch in skills, industries, offered compensation, etc. However, existing approaches focus on studying writing styles, linguistics, and context-based features, and ignore the relationships among domain-specific entities. To bridge this gap, we propose an approach based on the Knowledge Graph (KG) of domain-specific entities to detect fraudulent jobs. In this paper, we present a multi-tier novel end-to-end framework called FRaudulent Jobs Detection (FRJD) Engine, which considers a) fact validation module using KGs, b) contextual module using deep neural networks c) meta-data module to capture the semantics of job postings. We conduct our experiments using a fact validation dataset containing 4 million facts extracted from job postings. Extensive evaluation shows that FRJD yields a 0.96 F1-score on the curated dataset of 157,880 job postings. Finally, we provide insights on the performance of different fact-checking algorithms on recruitment domain datasets.
Surrogate Gradient Field for Latent Space Manipulation

Generative adversarial networks (GANs) can generate high-quality images from sampled latent codes. Recent works attempt to edit an image by manipulating its under-lying latent code, but rarely go beyond the basic task of at-tribute adjustment. We propose the first method that enables manipulation with multidimensional condition such as key-points and captions. Specifically, we design an algorithm that searches for a new latent code that satisfies the target condition based on the Surrogate Gradient Field (SGF) induced by an auxiliary mapping network. For quantitative comparison, we propose a metric to evaluate the disentanglement of manipulation methods. Thorough experimental analysis on the facial attribute adjustment task shows that our method outperforms state-of-the-art methods in disentanglement. We further apply our method to tasks of various condition modalities to demonstrate that our method can alter complex image properties such as keypoints and captions.
Initialization for non-negative matrix factorization: a comprehensive review

Non-negative matrix factorization (NMF) has become a popular method for representing meaningful data by extracting a non-negative basis feature from an observed non-negative data matrix. Some of the unique features of this method in identifying hidden data place this method among the powerful methods in the machine learning area. The NMF is a known non-convex optimization problem, and the initial point has a significant effect on finding an efficient local solution. In this paper, we investigate the most popular initialization procedures proposed for NMF so far. We describe each method and present some of their advantages and disadvantages. Finally, some numerical results to illustrate the performance of each algorithm are presented.
On group-characterizability of homomorphic secret sharing schemes

A group-characterizable (GC) random variable is induced by a finite group, called main group, and a collection of its subgroups. The notion extends directly to secret sharing schemes (SSSs). It is known that linear and abelian SSSs can be equivalently described in terms of GC SSSs. In this paper, we present a necessary and sufficient condition for a SSS to be equivalent to a GC one. Using this result, we show that homomorphic SSSs (HSSSs) are equivalent to GC SSSs whose subgroups are normal in the main group. We also present two applications for this equivalent description of HSSSs. One concerns lower bounding the information ratio of access structures for the class of HSSSs, and the other is about the coincidence between statistical, almost-perfect and perfect security notions for the same class.
Visualizing dynamic data with heat triangles

In this paper, an overview-based interactive visualization for temporally long dynamic data sequences is described. To reach this goal, each data object at a certain time point can be mapped to a number value based on a given property. Among others, a property is application-dependent and can be number of vertices, number of edges, average degree, density, number of self-loops, degree (maximum and total), or edge weight (minimum, maximum, and total) for dynamic graph data, but it can as well be the number of ball contacts in a football match, or the time-dependent visual attention paid to a stimulus in an eye tracking study. To achieve an overview over time, an aggregation strategy based on either the mean, minimum, or maximum of two values is applied. This temporal value aggregation generates a triangular shape with an overview of the entire data sequence as the peak. The color coding can be adjusted, forming visual patterns that can be rapidly explored for certain data features over time, supporting comparison tasks between the properties. The usefulness of the approach is illustrated by means of applying it to dynamic graphs generated from US domestic flight data as well as to dynamic Covid-19 infections on country levels.
Japanese Sign Language Recognition based on a Video accompanied by the Finger Images

Sign language is one of the most important tools for hearing impaired people to communicate with each other, and also a good tool to convey messages between hearing impaired and non-impaired people. Most non-impaired people, however, do not know the sign language, and there are many sign languages in the world, which are different depending on countries as with spoken languages. Then, many kinds of research have been performed in the world. Sign language is accompanied by the action using fingers, and the result of our previous work showed that the recognition rate of the words that have the same action with a different number of fingers was low. Therefore, we propose a sign language recognition method based on a video, which is accompanied by the finger images extracted from the overall image. As the result of the experiment using 3D CNN, the average rate of the recognition was 96.9%.
Augmented Lineage: Traceability of Data Analysis Including Complex UDFs

