Reviewerly/exHarmony huggingface dataset:
Topic-Transformer for Document-Level Language Understanding

As long as natural language processing applications are considered prediction problems with insufficient context, usually referred to as a single sentence or paragraph, this does not reveal how humans perceive natural language.When reading a text, humans are sensitive to much more context, such as the rest or other relevant documents.This study focuses on simultaneously capturing syntax and global semantics from a text, thus acquiring document-level understanding.Accordingly, we introduce a Topic-Transformer that combines the benefits of a neural topic model that captures global semantic information and a transformer-based language model, which can capture the local structure of texts both semantically and syntactically.Experiments on various datasets confirm that our model has a lower perplexity metric compared to standard transformer architecture and the recent topic-guided language models and generates topics that are conceivably coherent compared to those of regular Latent Dirichlet Allocation (LDA) topic model.
Qubit Regularization and Qubit Embedding Algebras

Qubit regularization is a procedure to regularize the infinite dimensional local Hilbert space of bosonic fields to a finite dimensional one, which is a crucial step when trying to simulate lattice quantum field theories on a quantum computer. When the qubit-regularized lattice quantum fields preserve important symmetries of the original theory, qubit regularization naturally enforces certain algebraic structures on these quantum fields. We introduce the concept of qubit embedding algebras (QEAs) to characterize this algebraic structure associated with a qubit regularization scheme. We show a systematic procedure to derive QEAs for the O(N) lattice spin models and the SU(N) lattice gauge theories. While some of the QEAs we find were discovered earlier in the context of the D-theory approach, our method shows that QEAs are far more richer. A more complete understanding of the QEAs could be helpful in recovering the fixed points of the desired quantum field theories.
Hybrid Bayesian network discovery with latent variables by scoring multiple interventions

In Bayesian Networks (BNs), the direction of edges is crucial for causal reasoning and inference. However, Markov equivalence class considerations mean it is not always possible to establish edge orientations, which is why many BN structure learning algorithms cannot orientate all edges from purely observational data. Moreover, latent confounders can lead to false positive edges. Relatively few methods have been proposed to address these issues. In this work, we present the hybrid mFGS-BS (majority rule and Fast Greedy equivalence Search with Bayesian Scoring) algorithm for structure learning from discrete data that involves an observational data set and one or more interventional data sets. The algorithm assumes causal insufficiency in the presence of latent variables and produces a Partial Ancestral Graph (PAG). Structure learning relies on a hybrid approach and a novel Bayesian scoring paradigm that calculates the posterior probability of each directed edge being added to the learnt graph. Experimental results based on well-known networks of up to 109 variables and 10k sample size show that mFGS-BS improves structure learning accuracy relative to the state-of-the-art and it is computationally efficient.
CO-AutoML: An Optimizable Automated Machine Learning System

In recent years, many automated machine learning (AutoML) techniques are proposed for the automatic selection or design machine learning models. They bring great convenience to the use of machine learning techniques, but are difficult for users without programming experiences to use, and lack of effective optimization scheme to respond to users’ dissatisfaction with final results. To overcome these defects, we develop CO-AutoML, a user-friendly and optimizable AutoML system. CO-AutoML allows users to interact with the system in a customized mode. Besides, it can continuously optimize the search space of the AutoML technique based on reinforce policy and graph neural network (GNN), and thus provide users with more powerful machine learning schemes. Our system empowers ordinary users to easily and more effectively use AutoML techniques, which has a certain application value and practical significance. Our demonstration video: https://youtu.be/nGnmA7noeJA .
Poisson-Birnbaum-Saunders Regression Model for Clustered Count Data

The premise of independence among subjects in the same cluster/group often fails in practice, and models that rely on such untenable assumption can produce misleading results. To overcome this severe deficiency, we introduce a new regression model to handle overdispersed and correlated clustered counts. To account for correlation within clusters, we propose a Poisson regression model where the observations within the same cluster are driven by the same latent random effect that follows the Birnbaum-Saunders distribution with a parameter that controls the strength of dependence among the individuals. This novel multivariate count model is called Clustered Poisson Birnbaum-Saunders (CPBS) regression. As illustrated in this paper, the CPBS model is analytically tractable, and its moment structure can be explicitly obtained. Estimation of parameters is performed through the maximum likelihood method, and an Expectation-Maximization (EM) algorithm is also developed. Simulation results to evaluate the finite-sample performance of our proposed estimators are presented. We also discuss diagnostic tools for checking model adequacy. An empirical application concerning the number of inpatient admissions by individuals to hospital emergency rooms, from the Medical Expenditure Panel Survey (MEPS) conducted by the United States Agency for Health Research and Quality, illustrates the usefulness of our proposed methodology.
Investigating the Potential of Auxiliary-Classifier GANs for Image Classification in Low Data Regimes

