Reviewerly/exHarmony huggingface dataset:
AR-ELBO: Preventing Posterior Collapse Induced by Oversmoothing in Gaussian VAE

Variational autoencoders (VAEs) often suffer from posterior collapse, which is a phenomenon that the learned latent space becomes uninformative. This is related to local optima introduced by a fixed hyperparameter resembling the data variance in the objective function. We suggest that this variance parameter regularizes the VAE and affects its smoothness, which is the magnitude of its gradient. An inappropriate choice of this parameter causes oversmoothness and leads to posterior collapse. This is shown theoretically by analysis on the linear approximated objective function and empirically in general cases. We propose AR-ELBO, which stands for adaptively regularized ELBO~(Evidence Lower BOund). It controls the strength of regularization by adapting the variance parameter, and thus avoids oversmoothing the model. Generation models trained by proposed objectives show improved Frechet inception distance~(FID) of images generated from the MNIST and CelebA datasets.
Introduction to data deduplication approaches

In the field of computing, data deduplication methods are used to reduce duplicate copies and repeated data. The data storage and its utilization are improved by deduplication in addition to better network transfer. Due to reduced data storage requirement, the data transfer also becomes fats and efficient. Unique data chunks are identified, and by using suitable approach the duplicate data is eliminated by replacing the redundant chunks. This chapter presents an overview of data deduplication methods, types, file chunking, metadata, and the applications of deduplication process.
Improvisación, composiciones y arreglos para formato de jazz mediante la síntesis auditiva del R&B alternativo estadounidense

The listening synthesis processes were applied through intensive listening of alternative R&B from the united states to improve, compose and arrange the music for jazz band.
Accounting and non-financial firm data tokens in permissioned DLT networks

We herein explore the usefulness of blockchain permissioned distributed-ledger networks for accounting and registration of financial and non-financial data, aiming to gain efficiency in inner and external audit processes. We also outline the main obstacles still impeding the generalisation and scalability of this kind of project and the corresponding distributed exchange of data tokens within this field, emphasising the benefits for stakeholder integration brought by smart contracts deployed in public permissioned blockchains, from the viewpoint of the audit profession.
Finite impulse response low-pass digital filter based on particle swarm optimisation for image denoising

Noise has very important influence on image quality. Finite Impulse Response (FIR) digital filter is an effective technique for removing image noise. The effectiveness of FIR digital filters designed by traditional methods in image denoising needs to be improved further because of their demerits such as lower accuracy. Therefore, to suppress noise and improve image quality, FIR low-pass digital filter designed by the improved particle swarm optimisation algorithm based on refraction principle (refrPSO) is employed. In the method, firstly, coefficients of FIR low-pass digital filter are mapped to a candidate solution of refrPSO, and then refrPSO is invoked to optimise it. Finally, the designed FIR low-pass digital filter is employed to denoise images. Experimental results of two images with different sizes show that the FIR digital filter designed by refrPSO has better image denoising effect compared with ones designed by window function method, best equivalent ripple approximation method, and PSO.
Learning and Generalization in Univariate Overparameterized Normalizing Flows

In supervised learning, it is known that overparameterized neural networks with one hidden layer provably and efficiently learn and generalize, when trained using Stochastic Gradient Descent (SGD). In contrast, the benefit of overparameterization in unsupervised learning is not well understood. Normalizing flows (NFs) learn to map complex real-world distributions into simple base distributions, and constitute an important class of models in unsupervised learning for sampling and density estimation. In this paper, we theoretically and empirically analyze these models when the underlying neural network is one hidden layer overparametrized network. On the one hand we provide evidence that for a class of NFs, overparametrization hurts training. On the other, we prove that another class of NFs, with similar underlying networks can efficiently learn any reasonable data distribution under minimal assumptions. We extend theoretical ideas on learning and generalization from overparameterized neural networks in supervised learning to overparameterized normalizing flows in unsupervised learning. We also provide experimental validation to support our theoretical analysis in practice.
LibGuides: Keep Calm and Study On: Diversity and Inclusion Resources

LibGuide serves as a platform to support Butler University students, faculty and staff during remote learning through a variety of resources to engage students academically and socially.
Gauge equivariant neural networks for quantum lattice gauge theories

