Reviewerly/exHarmony huggingface dataset:
Dynamics-Aware Unsupervised Skill Discovery

Conventionally, model-based reinforcement learning (MBRL) aims to learn a global model for the dynamics of the environment. A good model can potentially enable planning algorithms to generate a large variety of behaviors and solve diverse tasks. However, learning an accurate model for complex dynamical systems is difficult, and even then, the model might not generalize well outside the distribution of states on which it was trained. In this work, we combine model-based learning with model-free learning of primitives that make model-based planning easy. To that end, we aim to answer the question: how can we discover skills whose outcomes are easy to predict? We propose an unsupervised learning algorithm, Dynamics-Aware Discovery of Skills (DADS), which simultaneously discovers predictable behaviors and learns their dynamics. Our method can leverage continuous skill spaces, theoretically, allowing us to learn infinitely many behaviors even for high-dimensional state-spaces. We demonstrate that zero-shot planning in the learned latent space significantly outperforms standard MBRL and model-free goal-conditioned RL, can handle sparse-reward tasks, and substantially improves over prior hierarchical RL methods for unsupervised skill discovery.
A Bayesian Hierarchical Model for Criminal Investigations

Potential violent criminals will often need to go through a sequence of preparatory steps before they can execute their plans. During this escalation process police have the opportunity to evaluate the threat posed by such people through what they know, observe and learn from intelligence reports about their activities. In this paper we customise a three-level Bayesian hierarchical model to describe this process. This is able to propagate both routine and unexpected evidence in real time. We discuss how to set up such a model so that it calibrates to domain expert judgments. The model illustrations include a hypothetical example based on a potential vehicle based terrorist attack.
The Impact of Financial Development on Innovation Activities in Emerging and Developing Countries

We investigate in this paper the effect of financial development on innovation in emerging and developing countries. The estimation of panel threshold model for a sample 54 countries during the period 1980-2009 shows the presence of non linear effects in the relationship between financial development and innovation. We find a threshold value of economic development below which the financial development level has no significant impact on innovation and above which financial development has a significant positive impact on innovation. In sum, our findings suggest that the presence of a healthy economic environment is crucial for financial institutions to offer high-quality financial services, promoting more innovation.
Fast approximate inference for variable selection in Dirichlet process mixtures, with an application to pan-cancer proteomics

The Dirichlet Process (DP) mixture model has become a popular choice for model-based clustering, largely because it allows the number of clusters to be inferred. The sequential updating and greedy search (SUGS) algorithm (Wang & Dunson, 2011) was proposed as a fast method for performing approximate Bayesian inference in DP mixture models, by posing clustering as a Bayesian model selection (BMS) problem and avoiding the use of computationally costly Markov chain Monte Carlo methods. Here we consider how this approach may be extended to permit variable selection for clustering, and also demonstrate the benefits of Bayesian model averaging (BMA) in place of BMS. Through an array of simulation examples and well-studied examples from cancer transcriptomics, we show that our method performs competitively with the current state-of-the-art, while also offering computational benefits. We apply our approach to reverse-phase protein array (RPPA) data from The Cancer Genome Atlas (TCGA) in order to perform a pan-cancer proteomic characterisation of 5157 tumour samples. We have implemented our approach, together with the original SUGS algorithm, in an open-source R package named sugsvarsel, which accelerates analysis by performing intensive computations in C++ and provides automated parallel processing. The R package is freely available from: https://github.com/ococrook/sugsvarsel.
The Algonauts Project

A new open challenge tests whether algorithmic models can explain human brain activity in cognitive tasks and encourages interaction between researchers studying natural and artificial intelligence.
Top-down induction of decision trees: rigorous guarantees and inherent limitations

