Reviewerly/exHarmony huggingface dataset:
Amortized Proximal Optimization

We propose a framework for online meta-optimization of parameters that govern optimization, called Amortized Proximal Optimization (APO). We first interpret various existing neural network optimizers as approximate stochastic proximal point methods which trade off the current-batch loss with proximity terms in both function space and weight space. The idea behind APO is to amortize the minimization of the proximal point objective by meta-learning the parameters of an update rule. We show how APO can be used to adapt a learning rate or a structured preconditioning matrix. Under appropriate assumptions, APO can recover existing optimizers such as natural gradient descent and KFAC. It enjoys low computational overhead and avoids expensive and numerically sensitive operations required by some second-order optimizers, such as matrix inverses. We empirically test APO for online adaptation of learning rates and structured preconditioning matrices for regression, image reconstruction, image classification, and natural language translation tasks. Empirically, the learning rate schedules found by APO generally outperform optimal fixed learning rates and are competitive with manually tuned decay schedules. Using APO to adapt a structured preconditioning matrix generally results in optimization performance competitive with second-order methods. Moreover, the absence of matrix inversion provides numerical stability, making it effective for low precision training.
Embedding With Preservation of Semantics of the Original Data

In the modern world, the data used to describe objects is often presented as sparse vectors with a large number of features. Working with them can be computationally inefficient, and often leads to overfitting; therefore, the data dimension reduction algorithms are used, one of which is auto encoders. In this article, we propose a new approach for evaluating the properties of the obtained vectors of lower dimension, as well as a loss function based on this approach. The idea of the suggested loss function is to evaluate the quality of preserving the semantic structure in the embedding space, and to add that metric to loss function to save object relations in the embedding space and thus save more useful information about objects. The results obtained show that using a combination of the mean squared loss function together with the suggested one allows to improve the quality of the embeddings.
GAN-Supervised Dense Visual Alignment

We propose GAN-Supervised Learning, a framework for learning discriminative models and their GAN-generated training data jointly end-to-end. We apply our framework to the dense visual alignment problem. Inspired by the classic Congealing method, our GANgealing algorithm trains a Spatial Transformer to map random samples from a GAN trained on unaligned data to a common, jointly-learned target mode. We show results on eight datasets, all of which demonstrate our method successfully aligns complex data and discovers dense correspondences. GANgealing significantly outperforms past self-supervised correspondence algorithms and performs on-par with (and sometimes exceeds) state-of-the-art supervised correspondence algorithms on several datasets -- without making use of any correspondence supervision or data augmentation and despite being trained exclusively on GAN-generated data. For precise correspondence, we improve upon state-of-the-art supervised methods by as much as $3\times$. We show applications of our method for augmented reality, image editing and automated pre-processing of image datasets for downstream GAN training.
Forced Phonetic Alignment in Brazilian Portuguese Using Time-Delay Neural Networks

Forced phonetic alignment (FPA) is the task of assessing the time boundaries of phonetic units, i.e., calculating when in the speech utterance a certain phoneme starts and ends. This paper describes experiments on FPA for Brazilian Portuguese using Kaldi toolkit. Based on time-delay neural networks (TDNN), several acoustic models were trained on the top of the combination between hidden Markov models (HMM) and Gaussian mixture models (GMM). The nature of the input features and the topology of the HMMs have been varied in order to analyze each one’s influence. Results with respect to the phone boundary metric over a dataset of 385 hand-aligned utterances show that the network is mostly invariant to the input features, while regular HMM topologies do perform better in comparison to a modified version used in chain models. Conversely, the neural network still does not outperform GMM models for phonetic alignment.
Stationary Behavior of Constant Stepsize SGD Type Algorithms: An Asymptotic Characterization

