Reviewerly/exHarmony huggingface dataset:
Meta-Reinforcement Learning With Informed Policy Regularization

Meta-reinforcement learning aims at finding a policy able to generalize to new environments. When facing a new environment, this policy must explore to identify its particular characteristics and then exploit this information for collecting reward. We consider the online adaptation setting where the agent needs to trade-off between the two types of behaviour within the same episode. Even though policies based on recurrent neural networks can be used in this setting by training them on multiple environments, they often fail to model this trade-off, or solve it at a very high computational cost. In this paper, we propose a new algorithm that uses privileged information in the form of a task descriptor at train time to improve the learning of recurrent policies. Our method learns an informed policy (i.e., a policy receiving as input the description of the current task) that is used to both construct task embeddings from the descriptors, and to regularize the training of the recurrent policy through parameters sharing and an auxiliary objective. This approach significantly reduces the learning sample complexity without altering the representational power of RNNs, by focusing on the relevant characteristics of the task, and by exploiting them efficiently. We evaluate our algorithm in a variety of environments that require sophisticated exploration/exploitation strategies and show that it outperforms vanilla RNNs, Thompson sampling and the task-inference approaches to meta-reinforcement learning.
Fostering gifted students in a technical university: BMSTU case study

The article deals with the analysis, generalization and assessment of BMSTU experience in the field of identifying giftedness, ways, and technologies for its development, risks in psychological and pedagogical work while identifying and fostering gifted students. The criteria of a modern university educational environment are presented to identify its capabilities and risks of improving the syllabus and courses in order to foster gifted students, to shape and develop students’ giftedness, creative and intellectual potential in harmony with the university graduates’ competencies formed during their training course. The authors share their experience in designing special courses for university educators since a number of innovative educational programs for teachers in the field of specialized engineering and technical training have been realized at Bauman Moscow State Technical University in order to improve the quality of education and educational environment as the main condition for the development of teachers’ and students’ creative abilities so as to implement the program of the Moscow government «The education development in Moscow." As a result, advanced professional courses for pedagogical workers and educators of Moscow have been developed.
IMPACT OF FAMILY BACKGROUND AND SOCIO-ECONOMIC FACTORS ON ACADEMIC PERFORMANCE OF SECONDARY SCHOOL STUDENTS IN ILORIN :

This study examined the impact of family background and socio-economic factors on academic performance of secondary school students in Ilorin. Two hundred students selected from five secondary schools in Ilorin participated in the study. A reliable self-developed questionnaire was used to elicit information on family background and socio-economic status from the participants. The students’ average scores in the most recent examination was used to rate their academic performance. Data were analyzed using descriptive statistics (mean and proportions), independent t-test and one-way ANOVA. The result revealed that parent income has significant (p 0.05) influence on students’ academic performance. The educational implications of the research finding were discussed and appropriate recommendations made with additional suggestion for investigation.
Study of interiority application in deaf space based lecture space: Case study: the Center of Art, Design & Language in ITB building

Many lecture space facilities today are included in audism category because they do not yet have space design regulations for hearing disabilities. As a result, students with hearing disabilities, who are not identified from the start, require more time to sustain their education. This study aims to apply related theories including DeafSpace principles and interiority to find lecture space settings that can support the behavior of SHD’s. Lecture spaces in the CADL-BIT building are not currently designed specifically for users with disabilities in application of interiority. This research used a qualitative method with a case study approach. Data is collected through observation, simulation, and interviews to explore the experience of space users. The research results showed that spatial experience was influenced by behavior, activities and space. The behavior of students with hearing disabilities in lecture space settings is influenced by interiority. There are lecture classrooms found to form of interiority, which are u-shaped furniture settings, easy visibility to read facial expressions and lips movements, and can interact with each other, bright lighting (not dim), wall colors according to pastel colors, plain wall material, and acoustics are used sound absorbers. This research is expected to be beneficial for scholarship about the DeafSpace design, community, and building based on social culture.
Lesion-wise evaluation for effective performance monitoring of small object segmentation