Generative Adversarial Networks (GANs) have shown promise in augmenting datasets and boosting convolutional neural networks' (CNN) performance on image classification tasks. But they introduce more hyperparameters to tune as well as the need for additional time and computational power to train supplementary to the CNN. In this work, we examine the potential for Auxiliary-Classifier GANs (AC-GANs) as a 'one-stop-shop' architecture for image classification, particularly in low data regimes. Additionally, we explore modifications to the typical AC-GAN framework, changing the generator's latent space sampling scheme and employing a Wasserstein loss with gradient penalty to stabilize the simultaneous training of image synthesis and classification. Through experiments on images of varying resolutions and complexity, we demonstrate that AC-GANs show promise in image classification, achieving competitive performance with standard CNNs. These methods can be employed as an 'all-in-one' framework with particular utility in the absence of large amounts of training data.
Analysis of the Level of Satisfaction and Importance among Trainees towards Distance Training in the Covid-19 Pandemic Era

This study aims to analyze the level of satisfaction and importance among trainees towards distance training and identify the strengths and weaknesses of distance training implementation so that the organizers can maximize the satisfaction of the trainees towards the implementation of distance training. This research used quantitative and qualitative analysis within the framework of Level 1 (Reaction) of the Kirkpatrik Evaluation Model, as well as the Importance Performance Analysis (IPA) method. The results showed that: (1) the trainees were very satisfied with the implementation of distance training; (2) the implementation of distance training is considered very important to be implemented; (3) Some evaluation indicators have positive gap values, while others have negative gap values, meaning that some evaluation indicators have not met the expectations of stakeholders; and (4) most evaluation indicators are in quadrants A and B, meaning that they either must be prioritized to improve the performance, or maintained because they are already considered good. Therefore, the Financial Education and Training Agency through work units in the region, especially the Makassar branch, needs to improve the performance of distance training implementation, especially in the evaluation items/indicators in quadrant A to increase the satisfaction of participants in the next training sessions.
Artificial Intelligence and Applications

Artificial intelligence and machine-learning are widely applied in all domain applications, including computer vision and natural language processing (NLP). We briefly discuss the development of edge detection, which plays an important role in representing the salience features in a wide range of computer vision applications. Meanwhile, transformer-based deep models facilitate the usage of NLP application. We introduce two ongoing research projects for pharmaceutical industry and business negotiation. We also selected five papers in the related areas for this journal issue.
How and When Random Feedback Works: A Case Study of Low-Rank Matrix Factorization

The success of gradient descent in ML and especially for learning neural networks is remarkable and robust. In the context of how the brain learns, one aspect of gradient descent that appears biologically difficult to realize (if not implausible) is that its updates rely on feedback from later layers to earlier layers through the same connections. Such bidirected links are relatively few in brain networks, and even when reciprocal connections exist, they may not be equi-weighted. Random Feedback Alignment (Lillicrap et al., 2016), where the backward weights are random and fixed, has been proposed as a bio-plausible alternative and found to be effective empirically. We investigate how and when feedback alignment (FA) works, focusing on one of the most basic problems with layered structure -- low-rank matrix factorization. In this problem, given a matrix $Y_{n\times m}$, the goal is to find a low rank factorization $Z_{n \times r}W_{r \times m}$ that minimizes the error $\|ZW-Y\|_F$. Gradient descent solves this problem optimally. We show that FA converges to the optimal solution when $r\ge \mbox{rank}(Y)$. We also shed light on how FA works. It is observed empirically that the forward weight matrices and (random) feedback matrices come closer during FA updates. Our analysis rigorously derives this phenomenon and shows how it facilitates convergence of FA*, a closely related variant of FA. We also show that FA can be far from optimal when $r < \mbox{rank}(Y)$. This is the first provable separation result between gradient descent and FA. Moreover, the representations found by gradient descent and FA can be almost orthogonal even when their error $\|ZW-Y\|_F$ is approximately equal. As a corollary, these results also hold for training two-layer linear neural networks when the training input is isotropic, and the output is a linear function of the input.
Portrait Segmentation Using Deep Learning

A portrait is a painting, drawing, photograph, or engraving of a person, especially one depicting only the face or head and shoulders. In the digital world the portrait of a person is captured by having the person as a subject in the image and capturing the image of the person such that the background is blurred. DSLRs generally do it by reducing the aperture to focus on very close regions of interest and automatically blur the background. In this paper I have come up with a novel approach to replicate the portrait mode from DSLR using any smartphone to generate high quality portrait images.
Modulated Contrast for Versatile Image Synthesis

