Reviewerly/exHarmony huggingface dataset:
Optimistic Linear Support and Successor Features as a Basis for Optimal Policy Transfer

In many real-world applications, reinforcement learning (RL) agents might have to solve multiple tasks, each one typically modeled via a reward function. If reward functions are expressed linearly, and the agent has previously learned a set of policies for different tasks, successor features (SFs) can be exploited to combine such policies and identify reasonable solutions for new problems. However, the identified solutions are not guaranteed to be optimal. We introduce a novel algorithm that addresses this limitation. It allows RL agents to combine existing policies and directly identify optimal policies for arbitrary new problems, without requiring any further interactions with the environment. We first show (under mild assumptions) that the transfer learning problem tackled by SFs is equivalent to the problem of learning to optimize multiple objectives in RL. We then introduce an SF-based extension of the Optimistic Linear Support algorithm to learn a set of policies whose SFs form a convex coverage set. We prove that policies in this set can be combined via generalized policy improvement to construct optimal behaviors for any new linearly-expressible tasks, without requiring any additional training samples. We empirically show that our method outperforms state-of-the-art competing algorithms both in discrete and continuous domains under value function approximation.
FINGER: Fast Inference for Graph-based Approximate Nearest Neighbor Search

Approximate K-Nearest Neighbor Search (AKNNS) has now become ubiquitous in modern applications, for example, as a fast search procedure with two tower deep learning models. Graph-based methods for AKNNS in particular have received great attention due to their superior performance. These methods rely on greedy graph search to traverse the data points as embedding vectors in a database. Under this greedy search scheme, we make a key observation: many distance computations do not influence search updates so these computations can be approximated without hurting performance. As a result, we propose FINGER, a fast inference method to achieve efficient graph search. FINGER approximates the distance function by estimating angles between neighboring residual vectors with low-rank bases and distribution matching. The approximated distance can be used to bypass unnecessary computations, which leads to faster searches. Empirically, accelerating a popular graph-based method named HNSW by FINGER is shown to outperform existing graph-based methods by 20%-60% across different benchmark datasets.
LED: Latent Variable-based Estimation of Density

Modern generative models are roughly divided into two main categories: (1) models that can produce high-quality random samples, but cannot estimate the exact density of new data points and (2) those that provide exact density estimation, at the expense of sample quality and compactness of the latent space. In this work we propose LED, a new generative model closely related to GANs, that allows not only efficient sampling but also efficient density estimation. By maximizing log-likelihood on the output of the discriminator, we arrive at an alternative adversarial optimization objective that encourages generated data diversity. This formulation provides insights into the relationships between several popular generative models. Additionally, we construct a flow-based generator that can compute exact probabilities for generated samples, while allowing low-dimensional latent variables as input. Our experimental results, on various datasets, show that our density estimator produces accurate estimates, while retaining good quality in the generated samples.
A Novel Approach to Low Light Object Detection Using Exclusively Dark Images

The efficiency of our vision highly depends on the light’s intensity. In dark images, the intensity of light in our surroundings is generally lower, reducing the efficiency of vision and the capability to distinguish different objects. An analysis of lowlight images is possible with handcrafted and learned features. This process of object recognition also needs to take into consideration the intensity of light that is produced by a particular pixel varies depending on the color space used for a particular image since different colors produce different intensities of light. Therefore, the exclusively dark dataset has been used recently as a benchmark dataset for object recognition in the dark that contains 10 low light illumination types and 12 different categories of objects, and it has the potential to be used as the standard database for benchmarking research in the domain of low light. CSPNet is essential for the purpose of feature extraction. This reduces the computational load required by our model and also ensures that the accuracy does not significantly reduce. When it is coupled with the CNN, the results show potential for practical applications. The goal of this paper is to further improve the recognition rate of various objects in the dark.
Increasing Vocabulary of the Seventh Grade Students of SMPN 6 Bambalamotu Through Realia

The objective of this research was to find out whether the application of realia can increase the students’ vocabulary of especially noun, adjective and verb or not. This research was a pre experimental research design consisting of one group pre-test and post-test. There were two variables of this research. The independent variable was realia and the dependent variable was vocabulary of the seventh grade students of SMP N 6 Bambalamotu. The population of this research was seventh grade students of SMP N 6 Bambalamotu the 2020/2021 academic year. The sample of this research was selected by using total sampling technique and the selected class was grade seven consisting of 20 students. In collecting the data, the researcher conducted pre-test and post-test in order to measure the students’ vocabulary (noun, adjective and verb) before and after applying realia. The researcher analized the data statistically. The result of the test shows that the students’ mean score in the post-test (90,5) was higher than pre-test (30). To find out the significant difference between the pre-test and the post-test, the researcher compared the value of t-counted and t-table by applying 0.05 level of significance for one-tailed test with 19 degree of freedom (20-1=19), the researcher found that the t–table is 1,729. It means that the t-counted (19,139) is higher than the t-table (1,729) and the hypothesis is accepted. The researcher concludes that applying realia can increase the vocabulary of seventh grade students of SMP N 6 Bambalamotu especially noun, adjective and verb. j
PENGEMBANGAN MEDIA MAGIC BOX DALAM PEMBELAJARAN MATEMATIKA PADA SISWA KELAS III SDN 13 LUBUKLINGGAU