Object detection in medical images using deep learning is a challenging task, due to the imbalance often present in the data. Deep learning algorithms require large amount of balanced data to achieve optimal performance, as well as close monitoring and ne-tuning of hyper parameters. For most applications, such performance monitoring is done by simply feeding unseen data trough the network, and then using the loss function for evaluation. In the case of small or sparse objects, the loss function might not able to describe the features needed, but such features can be hard to capture in a loss function. In this paper we introduce a lesion-wise whole volume validation tool, which allows more a more accurate performance monitoring of segmentation of small and sparse objects. We showcase the efficacy of our tool by applying it to the task of microbleed segmentation, and compare the behaviour of lesionwise-whole volume validation compared to well known segmentation loss functions. Microbleeds are visible as small (less than 10 mm), ovoid hypo-intensities on T2*-weighted and susceptibility weighted magnetic resonance images. Detection of microbleeds is clinically relevant, as microbleeds can indicate the risk of recurrent stroke, and are used as imaging biomarker for various neurodegenerative diseases. Manual detection or segmentation is time consuming and error prone, and suffers from high inter- and intraobserver variability. Due to the sparsity and small size of the lesions, the data is severely imbalanced.
Notes from My Experience

While learning to build a custom SSIS task, I encountered elementary errors that I believe experienced .Net developers avoid. When I searched for solutions, I found very little help. The reason? I believe the issues I encountered are not typical for developers building solutions targeted at the Global Assembly Cache (GAC). When one begins developing in the controls space, one is expected to Just Know certain things about .Net development.
The Role of Virtual Reality as a Promotion Tool to Increase Travel Interest during the Covid 19 Pandemic: A Phenomenological Approach of Tourist Objects in Jakarta

This study is aimed at answering the question of how Indonesians are interested in traveling during a pandemic, and how virtual reality is applied as a promotional tool in order to attract people to travel. This is a qualitative research with a phenomenological approach. The data collection was carried out by in-depth interviews with 22 participants who were classified into 3 categories of ages, namely Gen Z (5-25 years), Gen Y / Millennial (26-40 years), Gen X (41-55 years). This study proves that conducting tourism promotion using Virtual Reality (VR) is proven to be effective, as long as it fulfils 4 important elements of VR, namely: virtual world, which is a content that creates a virtual world in the form of screenplay and script, immersion, namely the sensation that brings technology users VR feels that it is in a real environment even though it is fictitious or not real, sensory feedback serves to convey information from the virtual world to the user's senses. These elements include visual (sight), audio (hearing) and touch, interactivity, is one of the most important elements in VR because VR simulates a real-world sensation for its users. and if the simulated virtual world is rigid and there is no interaction, the resulting sensation is not optimal.
Keyphrase Extraction in Russian and English Scientific Articles Using Sentence Embeddings

Keyphrases provide an overview of the articles, making it a powerful tool for categorizing scientific articles. This paper introduces and describes our supervised machine learning model for automatic keywords extraction. The model calculates features from traditional statistical metrics and new state-of-the-art sentence embeddings to predict a confidence score annotating conformity of keyphrase candidate. The model is tested on corpora of Russian as well as English scientific articles. When compared to the chosen baseline methods of the experiment, our model achieved a comparable F1 score when applied to the Russian corpora; and outperformed them when applied to the English corpora. Using F1-score as the evaluation metric, we also experimented with the model's parameters, such as the embedder and the set of features used as input. We found the pre-trained embedder that provides the best possible outcome for our task and confirmed that our model works best with the full set of features - non of the input to the model is redundant. For future works, we set our goal on deploying the model on existing system. Moreover, we suggest training a delicated embedding module to improve the model performance when working with articles written in Russian.
Using MMD GANs to correct physics models and improve Bayesian parameter estimation

Bayesian parameter estimation methods are robust techniques for quantifying properties of physical systems which cannot be observed directly. In estimating such parameters, one first requires a physics model of the phenomenon to be studied. Often, such a model follows a series of assumptions to make parameter inference feasible. When simplified models are used for inference, however, systematic differences between model predictions and observed data may propagate throughout the parameter estimation process, biasing inference results. In this work, we use generative adversarial networks (GANs) based on the maximum mean discrepancy (MMD) to learn small stochastic corrections to physics models in order to minimize inference bias. We further propose a hybrid training procedure utilizing both the MMD and the standard GAN objective functionals. We demonstrate the ability to learn stochastic model corrections and eliminate inference bias on a toy problem wherein the true data distribution is known. Subsequently, we apply these methods to a mildly ill-posed inference problem in magnetic resonance imaging (MRI), showing improvement over an established inference method. Finally, because 3D MRI images often contain millions of voxels which would each require parameter inference, we train a conditional variational autoencoder (CVAE) network on the corrected MRI physics model to perform fast inference and make this approach practical.
Individually Fair Gradient Boosting

