Reviewerly/exHarmony huggingface dataset:
INTERDISCIPLINARY INTEGRATION AS THE FACTOR IN IMPROVING THE QUALITY OF EDUCATION

In this paper, the problem of interdisciplinary integration in the educational system is investigated. It was shown that interdisciplinary integration is presented as a means of increasing students' independent creative abilities, increasing and shaping their worldview and scientific thinking, and ultimately improving the entire educational process
Learning Markov State Abstractions for Deep Reinforcement Learning

The fundamental assumption of reinforcement learning in Markov decision processes (MDPs) is that the relevant decision process is, in fact, Markov. However, when MDPs have rich observations, agents typically learn by way of an abstract state representation, and such representations are not guaranteed to preserve the Markov property. We introduce a novel set of conditions and prove that they are sufficient for learning a Markov abstract state representation. We then describe a practical training procedure that combines inverse model estimation and temporal contrastive learning to learn an abstraction that approximately satisfies these conditions. Our novel training objective is compatible with both online and offline training: it does not require a reward signal, but agents can capitalize on reward information when available. We empirically evaluate our approach on a visual gridworld domain and a set of continuous control benchmarks. Our approach learns representations that capture the underlying structure of the domain and lead to improved sample efficiency over state-of-the-art deep reinforcement learning with visual features -- often matching or exceeding the performance achieved with hand-designed compact state information.
Learning latent causal graphs via mixture oracles

We study the problem of reconstructing a causal graphical model from data in the presence of latent variables. The main problem of interest is recovering the causal structure over the latent variables while allowing for general, potentially nonlinear dependence between the variables. In many practical problems, the dependence between raw observations (e.g. pixels in an image) is much less relevant than the dependence between certain high-level, latent features (e.g. concepts or objects), and this is the setting of interest. We provide conditions under which both the latent representations and the underlying latent causal model are identifiable by a reduction to a mixture oracle. The proof is constructive, and leads to several algorithms for explicitly reconstructing the full graphical model. We discuss efficient algorithms and provide experiments illustrating the algorithms in practice.
Closed-Loop Data Transcription to an LDR via Minimaxing Rate Reduction.

This work proposes a new computational framework for learning an explicit generative model for real-world datasets. In particular we propose to learn {\em a closed-loop transcription} between a multi-class multi-dimensional data distribution and a { linear discriminative representation (LDR)} in the feature space that consists of multiple independent multi-dimensional linear subspaces. In particular, we argue that the optimal encoding and decoding mappings sought can be formulated as the equilibrium point of a {\em two-player minimax game between the encoder and decoder}. A natural utility function for this game is the so-called {\em rate reduction}, a simple information-theoretic measure for distances between mixtures of subspace-like Gaussians in the feature space. Our formulation draws inspiration from closed-loop error feedback from control systems and avoids expensive evaluating and minimizing approximated distances between arbitrary distributions in either the data space or the feature space. To a large extent, this new formulation unifies the concepts and benefits of Auto-Encoding and GAN and naturally extends them to the settings of learning a {\em both discriminative and generative} representation for multi-class and multi-dimensional real-world data. Our extensive experiments on many benchmark imagery datasets demonstrate tremendous potential of this new closed-loop formulation: under fair comparison, visual quality of the learned decoder and classification performance of the encoder is competitive and often better than existing methods based on GAN, VAE, or a combination of both. We notice that the so learned features of different classes are explicitly mapped onto approximately {\em independent principal subspaces} in the feature space; and diverse visual attributes within each class are modeled by the {\em independent principal components} within each subspace.
Evaluating the Evaluation Metrics for Style Transfer: A Case Study in Multilingual Formality Transfer

While the field of style transfer (ST) has been growing rapidly, it has been hampered by a lack of standardized practices for automatic evaluation. In this paper, we evaluate leading automatic metrics on the oft-researched task of formality style transfer. Unlike previous evaluations, which focus solely on English, we expand our focus to Brazilian-Portuguese, French, and Italian, making this work the first multilingual evaluation of metrics in ST. We outline best practices for automatic evaluation in (formality) style transfer and identify several models that correlate well with human judgments and are robust across languages. We hope that this work will help accelerate development in ST, where human evaluation is often challenging to collect.
Self-Attention Between Datapoints: Going Beyond Individual Input-Output Pairs in Deep Learning