This study aims to be able to develop Magic Box media in learning Mathematics for third grade students of SDN.13 Lubuklinggau and to produce valid and practical media. This type of research is Research and Development using a 4D development model. The subjects in this study were students of class III SDN. 13 Lubuklinggau, totaling 11 students. Data collection techniques used in the form of interviews and questionnaires. Based on the results of the validity test of the three validators, namely linguists, material experts and media experts, the overall average score was 4.27 with a very valid category. While the results obtained from the practicality test of educators and students with an overall average score of 95.31% with a very practical category, so it can be concluded that the Magic Box media has met the valid and practical category and can be used in the mathematics learning process for students. grade III Elementary School.
Ethical Considerations of Distributed Participatory Design with Children

In this workshop, we invite researchers, practitioners and designers to reflect on ethical issues arising from Distributed Participatory Design (DPD) research with children. As participatory design research practices require rethinking and innovative adaptation in light of the COVID-19 pandemic, distributed, asynchronous and online (D)PD approaches may provide solutions to participation barriers. However, in light of this adaptation, additional ethical complexities may arise. Ongoing collaborative discussion is required to identify and address the different types of ethical issues which may arise when planning and conducting DPD projects with children. This workshop builds on previous workshops held at IDC 2021 and 2020, which provided insights into developing a protocol for a world-wide DPD project with children.
Disentangling representations in Restricted Boltzmann Machines without adversaries

A goal of unsupervised machine learning is to build representations of complex high-dimensional data, with simple relations to their properties. Such disentangled representations make easier to interpret the significant latent factors of variation in the data, as well as to generate new data with desirable features. Methods for disentangling representations often rely on an adversarial scheme, in which representations are tuned to avoid discriminators from being able to reconstruct information about the data properties (labels). Unfortunately adversarial training is generally difficult to implement in practice. Here we propose a simple, effective way of disentangling representations without any need to train adversarial discriminators, and apply our approach to Restricted Boltzmann Machines (RBM), one of the simplest representation-based generative models. Our approach relies on the introduction of adequate constraints on the weights during training, which allows us to concentrate information about labels on a small subset of latent variables. The effectiveness of the approach is illustrated with four examples: the CelebA dataset of facial images, the two-dimensional Ising model, the MNIST dataset of handwritten digits, and the taxonomy of protein families. In addition, we show how our framework allows for analytically computing the cost, in terms of log-likelihood of the data, associated to the disentanglement of their representations.
THE ACTIVITY OF HIGHER EDUCATIONAL INSTITUTION CURATOR IN THE FORMATION OF THE STUDENT GROUP COLLECTIVE

The article presents the results of the pedagogical experiment with the activity of the higher educational institution curator forming the university student collective. The main features of the student collective, the stages of its formation are revealed and characterized, criteria and indicators of its formation are developed, the content of the curators’ activity is described. The level of the collective formation was measured based on students’ self-analysis and expert assessments. The study led to the conclusion that the purposeful activity of curators contributes to the development of group self-government as an efficient form of student democracy.
Score-Guided Intermediate Layer Optimization: Fast Langevin Mixing for Inverse Problems

We prove fast mixing and characterize the stationary distribution of the Langevin Algorithm for inverting random weighted DNN generators. This result extends the work of Hand and Voroninski from efficient inversion to efficient posterior sampling. In practice, to allow for increased expressivity, we propose to do posterior sampling in the latent space of a pre-trained generative model. To achieve that, we train a score-based model in the latent space of a StyleGAN-2 and we use it to solve inverse problems. Our framework, Score-Guided Intermediate Layer Optimization (SGILO), extends prior work by replacing the sparsity regularization with a generative prior in the intermediate layer. Experimentally, we obtain significant improvements over the previous state-of-the-art, especially in the low measurement regime.
Establishing pertinence between Sorting Algorithms prevailing in n log (n) time