Gauge symmetries play a key role in physics appearing in areas such as quantum field theories of the fundamental particles and emergent degrees of freedom in quantum materials. Motivated by the desire to efficiently simulate many-body quantum systems with exact local gauge invariance, gauge equivariant neural-network quantum states are introduced, which exactly satisfy the local Hilbert space constraints necessary for the description of quantum lattice gauge theory with Zd gauge group on different geometries. Focusing on the special case of Z2 gauge group on a periodically identified square lattice, the equivariant architecture is analytically shown to contain the loop-gas solution as a special case. Gauge equivariant neural-network quantum states are used in combination with variational quantum Monte Carlo to obtain compact descriptions of the ground state wavefunction for the Z2 theory away from the exactly solvable limit, and to demonstrate the confining/deconfining phase transition of the Wilson loop order parameter.
TOKOH DAN PENOKOHAN DALAM TEKS CERPEN KARYA SISWA KELAS IX SMP NEGERI 21 PADANG

ABSTRACT This research was conducted to describe the types of figures and techniques for the presentation of the characters used in the short text of grade IX SMP Negeri 21 Padang. This type of research is qualitative with descriptive method. The data of this study are qualitative data in the form of the use of types of figures and techniques for the presentation of the character's character in the students' short stories. The data were analyzed by steps, namely (1) the researcher identified a general description of the data analyzed as well as coding the text, (2) inventorying the research data coding, (3) analyzing the characters and characterizing short story texts by students, (4) interpreting data based on theory and conclude the findings then write the report. Based on the results of the study, the results of the research there were two. First, the types of figures that tend to be used by grade IX SMP Negeri 21 Padang in the short story texts written are additional figures, protagonists, simple characters, static figures and neutral figures. Second, the technique of presenting figures that tends to be used by IX grade students at SMP Negeri 21 Padang in writing short stories is analytical techniques, action techniques, and word techniques. Kata Kunci: Tokoh, Penokohan, Teks Cerpen
Adaptive Gradient Methods Can Be Provably Faster than SGD with Random Shuffling

Adaptive gradient methods have been shown to outperform SGD in many tasks of training neural networks. However, the acceleration effect is yet to be explained in the non-convex setting since the best convergence rate of adaptive gradient methods is worse than that of SGD in literature. In this paper, we prove that adaptive gradient methods exhibit an O~(T−1/2)-convergence rate for finding first-order stationary points under the strong growth condition, which improves previous best convergence results of adaptive gradient methods and random shuffling SGD by factors of O(T−1/4) and O(T−1/6), respectively. In particular, we study two variants of AdaGrad with random shuffling for finite sum minimization. Our analysis suggests that the combination of random shuffling and adaptive learning rates gives rise to better convergence.
Trust establishment between OAuth 2.0 resource servers using claims-based authorization

The OAuth 2.0 authorisation framework is one of the most commonly used authorisation frameworks. In its specification, many implementation details are loosely defined including the relationship between resource servers and authorisation servers. This paper presents an approach for establishing trust between servers by using the authorisation server as a broker, and examines an implementation for secure exchange of scholarship information between parties. To specify access rights, claims such as roles and capabilities are assigned to resource servers. These claims are asserted by the authorisation server in the form of access tokens. Instead of relying on shared databases, the issued access tokens are used to exchange messages between resource servers. This approach is useful in scenarios where applications have no shared infrastructure or are implemented by different parties.
The Cybersecurity Governance in Changing the Security Psychology and Security Posture: Insights into E-Procurement

Security is a consistent and growing concern for e-commerce and e-procurement solutions which demand for secure transactions to ensure the confidentiality, integrity and availability of data. E-procurement is vulnerable to cyberattacks resulting in increasing demand for cybersecurity governance. Cybersecurity governance is needed to manage the cyberattacks and ensure the important assets of the company is well protected. The aim of this study is to identify the practices for an effective cybersecurity governance by examining and synthesising existing cybersecurity and cybersecurity governance maturity models and framework from the literature and industry. This study has selected and compared prominent cybersecurity maturity models such as cybersecurity governance maturity model (CSGMM) and cyber preparedness (cyber prep) framework by adapting the taxonomy of software improvement environments method. From the synthesis, 12 practical measures were identified and recommended to manufacturing firms for an effective cybersecurity governance.
Human context recognition with reduced feature space vectors for resource constraint gadgets

The aim of this paper is to determine the minimal features to train classification algorithms pertaining to context recognition using data collected from accelerometer sensor in smartphones. A detailed experimental evaluation of various time and frequency domain features on raw accelerometer sensor data collected from smartphones leads to the most influential minimal collection that aid in recognizing human context in resource constraint gadgets. To substantiate the study, four classifiers, namely, Logistic Regression, Support Vector Machine, Artificial Neural Network and Long Short Term Memory Recurrent Neural Networks are trained on six activities - Sitting, Standing and lying (sedentary activities), Walking, Walking Upstairs and Walking Downstairs (dynamic activities). The raw accelerometer values from UCI public dataset and the data collected from Android application is used to build classification model. Classifier performance on both datasets showed 90% accuracy with four features taken over each axes of 3-axis accelerometer.
Proceedings of the Workshop on Computational Humanities Research