We challenge a common assumption underlying most supervised deep learning: that a model makes a prediction depending only on its parameters and the features of a single input. To this end, we introduce a general-purpose deep learning architecture that takes as input the entire dataset instead of processing one datapoint at a time. Our approach uses self-attention to reason about relationships between datapoints explicitly, which can be seen as realizing non-parametric models using parametric attention mechanisms. However, unlike conventional non-parametric models, we let the model learn end-to-end from the data how to make use of other datapoints for prediction. Empirically, our models solve cross-datapoint lookup and complex reasoning tasks unsolvable by traditional deep learning models. We show highly competitive results on tabular data, early results on CIFAR-10, and give insight into how the model makes use of the interactions between points.
DATA LOSS PREVENTION USING POST QUANTUM CRYPTOGRAPHY: OVERVIEW OF ROUND-3 ALGORITHMS

The current hype of quantum computing has necessitated the need for computer security stakeholders to call for the design of security algorithms that will be quantum efficient when quantum computers finally grace our computing sphere. Recent advancements in quantum computing have made cryptographic schemes more vulnerable to quantum attacks like Shor’s algorithm and Grove’s algorithm. Therefore NIST call for a new set of algorithms known as Post-Quantum cryptography that would be quantum proof is imminent.  Many Post quantum algorithms have been designed and tested. But only few of them made it to the round 3 (the final round). This paper reviewed these post quantum candidates. Literatures highlighting their scheme, properties, implementation and areas of security coverage was reviewed. Recommendations on future research areas in this field was itemized for this novel security paradigm as we await the final standardization of this cryptosystems.
DISTANCE EDUCATION: CURRENT STATUS, PROBLEMS AND PROSPECTS

Nowadays distance learning is one of the key areas of renewal of all parts of educational system of Ukraine. With the spread of global corona virus pandemic, more than 1.3 billion students and young people around the world have been forced to move to an online learning format. Therefore, higher educational establishments faced the problem of changing the methodology of organization of the educational process. An urgent task was the organization of educational interaction in a distance format, namely with the help of distance learning technologies. The purpose of the article is to outline the current state, advantages, disadvantages and prospects of distance education in higher education in Ukraine. The article considers the tendencies of higher education development in modern conditions. The current state of distance education in Ukraine is analyzed. Using his own experience of implementing distance learning in higher education, the main problems and advantages of its use in the educational process are highlighted. Prospects for the introduction of distance education in Ukrainian universities are studied. In the process of research, general scientific theoretical methods were used: system analysis - to clarify the main categories of research; method of induction - at the stage of collecting, systematizing and processing information for the study; method of deduction - in the process of theoretical understanding of the problem; abstract-logical method - for the implementation of theoretical generalizations and conclusions about the current state, advantages, disadvantages and prospects of distance education in higher education in METHODS OF TEACHING AT HIGHER EDUCATIONAL ESTABLISHMENTS Issue I (81), 2021 157 Ukraine and empirical methods - diagnostic, namely direct and indirect observation, conversations with teachers and students, analysis of work experience teachers. The practical significance of the results is that the advantages and disadvantages identified in the study of distance learning will allow teachers to take into account these factors in the implementation of distance learning in higher education and make effective adjustments to further experimental work. A promising area of further research is to study and summarize the experience of online learning in universities that are leaders in education and have achieved significant success in various innovations aimed at improving its quality and diversifying forms and methods of teaching. It is advisable to conduct more research on the feedback of students and teachers, as well as wishes for the improvement of distance education, as they are objects and subjects of educational activities that are experiencing a period of formation of distance learning at the stage of its formation and early development.
Efficient and Local Parallel Random Walks

Random walks are a fundamental primitive used in many machine learning algorithms with several applications in clustering and semi-supervised learning. Despite their relevance, the first efficient parallel algorithm to compute random walks has been introduced very recently (Lacki et al.). Unfortunately their method has a fundamental shortcoming: their algorithm is non-local in that it heavily relies on computing random walks out of all nodes in the input graph, even though in many practical applications one is interested in computing random walks only from a small subset of nodes in the graph. In this paper, we present a new algorithm that overcomes this limitation by building random walk efficiently and locally at the same time. We show that our technique is both memory and round efficient, and in particular yields an efficient parallel local clustering algorithm. Finally, we complement our theoretical analysis with experimental results showing that our algorithm is significantly more scalable than previous approaches.
Differential Privacy Dynamics of Langevin Diffusion and Noisy Gradient Descent

