Reviewerly/exHarmony huggingface dataset:
Vectorial GP for Alzheimer’s Disease Prediction Through Handwriting Analysis

Alzheimer's Disease (AD) is a neurodegenerative disease which causes a continuous cognitive decline. This decline has a strong impact on daily life of the people affected and on that of their relatives. Unfortunately, to date there is no cure for this disease. However, its early diagnosis helps to better manage the course of the disease with the treatments currently available. In recent years, AI researchers have become increasingly interested in developing tools for early diagnosis of AD based on handwriting analysis. In most cases, they use a feature engineering approach: domain knowledge by clinicians is used to define the set of features to extract from the raw data. In this paper, we present a novel approach based on vectorial genetic programming (VE_GP) to recognize the handwriting of AD patients. VE_GP is a recently defined method that enhances Genetic Programming (GP) and is able to directly manage time series in such a way to automatically extract informative features, without any need of human intervention. We applied VE_GP to handwriting data in the form of time series consisting of spatial coordinates and pressure. These time series represent pen movements collected from people while performing handwriting tasks. The presented experimental results indicate that the proposed approach is effective for this type of application. Furthermore, VE_GP is also able to generate rather small and simple models, that can be read and possibly interpreted. These models are reported and discussed in the Last part of the paper.
A Unified Framework for Multi-distribution Density Ratio Estimation

Binary density ratio estimation (DRE), the problem of estimating the ratio $p_1/p_2$ given their empirical samples, provides the foundation for many state-of-the-art machine learning algorithms such as contrastive representation learning and covariate shift adaptation. In this work, we consider a generalized setting where given samples from multiple distributions $p_1, \ldots, p_k$ (for $k > 2$), we aim to efficiently estimate the density ratios between all pairs of distributions. Such a generalization leads to important new applications such as estimating statistical discrepancy among multiple random variables like multi-distribution $f$-divergence, and bias correction via multiple importance sampling. We then develop a general framework from the perspective of Bregman divergence minimization, where each strictly convex multivariate function induces a proper loss for multi-distribution DRE. Moreover, we rederive the theoretical connection between multi-distribution density ratio estimation and class probability estimation, justifying the use of any strictly proper scoring rule composite with a link function for multi-distribution DRE. We show that our framework leads to methods that strictly generalize their counterparts in binary DRE, as well as new methods that show comparable or superior performance on various downstream tasks.
Sparse Latent Space Acquisition with Variational Autoencoders Based on Tsallis Statistics

In machine learning, transforming features into a low-dimensional latent space has advantages such as speeding up learning and suppressing overfitting. In addition, when each feature in the latent space is independent and latent space is sparse, the overlap of information between features can be reduced. Such a sparse latent space can be useful for acquiring a policy of robot with high-dimensional sensors like a camera. To obtain the sparse latent space, a variational autoencoder based on Tsallis statistics is rearranged and analyzed. From vision information on a car racing simulation, the proposed method, which is with a more natural implementation than the previous method, can extract the sparse latent space appropriately.
College Enquiry Chat Bot System with Text to Speech

Chat bots are intelligent systems that interpret and react to users' questions in their native language. In a conversation, the chat bot reacts in the same way as a human would. It functions as a virtual assistant, and its accuracy is assessed by determining a correlation between user questions and chat bot responses. For a better user experience, the implemented Chat bot has two modes: text mode and audio mode. It provides an interactive approach of answering through voice messages when in audio mode. There is a long line at the inquiry window during the Institute's Academic Admission procedure. Even more challenging is the situation for parents who live in various cities, states, and nations. The purpose of this system is to give students and parents a place to ask questions and get answers via easy English language text messages or audio commands. Instead of queuing at an information desk to ask questions about the admissions process, students and parents will collaborate with a bot. Artificial intelligence (AI) and natural language processing (NLP) algorithms are used to create chatbots, which are intelligent systems. It effectively interacts with users and responds to their questions. Organizations, government groups, and non-profit associations are the most common users of dialogue/conversation operators. These conversational experts work for a wide range of businesses, from small start-ups to large corporations. There are a variety of code-based and interface-based chatbot development platforms available on the market.
Adversarial Attacks on Speech Recognition Systems for Mission-Critical Applications: A Survey

