Reviewerly/exHarmony huggingface dataset:
Spatio-Temporal Resource Meshes for Serverless Computing

Serverless computing is leading the way to a simplified and general purpose programming model for the cloud. A key enabler behind serverless computing is efficient load balancing, which routes function invocations to appropriate backend VM nodes. However, current load balancing algorithms implemented in serverless platforms are static and simple to operate without advantageous performance guarantee. Although policies such as Pod or JFIQ yield asymptotically optimal average response time, the information they depend on are usually unavailable. In addition, dispatching serverless functions with strict deadlines to heterogenous VM nodes with limited input data processing speeds online is difficult because the resource contention is intensified with frequently creating and evicting of function instances. To design an online load balancing algorithm without assumptions on the arrival patterns of serverless functions and the processing capacity of VM nodes while maximizing the social welfare, i.e., the sum of utility of functions and the revenue of the serverless platform, we construct several pseudo-social welfare functions and marginal cost functions, where the latter is to estimate the marginal cost for provisioning resources to every newly arrived function based on present resource surplus. The proposed algorithm, named OnSocMax, works by following the solutions of several convex optimization problems. It is proved to be $\alpha$-competitive for some $\alpha$ at least 2. We also validate the theoretical superiority of OnSocMax with simulations and the results show that it significantly outperforms two handcrafted benchmarking policies on the social welfare.
Evaluation of quadratic residue with the help of Legendre symbol

Cryptography is the study of "Mathematical Systems," which includes two types of security protocols: privacy and authentication. Quadratic residue, a mathematical notion from the discipline of number theory known as Modular arithmetic, is extremely valuable in cryptography. Cryptography is deals with huge numbers, such as integers system with millions of digits or more. In this case, the Legendre symbols may be used to determine if an integer "x" has quadratic residue modulo "p" when p is Prime. This research article explains the mathematical ideas of quadratic residue, Fermat's little theorem, Euler's criteria, and the Legendre symbols. The main goal of this article is to explore the calculation problem of the number of solutions for one kind congruence equation modulo p (an odd prime) using simple methods and character sum properties, and to provide some interesting identities and asymptotic formulas for it.
Defining Gaze Patterns for Process Model Literacy -- Exploring Visual  Routines in Process Models with Diverse Mappings

Process models depict crucial artifacts for organizations regarding documentation, communication, and collaboration. The proper comprehension of such models is essential for an effective application. An important aspect in process model literacy constitutes the question how the information presented in process models is extracted and processed by the human visual system? For such visuospatial tasks, the visual system deploys a set of elemental operations, from whose compositions different visual routines are produced. This paper provides insights from an exploratory eye tracking study, in which visual routines during process model comprehension were contemplated. More specifically, n = 29 participants were asked to comprehend n = 18 process models expressed in the Business Process Model and Notation 2.0 reflecting diverse mappings (i.e., straight, upward, downward) and complexity levels. The performance measures indicated that even less complex process models pose a challenge regarding their comprehension. The upward mapping confronted participants' attention with more challenges, whereas the downward mapping was comprehended more effectively. Based on recorded eye movements, three gaze patterns applied during model comprehension were derived. Thereupon, we defined a general model which identifies visual routines and corresponding elemental operations during process model comprehension. Finally, implications for practice as well as research and directions for future work are discussed in this paper.
Time-domain source parameter estimation of Mw 3-7 earthquakes in Japan from a large database of moment-rate functions

Earth and Space Science Open Archive This preprint has been submitted to and is under consideration at Geophysical Journal International. ESSOAr is a venue for early communication or feedback before peer review. Data may be preliminary.Learn more about preprints preprintOpen AccessYou are viewing the latest version by default [v2]Time-domain source parameter estimation of Mw 3-7 earthquakes in Japan from a large database of moment-rate functionsAuthorsKeisukeYoshidaiDHirooKanamoriiDSee all authors Keisuke YoshidaiDCorresponding Authorâ€¢ Submitting AuthorTohoku UniversityiDhttps://orcid.org/0000-0002-1058-9811view email addressThe email was not providedcopy email addressHiroo KanamoriiDCalifornia Institute of TechnologyiDhttps://orcid.org/0000-0001-8219-9428view email addressThe email was not providedcopy email addressPublished Online:Wed, 6 Apr 2022DOI:https://doi.org/10.1002/essoar.10510900.2Download PDFDownload PDFCiteAbout ToolsAdd to FavoritesTrack Citations ShareShare onFacebookTwitterLinked InRedditEmailQR Code Abstract
Analysis of electronic bill authentication and security storage performance utilising machine learning algorithm