What is the information leakage of an iterative learning algorithm about its training data, when the internal state of the algorithm is \emph{not} observable? How much is the contribution of each specific training epoch to the final leakage? We study this problem for noisy gradient descent algorithms, and model the \emph{dynamics} of Renyi differential privacy loss throughout the training process. Our analysis traces a provably tight bound on the Renyi divergence between the pair of probability distributions over parameters of models with neighboring datasets. We prove that the privacy loss converges exponentially fast, for smooth and strongly convex loss functions, which is a significant improvement over composition theorems. For Lipschitz, smooth, and strongly convex loss functions, we prove optimal utility for differential privacy algorithms with a small gradient complexity.
The role of democracy in economic growth : A (brief) literature review

Abstract: This article presents a brief review of recent work by economists studying the effects of democracy on economic growth. These studies are divided into three groups: the first one supports the general view that democracy has a positive effect on economic growth; democratic regimes increase the level of GDP by creating the climate of trust, guaranteeing the protection of property rights, as well as reducing transaction costs, while dictatorship regimes strongly hinder economic growth. The second group of researchers stipulated that non-democratic regimes would be more favorable to increasing economic growth. For the third research group, there is no significant relationship between the two variables.The common point of these studies, despite the absence of a consensus, is their insistence that the political regime matters for economic growth and development.Keywords: Economic growth - Democracy - Institutions
Towards Optimal Strategies for Training Self-Driving Perception Models in Simulation

Autonomous driving relies on a huge volume of real-world data to be labeled to high precision. Alternative solutions seek to exploit driving simulators that can generate large amounts of labeled data with a plethora of content variations. However, the domain gap between the synthetic and real data remains, raising the following important question: What are the best ways to utilize a self-driving simulator for perception tasks? In this work, we build on top of recent advances in domain-adaptation theory, and from this perspective, propose ways to minimize the reality gap. We primarily focus on the use of labels in the synthetic domain alone. Our approach introduces both a principled way to learn neural-invariant representations and a theoretically inspired view on how to sample the data from the simulator. Our method is easy to implement in practice as it is agnostic of the network architecture and the choice of the simulator. We showcase our approach on the bird's-eye-view vehicle segmentation task with multi-sensor data (cameras, lidar) using an open-source simulator (CARLA), and evaluate the entire framework on a real-world dataset (nuScenes). Last but not least, we show what types of variations (e.g. weather conditions, number of assets, map design, and color diversity) matter to perception networks when trained with driving simulators, and which ones can be compensated for with our domain adaptation technique.
Escape saddle points by a simple gradient-descent based algorithm

Escaping saddle points is a central research topic in nonconvex optimization. In this paper, we propose a simple gradient-based algorithm such that for a smooth function $f\colon\mathbb{R}^n\to\mathbb{R}$, it outputs an $\epsilon$-approximate second-order stationary point in $\tilde{O}(\log n/\epsilon^{1.75})$ iterations. Compared to the previous state-of-the-art algorithms by Jin et al. with $\tilde{O}((\log n)^{4}/\epsilon^{2})$ or $\tilde{O}((\log n)^{6}/\epsilon^{1.75})$ iterations, our algorithm is polynomially better in terms of $\log n$ and matches their complexities in terms of $1/\epsilon$. For the stochastic setting, our algorithm outputs an $\epsilon$-approximate second-order stationary point in $\tilde{O}((\log n)^{2}/\epsilon^{4})$ iterations. Technically, our main contribution is an idea of implementing a robust Hessian power method using only gradients, which can find negative curvature near saddle points and achieve the polynomial speedup in $\log n$ compared to the perturbed gradient descent methods. Finally, we also perform numerical experiments that support our results.
NEED ANALYSIS: ENGLISH FOR CUSTOMER SERVICE OFFICER AT BPJS KETENAGAKERJAAN

As an Indonesian public legal body that provides social security for not only Indonesian but also foreign workers, having the English ability is one of the important abilities for customer service at BPJS Ketenagakerjaan. Therefore, it is deemed necessary to design English learning materials following the needs for customer service which is also in line with the scope of [ESP?]. This study aims to determine the need for proper English learning to design ESP materials for customer service in BPJS Ketenagakerjaan based on the result of the need analysis. The subjects of this study are 15 customers service at BPJS Ketenagakerjaan while the quantitative research method is applied by utilizing Google Form for collecting information. The results of this study indicate that customer service at BPJS Ketenagakerjaan requires English more as a communication medium in the process of interviewing foreign customers where 60% of respondents stated that English is used more for listening and speaking. Furthermore, 40% stated that they use English in listening, speaking, and writing. This needs analysis is expected to provide relevant data related to the English needs for customers service at BPJS Ketenagakerjaan and can be useful in designing more suitable learning materials.
Internetowe szaty filozofii epoki informatycznej