Stochastic approximation (SA) and stochastic gradient descent (SGD) algorithms are work-horses for modern machine learning algorithms. Their constant stepsize variants are preferred in practice due to fast convergence behavior. However, constant step stochastic iterative algorithms do not converge asymptotically to the optimal solution, but instead have a stationary distribution, which in general cannot be analytically characterized. In this work, we study the asymptotic behavior of the appropriately scaled stationary distribution, in the limit when the constant stepsize goes to zero. Specifically, we consider the following three settings: (1) SGD algorithms with smooth and strongly convex objective, (2) linear SA algorithms involving a Hurwitz matrix, and (3) nonlinear SA algorithms involving a contractive operator. When the iterate is scaled by $1/\sqrt{\alpha}$, where $\alpha$ is the constant stepsize, we show that the limiting scaled stationary distribution is a solution of an integral equation. Under a uniqueness assumption (which can be removed in certain settings) on this equation, we further characterize the limiting distribution as a Gaussian distribution whose covariance matrix is the unique solution of a suitable Lyapunov equation. For SA algorithms beyond these cases, our numerical experiments suggest that unlike central limit theorem type results: (1) the scaling factor need not be $1/\sqrt{\alpha}$, and (2) the limiting distribution need not be Gaussian. Based on the numerical study, we come up with a formula to determine the right scaling factor, and make insightful connection to the Euler-Maruyama discretization scheme for approximating stochastic differential equations.
Software Defect Prediction and Software Quality Assessment Using Dlr-Lvq and Fuzzy Rules

Recently, Software development has been considerably grown. Fault in the software causes fault and interrupts the output. Characteristics like these make it much challenging to avert software flaws. Spontaneously forecasting the amount of flaws within the software modules is essential and also can assist developers to proficiently allot restricted resources. Recently, numerous Software Defect Prediction (SDP) techniques are developed. But, the accuracy and time consuming challenges still remain to be solved. Also, a few top-notch techniques don't properly classify the software whereas it is a needed metric to ensure quality standards. This work proffers a novel Decaying Learning Rate – Learning vector Quantization (DLR-LVQ) classifier to forecast the software defect. The proposed methods consist of the following steps: redundant data removal, feature extraction (FE), feature oversampling, data normalization, defect prediction (DP), and quality prediction. The proposed DLR-LVQ’s attained outcome is assessed with the existent methodologies. The outcomes exhibit that the methodology proposed attains efficient classification outcomes are examined. Keywords: Software Defect Prediction (SDP), Non defective software quality prediction, BM-SMOTE, Decaying Learning Rate, Learning Vector Quantization, Fuzzy rules, HDFS and Map Reduce.
Videotext Over Mall Elevate Reading Fluency for Remote Area’s Intensive Program University Student

Objectives: this study aimed to perceive the impact of videotext on reading fluency over mall in remote area student. Nonetheless, the writing is moderately simulating the concurrences with the impacts of videotext, drained of any current methodology investigating the impacts of videotext as a part to small participants reading intercessions. Methods: this study used quasi-experimental, non-randomized pre-test and post-test design with 60 students over remote areas and interview case study plan as testimonial with six pseudonyms contributors. along with comparation of two intercession correspondences to one another. Well-separated of all participants scores mark. Findings: The results demonstrated moderate to effective impacts for the two intercessions across all members, with little distinction betwixt the two instruments. Conclusion: the established hypothesis has been accepted. Keywords: videotext, mobile-assisted language learning, reading fluency, remote area. Abstrak: Tujuan: Penelitian ini bertujuan untuk mengetahui dampak dari videoteks terhadap kelancaran membaca melalui mall pada siswa daerah terpencil. Namun, penelitian ini mensimulasikan keadaan dengan dampak videoteks, keterbatasan metodologi saat ini yang menyelidiki dampak videoteks sebagai bagian dari penggunaan teknik membaca. Metode: Penelitian ini menggunakan desain quasi-experimental, non-randomized pre-test and post-test design dengan 60 siswa di daerah terpencil dan wawancara studi kasus sebagai testimonial dengan enam kontributor nama samaran. bersama dengan perbandingan dua korespondensi yang berbeda satu sama lain. Terpisah dengan baik dari semua tanda skor peserta. Temuan: Hasil penelitian menunjukkan dampak sedang hingga efektif untuk dua perbedaan di semua anggota, dengan sedikit perbedaan antara kedua instrumen. Kesimpulan: hipotesis yang ditetapkan dinyatakan diterima. Kata kunci: teks video, pembelajaran Bahasa berbantuan ponsel, kelancaran membaca, daerah terpencil. DOI: http://dx.doi.org/10.23960/jpp.v12.i1.202225
Pengaruh Literasi Digital Terhadap Hasil Belajar Ekonomi Di Era Pandemik Covid-19 Siswa Kelas XI SMA Negeri 6 Kota Tangerang Selatan