In the humanities and related social sciences, the use of computational, statistical, and mathematical approaches has increased in recent years. The research can be characterized by (i) relying on quantifiable rather than strictly qualitative evidence, (ii) making explicit and formalizing theory with the help of mathematical and computational models, (iii) making statistical inferences about model parameters, (iv) the use of computational and automatic procedures for processing and analyzing data, and (v) searching for computational analogies through which theories from different disciplines can be unified.
NASOA: Towards Faster Task-oriented Online Fine-tuning

Fine-tuning from pre-trained ImageNet models has been a simple, effective, and popular approach for various computer vision tasks. The common practice of fine-tuning is to adopt a default hyperparameter setting with a fixed pre-trained model, while both of them are not optimized for specific tasks and time constraints. Moreover, in cloud computing or GPU clusters where the tasks arrive sequentially in a stream, faster online fine-tuning is a more desired and realistic strategy for saving money, energy consumption, and CO2 emission. In this paper, we propose a joint Neural Architecture Search and Online Adaption framework named NASOA towards a faster task-oriented fine-tuning upon the request of users. Specifically, NASOA first adopts an offline NAS to identify a group of training-efficient networks to form a pretrained model zoo. We propose a novel joint block and macro-level search space to enable a flexible and efficient search. Then, by estimating fine-tuning performance via an adaptive model by accumulating experience from the past tasks, an online schedule generator is proposed to pick up the most suitable model and generate a personalized training regime with respect to each desired task in a one-shot fashion. The resulting model zoo is more training efficient than SOTA NAS models, e.g. 6x faster than RegNetY-16GF, and 1.7x faster than EfficientNetB3. Experiments on multiple datasets also show that NASOA achieves much better fine-tuning results, i.e. improving around 2.1% accuracy than the best performance in RegNet series under various time constraints and tasks; 40x faster compared to the BOHB method.
On the Universal Approximability and Complexity Bounds of Deep Learning in Hybrid Quantum-Classical Computing

With the continuously increasing number of quantum bits in quantum computers, there are growing interests in exploring applications that can harvest the power of them. Recently, several attempts were made to implement neural networks, known to be computationally intensive, in hybrid quantum-classical scheme computing. While encouraging results are shown, two fundamental questions need to be answered: (1) whether neural networks in hybrid quantum-classical computing can leverage quantum power and meanwhile approximate any function within a given error bound, i.e., universal approximability; (2) how do these neural networks compare with ones on a classical computer in terms of representation power? This work sheds light on these two questions from a theoretical perspective.
Slice, Dice, and Optimize: Measuring the Dimension of Neural Network Class Manifolds

Deep neural network classifiers naturally partition input space into regions belonging to different classes. The geometry of these class manifolds (CMs) is widely studied and is intimately related to model performance; for example, the margin is defined via boundaries between these CMs. We present a simple technique to estimate the effective dimension of CMs as well as boundaries between multiple CMs, by computing their intersection with random affine subspaces of varying dimension. We provide a theory for the technique and verify that our theoretical predictions agree with measurements on real neural networks. Through extensive experiments, we leverage this method to show deep connections between the geometry of CMs, generalization, and robustness. In particular we investigate how CM dimension depends on 1) the dataset, 2) architecture, 3) random initialization, 4) stage of training, 5) class, 6) ensemble size, 7) label randomization, 8) training set size, and 9) model robustness to data corruption. Together a picture emerges that well-performing, robust models have higher dimensional CMs than worse performing models. Moreover, we offer a unique perspective on ensembling via intersections of CMs. Our core code is available on Github (link in the PDF abstract).
A Black-Box Adversarial Attack via Deep Reinforcement Learning on the Feature Space

