Reviewerly/exHarmony huggingface dataset:
Time-frequency sparse Gabor transform for detecting microseismic events

PreviousNext No AccessFirst International Meeting for Applied Geoscience & Energy Expanded AbstractsTime-frequency sparse Gabor transform for detecting microseismic eventsAuthors: Hanh BuiMauricio D. SacchiMirko van der BaanHanh BuiUniversity of AlbertaSearch for more papers by this author, Mauricio D. SacchiUniversity of AlbertaSearch for more papers by this author, and Mirko van der BaanUniversity of AlbertaSearch for more papers by this authorhttps://doi.org/10.1190/segam2021-3583114.1 SectionsAboutPDF/ePub ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InRedditEmail AbstractEvent detection is a challenging and time-consuming step in microseismic processing because microseismic signals usually have low amplitudes and are often embedded in high-amplitude noise. This study proposes a fast, automatic, and promising method to detect microseismic arrivals using a time-frequency sparse Gabor transform. First, we perform time-frequency denoising based on an automatic noise-level estimation and a neighboring block thresholding technique. Then, we estimate the spectrum of the denoised data using the Gabor transform via a sparsity constrained inversion scheme. Microseismic events are detected based on a characteristic function calculated from the resulting Gabor spectrum. This study uses a robust sparse inversion to obtain high-resolution Gabor spectra and automatically detected events based on abrupt changes in the energy distribution in the time-frequency representation. We test the algorithm on a real microseismic data set in a Montney reservoir. The results show that the proposed method can automatically detect potential microseismic events with an increased precision rate and works well with noisy data.Note: This paper was accepted into the Technical Program but was not presented at IMAGE 2021 in Denver, Colorado.Keywords: microseismic, processing, inversion, algorithm, effectivePermalink: https://doi.org/10.1190/segam2021-3583114.1FiguresReferencesRelatedDetails 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 Hanh Bui, Mauricio D. Sacchi, and Mirko van der Baan, (2021), "Time-frequency sparse Gabor transform for detecting microseismic events," SEG Technical Program Expanded Abstracts : 2016-2020. https://doi.org/10.1190/segam2021-3583114.1 Plain-Language Summary KeywordsmicroseismicprocessinginversionalgorithmeffectivePDF DownloadLoading ...
A Needs-Based Augmented Reality System

Augmented Reality aims to enhance the real world with computer-generated information. AR technology is both attractive and promising. Current AR experiences depend on external elements to launch, such as markers, images, and location. For an AR experience to be more personalized, this research proposes a scheme to trigger AR experiences based on human needs. This approach should enable human needs to be captured, and analyze them to select the most suited experiences that fulfill or aids in fulfilling needs. The contribution of this paper includes (1) a study of current AR technologies and triggers, (2) an analysis of human needs into measurable elements, and (3) a description of a needs-based AR application process with a demonstration of the process guidelines. The research presents a proof of concept prototype of a restaurant that satisfies the subsistence need for hunger. The results show the effectiveness of the guidelines in detecting human needs and recommending AR experiences; however, producing correct predictions and recommendations requires a well-established dataset.
A new Modified method of Cryptography using Caesar Cipher

Nowadays the modern world depends on connections through the internet brings different teams together. The essential components of communication are sender, medium, and receiver. Data transmission over the internet is not possible without any encryption method due to security issues. Different types of areas like corporate sectors, banking sectors, government sectors, and many other sectors share their data through the internet. Hackers always try to attack the transmitted data and try to recover the data. Various techniques are developed for providing data security. Cryptography is used for the safe transmission of data. Encryption is done at the sender side in cryptography, and decryption is done at the receiver side. In the encryption technique, Caesar cipher is one of the best examples. The analysis of Basic Caesar cipher, Delta formation Caesar cipher, and XOR Caesar cipher is done based on many parameters like Avalanche Effect, Frequency Test, and Brute force attack. The authors of this paper have tried to modify the caesar cipher method, which produces ciphertext that can be read. After that with the new ciphertext that can be read, then cryptanalysis not suspicious of the ciphertext.
Adaptive oblivious transfer with access control from lattice assumptions