Perceiving the similarity between images has been a long-standing and fundamental problem underlying various visual generation tasks. Predominant approaches measure the inter-image distance by computing pointwise absolute deviations, which tends to estimate the median of instance distributions and leads to blurs and artifacts in the generated images. This paper presents MoNCE, a versatile metric that introduces image contrast to learn a calibrated metric for the perception of multifaceted inter-image distances. Unlike vanilla contrast which indiscriminately pushes negative samples from the anchor regardless of their similarity, we propose to re-weight the pushing force of negative samples adaptively according to their similarity to the anchor, which facilitates the contrastive learning from informative negative samples. Since multiple patch-level contrastive objectives are involved in image distance measurement, we introduce optimal transport in MoNCE to modulate the pushing force of negative samples collaboratively across multiple contrastive objectives. Extensive experiments over multiple image translation tasks show that the proposed MoNCE outperforms various prevailing metrics substantially. The code is available at https://github.com/fnzhan/MoNCE.
Label-Free Explainability for Unsupervised Models

Unsupervised black-box models are challenging to interpret. Indeed, most existing explainability methods require labels to select which component(s) of the black-box's output to interpret. In the absence of labels, black-box outputs often are representation vectors whose components do not correspond to any meaningful quantity. Hence, choosing which component(s) to interpret in a label-free unsupervised/self-supervised setting is an important, yet unsolved problem. To bridge this gap in the literature, we introduce two crucial extensions of post-hoc explanation techniques: (1) label-free feature importance and (2) label-free example importance that respectively highlight influential features and training examples for a black-box to construct representations at inference time. We demonstrate that our extensions can be successfully implemented as simple wrappers around many existing feature and example importance methods. We illustrate the utility of our label-free explainability paradigm through a qualitative and quantitative comparison of representation spaces learned by various autoencoders trained on distinct unsupervised tasks.
Effectiveness of Group Guidance Services using Simulation Games Techniques to Improve Students’ Prosocial Behavior in Grade VIII of State Junior High School 1 Bantul

Prosocial behavior is actions taken or planned to help others, regardless of the actor's motives.Individuals who have high prosocial behavior will help others without expecting anything in return.However, prosocial values are declining.Therefore, action is needed to improve students' prosocial behavior, including by providing group guidance services.This study aims to determine the effectiveness of group guidance service using simulation game techniques to improve students' prosocial behavior in Grade VIII of State Junior High School 1 Bantul.This quantitative study used a pre-experimental design, one group pretest-posttest design model.The simulation game technique was carried out for six treatments.The subjects of this study were 10 students in grade VIII of State Junior High School 1 Bantul with a low category of prosocial behavior.The data were collected by using a prosocial behavior scale.The data were analyzed using paired sample t-test.Based on the results of the study, it can be seen that the group guidance service using simulation game techniques are effective in improving the students' prosocial behavior in Grade VIII of State Junior High School 1 Bantul.
CSG0: Continual Urban Scene Generation with Zero Forgetting

With the rapid advances in generative adversarial networks (GANs), the visual quality of synthesised scenes keeps improving, including for complex urban scenes with applications to automated driving. We address in this work a continual scene generation setup in which GANs are trained on a stream of distinct domains; ideally, the learned models should eventually be able to generate new scenes in all seen domains. This setup reflects the real-life scenario where data are continuously acquired in different places at different times. In such a continual setup, we aim for learning with zero forgetting, \IE, with no degradation in synthesis quality over earlier domains due to catastrophic forgetting. To this end, we introduce a novel framework that not only (i) enables seamless knowledge transfer in continual training but also (ii) guarantees zero forgetting with a small overhead cost. While being more memory efficient, thanks to continual learning, our model obtains better synthesis quality as compared against the brute-force solution that trains one full model for each domain. Especially, under extreme low-data regimes, our approach outperforms the brute-force one by a large margin.
Real Time Action Recognition from Video Footage

Crime rate is increasing proportionally with the increasing rate of the population. The most prominent approach was to introduce Closed-Circuit Television (CCTV) camera-based surveillance to tackle the issue. Video surveillance cameras have added a new dimension to detect crime. Several research works on autonomous security camera surveillance are currently ongoing, where the fundamental goal is to discover violent activity from video feeds. From the technical viewpoint, this is a challenging problem because analyzing a set of frames, i.e., videos in temporal dimension to detect violence might need careful machine learning model training to reduce false results. This research focuses on this problem by integrating state-of-the-art Deep Learning methods to ensure a robust pipeline for autonomous surveillance for detecting violent activities, e.g., kicking, punching, and slapping. Initially, we designed a dataset of this specific interest, which contains 600 videos (200 for each action). Later, we have utilized existing pre-trained model architectures to extract features, and later used deep learning network for classification. Also, We have classified our models' accuracy, and confusion matrix on different pre-trained architectures like VGG16, InceptionV3, ResNet50, Xception and MobileNet V2 among which VGG16 and MobileNet V2 performed better.
On the Acquisition of Stationary Signals Using Uniform ADCs