The study aims to ensure the security and authentication efficiency of the bill image, and the encryption and decryption methods and security protection of the electronic bill are studied in the experiment. First, aiming at the not high traditional electronic bill security performance, a method is proposed, namely, embedding a watermark into a binary image with edge information. Second, aiming at the weak compression robust character of electronic bill image, the method of chaotic encryption of digital watermark through wavelet coefficient matrix algorithm is proposed to be combined with the binary sequence. Finally, the deep learning algorithm combined with the convolution algorithm can detect the quality of electronic bill watermark images. The results show that the method of embedding watermark with edge information effectively has improved the confidentiality of electronic bills. The method of chaotic encryption of digital watermarks by wavelet coefficient matrix algorithm combined with binary sequence has improved the anti-compression ability of digital watermarks. The multi-watermark encryption method has enhanced the tamper-proof ability of electronic bills and has improved the security performance of bills, and the deep convolution algorithm has improved the security and efficiency of electronic bill processing.
Brain tumor segmentation using graph coloring approach in magnetic resonance images

It is important to have an accurate and reliable brain tumor segmentation for cancer diagnosis and treatment planning. There are few unsupervised approaches for brain tumor segmentation. In this paper, a new unsupervised approach based on graph coloring for brain tumor segmentation is introduced. In this study, a graph coloring approach is used for brain tumor segmentation. For this aim, each pixel of brain image assumed as a node of graph and difference between brightness of a couple of pixels considered as edge. This method was applied on T1-enhanced magnetic resonance images of low-grade and high-grade patients. Since a rigid graph was needed for graph coloring, edges must be divided into existing or nonexisting edge using a threshold. The value of this threshold has affected the accuracy of image segmentation, so the choice of the optimal threshold was important. The optimal value for this threshold was 0.42 of maximum value of difference of brightness between pixels that caused the 83.62% of correlation accuracy. The results showed that graph coloring approach can be a reliable unsupervised approach for brain tumor segmentation. This approach, as an unsupervised approach, shows better accuracy in comparison with neural networks and neuro-fuzzy networks. However, as a limitation, the accuracy of this approach is dependent on the threshold of edges.
A Survey of Unsupervised Domain Adaptation for Visual Recognition

While huge volumes of unlabeled data are generated and made available in many domains, the demand for automated understanding of visual data is higher than ever before. Most existing machine learning models typically rely on massive amounts of labeled training data to achieve high performance. Unfortunately, such a requirement cannot be met in real-world applications. The number of labels is limited and manually annotating data is expensive and time-consuming. It is often necessary to transfer knowledge from an existing labeled domain to a new domain. However, model performance degrades because of the differences between domains (domain shift or dataset bias). To overcome the burden of annotation, Domain Adaptation (DA) aims to mitigate the domain shift problem when transferring knowledge from one domain into another similar but different domain. Unsupervised DA (UDA) deals with a labeled source domain and an unlabeled target domain. The principal objective of UDA is to reduce the domain discrepancy between the labeled source data and unlabeled target data and to learn domain-invariant representations across the two domains during training. In this paper, we first define UDA problem. Secondly, we overview the state-of-the-art methods for different categories of UDA from both traditional methods and deep learning based methods. Finally, we collect frequently used benchmark datasets and report results of the state-of-the-art methods of UDA on visual recognition problem.
Efficient Anomaly Detection Using Self-Supervised Multi-Cue Tasks

Deep anomaly detection has proven to be an efficient and robust approach in several fields. The introduction of self-supervised learning has greatly helped many methods including anomaly detection where simple geometric transformation recognition tasks are used. However these methods do not perform well on fine-grained problems since they lack finer features and are usually highly dependent on the anomaly type. In this paper, we explore each step of self-supervised anomaly detection with pretext tasks. First, we introduce novel discriminative and generative tasks which focus on different visual cues. A piece-wise jigsaw puzzle task focuses on structure cues, while a tint rotation recognition is used on each piece for colorimetry and a partial re-colorization task is performed. In order for the re-colorization task to focus more on the object rather than on the background, we propose to include the contextual color information of the image border. Then, we present a new out-of-distribution detection function and highlight its better stability compared to other out-of-distribution detection methods. Along with it, we also experiment different score fusion functions. Finally, we evaluate our method on a comprehensive anomaly detection protocol composed of object anomalies with classical object recognition, style anomalies with fine-grained classification and local anomalies with face anti-spoofing datasets. Our model can more accurately learn highly discriminative features using these self-supervised tasks. It outperforms state-of-the-art with up to 36% relative error improvement on object anomalies and 40% on face anti-spoofing problems.
Towards Practical Deployment-Stage Backdoor Attack on Deep Neural  Networks