Adaptive oblivious transfer (OT) is a protocol where a sender initially commits to a database {Mi}i=1N. Then, a receiver can query the sender up to k times with private indexes ρ1,…,ρk so as to obtain Mρ1,…,Mρk and nothing else. Moreover, for each i∈[k], the receiver's choice ρi may depend on previously obtained messages {Mρj}j<i. Oblivious transfer with access control (OT-AC) is a flavor of adaptive OT where database records are protected by distinct access control policies that specify which credentials a receiver should obtain in order to access each Mi. So far, all known OT-AC protocols only support access policies made of conjunctions or rely on ad hoc assumptions in pairing-friendly groups (or both). In this paper, we provide an OT-AC protocol where access policies may consist of any branching program of polynomial length, which is sufficient to realize any access policy in NC1. The security of our protocol is proved under the Learning-with-Errors (LWE) and Short-Integer-Solution (SIS) assumptions. As a result of independent interest, we provide protocols for proving the correct evaluation of a committed branching program on a committed input.
Eye Accommodation, Near Vision, and Far Vision from a Geometrical Optics Point of View

The eye should have a particular focal length in order to see an object. As there are many things to see, the focal length of the eye must vary because these things are not all at the same distance from the eye. We explore the eye accommodation from a geometrical optics point of view and present our results in this paper; the study includes a description of the accommodation mechanism and a scientific definition of near vision and far vision.
Just move it!

Parameter servers (PSs) ease the implementation of distributed machine learning systems, but their performance can fall behind that of single machine baselines due to communication overhead. We demonstrate Lapse, an open source PS with dynamic parameter allocation . Previous work has shown that dynamic parameter allocation can improve PS performance by up to two orders of magnitude and lead to near-linear speed-ups over single machine baselines. This demonstration illustrates how Lapse is used and why it can provide order-of-magnitude speed-ups over other PSs. To do so, this demonstration interactively analyzes and visualizes how dynamic parameter allocation looks like in action.
Research on automatic evaluation algorithm of subjective questions based on vector space model and synonym lexical forest

Word2vec word vector algorithm and Doc2vec text vectoring algorithm calculate the similarity between words and between texts from the perspective of vector space without considering the semantic similarity between texts. Thus, in this paper, an automatic evaluation algorithm is proposed to combine semantic similarity based on synonym lexical forest and text similarity based on vector space. Meanwhile, Sentiment analysis was used to obtain the emotional score of the reference answers and students' answers in the subjective questions. Finally, the final score of students' answers was obtained by combining text similarity and emotion scores. In this paper, logistics professional education is the experimental object of the experiment. The experimental results show that the algorithm has achieved good scoring results.
College English Paradigm in the Ubiquitous Learning Era: A Literature Review

Ubiquitous learning (u-learning) is an expansion of e-learning and mobile-assisted learning (m-learning). This paper is aimed to provide a review of the ubiquitous teaching paradigm within the specific area of College English as a foreign language. This article discusses, analyses and summarizes the basic concepts, theoretical basis, research approaches and points out the tendency of College English paradigm in the u-leaning era. The findings show that China’s College English learning has entered into the u-learning era with big data and AI+ as the two main features. The future researches are recommended to focus on the effectiveness of smart learning mode based on ecological theory.
Challenges in Streaming Data Analysis for Building an Adaptive Model for Handling Concept Drifts

Incremental learning from non-stationary environments with evolving streaming data is one of the main challenges in streaming data analysis (SDA). The advances in data acquisition technology have ignited the importance of data analysis on streaming data. Analysis on streaming data has to be performed with constrained memory and single pass over the data. The main challenges are in window adjustments, various drift detections, feature selection, dynamic model selection, adaptability of models and it’s real-time processing. The change in characteristics of data stream often make our model obsolete. Such changes are called concept drift. This concept drift mandates model adaptations in SDA. Adaptations are required at the feature selection, parametric adjustments and model selection. Making models with the capability of detecting abrupt, gradual and virtual drift is one of the challenges addressed by the researchers in this field. Upon detecting concept drifts, it is required to make necessary changes to the models for its acceptance. Statistical constraints have to be overcome for developing such adaptive algorithms. The model adaptability can be achieved much faster with good feature selection strategies which in turn reduces resource conception without affecting performance of model. Here the objective is to survey on building an adaptive model which is capable of handling concept drifts occurring in non-stationary data with limited resources without degradation in performance.
Edge Profile Super Resolution

The purpose of single-image super resolution (SISR) is to reconstruct an accurate high-resolution image from a degraded low-resolution image. Owing to the lack of information in low-resolution images, SISR is a challenging problem. In particular, it is difficult to represent details, including high-frequency components, such as texture and structural information. We propose the edge profile super-resolution (EPSR) method to preserve structural information and restore texture. EPSR is achieved by stacking modified fractal residual network (mFRN) structures hierarchically and repeatedly. Each mFRN is composed of many residual edge profile blocks (REPBs) that extract features and preserve the edge, structure, and texture information of the image. For implementing REPB, we design three main modules: Residual Efficient Channel Attention Block(RECAB) module, Edge Profile(EP) module, and Context Network(CN) module. By repeating the procedure in the mFRN structure, the EPSR method could be used to extract high-fidelity features, thus preventing texture loss and preserving the structure with appropriate sharpness. Experimental results show that EPSR achieves competitive performance against state-of-the-art methods in terms of the peak signal-to-noise ratio(PSNR) and structural similarity index measure(SSIM) evaluation metrics, as well as visual results.
Active Learning of Sequential Transducers with Side Information About the Domain