In this paper we propose a novel black-box adversarial attack by using the reinforcement learning to learn the characteristics of the target classifier C. Our method does not need to find a substitute classifier that resembles C with respect to its structure and parameters. Instead, our method learns an optimal attacking policy of guiding the attacker to build an adversarial image from the original image. We work on the feature space of images, instead of the pixels of images directly. Our method achieves better results on many measures. Our method achieves 94.5 % attack success rate on a well-trained digit classifier. Our adversarial images have better imperceptibility even though the norm distances to original images are larger than other methods. Since our method works on the characteristics of a classifier, it has better transferability. The transfer rate of our method could reach 52.1 % for a targeted class and 65.9% for a non-targeted class. This improves over previous results of single-digit transfer rates. Also, we show that it is harder to defend our attack by incorporating defense mechanisms, such as MagNet, which uses a denoising technique. We show that our method achieves 65% attack success rate even though the target classifier employs MagNet to defend.
Median DC for Sign Recovery: Privacy can be Achieved by Deterministic Algorithms

Privacy-preserving data analysis becomes prevailing in recent years. It is a common sense in privacy literature that strict differential privacy can only be obtained by imposing additional randomness in the algorithm. In this paper, we study the problem of private sign recovery for sparse mean estimation and sparse linear regression in a distributed setup. By taking a coordinate-wise median among the reported local sign vectors, which can be referred to as a median divide-and-conquer (Med-DC) approach, we can recover the signs of the true parameter with a provable consistency guarantee. Moreover, without adding any extra randomness to the algorithm, our Med-DC method can protect data privacy with high probability. Simulation studies are conducted to demonstrate the effectiveness of our proposed method.
One-Class Classification Criterion Robust to Anomalies in Training Dataset

A new version of one-class classification criterion robust to anomalies in the training dataset is proposed based on support vector data description (SVDD). The original formulation of the problem is not geometrically correct, since the value of the penalty for the admissible escape of the training sample objects outside the describing hypersphere is incommensurable with the distance to its center in the optimization problem and the presence of outliers can greatly affect the decision boundary. The proposed criterion is intended to eliminate this inconsistency. The equivalent form of criterion without constraints lets us use a kernel-based approach without transition to the dual form to make a flexible description of the training dataset. The substitution of the non-differentiable objective function by the smooth one allows us to apply an algorithm of sequential optimizations to solve the problem. We apply the Jaccard measure for a quantitative assessment of the robustness of a decision rule to the presence of outliers. A comparative experimental study of existing one-class methods shows the superiority of the proposed criterion in anomaly detection.
LibGuides: Library automation machine @ VinUni Library: One Button Studio Instruction Guide

You can use the self - check machine on either the 1st floor or the 2nd floor to borrow or return print books. The 24/7 book return machine at the library main entrance is just for checking in books only.
Indonesia terrestrial border control information system and business processes alignment

The information technology (IT) has enhanced the effectiveness of the business processes in all sectors. IT has rapidly developed but becomes a technological disruption for the industries too. IT professional decisions are based on specific requirements and user needs. The investment bases are business processes and the IT alignments. The terrestrial border control systems of customs, immigration, and quarantine (CIQ) are still manual. The traveller passports or 'Pas Lintas Batas' (PLB) documents are collected, based on the customers' handwriting on the arrival or departure immigration cards. These are challenging works for the officers in organising the archiving, retrieving data, and developing reports. The researchers use AS-IS and TO-BE methodology within the software development lifecycle and aim to produce an effective, efficient, and secure 'Sistem Informasi Pelintas Batas Wilayah Indonesia' (SINTASWIN) prototype. This prototype application system is user and environmentally friendly, crossplatform, and has a high return on investment.
GANMEX: Class-Targeted One-vs-One Attributions using GAN-based Model Explainability

Attribution methods have been shown as promising approaches for identifying key features that led to learned model predictions. While most existing attribution methods rely on a baseline input for performing feature perturbations, limited research has been conducted to address the baseline selection issues. Poor choices of baselines can lead to unfair attributions as well as limited ability of one-vs-one explanations for multi-class classifiers, which means explaining why the input belongs to its original class but not the other specified target class. Achieving one-vs-one explanation is crucial when certain classes are more similar than others, e.g. two bird types among multiple animals. One-vs-one explanations focus on key differentiating features rather than features shared across the original and the target classes. In this paper, we present GANMEX, a novel algorithm applying Generative Adversarial Networks (GAN) by incorporating the to-be-explained classifier as part of the adversarial networks. Our approach effectively selects the baseline as the closest realistic sample belong to the target class, which allows attribution methods to provide true one-vs-one explanations. We showed that GANMEX baselines improved the saliency maps visually and led to stronger performance on perturbation-based evaluation metrics over the existing baselines. Attribution results with the existing baselines are known to be insensitive to model randomization, and we demonstrated that GANMEX baselines led to better outcome under the randomization sanity checks.