Reviewerly/exHarmony huggingface dataset:
No RL, No Simulation: Learning to Navigate without Navigating

Most prior methods for learning navigation policies require access to simulation environments, as they need online policy interaction and rely on ground-truth maps for rewards. However, building simulators is expensive (requires manual effort for each and every scene) and creates challenges in transferring learned policies to robotic platforms in the real-world, due to the sim-to-real domain gap. In this paper, we pose a simple question: Do we really need active interaction, ground-truth maps or even reinforcement-learning (RL) in order to solve the image-goal navigation task? We propose a self-supervised approach to learn to navigate from only passive videos of roaming. Our approach, No RL, No Simulator (NRNS), is simple and scalable, yet highly effective. NRNS outperforms RL-based formulations by a significant margin. We present NRNS as a strong baseline for any future image-based navigation tasks that use RL or Simulation.
On the Second-order Convergence Properties of Random Search Methods

We study the theoretical convergence properties of random-search methods when optimizing non-convex objective functions without having access to derivatives. We prove that standard random-search methods that do not rely on second-order information converge to a second-order stationary point. However, they suffer from an exponential complexity in terms of the input dimension of the problem. In order to address this issue, we propose a novel variant of random search that exploits negative curvature by only relying on function evaluations. We prove that this approach converges to a second-order stationary point at a much faster rate than vanilla methods: namely, the complexity in terms of the number of function evaluations is only linear in the problem dimension. We test our algorithm empirically and find good agreements with our theoretical results.
정책에 의해 비자발적으로 구성된 교사학습공동체 : 교사의 경험과 성찰

This study exploresd teacher learning communities in an elementary school involuntarily organized by policy based on qualitative action research. It investigateds what teachers experienced and reflected on through the participation of the learning community. Teachers expressed the joy of teaching children, the joy of teacher growth, and the joy of studying with the community. Teachers discussed ways to become a better teacher learning community at the interim evaluation meeting. It wias suggested to get closebecome familiar with each other first, stand together at the starting line, become receptive leaders and cooperative participants, compromise on different needs, and focus on class improvement. At this teacher learning community, teachers do not show formal cooperation, demonstrative performance, and loss of autonomy that are criticized by other studies on involuntary teacher learning communities. This This isresult was found to befound to be because the principal and leaders were persuadedd the teachers of its necessity rather than unilaterally forcing the participation of the learning community, while t. The community was focusinged on class improvement and was was running with respect to other voices.
SupCL-Seq: Supervised Contrastive Learning for Downstream Optimized Sequence Representations.

While contrastive learning is proven to be an effective training strategy in computer vision, Natural Language Processing (NLP) is only recently adopting it as a self-supervised alternative to Masked Language Modeling (MLM) for improving sequence representations. This paper introduces SupCL-Seq, which extends the supervised contrastive learning from computer vision to the optimization of sequence representations in NLP. By altering the dropout mask probability in standard Transformer architectures (e.g. BERT-base), for every representation (anchor), we generate augmented altered views. A supervised contrastive loss is then utilized to maximize the system’s capability of pulling together similar samples (e.g., anchors and their altered views) and pushing apart the samples belonging to the other classes. Despite its simplicity, SupCL-Seq leads to large gains in many sequence classification tasks on the GLUE benchmark compared to a standard BERT-base, including 6% absolute improvement on CoLA, 5.4% on MRPC, 4.7% on RTE and 2.6% on STS-B. We also show consistent gains over self-supervised contrastively learned representations, especially in non-semantic tasks. Finally we show that these gains are not solely due to augmentation, but rather to a downstream optimized sequence representation.
Provable Representation Learning for Imitation with Contrastive Fourier Features

In imitation learning, it is common to learn a behavior policy to match an unknown target policy via max-likelihood training on a collected set of target demonstrations. In this work, we consider using offline experience datasets - potentially far from the target distribution - to learn low-dimensional state representations that provably accelerate the sample-efficiency of downstream imitation learning. A central challenge in this setting is that the unknown target policy itself may not exhibit low-dimensional behavior, and so there is a potential for the representation learning objective to alias states in which the target policy acts differently. Circumventing this challenge, we derive a representation learning objective which provides an upper bound on the performance difference between the target policy and a lowdimensional policy trained with max-likelihood, and this bound is tight regardless of whether the target policy itself exhibits low-dimensional structure. Moving to the practicality of our method, we show that our objective can be implemented as contrastive learning, in which the transition dynamics are approximated by either an implicit energy-based model or, in some special cases, an implicit linear model with representations given by random Fourier features. Experiments on both tabular environments and high-dimensional Atari games provide quantitative evidence for the practical benefits of our proposed objective.
INCREASING THE FIRST YEAR STUDENTS’ SPEAKING ABILITY BY USING PARTICIPATION POINT STRATEGY IN TEACHING ENGLISH AT SMAN 14 MAKASSAR