Active learning is a setting in which a student queries a teacher, through membership and equivalence queries, in order to learn a language. Performance on these algorithms is often measured in the number of queries required to learn a target, with an emphasis on costly equivalence queries. In graybox learning, the learning process is accelerated by foreknowledge of some information on the target. Here, we consider graybox active learning of subsequential string transducers, where a regular overapproximation of the domain is known by the student. We show that there exists an algorithm to learn subsequential string transducers with a better guarantee on the required number of equivalence queries than classical active learning.
Mix Broadcast Network With User Untraceability for Resource-Constrained IoT

With the tremendous growth of information and communications technology, the needs of user untraceability have opened the door for many potential applications in Internet of Things (IoT). The network (MN) was invented in 1981 to provide untraceability of a message sender. Most MNs are implemented by traffic routing in end-to-end networks. Each message is layered encrypted following the chain of proxy servers in an MN using public-key cryptography. Each proxy server strips off its own layer of encryption to reveal where to send the message next. This breaks the link between the source and the destination, making it harder for eavesdroppers to trace end-to-end communications. If all but one of the proxy servers is compromised by the tracer, untraceability of a message can still be achieved. Important applications of MNs are secure electronic voting, anonymous email, anonymous telecommunication and hiding of IP address, etc. In this letter, we propose a novel design of a mix broadcast network. The untraceability is achieved by a simple cryptographic algorithm to mix information in a broadcast channel. The computation needed in this algorithm is the logic Exclusive-OR operation, which is one of the fastest operations so far. Our proposal is especially suitable for wireless resource-constrained IoT.
A Multi-agent Specification for the Tetris Game

In the video game development industry, tasks related to design and specification require support to translate game features into implementations. These support systems must clearly define the elements, functionalities, and interactions of the game elements, and they must also be established independently of the target platform for its development. Based on a study for the specification of games that allows the generation of games as multi-agent systems, this work tries to check if the results can be cross-platform applied. As a case study the classic game Tetris has been used, a game whose very nature suggests that its implementation should be composed of vector and matrix data structures. The purpose is to validate the usage of a game specification based on multi-agent systems for the game's implementation on different platforms.
WorldBuilding and Mandala as a Tool for Co-Speculating on the Healthcare Domain in 2050

Covid-19 pandemic that demanded distancing from social life had a drastic impact on our societal systems from economy to education but especially healthcare, which became the center of attention in 2020. While making gaps in the social systems visible, these times have reminded us the significance of exploring possible futures, scenarios and how design methods can help co-speculating on possible futures. With a motivation to co-speculate on possible futures with the healthcare domain experts, discuss how 2020 might effect the world in general and reflect back on the future of the healthcare domain based on these speculations, we have started a series of worldbuilding workshops. Here in this paper, we are sharing the lessons learned through the first three workshops conducted in this long term project, where we worked with 8 domain experts, used design fiction stories and the worldbuilding mandala tool to co-speculate on possible futures of 2030, 2040, and finally 2050. This paper shares insights regarding the methods used and how they helped the co-speculation process, but not the implications regarding healthcare sector.
Scalable Vector Gaussian Information Bottleneck

In the context of statistical learning, the Information Bottleneck (IB) method seeks a right balance between accuracy and generalization capability through a suitable tradeoff between compression complexity, measured by minimum description length, and distortion evaluated under logarithmic loss measure. In this paper, we study a variation of the problem, called scalable information bottleneck, in which the encoder outputs multiple descriptions of the observation with increasingly richer features. The model, which is of successive-refinement type with degraded side information streams at the decoders, is motivated by some application scenarios that require varying levels of accuracy depending on the allowed level of complexity. We establish an analytic characterization of the optimal relevance-complexity region for vector Gaussian sources. Then, we derive a variational inference type algorithm for general sources with unknown distribution; and show means of parametrizing it using neural networks. Finally, we provide experimental results on the MNIST dataset which illustrate that the proposed method generalizes better to unseen data compared to the standard IB with a single description.
High-Precision 3D Reconstruction of Cooperative Markers under Motion Blur