In this review, we present the philosophical blog “Cafe Aleph” (https://marciszewski.eu), which was founded by Witold Marciszewski in collaboration with Pawel Stacewicz. The blog contains numerous expert entries on issues from the border of philosophy, science and technology. “Cafe Aleph” is an area for developing, popularizing and discussing the idea of philosophy of computing.
Demystifying and Generalizing BinaryConnect

BinaryConnect (BC) and its many variations have become the de facto standard for neural network quantization. However, our understanding of the inner workings of BC is still quite limited. We attempt to close this gap in four different aspects: (a) we show that existing quantization algorithms, including post-training quantization, are surprisingly similar to each other; (b) we argue for proximal maps as a natural family of quantizers that is both easy to design and analyze; (c) we refine the observation that BC is a special case of dual averaging, which itself is a special case of the generalized conditional gradient algorithm; (d) consequently, we propose ProxConnect (PC) as a generalization of BC and we prove its convergence properties by exploiting the established connections. We conduct experiments on CIFAR-10 and ImageNet, and verify that PC achieves competitive performance.
Credal Self-Supervised Learning

Self-training is an effective approach to semi-supervised learning. The key idea is to let the learner itself iteratively generate pseudo-supervision for unlabeled instances based on its current hypothesis. In combination with consistency regularization, pseudo-labeling has shown promising performance in various domains, for example in computer vision. To account for the hypothetical nature of the pseudo-labels, these are commonly provided in the form of probability distributions. Still, one may argue that even a probability distribution represents an excessive level of informedness, as it suggests that the learner precisely knows the ground-truth conditional probabilities. In our approach, we therefore allow the learner to label instances in the form of credal sets, that is, sets of (candidate) probability distributions. Thanks to this increased expressiveness, the learner is able to represent uncertainty and a lack of knowledge in a more flexible and more faithful manner. To learn from weakly labeled data of that kind, we leverage methods that have recently been proposed in the realm of so-called superset learning. In an exhaustive empirical evaluation, we compare our methodology to state-of-the-art self-supervision approaches, showing competitive to superior performance especially in low-label scenarios incorporating a high degree of uncertainty.
Quantum homotopy perturbation method for nonlinear dissipative ordinary differential equations.

While quantum computing provides an exponential advantage in solving linear differential equations, there are relatively few quantum algorithms for solving nonlinear differential equations. In our work, based on the homotopy perturbation method, we propose a quantum algorithm for solving $n$-dimensional nonlinear dissipative ordinary differential equations (ODEs). Our algorithm first converts the original nonlinear ODEs into the other nonlinear ODEs which can be embedded into finite-dimensional linear ODEs. Then we solve the embedded linear ODEs with quantum linear ODEs algorithm and obtain a state $\epsilon$-close to the normalized exact solution of the original nonlinear ODEs with success probability $\Omega(1)$. The complexity of our algorithm is $O(g\eta T{\rm poly}(\log(nT/\epsilon)))$, where $\eta$, $g$ measure the decay of the solution. Our algorithm provides exponential improvement over the best classical algorithms or previous quantum algorithms in $n$ or $\epsilon$.
The agency of immersive virtual reality systems in a codesign situation: Comparative study between the Hyve-3D and a traditional environment

This article presents a comparative study between the agency—that is, the power to act—of an immersive social virtual reality device (without glasses) and that of a traditional environment (paper and pencil) in a codesign situation. It is interesting to note that these immersive devices rely on a unique spatial perception by projecting the designers, via simulation, into the heart of problems and conceptual solutions. This grants an agency consisting of a critical perception articulated dynamically through the assimilation of the past, reflection around the present, and projection into the future.
LMSOC: An Approach for Socially Sensitive Pretraining.

While large-scale pretrained language models have been shown to learn effective linguistic representations for many NLP tasks, there remain many real-world contextual aspects of language that current approaches do not capture. For instance, consider a cloze test “I enjoyed the _____ game this weekend”: the correct answer depends heavily on where the speaker is from, when the utterance occurred, and the speaker’s broader social milieu and preferences. Although language depends heavily on the geographical, temporal, and other social contexts of the speaker, these elements have not been incorporated into modern transformer-based language models. We propose a simple but effective approach to incorporate speaker social context into the learned representations of large-scale language models. Our method first learns dense representations of social contexts using graph representation learning algorithms and then primes language model pretraining with these social context representations. We evaluate our approach on geographically-sensitive language modeling tasks and show a substantial improvement (more than 100% relative lift on MRR) compared to baselines.