One major goal of the AI security community is to securely and reliably produce and deploy deep learning models for real-world applications. To this end, data poisoning based backdoor attacks on deep neural networks (DNNs) in the production stage (or training stage) and corresponding defenses are extensively explored in recent years. Ironically, backdoor attacks in the deployment stage, which can often happen in unprofessional users' devices and are thus arguably far more threatening in real-world scenarios, draw much less attention of the community. We attribute this imbalance of vigilance to the weak practicality of existing deployment-stage backdoor attack algorithms and the insufficiency of real-world attack demonstrations. To fill the blank, in this work, we study the realistic threat of deployment-stage backdoor attacks on DNNs. We base our study on a commonly used deployment-stage attack paradigm -- adversarial weight attack, where adversaries selectively modify model weights to embed backdoor into deployed DNNs. To approach realistic practicality, we propose the first gray-box and physically realizable weights attack algorithm for backdoor injection, namely subnet replacement attack (SRA), which only requires architecture information of the victim model and can support physical triggers in the real world. Extensive experimental simulations and system-level real-world attack demonstrations are conducted. Our results not only suggest the effectiveness and practicality of the proposed attack algorithm, but also reveal the practical risk of a novel type of computer virus that may widely spread and stealthily inject backdoor into DNN models in user devices. By our study, we call for more attention to the vulnerability of DNNs in the deployment stage.
Data Access, Privacy, Protection Methods, and Challenges

Data privacy is an intricate job and is becoming a key area of research as far as cloud technologies are concerned. This is because the information is massively generated from many sources. It's collected, shared, and disseminated in many different segments. Due to this factor, countless individuals or organizations have absconded the cloud services despite the fact of their endless fruitiness benefits. Providing security to this data is a major concern. There are numerous ways this data can be protected from unauthorized users. Hence, data security and privacy are becoming very important fields of research for the future development and improvement of cloud technologies for the government, business, and industry. Data privacy protection issues are relevant to both hardware and software in the cloud architecture. Therefore, this study will review different data privacy concepts, data access protection methods, approaches to manage data privacy, data privacy techniques, challenges faced during data access, and some research trends in data privacy.
Double Control Variates for Gradient Estimation in Discrete Latent  Variable Models

Stochastic gradient-based optimisation for discrete latent variable models is challenging due to the high variance of gradients. We introduce a variance reduction technique for score function estimators that makes use of double control variates. These control variates act on top of a main control variate, and try to further reduce the variance of the overall estimator. We develop a double control variate for the REINFORCE leave-one-out estimator using Taylor expansions. For training discrete latent variable models, such as variational autoencoders with binary latent variables, our approach adds no extra computational cost compared to standard training with the REINFORCE leave-one-out estimator. We apply our method to challenging high-dimensional toy examples and training variational autoencoders with binary latent variables. We show that our estimator can have lower variance compared to other state-of-the-art estimators.
Exit Decisions Inspired by Reinforcement Learning

Bounded rationality is one of the key components of human behaviors. Human action normally involves experience accumulation, exploration, and a combination of them. Though it is invisible and difficult to model, in this work, we propose a reinforcement learning-based model and applied it to a particular escaping case to evaluate the behavior of an intelligent agent who wants to optimize its efficiency to escape a typical map. The result resembles the tendency of humans in a procedure of escape including a strong tendency to move out near the exit, randomness when further away from the exit, and some tendency to move near the boundary of the map. Such behaviors are very close to human behaviors which indicates that the decisions made in emergence are mainly based on experience. Such a perception-action model of RL learning can be used in a multi-agent model to investigate collective behaviors.
Health Monitoring of Industrial machines using Scene-Aware Threshold  Selection

This paper presents an autoencoder based unsupervised approach to identify anomaly in an industrial machine using sounds produced by the machine. The proposed framework is trained using log-melspectrogram representations of the sound signal. In classification, our hypothesis is that the reconstruction error computed for an abnormal machine is larger than that of the a normal machine, since only normal machine sounds are being used to train the autoencoder. A threshold is chosen to discriminate between normal and abnormal machines. However, the threshold changes as surrounding conditions vary. To select an appropriate threshold irrespective of the surrounding, we propose a scene classification framework, which can classify the underlying surrounding. Hence, the threshold can be selected adaptively irrespective of the surrounding. The experiment evaluation is performed on MIMII dataset for industrial machines namely fan, pump, valve and slide rail. Our experiment analysis shows that utilizing adaptive threshold, the performance improves significantly as that obtained using the fixed threshold computed for a given surrounding only.
Coping with Changing Contexts: A Healthcare Security Perspective