Data lineage allows information to be traced to its origin in data analysis by showing how the results were derived. Although many methods have been proposed to identify the source data from which the analysis results are derived, analysis is becoming increasingly complex both with regard to the target (e.g., images, videos, and texts) and technology (e.g., AI and machine learning). In such complex data analysis, simply showing the source data may not ensure traceability. Analysts often need to know which parts of images are relevant to the output and why the classifier made a decision. Recent studies have intensively investigated interpretability and explainability in the machine learning (ML) domain. Integrating these techniques into the lineage framework will greatly enhance the traceability of complex data analysis, including the basis for decisions. In this paper, we propose the concept of augmented lineage, which is an extended lineage, and an efficient method to derive the augmented lineage for complex data analysis. We express complex data analysis flows using relational operators by combining user defined functions (UDFs). UDFs can represent invocations of AI/ML models within the data analysis. Then we present an algorithm to derive the augmented lineage for arbitrarily chosen tuples among the analysis results. We also experimentally demonstrate the efficiency of the proposed method.
The smallest extraction problem

We introduce landmark grammars , a new family of context-free grammars aimed at describing the HTML source code of pages published by large and templated websites and therefore at effectively tackling Web data extraction problems. Indeed, they address the inherent ambiguity of HTML, one of the main challenges of Web data extraction, which, despite over twenty years of research, has been largely neglected by the approaches presented in literature. We then formalize the Smallest Extraction Problem (SEP), an optimization problem for finding the grammar of a family that best describes a set of pages and contextually extract their data. Finally, we present an unsupervised learning algorithm to induce a landmark grammar from a set of pages sharing a common HTML template, and we present an automatic Web data extraction system. The experiments on consolidated benchmarks show that the approach can substantially contribute to improve the state-of-the-art.
Refined PSO Clustering for Not Well-Separated Data

Cluster analysis is the most popular and often the foremost task in big data analytics as it helps in unearthing hidden patterns and trends in data. Traditional single-objective clustering techniques often suffer from accuracy fluctuations especially when applied over data groups of varying densities and imbalanced distribution as well as in the presence of outliers. This paper presents a multi-phase clustering solution that achieves good accuracy measures even in the case of noisy and not- well-separated data (linearly not separable data). The proposed design combines a two-stage Particle Swarm Optimisation (PSO) clustering with K-means logic and a state-of-the-art outlier removal technique. The use of two different optimisation criteria in the two stages of PSO clustering equips the model with the ability to escape local minima traps in the process of convergence. Extensive experiments featuring a wide variety of data have been carried out and the system could achieve accuracy levels as high as 99.9% and an average of 87.4% on notwell-separated data. The model has also been proved to be robust on eight out of the ten datasets of the Fundamental Clustering Problem Suit (FCPS), a benchmark for clustering algorithms.
A Survey on Secured Data Sharing using Ciphertext Policy Attribute Based Encryption in Cloud

Cloud computing facilitates the access of applications and data from any location by using any device with an internet connection. It enables multiple applications and users to access the same data resources. Cloud based information sharing is a technique that allows researchers to communicate and collaborate, that leads to major new developments in the field. It also enables users to access data over the cloud easily and conveniently. Privacy, authenticity and confidentiality are the three main challenges while sharing data in cloud. There are many methods which support secure data sharing in cloud environment such as Attribute Based Encryption(ABE), Role Based Encryption, Hierarchical Based Encryption, and Identity Based Encryption. ABE provides secure access control mechanisms for integrity. It is classified as Key Policy Attribute Based Encryption(KP-ABE) and Ciphertext Policy Attribute Based Encryption(CP-ABE) based on access policy integration. In KPABE, access structure is incorporated with user’s private key, and data are encrypted over a defined attributes. Moreover, in CPABE, access structure is embedded with ciphertext. This paper reviews CP-ABE methods that have been developed so far for achieving secured data sharing in cloud environment.
Incorporating Concreteness in Multi-Modal Language Models with Curriculum Learning

Over the last few years, there has been an increase in the studies that consider experiential (visual) information by building multi-modal language models and representations. It is shown by several studies that language acquisition in humans starts with learning concrete concepts through images and then continues with learning abstract ideas through the text. In this work, the curriculum learning method is used to teach the model concrete/abstract concepts through images and their corresponding captions to accomplish multi-modal language modeling/representation. We use the BERT and Resnet-152 models on each modality and combine them using attentive pooling to perform pre-training on the newly constructed dataset, which is collected from the Wikimedia Commons based on concrete/abstract words. To show the performance of the proposed model, downstream tasks and ablation studies are performed. The contribution of this work is two-fold: A new dataset is constructed from Wikimedia Commons based on concrete/abstract words, and a new multi-modal pre-training approach based on curriculum learning is proposed. The results show that the proposed multi-modal pre-training approach contributes to the success of the model.
Automated detection of bad borehole sections and evaluation of log quality using Toeplitz Inverse Covariance-based Clustering