The research dealt with increasing students' speaking ability by using a participation point strategy in teaching English. Participation point strategy was used in order to improve students' speaking ability. The problem statement of this research was to find out whether the participation point strategy is effective in improving the students speaking ability or not. This study used a quasi-experimental design. The population of this research was the first-year students of SMAN 14 Makassar, which consisted of 54 students. The sample was taken using the purposive sampling technique, which consisted of 27 students of class X.1 as an experimental class and 27 students of class X.2 as a control class. It takes 12 meetings which are covered in a pre-test, treatment, and post-test. The data were taken from the speaking test in the pre-test and post-test.Based on the result of the research, it is found that after being treated with the participation point strategy, the students are encouraged to speak in their English classes. They are actively involved in oral participation during the teaching and learning process. The giving points applied in this research have succeeded in solving the students' problem in participating in oral task in their English classes. Moreover, the participation point strategy effectively increases the first-year students' speaking ability of SMAN 14 Makassar.
A single gradient step finds adversarial examples on random two-layers neural networks

Daniely and Schacham recently showed that gradient descent finds adversarial examples on random two-layers ReLU neural networks. The term undercomplete refers to the fact that their proof only holds when the number of neurons is a vanishing fraction of the ambient dimension. We extend their result to the overcomplete case, where the number of neurons is larger than the dimension (yet also subexponential in the dimension). In fact we prove that a single step of gradient descent suffices. We also show this result for any subexponential width random neural network with smooth activation function.
Fast Axiomatic Attribution for Neural Networks.

Mitigating the dependence on spurious correlations present in the training dataset is a quickly emerging and important topic of deep learning. Recent approaches include priors on the feature attribution of a deep neural network (DNN) into the training process to reduce the dependence on unwanted features. However, until now one needed to trade off high-quality attributions, satisfying desirable axioms, against the time required to compute them. This in turn either led to long training times or ineffective attribution priors. In this work, we break this trade-off by considering a special class of efficiently axiomatically attributable DNNs for which an axiomatic feature attribution can be computed with only a single forward/backward pass. We formally prove that nonnegatively homogeneous DNNs, here termed $\mathcal{X}$-DNNs, are efficiently axiomatically attributable and show that they can be effortlessly constructed from a wide range of regular DNNs by simply removing the bias term of each layer. Various experiments demonstrate the advantages of $\mathcal{X}$-DNNs, beating state-of-the-art generic attribution methods on regular DNNs for training with attribution priors.
Understanding Instance-based Interpretability of Variational Auto-Encoders

Instance-based interpretation methods have been widely studied for supervised learning methods as they help explain how black box neural networks predict. However, instance-based interpretations remain ill-understood in the context of unsupervised learning. In this paper, we investigate influence functions [20], a popular instance-based interpretation method, for a class of deep generative models called variational auto-encoders (VAE). We formally frame the counter-factual question answered by influence functions in this setting, and through theoretical analysis, examine what they reveal about the impact of training samples on classical unsupervised learning methods. We then introduce VAE-TracIn, a computationally efficient and theoretically sound solution based on Pruthi et al. [28], for VAEs. Finally, we evaluate VAE-TracIn on several real world datasets with extensive quantitative and qualitative analysis.
Bayesian Optimization with High-Dimensional Outputs

Bayesian Optimization is a sample-efficient black-box optimization procedure that is typically applied to problems with a small number of independent objectives. However, in practice we often wish to optimize objectives defined over many correlated outcomes (or ``tasks). For example, scientists may want to optimize the coverage of a cell tower network across a dense grid of locations. Similarly, engineers may seek to balance the performance of a robot across dozens of different environments via constrained or robust optimization. However, the Gaussian Process (GP) models typically used as probabilistic surrogates for multi-task Bayesian Optimization scale poorly with the number of outcomes, greatly limiting applicability. We devise an efficient technique for exact multi-task GP sampling that combines exploiting Kronecker structure in the covariance matrices with Matheron's identity, allowing us to perform Bayesian Optimization using exact multi-task GP models with tens of thousands of correlated outputs. In doing so, we achieve substantial improvements in sample efficiency compared to existing approaches that only model aggregate functions of the outcomes. We demonstrate how this unlocks a new class of applications for Bayesian Optimization across a range of tasks in science and engineering, including optimizing interference patterns of an optical interferometer with more than 65,000 outputs.
What does the decline in the U.S. dollar’s global role mean for cryptocurrencies?