We consider the task of enforcing individual fairness in gradient boosting. Gradient boosting is a popular method for machine learning from tabular data, which arise often in applications where algorithmic fairness is a concern. At a high level, our approach is a functional gradient descent on a (distributionally) robust loss function that encodes our intuition of algorithmic fairness for the ML task at hand. Unlike prior approaches to individual fairness that only work with smooth ML models, our approach also works with non-smooth models such as decision trees. We show that our algorithm converges globally and generalizes. We also demonstrate the efficacy of our algorithm on three ML problems susceptible to algorithmic bias.
Forecasting Method Using the Minitab Program

The Corona Virus 2019 (COVID-19) pandemic has yet to subside. This epidemic has spread to almost all countries in the world. This pandemic has resulted in the decrease of the activities of people and the economy. The COVID-19 pandemic itself spread in Indonesia on March 2, 2020, to be precise. Two people tested positive for COVID-19, and they were referred to as case 1 and 2. After the detection of the COVID-19 pandemic in Indonesia, Indonesia experienced additional positive cases of COVID-19 every day. This study aims - to build a model to predict the development of COVID-19 cases based on time series data and the number of COVID-19 sufferers, using seven forecasting methods, namely, Exponential Smoothing, Exponential Smoothing with Trend, Linear Regression/Least Squares, Moving Average, Trend Analysis (Regression Over Time), Additive Decomposition (Seasonal), and Multiplicative Decomposition (Seasonal), with the Minitab program, including in Indonesia. The results showed that the Exponential Smoothing with Trend method, has mean absolute deviation (MAD) of 220.6and mean squared error (MSE) of 69,994.2; the forecast resulted in 177,621 number of COVID-19 cases, which is predicted to occur on September 1, 2020.
Data Driven-based Operational Risk Control System for Whole Process Risk Prevention

Mobile Internet and its applications have greatly promoted the process of social development and facilitated people's production and life. However, the dark industry chain attached to the network is also undermining the dividends brought by the Mobile Internet to the whole society, bringing risks that cannot be ignored by the operation and application of the Mobile Internet. Therefore, it is very necessary to establish a sound risk control system by Mobile Internet operators to avoid and resist the risks that may occur in the operation process. In this paper, a certain type of App is taken as an example to elaborate the elements and significance of establishing an operational risk control system based on big data. Through the study and discussion of data architecture, model, rules and flow involved in the process of operational risk control, the method of operational risk warning and prevention through data analysis is proposed.
The Process Aspect of Project Success

This paper proposes an empirically tested model that explains the significance of project development phases for the project success, and the impact of project people on each phase. The conceptual model includes six inter-related components: project success as the ultimate target, project team, customer, and three process steps: planning, execution, and control. The empirical test was performed in the context of information systems (IS) projects. Usable data were obtained from a survey of 603 IS professionals and were analysed through structural equation modeling, factor analysis, and descriptive analysis. The results provide good empirical support for the proposed theoretical model. They reveal a significant direct relationship between project planning and control components and project success, and the indirect impact of project execution phase on the project success through mediating project control component. Furthermore, results emphasize the importance of the people aspect for successful execution of each of the introduced process steps.
Variational Deterministic Uncertainty Quantification

Building on recent advances in uncertainty quantification using a single deep deterministic model (DUQ), we introduce variational Deterministic Uncertainty Quantification (vDUQ). We overcome several shortcomings of DUQ by recasting it as a Gaussian process (GP) approximation. Our principled approximation is based on an inducing point GP in combination with Deep Kernel Learning. This enables vDUQ to use rigorous probabilistic foundations, and work not only on classification but also on regression problems. We avoid uncertainty collapse away from the training data by regularizing the spectral norm of the deep feature extractor. Our method matches SotA accuracy, 96.2\% on CIFAR-10, while maintaining the speed of softmax models, and provides uncertainty estimates competitive with Deep Ensembles. We demonstrate our method in regression problems and by estimating uncertainty in causal inference for personalized medicine
Human Attributes in Humanoid Robots

The robotics world has made robots more user friendly, intelligent and affordable. From industrial manufacturing to the medical field, robots are used. Humanoid robots resemble humans and can-do work more efficiently than a human can. With growing technology, we are able to add up human attributes to a humanoid, specifically the sweat gland. This work emphasizes the sweating robot to keep cool during intense activities as these types of robotic technologies help humans and the societies in a good way.
An Automated Domain Understanding Technique for Knowledge Graph Generation