A Machine-Critical Application is a system that is fundamentally necessary to the success of specific and sensitive operations such as search and recovery, rescue, military, and emergency management actions. Recent advances in Machine Learning, Natural Language Processing, voice recognition, and speech processing technologies have naturally allowed the development and deployment of speech-based conversational interfaces to interact with various machine-critical applications. While these conversational interfaces have allowed users to give voice commands to carry out strategic and critical activities, their robustness to adversarial attacks remains uncertain and unclear. Indeed, Adversarial Artificial Intelligence (AI) which refers to a set of techniques that attempt to fool machine learning models with deceptive data, is a growing threat in the AI and machine learning research community, in particular for machine-critical applications. The most common reason of adversarial attacks is to cause a malfunction in a machine learning model. An adversarial attack might entail presenting a model with inaccurate or fabricated samples as it's training data, or introducing maliciously designed data to deceive an already trained model. While focusing on speech recognition for machine-critical applications, in this paper, we first review existing speech recognition techniques, then, we investigate the effectiveness of adversarial attacks and defenses against these systems, before outlining research challenges, defense recommendations, and future work. This paper is expected to serve researchers and practitioners as a reference to help them in understanding the challenges, position themselves and, ultimately, help them to improve existing models of speech recognition for mission-critical applications. Keywords: Mission-Critical Applications, Adversarial AI, Speech Recognition Systems.
Reward-Free Attacks in Multi-Agent Reinforcement Learning

We investigate how effective an attacker can be when it only learns from its victim's actions, without access to the victim's reward. In this work, we are motivated by the scenario where the attacker wants to behave strategically when the victim's motivations are unknown. We argue that one heuristic approach an attacker can use is to maximize the entropy of the victim's policy. The policy is generally not obfuscated, which implies it may be extracted simply by passively observing the victim. We provide such a strategy in the form of a reward-free exploration algorithm that maximizes the attacker's entropy during the exploration phase, and then maximizes the victim's empirical entropy during the planning phase. In our experiments, the victim agents are subverted through policy entropy maximization, implying an attacker might not need access to the victim's reward to succeed. Hence, reward-free attacks, which are based only on observing behavior, show the feasibility of an attacker to act strategically without knowledge of the victim's motives even if the victim's reward information is protected.
Understanding and Testing Generalization of Deep Networks on  Out-of-Distribution Data

Deep network models perform excellently on In-Distribution (ID) data, but can significantly fail on Out-Of-Distribution (OOD) data. While developing methods focus on improving OOD generalization, few attention has been paid to evaluating the capability of models to handle OOD data. This study is devoted to analyzing the problem of experimental ID test and designing OOD test paradigm to accurately evaluate the practical performance. Our analysis is based on an introduced categorization of three types of distribution shifts to generate OOD data. Main observations include: (1) ID test fails in neither reflecting the actual performance of a single model nor comparing between different models under OOD data. (2) The ID test failure can be ascribed to the learned marginal and conditional spurious correlations resulted from the corresponding distribution shifts. Based on this, we propose novel OOD test paradigms to evaluate the generalization capacity of models to unseen data, and discuss how to use OOD test results to find bugs of models to guide model debugging.
The Relationship of Indonesian Learning with the Environment

Learning Indonesian is closely related to the conditions of the surrounding environment. The environment is one of the influential components in the implementation of teaching and learning activities. The environment also greatly affects the success of students in the teaching and learning process. Language language that is associated with the environment directly or indirectly can affect the mindset of students. In Indonesian learning, the environment is one of the indications to form the intelligence of the heart, mind, and hands where the threecoexistences are related to each other.
Simple Control Baselines for Evaluating Transfer Learning

Transfer learning has witnessed remarkable progress in recent years, for example, with the introduction of augmentation-based contrastive self-supervised learning methods. While a number of large-scale empirical studies on the transfer performance of such models have been conducted, there is not yet an agreed-upon set of control baselines, evaluation practices, and metrics to report, which often hinders a nuanced and calibrated understanding of the real efficacy of the methods. We share an evaluation standard that aims to quantify and communicate transfer learning performance in an informative and accessible setup. This is done by baking a number of simple yet critical control baselines in the evaluation method, particularly the blind-guess (quantifying the dataset bias), scratch-model (quantifying the architectural contribution), and maximal-supervision (quantifying the upper-bound). To demonstrate how the evaluation standard can be employed, we provide an example empirical study investigating a few basic questions about self-supervised learning. For example, using this standard, the study shows the effectiveness of existing self-supervised pre-training methods is skewed towards image classification tasks versus dense pixel-wise predictions. In general, we encourage using/reporting the suggested control baselines in evaluating transfer learning in order to gain a more meaningful and informative understanding.
A Color Image Steganography Based on Frequency Sub-band Selection