Cryptocurrencies have been at the forefront of a number of newsworthy events so far in 2021. One of those stories suggests that Bitcoin might be well-positioned to replace the U.S. dollar as the most popular global reserve currency. This article reviews what it means to be a reserve currency, as well as the costs and benefits for the U.S. to have its dollar serve as the medium of exchange for most international commercial transactions and to have it held by so many governments, central banks, and financial institutions. It then evaluates whether recent events that weaken the dollar’s influence and popularity create a path for Bitcoin or other cryptoassets to step into this role. In addition to Bitcoin, private stablecoins and central bank digital currencies (CBDCs) will be evaluated before concluding that, while the U.S. dollar might eventually be replaced as the most popular reserve currency, the time for that development has not yet arrived. Thus, the current, relatively cautious approach taken by the U.S. Federal Reserve with regard to a U.S. CBDC is probably the best option.
mDAPT: Multilingual Domain Adaptive Pretraining in a Single Model

Domain adaptive pretraining, i.e. the continued unsupervised pretraining of a language model on domain-specific text, improves the modelling of text for downstream tasks within the domain. Numerous real-world applications are based on domain-specific text, e.g. working with financial or biomedical documents, and these applications often need to support multiple languages. However, large-scale domain-specific multilingual pretraining data for such scenarios can be difficult to obtain, due to regulations, legislation, or simply a lack of language- and domain-specific text. One solution is to train a single multilingual model, taking advantage of the data available in as many languages as possible. In this work, we explore the benefits of domain adaptive pretraining with a focus on adapting to multiple languages within a specific domain. We propose different techniques to compose pretraining corpora that enable a language model to both become domain-specific and multilingual. Evaluation on nine domain-specific datasets—for biomedical named entity recognition and financial sentence classification—covering seven different languages show that a single multilingual domain-specific model can outperform the general multilingual model, and performs close to its monolingual counterpart. This finding holds across two different pretraining methods, adapter-based pretraining and full model pretraining.
Application of the augmented reality method in training of radio engineering students

Third quarter of 2021 was marked by the appearance of technologically mature augmented reality (AR) equipment, which is positioned by the leaders of all leading IT companies as the next wave of radical changes in digital technology after the spread of smartphones. The process is accompanied by the beginning of the introduction of AR in education, applications of this kind are becoming widespread. Due to the comparative novelty and attractiveness for users, the educational practice of AR is ahead of the accompanying research activity. Despite the already existing large volume of publications on this topic, most of them are preliminary, dedicated to certain private aspects. A very problematic, although natural for a new technology, circumstance is the lack of publications on the consequences of long-term use of this tool in training. At the same time, there is no doubt that there is a significant potential for improving the efficiency of educational processes via AR, especially in university practice and when teaching students of radio engineering specialties. In the Russian Federation, there is a noticeable lag in the application of augmented reality technology in educational practice for students of specialties that are not directly related to software development. The purpose of this paper is to fill in this didactic gap in radio engineering education. The paper highlights the practice of using an augmented reality application for smartphones in the educational process for three months in 2021 at Moscow State Technical University of Civil Aviation. The specific capabilities of virtual and augmented reality technologies are correlated, the expediency of their use in education compared. The results of a long-term educational application of augmented reality are shown. The paper allows to identify promising problems of using augmented reality technology in radio engineering education from a research perspective, as well as to formulate didactic recommendations.
Using the Community of Inquiry framework to analyze emojis as an emerging language in an online educational experience via WhatsApp

With the ever-growing interest in WhatsApp as a social space to accommodate pedagogical initiatives, the role of emojis as an emergent Internet language in this social space needs to be better understood as mobile technologies have been integrated in education. With that in mind, this paper reports a study that employed an embedded correlational mixed methods design, and aimed to identify: the emojis used to support cognitive, teaching, and social presences in a teacher education course via WhatsApp; participants’ attitudes regarding the use of emojis, and their age and gender relation to this use. The data generated from the interactions of the teachers via WhatsApp were analyzed and discussed based on Garrison et al.’s (1999) Community of Inquiry Model. This conceptual framework identifies crucial elements for successful online educational experiences and assumes learning occurs through the interaction of three core elements: social, cognitive and teaching presences. The findings show that emojis can effectively be used to support teaching, cognitive, and social presences. The high frequency of emojis seem to be both culturally situated and gender related.
EXCHANGE RATE VOLATILITY AND FOREIGN DIRECT INVESTMENT IN SELECTED WEST AFRICAN COUNTRIES