This study aims to determine the Effect of Digital Literacy on Economic Learning Outcomes in the Era of the Covid-19 Pandemic for Class XI Students at SMA Negeri 6 Tangerang Selatan City. The research methodology used is the associative method with a quantitative research approach. The population that the researcher uses is all students of SMA Negeri 6 Tangerang Selatan City using the Rendom Sampling technique by determining the number of samples using the Slovin formula and obtained as many as 114 students with an error rate of 5%. The data collection instruments used were Economic Daily Examination (UH) documents and questionnaires. Prerequisite test of analysis using normality test and linearity test with data analysis technique used is simple regression analysis with the help of IMB SPSS Statistic 22 program.The results showed that digital literacy had a fairly low influence on economic learning outcomes in the pandemic era with 24.2% results which gave changes to the digital literacy variable, while 75.8% was influenced by other variables that were not found in this study.
Neurosymbolic Systems of Perception & Cognition: The Role of Attention

A cognitive architecture aimed at cumulative learning must provide the necessary information and control structures to allow agents to learn incrementally and autonomously from their experience. This involves managing an agent's goals as well as continuously relating sensory information to these in its perception-cognition information stack. The more varied the environment of a learning agent is, the more general and flexible must be these mechanisms to handle a wider variety of relevant patterns, tasks, and goal structures. While many researchers agree that information at different levels of abstraction likely differs in its makeup and structure and processing mechanisms, agreement on the particulars of such differences is not generally shared in the research community. A binary processing architecture (often referred to as System-1 and System-2) has been proposed as a model of cognitive processing for low- and high-level information, respectively. We posit that cognition is not binary in this way and that knowledge at any level of abstraction involves what we refer to as neurosymbolic information, meaning that data at both high and low levels must contain both symbolic and subsymbolic information. Further, we argue that the main differentiating factor between the processing of high and low levels of data abstraction can be largely attributed to the nature of the involved attention mechanisms. We describe the key arguments behind this view and review relevant evidence from the literature.
Predicting the intended action using internal simulation of perception

This article proposes an architecture, which allows the prediction of intention by internally simulating perceptual states represented by action pattern vectors. To this end, associative self-organising neural networks (A-SOM) is utilised to build a hierarchical cognitive architecture for recognition and simulation of the skeleton based human actions. The abilities of the proposed architecture in recognising and predicting actions is evaluated in experiments using three different datasets of 3D actions. Based on the experiments of this article, applying internally simulated perceptual states represented by action pattern vectors improves the performance of the recognition task in all experiments. Furthermore, internal simulation of perception addresses the problem of having limited access to the sensory input, and also the future prediction of the consecutive perceptual sequences. The performance of the system is compared and discussed with similar architecture using self-organizing neural networks (SOM).
Understanding Why Generalized Reweighting Does Not Improve Over ERM