The application of artificial intelligence and deep learning in the fields of wireless communication, image and speech recognition, and 3D reconstruction has successfully solved some difficult modeling problems. This paper focuses on the high-precision 3D reconstruction of the motion-blurred cooperative markers, including the Chinese character coded targets (CCTs) and the noncoded circular markers. A simulation-based motion-blurred image generation model is constructed to provide sufficient samples for training the convolutional neural network to identify and match the motion-blurred CCTs on the moving object. The blurred noncoded marker matching is performed through homography. The 3D reconstruction of the markers is realized via the optimization of the spatial moving path within the exposure period. The midpoint of the moving path of the markers is taken as the final reconstruction result. The experimental results show that the 3D reconstruction accuracy of the markers with a certain motion blur effect is about 0.08 mm.
Ranking by Language Similarity for Resource Scarce Southern Bantu Languages

Resource Scarce Languages (RSLs) lack sufficient resources to use Cross-Lingual Information Retrieval (CLIR) techniques and tools such as machine translation. Consequentially, searching using RSLs is frustrating and usually ends in unsuccessful struggling search. In such search tasks, search engines return low-quality results; relevant documents are either limited and lowly ranked or non-existent. Previous work has shown that alternative relevant results written in similar languages, including dialects, neighbouring and genetically related languages, can assist multilingual RSLs speakers to complete their search tasks. To improve the quality of search results in this context, we propose the re-ranking of documents based on the similarity between the language of the document and the language of the query. Accordingly, we created a dataset of four Southern Bantu languages that includes documents, topics, topical relevance and intelligibility features, and document utility annotations. To understand the intelligibility dimension of the studied languages, we conducted online intelligibility test experiments and used the data for feature selection and intelligibility prediction. We performed re-ranking of search results using offline evaluation, exploring Learning To Rank (LTR). Our results show that integrating topical relevance and intelligibility in ranking slightly improves retrieval effectiveness. Further, results on intelligibility prediction show that classification of intelligibility is feasible at a fair accuracy.
Secure Anonymous Communication on Corrupted Machines With Reverse Firewalls

The Snowden revelations in 2013 showed that user machines running cryptographic protocols might be stealthily corrupted by attackers (e.g., manufacturers and supply-chain intermediaries) who could tamper cryptographic implementations to insert backdoors to undermine cryptographic tools. To formalize such attacks, in CRYPTO 2014, Bellare <i>et al.</i> proposed the notion of Algorithm-Substitution Attack (ASA) which has been extensively studied since then. In this work, we turn to investigate the security of anonymous communication (AC) protocol—a well-known tool to protect user privacy on the Internet—in the case when user machines are corrupted. Specifically, we give a formal treatment of ASAs on the universal mixnet-based AC ( <inline-formula><tex-math notation="LaTeX">$\mathsf{U\text{-}Mix\text{-}AC}$</tex-math></inline-formula> ) protocols. We show that ASAs on <inline-formula><tex-math notation="LaTeX">$\mathsf{U\text{-}Mix\text{-}AC}$</tex-math></inline-formula> protocols could be more dangerous than previously thought by presenting attacks that are extremely powerful. As countermeasure, we adopt cryptographic reverse firewall (CRF), originally proposed by Mironov and Stephens-Davidowitz in EUROCRYPT 2015, to restore the security of <inline-formula><tex-math notation="LaTeX">$\mathsf{U\text{-}Mix\text{-}AC}$</tex-math></inline-formula> protocols in the presence of ASAs. We also implement proposed AC protocol, ASAs and CRFs for experimental evaluations, and the results show that the execution time of subverted algorithms is almost the same as that of faithful ones and our designed CRFs are effective to guard the security of <inline-formula><tex-math notation="LaTeX">$\mathsf{U\text{-}Mix\text{-}AC}$</tex-math></inline-formula> protocol.
On (Multi-stage) Proof-of-Works

In this paper we analyze permissionless blockchain protocols, whose distributed consensus algorithm lies on a Proof-of-Work composed of $$k > 1$$ consecutive hash-puzzles, that have to be solved sequentially. Our contribution is twofold. First, under common assumptions in the literature, we provide a closed-form expression for the mining probability of a miner, that is, the probability that the miner completes the Proof-of-Work of the next block to be added to the blockchain before every other miner does. Second, we show that, contrary to single-stage Proof-of-Works (i.e., $$k=1$$ ), in multi-stage Proof-of-Works, the mining probability might not be strictly related to the miner hash rate. This feature could be exploited by a smart miner, and could cause fairness and centralization issues in mining, which make the design of practical multi-stage Proof-of-Work blockchain protocols not trivial.