Color image steganography based on deep learning is the art of hiding information in the color image. Among them, image hiding steganography(hiding image with image) has attracted much attention in recent years because of its great steganographic capacity. However, images generated by image hiding steganography may show some obvious color distortion or artificial texture traces. We propose a color image steganographic model based on frequency sub-band selection to solve the above problems. Firstly, we discuss the relationship between the characteristics of different color spaces/frequency sub-bands and the generated image quality. Then, we select the B channel of the RGB image as the embedding channel and the high-frequency sub-band as the embedding domain. DWT(discrete wavelet transformation) transforms B channel information and secret gray image into frequency domain information, and then the secret image is embedded and extracted in the frequency domain. Comprehensive experiments demonstrate that images generated by our model have better image quality, and the imperceptibility is significantly increased.
A Correlation Study between the Attitudes of the Students’ towards Mathematics with their Performance in Mathematics

The present study was conducted with an objective to identify the relation between the attitudes of students towards mathematics with their performance in mathematics. Based on the survey research design, the study was conducted in a sample of 154 ten grader students. The performances of the students in mathematics were obtained by the school administration whereas their attitudes towards mathematics were determined with the help of survey form entitled ‘Fennema-Sherman Mathematics Attitude Scale’. The survey form was divided into eight categories as attitudes towards success in mathematics, confidence in learning mathematics, mathematics anxiety, motivation in learning mathematics, teacher scale, parents scale, mathematics usefulness and mathematics as a male domain. The study concluded with the positive correlation between the performances of the students with their attitudes towards mathematics.
On the Stochastic Stability of Deep Markov Models

Deep Markov models (DMM) are generative models that are scalable and expressive generalization of Markov models for representation, learning, and inference problems. However, the fundamental stochastic stability guarantees of such models have not been thoroughly investigated. In this paper, we provide sufficient conditions of DMM's stochastic stability as defined in the context of dynamical systems and propose a stability analysis method based on the contraction of probabilistic maps modeled by deep neural networks. We make connections between the spectral properties of neural network's weights and different types of used activation functions on the stability and overall dynamic behavior of DMMs with Gaussian distributions. Based on the theory, we propose a few practical methods for designing constrained DMMs with guaranteed stability. We empirically substantiate our theoretical results via intuitive numerical experiments using the proposed stability constraints.
Visualization of COVID-19 Data in Yogyakarta City Using Data Studio

The Corona virus, which has a very fast spread rate, has spread to all provinces in Indonesia, including in the Special Region of Yogyakarta. Information related to the development of COVID-19 data is needed by the government to handle COVID-19 cases. The purpose of this study is to visualize COVID-19 data in the form of Confirmed, Died, and Recovered data in the City of Yogyakarta, which later the results of the visualization can assist the government in making policies related to handling COVID-19 cases in the City of Yogyakarta. The research method used is visualization data processing using Data Studio, the data used is from October 2021 to January 2022. The result of the research is visualization of data in the form of a COVID-19 dashboard in the City of Yogyakarta which displays Confirmed, Died, and Recovered Cases. The visualization results show that in October and November, the graph results are quite high, while the graph in December, the Confirmed and Died graph is almost non-existent or zero, while the Healing graph is quite increasing.
Relationship between Big Five Personality and Depression Index of High School Students

This research topic is about the Big Five personality and depression index, and the research question is the Relationship Between the Big Five Personality and Depression Index of High School Students.All the participants are high school students from Grade 10, Grade 11 and Grade 12, regardless of gender.The research method of this research is to first hypothesize, then collect and analyze data through GFFS and PHQ-9 questionnaire surveys, and finally overturn and confirm the hypothesis.The results of this research show that there is a certain relationship between the Big Five personality and the depression index of high school students.The data collected in this research has been calculated by SPSS for Descriptive Statistics, Correlation Coefficients, Multiple Regression, T-test and ANOVA tests.This research concludes that low conscientiousness, low extraversion and high neuroticism among the Big Five personality types of high school students have a significant relationship with the depression index.
Representing 3D Shapes with Probabilistic Directed Distance Fields

Differentiable rendering is an essential operation in modern vision, allowing inverse graphics approaches to 3D understanding to be utilized in modern machine learning frameworks. Explicit shape representations (voxels, point clouds, or meshes), while relatively easily rendered, often suffer from limited geometric fidelity or topological constraints. On the other hand, implicit representations (occupancy, distance, or radiance fields) preserve greater fidelity, but suffer from complex or inefficient rendering processes, limiting scalability. In this work, we endeavour to address both shortcomings with a novel shape representation that allows fast differentiable rendering within an implicit architecture. Building on implicit distance representations, we define Directed Distance Fields (DDFs), which map an oriented point (position and direction) to surface visibility and depth. Such a field can render a depth map with a single forward pass per pixel, enable differential surface geometry extraction (e.g., surface normals and curvatures) via network derivatives, be easily composed, and permit extraction of classical unsigned distance fields. Using probabilistic DDFs (PDDFs), we show how to model inherent discontinuities in the underlying field. Finally, we apply our method to fitting single shapes, unpaired 3D-aware generative image modelling, and single-image 3D reconstruction tasks, showcasing strong performance with simple architectural components via the versatility of our representation.
Converting single photons from an InAs/GaAs quantum dot into the ultraviolet: preservation of second-order correlations