Empirical risk minimization (ERM) is known in practice to be non-robust to distributional shift where the training and the test distributions are different. A suite of approaches, such as importance weighting, and variants of distributionally robust optimization (DRO), have been proposed to solve this problem. But a line of recent work has empirically shown that these approaches do not significantly improve over ERM in real applications with distribution shift. The goal of this work is to obtain a comprehensive theoretical understanding of this intriguing phenomenon. We first posit the class of Generalized Reweighting (GRW) algorithms, as a broad category of approaches that iteratively update model parameters based on iterative reweighting of the training samples. We show that when overparameterized models are trained under GRW, the resulting models are close to that obtained by ERM. We also show that adding small regularization which does not greatly affect the empirical training accuracy does not help. Together, our results show that a broad category of what we term GRW approaches are not able to achieve distributionally robust generalization. Our work thus has the following sobering takeaway: to make progress towards distributionally robust generalization, we either have to develop non-GRW approaches, or perhaps devise novel classification/regression loss functions that are adapted to the class of GRW approaches.
Auxiliary Learning for Self-Supervised Video Representation via  Similarity-based Knowledge Distillation

Despite the outstanding success of self-supervised pretraining methods for video representation learning, they generalise poorly when the unlabeled dataset for pretraining is small or the domain difference between unlabelled data in source task (pretraining) and labeled data in target task (finetuning) is significant. To mitigate these issues, we propose a novel approach to complement self-supervised pretraining via an auxiliary pretraining phase, based on knowledge similarity distillation, auxSKD, for better generalisation with a significantly smaller amount of video data, e.g. Kinetics-100 rather than Kinetics-400. Our method deploys a teacher network that iteratively distills its knowledge to the student model by capturing the similarity information between segments of unlabelled video data. The student model meanwhile solves a pretext task by exploiting this prior knowledge. We also introduce a novel pretext task, Video Segment Pace Prediction or VSPP, which requires our model to predict the playback speed of a randomly selected segment of the input video to provide more reliable self-supervised representations. Our experimental results show superior results to the state of the art on both UCF101 and HMDB51 datasets when pretraining on K100 in apple-to-apple comparisons. Additionally, we show that our auxiliary pretraining, auxSKD, when added as an extra pretraining phase to recent state of the art self-supervised methods (i.e. VCOP, VideoPace, and RSPNet), improves their results on UCF101 and HMDB51. Our code is available at https://github.com/Plrbear/auxSKD.
An Epistemic Interpretation of Tensor Disjunction

This paper aims to give an epistemic interpretation to the tensor disjunction in dependence logic, through a rather surprising connection to the so-called weak disjunction in Medvedev's early work on intermediate logic under the Brouwer-Heyting-Kolmogorov (BHK)-interpretation. We expose this connection in the setting of inquisitive logic with tensor disjunction discussed by Ciardelli and Barbero (2019}, but from an epistemic perspective. More specifically, we translate the propositional formulae of inquisitive logic with tensor into modal formulae in a powerful epistemic language of "knowing how" following the proposal by Wang (2021). We give a complete axiomatization of the logic of our full language based on Fine's axiomatization of S5 modal logic with propositional quantifiers. Finally, we generalize the tensor operator with parameters $k$ and $n$, which intuitively captures the epistemic situation that one knows $n$ potential answers to $n$ questions and is sure $k$ answers of them must be correct. The original tensor disjunction is the special case when $k=1$ and $n=2$. We show that the generalized tensor operators do not increase the expressive power of our logic, the inquisitive logic, and propositional dependence logic, though most of these generalized tensors are not uniformly definable in these logics, except in our dynamic epistemic logic of knowing how.
Assisted Text Annotation Using Active Learning to Achieve High Quality with Little Effort

Large amounts of annotated data have become more important than ever, especially since the rise of deep learning techniques. However, manual annotations are costly. We propose a tool that enables researchers to create large, high-quality, annotated datasets with only a few manual annotations, thus strongly reducing annotation cost and effort. For this purpose, we combine an active learning (AL) approach with a pre-trained language model to semi-automatically identify annotation categories in the given text documents. To highlight our research direction's potential, we evaluate the approach on the task of identifying frames in news articles. Our preliminary results show that employing AL strongly reduces the number of annotations for correct classification of even these complex and subtle frames. On the framing dataset, the AL approach needs only 16.3\% of the annotations to reach the same performance as a model trained on the full dataset.
Stratified Graph Spectra