Abstract Data is the new fuel. With the expansion of global technology, the increasing living standards, and modernization, data values have caught a great height. Nowadays, nearly all top MNCs feed on data. Now, storing all this data is a prime concern for all of them, which is relieved by the Data Structures, the systematic way of storing data. Now, once these data are stored and charged in secure vaults, it’s time to utilize them most efficiently. Now, many operations need to be performed on these massive chunks of data, like searching, sorting, inserting, deleting, merging, and so more. In this paper, we would be comparing all the major sorting algorithms, that have prevailed to date. Further, work has been done and inequality in the dimension of time between the three Sorting algorithms, operational in \(n{log}\left(n\right)\) time, Merge, Quick, and Heap, that have been discussed in the paper have been proposed.
Smart Wheelchair Based on IoT for Disabled and Elderly People

Abstract: Pervasive devices are becoming an integral part of people's daily lives. Smart gadgets make people's lives easier and are becoming an essential part of the lives of physically tested and matured people. The need for safe and unrestricted mobility for the elderly and physically challenged is critical. The study deals with the creation of an Intelligent Wheel-Chair (IWC) that focuses on a certain issue in an exceptionally moderate and prominent manner.Wheel-Chair is controlled by RTOS as its center working framework. It comprises of touchscreen based route navigation alongside obstacle avoidance and fall identification. As a result with voice command movement of the wheel chair is dependent which will help disable and old aged people. The Node MCU used successfully detected the commanded and he Arduino micro-controller used in this project has successfully delivered its purpose.
Automatic Zig-Zag sampling in practice

Novel Monte Carlo methods to generate samples from a target distribution, such as a posterior from a Bayesian analysis, have rapidly expanded in the past decade. Algorithms based on Piecewise Deterministic Markov Processes (PDMPs), non-reversible continuous-time processes, are developing into their own research branch, thanks their important properties (e.g., correct invariant distribution, ergodicity, and super-efficiency). Nevertheless, practice has not caught up with the theory in this field, and the use of PDMPs to solve applied problems is not widespread. This might be due, firstly, to several implementational challenges that PDMP-based samplers present with and, secondly, to the lack of papers that showcase the methods and implementations in applied settings. Here, we address both these issues using one of the most promising PDMPs, the Zig-Zag sampler, as an archetypal example. After an explanation of the key elements of the Zig-Zag sampler, its implementation challenges are exposed and addressed. Specifically, the formulation of an algorithm that draws samples from a target distribution of interest is provided. Notably, the only requirement of the algorithm is a closed-form function to evaluate the target density of interest, and, unlike previous implementations, no further information on the target is needed. The performance of the algorithm is evaluated against another gradient-based sampler, and it is proven to be competitive, in simulation and real-data settings. Lastly, we demonstrate that the super-efficiency property, i.e. the ability to draw one independent sample at a lesser cost than evaluating the likelihood of all the data, can be obtained in practice.
Learning Agile Skills via Adversarial Imitation of Rough Partial Demonstrations

Learning agile skills is one of the main challenges in robotics. To this end, reinforcement learning approaches have achieved impressive results. These methods require explicit task information in terms of a reward function or an expert that can be queried in simulation to provide a target control output, which limits their applicability. In this work, we propose a generative adversarial method for inferring reward functions from partial and potentially physically incompatible demonstrations for successful skill acquirement where reference or expert demonstrations are not easily accessible. Moreover, we show that by using a Wasserstein GAN formulation and transitions from demonstrations with rough and partial information as input, we are able to extract policies that are robust and capable of imitating demonstrated behaviors. Finally, the obtained skills such as a backflip are tested on an agile quadruped robot called Solo 8 and present faithful replication of hand-held human demonstrations.
CREATING A BACKLOG OF A SOFTWARE PRODUCT AS A METHOD OF PREPARING A FUTURE IT SPECIALIST FOR PROFESSIONAL ACTIVITIES

According to the agile software development methodology, the ability to work in a team for developing software products is relevant for a future IT specialist. The possibility of organizing the activities of future IT specialists using the technology of compiling a backlog related to the Scrum methodology is discussed. An algorithm for preparing students for compiling a project backlog is proposed, including the stages at which students are divided into teams, specify the product backlog, prioritize tasks in sprint backlogs, develop and present versions of the software product, and refl on the results obtained.
An Expanded Bibliometric Study of Articles on Emerging Markets