Domain-specific Knowledge Graph (KG) generation is a labor intensive task usually orchestrated and supervised by subject matter experts. Herein, we propose a strategy to automate the generation process following a two-step approach. Initially, the structure of the domain of interest is inferred from the corpus in the form of a metagraph. Afterwards, once the domain structure has been discovered, named entity recognition (NER) and relation extraction (RE) models can be used to generate a domain-specific KG. We argue why the automated definition of the domain's structure as a first step is beneficial both in terms of construction time and quality of the generated graph. Furthermore, we present a machine learning approach, based on Transformers, to infer the structure of a corpus's domain. The proposed method is extensively validated on three public datasets (WebNLG, NYT and DocRED) by comparing it with two reference methods using CNNs and RNNs. Lastly, we demonstrate how this work lays the foundation for fully automated and unsupervised KG generation.
Konstverket i spelmiljö : En undersökning av spelet Animal Crossing: New Horizons (2020) användande av konstverk och presentation av museet som institution

This essay focuses on the digitalization of art canonisation in the game Animal Crossing: New Horizons (Nintendo/Nintendo EPD, 2020). Furthermore, the purpose of this essay is to understand which a ...
SURVANT: An Innovative Semantics-Based Surveillance Video Archives Investigation Assistant

SURVANT is an innovative video archive investigation system that aims to drastically reduce the time required to examine large amounts of video content. It can collect the videos relevant to a specific case from heterogeneous repositories in a seamless manner. SURVANT employs Deep Learning technologies to extract inter/intra-camera video analytics, including object recognition, inter/intra-camera tracking, and activity detection. The identified entities are semantically indexed enabling search and retrieval of visual characteristics. Semantic reasoning and inference mechanisms based on visual concepts and spatio-temporal metadata allows users to identify hidden correlations and discard outliers. SURVANT offers the user a unified GIS-based search interface to unearth the required information using natural language query expressions and a plethora of filtering options. An intuitive interface with a relaxed learning curve assists the user to create specific queries and receive accurate results using advanced visual analytics tools. GDPR compliant management of personal data collected from surveillance videos is integrated in the system design.
VOCABULARY PROBLEMS’ ANALYSIS OF NUTRITION FRESHMAN STUDENTS IN LEARNING ENGLISH FOR SPECIFIC PURPOSES

Nutrition students of Poltekkes Banjarmasin face vocabulary problems in learningEnglish such as the difficulty of memorizing new strange words, the difficulty of utteringvocabulary, difficulty reading and translate word, the difficulty of writing a word, difficulty ofusing to be and es/s, lack of confidence, get nervous, not enough time to learn English in theclass, the environment does not support, and lack of motivation.The method of this study is Qualitative research. The purpose of this research is tofound out the problem students faced in learning vocabulary and to analyze how studentsovercome the problems. The subject of this research is Nutrition fresh students of PoltekkesBanjarmasin. The data collected by interview and questionnaire.Based on the research, the researcher found out what problem students faced inlearning vocabulary and how they overcome each of the problems, namely hard to recognizedand memorialized new word, the difficulty of reading and translating words, the difficulty inwriting words, the difficulty in using to be and s/es, the difficulty of organizing words intoanother sentence, the environment did not support, lack of self-confidence, get nervous, thereis no enough time, and lack of motivation, but the students also have the ways to overcometheir problems such as constantly practicing, starting with simple sentences, and helping withthe dictionary, make a note contained a list of vocabulary, reading English books, watchYoutube that using English, take an English course, listening to English song, playinginternational-basic video games, google translate, and share knowledge with friends.
Variational Structured Attention Networks for Dense Pixel-Wise Prediction

State-of-the-art performances in dense pixel-wise prediction tasks are obtained with specifically designed convolutional networks. These models often benefit from attention mechanisms that allow better learning of deep representations. Recent works showed the importance of estimating both spatial- and channel-wise attention tensors. In this paper, we propose a unified approach to jointly estimate spatial attention maps and channel attention vectors so as to structure the resulting attention tensor. Moreover, we integrate the estimation of the attention within a probabilistic framework, leading to VarIational STructured Attention networks(VISTA). We implement the inference rules within the neural network, thus allowing for joint learning of the probabilistic and the CNN front-end parameters. Importantly, as demonstrated by our extensive empirical evaluation on six large-scale datasets VISTA outperforms the state-of-the-art in multiple continuous and discrete pixel-level prediction tasks, thus confirming the benefit of structuring the attention tensor and of inferring it within a probabilistic formulation.