In classic graph signal processing, given a real-valued graph signal, its graph Fourier transform is typically defined as the series of inner products between the signal and each eigenvector of the graph Laplacian. Unfortunately, this definition is not mathematically valid in the cases of vector-valued graph signals which however are typical operands in the state-of-the-art graph learning modeling and analyses. Seeking a generalized transformation decoding the magnitudes of eigencomponents from vector-valued signals is thus the main objective of this paper. Several attempts are explored, and also it is found that performing the transformation at hierarchical levels of adjacency help profile the spectral characteristics of signals more insightfully. The proposed methods are introduced as a new tool assisting on diagnosing and profiling behaviors of graph learning models.
End-to-end speaker diarization with transformer

Speaker diarization is connected to semantic segmentation in computer vision. Inspired from MaskFormer \cite{cheng2021per} which treats semantic segmentation as a set-prediction problem, we propose an end-to-end approach to predict a set of targets consisting of binary masks, vocal activities and speaker vectors. Our model, which we coin \textit{DiFormer}, is mainly based on a speaker encoder and a feature pyramid network (FPN) module to extract multi-scale speaker features which are then fed into a transformer encoder-decoder to predict a set of diarization targets from learned query embedding. To account for temporal characteristics of speech signal, bidirectional LSTMs are inserted into the mask prediction module to improve temporal consistency. Our model handles unknown number of speakers, speech overlaps, as well as vocal activity detection in a unified way. Experiments on multimedia and meeting datasets demonstrate the effectiveness of our approach.
Optimization landscape in the simplest constrained random least-square  problem

We analyze statistical features of the ``optimization landscape'' in a random version of one of the simplest constrained optimization problems of the least-square type: finding the best approximation for the solution of an overcomplete system of $M>N$ linear equations $({\bf a}_k,{\bf x})=b_k, \, k=1,\ldots,M$ on the $N-$sphere ${\bf x}^2=N$. We treat both the $N-$component vectors ${\bf a}_k$ and parameters $b_k$ as independent mean zero real Gaussian random variables. First, we derive the exact expressions for the mean number of stationary points of the least-square loss function in the framework of the Kac-Rice approach combined with the Random Matrix Theory for Wishart Ensemble, and then perform its asymptotic analysis as $N\to \infty$ at a fixed $\alpha=M/N>1$ in various regimes. In particular, this analysis allows to extract the Large Deviation Function for the density of the smallest Lagrange multiplier $\lambda_{min}$ associated with the problem, and in this way to find its most probable value. This can be further used to predict the asymptotic minimal value ${\cal E}_{min}$ of the loss function as $N\to \infty$. Finally, we develop an alternative approach based on the replica trick to conjecture the form of the Large Deviation function for the density of ${\cal E}_{min}$ at $N\gg 1$. As a by-product, we find the value of the {\it compatibility threshold} $\alpha_c$ which is the minimal value of the asymptotic ratio $M/N$ such that the random linear system on the $N-$sphere is typically compatible.
Distributed Passive Sensor Trajectory Association and Fusion

The two key steps in the space-based optical passive tracking system are track association and fusion. The traditional distributed fusion methods are based on three-dimensional trajectory and are not suitable for the two-dimensional track association problem of passive sensors. For this reason, the probability distribution of the hinge angle difference statistic is modeled exactly in the correct and the wrong association situation. Then regarding the track association task as a two-dimensional assignment problem, we use Kuhn-Munkras algorithm to find the optimal association results of the bipartite graph. Finally, we model the target motion process and the sensor measurement process, and use UKF algorithm to fuse angular trajectories, which can accurately reconstruct the spatial position of the target. The simulation results under the STK platform show that our algorithm has favorable association and fusion performance.