Research Question: Bibliometric analysis of the literature on emerging markets aims to recognise the relationship between traditional metrics (i.e., citations) and alternative metrics (i.e., altmetrics) in this field. Motivation: This study is motivated by existing difficulties in measuring both the quality and societal effects of research papers. There are divided opinions among critics considering whether traditional metrics represent similar measures in comparison with altmetrics, or whether scientific and public attention are principally different categories. The study focuses on a specific field – emerging markets, considering that both opinions of scientists and the crowd are essential for driving appropriate societal changes. Understanding both metrics’ nature is crucial for their proper usage to support sustainable development efforts and link research project evaluation and financing in the field. Idea: The main idea is to examine the relationship between the number of citations and altmetric indicators and to determine the extent of overlapping individual papers/journals in the sample with the best ranking results in both categories. Data: The study uses data about articles on the topic of “emerging markets” exported from the Web of Science (WoS) database and expanded with altmetric indicators (either from providers Altmetric.com or PlumX, depending on the publisher). There are 3996 valid records collected during November 2019. Tools: This paper considers only altmetric indicators common to both providers. Analyses on the relationship of the number of citations and altmetric indicators (Pearson correlation and percentage of overlapping of top-ranked articles/journals) are performed across the entire sample or only considering selected records. Findings: Obtained results indicate that there is generally no significant relationship between observed metrics in circulation (except in the case of Mendeley). The same conclusion is reached by looking at 100 prominent individual papers in both categories in terms of overlapping, while the top 100 journals indicate a better overlap. The role of altmetrics is not to predict citation. Societal and academic impacts are rather different categories, and it is reasonable to consider the purpose of both metrics for initiating/enhancing development in emerging markets. Contribution: This expanded bibliometric study provides valuable information and orientation for researchers, journals, and academic institutions interested in emerging market growth, evaluation, and financially supporting related research projects.
Backward baselines: Is your model predicting the past?

When does a machine learning model predict the future of individuals and when does it recite patterns that predate the individuals? In this work, we propose a distinction between these two pathways of prediction, supported by theoretical, empirical, and normative arguments. At the center of our proposal is a family of simple and efficient statistical tests, called backward baselines, that demonstrate if, and to what extent, a model recounts the past. Our statistical theory provides guidance for interpreting backward baselines, establishing equivalences between different baselines and familiar statistical concepts. Concretely, we derive a meaningful backward baseline for auditing a prediction system as a black box, given only background variables and the system's predictions. Empirically, we evaluate the framework on different prediction tasks derived from longitudinal panel surveys, demonstrating the ease and effectiveness of incorporating backward baselines into the practice of machine learning.
ICT Sustained Remodeled HEV-ICON Design Model: A Conceptual Module to Foster Key Sustainability Competencies.

During the COVID-19, ICT has been recognized as the main driver of socioeconomic growth and congruently its synchronization with the SDG4 to bolster the fundamentals of access to inclusive and equitable quality education, invoked the existent education sector to transmute their pedagogical methods from a conventional approach to multifaceted active-engaging learning approaches that involve the integration of ICT into constructivist pedagogy in a well-formulated way, that would effectively reinforce both teachers and learners towards the acquisition of key sustainability competencies. But the major concern lies in accepting the integration of ICT into constructivist pedagogy that can be demanding as when inappropriately applied may bring about a flawed style of instruction because the articulation of a learning theory into an instructional theory can be quite overwhelming for an educator where every single constructivist approach has its specific plan of action. Hence, the present article map out a strategically designed proposed module contemplating the conjectured pioneering relationship between ICT Sustained Remodeled HEV-ICON Design and Eight Key Sustainability Competencies for the ongoing research, that will come off as one of the most benignant models in enhancing learner’s cognitive, socio-emotional and behavioral domain that has potential to bring prospective makeshift in revamping our curriculum framework in a new dimension, creating ‘Sustainability Citizens’.
Community Services using Internet of Things

This paper will go over community services, which are an Internet of Things application that relates to a prototypical category of cyber-physical platforms with cooperating components.Following that, basic smart community architecture is developed, and we explain how to ensure safe & strong communication throughout specific residences.It introduce two improved knowledge applications, Community Watch and Health, as well as enabling methodologies & challenges, that we anticipate some few valuation smart services to the community.
Online Learning Quality, Satisfaction, and Word-of-Mouth Promotion

This study aimed to verify and analyze the two influences. The first was the effect of online learning quality (OLQ) on student satisfaction and word-of-mouth (WOM) promotion. The second was the impact of student satisfaction on WOM promotion. Then, we used the population of 137 students taking online classes during the COVID-19 pandemic at Indonesia's Interkultural Edukasi Partner (IEP) in Bandung to support this intention. Furthermore, we applied the Slovin formula with a 5% border of fault to obtain a sample size of 103. After getting it, the samples were taken by simple random sampling. Unfortunately, only 45 students responded by filling out the questionnaire distributed. As a result, this study utilized the structural equation model based on variance to examine the proposed hypotheses. After testing and discussing them, this research concluded that OLQ positively influenced student satisfaction, but OLQ did not. Besides, student satisfaction positively affected this promotion.