With the fourth industrial revolution, there is a digitization wave going on for the transformation of existing systems into modern digital systems. This has opened the window for many opportunities, but at the same time, there is a multitude of cyber-security threats that need to be addressed. This paper considers one such threat posed by phishing and ransomware attacks to the healthcare infrastructures. Phishing has also been the most prevalent attack mechanism on the healthcare infrastructures during the ongoing COVID-19 pandemic. The paper proposes two intervention strategies as a step towards catering to the challenges posed by phishing and ransomware attacks in the context of healthcare infrastructures.
Deeper Learning with CoLU Activation

In neural networks, non-linearity is introduced by activation functions. One commonly used activation function is Rectified Linear Unit (ReLU). ReLU has been a popular choice as an activation but has flaws. State-of-the-art functions like Swish and Mish are now gaining attention as a better choice as they combat many flaws presented by other activation functions. CoLU is an activation function similar to Swish and Mish in properties. It is defined as f(x)=x/(1-xe^-(x+e^x)). It is smooth, continuously differentiable, unbounded above, bounded below, non-saturating, and non-monotonic. Based on experiments done with CoLU with different activation functions, it is observed that CoLU usually performs better than other functions on deeper neural networks. While training different neural networks on MNIST on an incrementally increasing number of convolutional layers, CoLU retained the highest accuracy for more layers. On a smaller network with 8 convolutional layers, CoLU had the highest mean accuracy, closely followed by ReLU. On VGG-13 trained on Fashion-MNIST, CoLU had a 4.20% higher accuracy than Mish and 3.31% higher accuracy than ReLU. On ResNet-9 trained on Cifar-10, CoLU had 0.05% higher accuracy than Swish, 0.09% higher accuracy than Mish, and 0.29% higher accuracy than ReLU. It is observed that activation functions may behave better than other activation functions based on different factors including the number of layers, types of layers, number of parameters, learning rate, optimizer, etc. Further research can be done on these factors and activation functions for more optimal activation functions and more knowledge on their behavior.
Adversarial Attack for Asynchronous Event-based Data

Deep neural networks (DNNs) are vulnerable to adversarial examples that are carefully designed to cause the deep learning model to make mistakes. Adversarial examples of 2D images and 3D point clouds have been extensively studied, but studies on event-based data are limited. Event-based data can be an alternative to a 2D image under high-speed movements, such as autonomous driving. However, the given adversarial events make the current deep learning model vulnerable to safety issues. In this work, we generate adversarial examples and then train the robust models for event-based data, for the first time. Our algorithm shifts the time of the original events and generates additional adversarial events. Additional adversarial events are generated in two stages. First, null events are added to the event-based data to generate additional adversarial events. The perturbation size can be controlled with the number of null events. Second, the location and time of additional adversarial events are set to mislead DNNs in a gradient-based attack. Our algorithm achieves an attack success rate of 97.95\% on the N-Caltech101 dataset. Furthermore, the adversarial training model improves robustness on the adversarial event data compared to the original model.
Grappling with the Scale of Born-Digital Government Publications: Toward  Pipelines for Processing and Searching Millions of PDFs

Official government publications are key sources for understanding the history of societies. Web publishing has fundamentally changed the scale and processes by which governments produce and disseminate information. Significantly, a range of web archiving programs have captured massive troves of government publications. For example, hundreds of millions of unique U.S. Government documents posted to the web in PDF form have been archived by libraries to date. Yet, these PDFs remain largely unutilized and understudied in part due to the challenges surrounding the development of scalable pipelines for searching and analyzing them. This paper utilizes a Library of Congress dataset of 1,000 government PDFs in order to offer initial approaches for searching and analyzing these PDFs at scale. In addition to demonstrating the utility of PDF metadata, this paper offers computationally-efficient machine learning approaches to search and discovery that utilize the PDFs' textual and visual features as well. We conclude by detailing how these methods can be operationalized at scale in order to support systems for navigating millions of PDFs.
Asymmetric Proxy Loss for Multi-View Acoustic Word Embeddings

Acoustic word embeddings (AWEs) are discriminative representations of speech segments, and learned embedding space reflects the phonetic similarity between words. With multi-view learning, where text labels are considered as supplementary input, AWEs are jointly trained with acoustically grounded word embeddings (AGWEs). In this paper, we expand the multi-view approach into a proxy-based framework for deep metric learning by equating AGWEs with proxies. A simple modification in computing the similarity matrix allows the general pair weighting to formulate the data-to-proxy relationship. Under the systematized framework, we propose an asymmetric-proxy loss that combines different parts of loss functions asymmetrically while keeping their merits. It follows the assumptions that the optimal function for anchor-positive pairs may differ from one for anchor-negative pairs, and a proxy may have a different impact when it substitutes for different positions in the triplet. We present comparative experiments with various proxy-based losses including our asymmetric-proxy loss, and evaluate AWEs and AGWEs for word discrimination tasks on WSJ corpus. The results demonstrate the effectiveness of the proposed method.