PreviousNext No AccessFirst International Meeting for Applied Geoscience & Energy Expanded AbstractsAutomated detection of bad borehole sections and evaluation of log quality using Toeplitz Inverse Covariance-based ClusteringAuthors: Shuvajit BhattacharyaShuvajit BhattacharyaThe University of Texas at AustinSearch for more papers by this authorhttps://doi.org/10.1190/segam2021-3581438.1 SectionsSupplemental MaterialAboutPDF/ePub ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InRedditEmail AbstractThe reliability of estimated reservoir properties depends a lot on the quality of openhole logs used in formation evaluation, which are intricately related to the borehole geometry and stand-offs. Openhole geophysical logs get affected due to bad borehole conditions. The state-of-the-art technology to identify bad log measurements includes a visual inspection of individual wells and applying deterministic cutoff values (i.e., large or small caliper log responses) to flag poor-quality borehole sections. These techniques are human-intensive and time-consuming. Often, the log responses across several wells do not always follow normal distributions due to different drilling bit sizes, log vintages, logging tools, scales, and calibration standards, etc. Well logs are multivariate time series (or depth series), where they share interdependencies to a large extent. To tackle these fundamental challenges, this study shows an example of unsupervised multivariate time series clustering (Toeplitz Inverse Covariance-based Clustering [TICC]) to automatically identify bad borehole sections in many wells in the Wolfcamp Formation in the Midland Basin, United States. The proposed workflow simultaneously clusters the wireline logs, resulting in a large, labeled regional-scale data set that can be further used for log quality assurance and processing.Keywords: artificial intelligence, borehole geophysics, wireline logging, machine learningPermalink: https://doi.org/10.1190/segam2021-3581438.1FiguresReferencesRelatedDetails First International Meeting for Applied Geoscience & Energy Expanded AbstractsISSN (print):1052-3812 ISSN (online):1949-4645Copyright: 2021 Pages: 3561 publication data© 2021 Published in electronic format with permission by the Society of Exploration GeophysicistsPublisher:Society of Exploration Geophysicists HistoryPublished: 01 Sep 2021 CITATION INFORMATION Shuvajit Bhattacharya, (2021), "Automated detection of bad borehole sections and evaluation of log quality using Toeplitz Inverse Covariance-based Clustering," SEG Technical Program Expanded Abstracts : 347-351. https://doi.org/10.1190/segam2021-3581438.1 Plain-Language Summary Keywordsartificial intelligenceborehole geophysicswireline loggingmachine learningPDF DownloadLoading ...
Universal classical-quantum multiple access channel coding

We derive universal classical-quantum superposition coding and universal classical-quantum multiple access channel code by using generalized packing lemmas for the type method. Using our classical-quantum universal superposition code, we establish the capacity region of a classical-quantum compound broadcast channel with degraded message sets. Our universal classical-quantum multiple access channel codes have two types of codes. One is a code with joint decoding and the other is a code with separate decoding. It is not so easy to construct a former code that universally achieves general points of the capacity region beyond corner points. First, we construct the latter code that universally achieves general points of the capacity region. Then, converting the latter code to the former code, we construct the above desired code with the former type. A full version of this paper is accessible at http://arxiv.org/abs/2011.00410
Semi-supervised multi-label feature selection with local logic information preserved

In reality, like single-label data, multi-label data sets have the problem that only some have labels. This is an excellent challenge for multi-label feature selection. This paper combines the logistic regression model with graph regularization and sparse regularization to form a joint framework (SMLFS) for semi-supervised multi-label feature selection. First of all, the regularization of the feature graph is used to explore the geometry structure of the feature, to obtain a better regression coefficient matrix, which reflects the importance of the feature. Second, the label graph regularization is used to extract the available label information, and constrain the regression coefficient matrix, so that the regression coefficient matrix can better fit the label information. Third, the $$L_{2,p}$$ -norm $$0<p\le 1$$ constraint is used to ensure the sparsity of the regression coefficient matrix so that it is more convenient to distinguish the importance of features. In addition, an iterative updating algorithm with convergence is designed and proved to solve the above problems. Finally, the proposed method is validated on eight classic multi-label data sets, and the experimental results show the effectiveness of the proposed algorithm.