In this work, we consider the acquisition of stationary signals using uniform analog-to-digital converters (ADCs), i.e., employing uniform sampling and scalar uniform quantization. We jointly optimize the pre-sampling and reconstruction filters to minimize the time-averaged mean-squared error (TMSE) in recovering the continuous-time input signal for a fixed sampling rate and quantizer resolution and obtain closed-form expressions for the minimal achievable TMSE. We show that the TMSE-minimizing pre-sampling filter omits aliasing and discards weak frequency components to resolve the remaining ones with higher resolution when the rate budget is small. In our numerical study, we validate our results and show that sub-Nyquist sampling often minimizes the TMSE under tight rate budgets at the output of the ADC.
FaSS-MVS -- Fast Multi-View Stereo with Surface-Aware Semi-Global Matching from UAV-borne Monocular Imagery

With FaSS-MVS, we present an approach for fast multi-view stereo with surface-aware Semi-Global Matching that allows for rapid depth and normal map estimation from monocular aerial video data captured by UAVs. The data estimated by FaSS-MVS, in turn, facilitates online 3D mapping, meaning that a 3D map of the scene is immediately and incrementally generated while the image data is acquired or being received. FaSS-MVS is comprised of a hierarchical processing scheme in which depth and normal data, as well as corresponding confidence scores, are estimated in a coarse-to-fine manner, allowing to efficiently process large scene depths which are inherent to oblique imagery captured by low-flying UAVs. The actual depth estimation employs a plane-sweep algorithm for dense multi-image matching to produce depth hypotheses from which the actual depth map is extracted by means of a surface-aware semi-global optimization, reducing the fronto-parallel bias of SGM. Given the estimated depth map, the pixel-wise surface normal information is then computed by reprojecting the depth map into a point cloud and calculating the normal vectors within a confined local neighborhood. In a thorough quantitative and ablative study we show that the accuracies of the 3D information calculated by FaSS-MVS is close to that of state-of-the-art approaches for offline multi-view stereo, with the error not even being one magnitude higher than that of COLMAP. At the same time, however, the average run-time of FaSS-MVS to estimate a single depth and normal map is less than 14 % of that of COLMAP, allowing to perform an online and incremental processing of Full-HD imagery at 1-2 Hz.
Stereo Magnification with Multi-Layer Images

Representing scenes with multiple semi-transparent colored layers has been a popular and successful choice for real-time novel view synthesis. Existing approaches infer colors and transparency values over regularly-spaced layers of planar or spherical shape. In this work, we introduce a new view synthesis approach based on multiple semi-transparent layers with scene-adapted geometry. Our approach infers such representations from stereo pairs in two stages. The first stage infers the geometry of a small number of data-adaptive layers from a given pair of views. The second stage infers the color and the transparency values for these layers producing the final representation for novel view synthesis. Importantly, both stages are connected through a differentiable renderer and are trained in an end-to-end manner. In the experiments, we demonstrate the advantage of the proposed approach over the use of regularly-spaced layers with no adaptation to scene geometry. Despite being orders of magnitude faster during rendering, our approach also outperforms a recently proposed IBRNet system based on implicit geometry representation. See results at https://samsunglabs.github.io/StereoLayers .
Locality-based Graph Reordering for Processing Speed-Ups and Impact of Diameter

Graph analysis involves a high number of random memory access patterns. Earlier research has shownthat the cache miss latency is responsible for more than half of the graph processing time, with the CPU execution having the smaller share. There has been significant study on decreasing the CPU computing time for example, by employing better cache prefetching and replacement policies. In thispaper, we study the various methods that do so by attempting to decrease the CPU cache miss ratio.Graph Reordering attempts to exploit the power-law distribution of graphs -- few sparsely-populated vertices in the graph have high number of connections -- to keep the frequently accessed vertices together locally and hence decrease the cache misses. However, reordering the graph by keeping the hot vertices together may affect the spatial locality of the graph, and thus add to the total CPU compute time.Also, we also need to have a control over the total reordering time and its inverse relation with thefinal CPU execution timeIn order to exploit this trade-off between reordering as per vertex hotness and spatial locality, we introduce the light-weight Community-based Reordering. We attempt to maintain the community-structureof the graph by storing the hot-members in the community locally together. The implementation also takes into consideration the impact of graph diameter on the execution time. We compare our implementation with other reordering implementations and find a significantly better result on five graph processing algorithms: BFS, CC, CCSV, PR and BC. Lorder achieved speed-up of upto 7x and an average speed-up of 1.2x as compared to other reordering algorithms