This paper empirically investigates the exchange rate volatility-FDI nexus in selected Economic Community of West African Sates (ECOWAS) countries using time series data from 1986-2017. Using Autoregressive Distributed Lag (ARDL) model and Toda-Yamamoto (1995) causality techniques, the effects of exchange rate volatility on FDI and causality relationship between the two are examined. The empirical results show that the estimated coefficient of nominal exchange rate volatility is negative in all the selected countries but significant only in Ghana, Sierra Leone, and Nigeria. Conversely, the effect of real exchange rate volatility is negatively significant as expected, in Nigeria, Togo, Sierra Leone, and Cote dâ€™lvore. However, the effect is positive but statistically insignificant in Ghana and Gambia. Furthermore, the causality test results show unidirectional causality from exchange rate volatility to FDI in all selected countries except in Ghana when the nominal exchange rate is employed. On the other hand, when real exchange rate volatility is employed, there is evidence of bidirectional causality between the two variables only in Nigeria and Sierra Leone.
Surviving Technical Change: How to Thrive when Software Gets Replaced

When that software you've been using for years gets replaced, your work life can be thrown into turmoil. Getting the simplest things done can take forever. If your technical support staff is overworked, understaffed, or nonexistent, things are even worse. Learn what you can do to learn the new stuff quicker and more effectively from those that have been there. We will share general guidelines based on our experience changing to a new learning management system.
Learning to Elect.

Voting systems have a wide range of applications including recommender systems, web search, product design and elections. Limited by the lack of general-purpose analytical tools, it is difficult to hand-engineer desirable voting rules for each use case. For this reason, it is appealing to automatically discover voting rules geared towards each scenario. In this paper, we show that set-input neural network architectures such as Set Transformers, fully-connected graph networks and DeepSets are both theoretically and empirically well-suited for learning voting rules. In particular, we show that these network models can not only mimic a number of existing voting rules to compelling accuracy --- both position-based (such as Plurality and Borda) and comparison-based (such as Kemeny, Copeland and Maximin) --- but also discover near-optimal voting rules that maximize different social welfare functions. Furthermore, the learned voting rules generalize well to different voter utility distributions and election sizes unseen during training.
A Comparative Study on Prejudiced Measurements of Da-tasets in three variants of Automatic Evolutionary Clustering using Teaching-Learning-Based Optimization

This paper aims to conduct automatic non-predictive learning by dividing datasets into several subsets on their preju-diced measurements. To notarize paramount recitals in this direction, the work presented in this paper is captivated by con-ducting automatic evolutionary clustering with Teaching-Learning-Based Optimization (TLBO) to organize the collection of pat-terns into clusters based on similarity and by upholding minimum intra-cluster and maximum inter-cluster distances. This work has set a goal to determine the number of clusters automatically, enriching absolute positions of clusters with optimal size, shape, low computational time and minimum error rate. The cogitative content of this work is to investigate possibilities for the improvement in classical clustering algorithms with TLBO and evolutionary automated clustering cognitions. This article opti-mizes multiple objective functions simultaneously and evaluates clustering quality regarding the goodness-of-fit of the resulting clusters against the existing methods. This treatise advances a new point of view results by testing the performance of TLBO and its advancements with automatic clustering techniques across real-time and micro-array datasets.
Latent Execution for Neural Program Synthesis Beyond Domain-Specific Languages

Program synthesis from input-output examples has been a long-standing challenge, and recent works have demonstrated some success in designing deep neural networks for program synthesis. However, existing efforts in input-output neural program synthesis have been focusing on domain-specific languages, thus the applicability of previous approaches to synthesize code in full-fledged popular programming languages, such as C, remains a question. The main challenges lie in two folds. On the one hand, the program search space grows exponentially when the syntax and semantics of the programming language become more complex, which poses higher requirements on the synthesis algorithm. On the other hand, increasing the complexity of the programming language also imposes more difficulties on data collection, since building a large-scale training set for input-output program synthesis require random program generators to sample programs and input-output examples. In this work, we take the first step to synthesize C programs from input-output examples. In particular, we propose LaSynth, which learns the latent representation to approximate the execution of partially generated programs, even if their semantics are not well-defined. We demonstrate the possibility of synthesizing elementary C code from input-output examples, and leveraging learned execution significantly improves the prediction performance over existing approaches. Meanwhile, compared to the randomly generated ground-truth programs, LaSynth synthesizes more concise programs that resemble human-written code. We show that training on these synthesized programs further improves the prediction performance for both Karel and C program synthesis, indicating the promise of leveraging the learned program synthesizer to improve the dataset quality for input-output program synthesis.