Wavelength conversion at the single-photon level is required to forge a quantum network from distinct quantum devices. Such devices include solid-state emitters of single or entangled photons, as well as network nodes based on atoms or ions. Here we demonstrate the conversion of single photons emitted from a III-V semiconductor quantum dot at 853 nm via sum frequency conversion to the wavelength of the strong transition of Yb+ ions at 370 nm. We measure the second-order correlation function of both the unconverted and the converted photon and show that the single-photon character of the quantum dot emission is preserved during the conversion process.
One Network Doesn't Rule Them All: Moving Beyond Handcrafted Architectures in Self-Supervised Learning

The current literature on self-supervised learning (SSL) focuses on developing learning objectives to train neural networks more effectively on unlabeled data. The typical development process involves taking well-established architectures, e.g., ResNet demonstrated on ImageNet, and using them to evaluate newly developed objectives on downstream scenarios. While convenient, this does not take into account the role of architectures which has been shown to be crucial in the supervised learning literature. In this work, we establish extensive empirical evidence showing that a network architecture plays a significant role in SSL. We conduct a large-scale study with over 100 variants of ResNet and MobileNet architectures and evaluate them across 11 downstream scenarios in the SSL setting. We show that there is no one network that performs consistently well across the scenarios. Based on this, we propose to learn not only network weights but also architecture topologies in the SSL regime. We show that "self-supervised architectures" outperform popular handcrafted architectures (ResNet18 and MobileNetV2) while performing competitively with the larger and computationally heavy ResNet50 on major image classification benchmarks (ImageNet-1K, iNat2021, and more). Our results suggest that it is time to consider moving beyond handcrafted architectures in SSL and start thinking about incorporating architecture search into self-supervised learning objectives.
Bayesian nonparametric model based clustering with intractable distributions: an ABC approach

Bayesian nonparametric mixture models offer a rich framework for model based clustering. We consider the situation where the kernel of the mixture is available only up to an intractable normalizing constant. In this case, most of the commonly used Markov chain Monte Carlo (MCMC) methods are not suitable. We propose an approximate Bayesian computational (ABC) strategy, whereby we approximate the posterior to avoid the intractability of the kernel. We derive an ABC-MCMC algorithm which combines (i) the use of the predictive distribution induced by the nonparametric prior as proposal and (ii) the use of the Wasserstein distance and its connection to optimal matching problems. To overcome the sensibility with respect to the parameters of our algorithm, we further propose an adaptive strategy. We illustrate the use of the proposed algorithm with several simulation studies and an application on real data, where we cluster a population of networks, comparing its performance with standard MCMC algorithms and validating the adaptive strategy.
Zero Shot Crosslingual Eye-Tracking Data Prediction using Multilingual Transformer Models

Eye tracking data during reading is a useful source of information to understand the cognitive processes that take place during language comprehension processes. Different languages account for different brain triggers , however there seems to be some uniform indicators. In this paper, we describe our submission to the CMCL 2022 shared task on predicting human reading patterns for multi-lingual dataset. Our model uses text representations from transformers and some hand engineered features with a regression layer on top to predict statistical measures of mean and standard deviation for 2 main eye-tracking features. We train an end to end model to extract meaningful information from different languages and test our model on two seperate datasets. We compare different transformer models and show ablation studies affecting model performance. Our final submission ranked 4th place for SubTask-1 and 1st place for SubTask-2 for the shared task.
Causal Modeling With Infinitely Many Variables

Structural-equations models (SEMs) are perhaps the most commonly used framework for modeling causality. However, as we show, naively extending this framework to infinitely many variables, which is necessary, for example, to model dynamical systems, runs into several problems. We introduce GSEMs (generalized SEMs), a flexible generalization of SEMs that directly specify the results of interventions, in which (1) systems of differential equations can be represented in a natural and intuitive manner, (2) certain natural situations, which cannot be represented by SEMs at all, can be represented easily, (3) the definition of actual causality in SEMs carries over essentially without change.