Consider the following heuristic for building a decision tree for a function $f : \{0,1\}^n \to \{\pm 1\}$. Place the most influential variable $x_i$ of $f$ at the root, and recurse on the subfunctions $f_{x_i=0}$ and $f_{x_i=1}$ on the left and right subtrees respectively; terminate once the tree is an $\varepsilon$-approximation of $f$. We analyze the quality of this heuristic, obtaining near-matching upper and lower bounds: $\circ$ Upper bound: For every $f$ with decision tree size $s$ and every $\varepsilon \in (0,\frac1{2})$, this heuristic builds a decision tree of size at most $s^{O(\log(s/\varepsilon)\log(1/\varepsilon))}$. $\circ$ Lower bound: For every $\varepsilon \in (0,\frac1{2})$ and $s \le 2^{\tilde{O}(\sqrt{n})}$, there is an $f$ with decision tree size $s$ such that this heuristic builds a decision tree of size $s^{\tilde{\Omega}(\log s)}$. We also obtain upper and lower bounds for monotone functions: $s^{O(\sqrt{\log s}/\varepsilon)}$ and $s^{\tilde{\Omega}(\sqrt[4]{\log s } )}$ respectively. The lower bound disproves conjectures of Fiat and Pechyony (2004) and Lee (2009). Our upper bounds yield new algorithms for properly learning decision trees under the uniform distribution. We show that these algorithms---which are motivated by widely employed and empirically successful top-down decision tree learning heuristics such as ID3, C4.5, and CART---achieve provable guarantees that compare favorably with those of the current fastest algorithm (Ehrenfeucht and Haussler, 1989). Our lower bounds shed new light on the limitations of these heuristics. Finally, we revisit the classic work of Ehrenfeucht and Haussler. We extend it to give the first uniform-distribution proper learning algorithm that achieves polynomial sample and memory complexity, while matching its state-of-the-art quasipolynomial runtime.
Short-Term Prediction and Multi-Camera Fusion on Semantic Grids

An environment representation (ER) is a substantial part of every autonomous system. It introduces a common interface between perception and other system components, such as decision making, and allows downstream algorithms to deal with abstract data without knowledge of the used sensor. In this work, we propose and evaluate a novel architecture that generates an egocentric, grid-based, predictive, and semantically-interpretable ER, which we call semantic grid. We show that our approach supports the spatio-temporal fusion of multiple camera sequences and short-term prediction in such an ER. Our design utilizes a strong semantic segmentation network together with depth and egomotion estimates to first extract semantic information from multiple camera streams and then transform these separately into egocentric temporally-aligned bird's-eye view grids. A deep encoder-decoder network is trained to fuse a stack of these grids into a unified semantic grid and to predict the dynamics of its surrounding. We evaluate this representation on real-world sequences of Cityscapes and show that our architecture can make accurate predictions in complex sensor fusion scenarios and significantly outperforms a model-driven baseline in a category-based evaluation.
Vision and language: from visual perception to content creation

Vision and language are two fundamental capabilities of human intelligence. Humans routinely perform tasks through the interactions between vision and language, supporting the uniquely human capacity to talk about what they see or hallucinate a picture on a natural-language description. The valid question of how language interacts with vision motivates us researchers to expand the horizons of computer vision area. In particular, "vision to language" is probably one of the most popular topics in the past five years, with a significant growth in both volume of publications and extensive applications, e.g., captioning, visual question answering, visual dialog, language navigation, etc. Such tasks boost visual perception with more comprehensive understanding and diverse linguistic representations. Going beyond the progresses made in "vision to language," language can also contribute to vision understanding and offer new possibilities of visual content creation, i.e., "language to vision." The process performs as a prism through which to create visual content conditioning on the language inputs. This paper reviews the recent advances along these two dimensions: "vision to language" and "language to vision." More concretely, the former mainly focuses on the development of image/video captioning, as well as typical encoder-decoder structures and benchmarks, while the latter summarizes the technologies of visual content creation. The real-world deployment or services of vision and language are elaborated as well.
Linear Context Transform Block

Squeeze-and-Excitation (SE) block presents a channel attention mechanism for modeling global context via explicitly capturing dependencies across channels. However, we are still far from understanding how the SE block works. In this work, we first revisit the SE block, and then present a detailed empirical study of the relationship between global context and attention distribution, based on which we propose a simple yet effective module, called Linear Context Transform (LCT) block. We divide all channels into different groups and normalize the globally aggregated context features within each channel group, reducing the disturbance from irrelevant channels. Through linear transform of the normalized context features, we model global context for each channel independently. The LCT block is extremely lightweight and easy to be plugged into different backbone models while with negligible parameters and computational burden increase. Extensive experiments show that the LCT block outperforms the SE block in image classification task on the ImageNet and object detection/segmentation on the COCO dataset with different backbone models. Moreover, LCT yields consistent performance gains over existing state-of-the-art detection architectures, e.g., 1.5∼1.7% APbbox and 1.0%∼1.2% APmask improvements on the COCO benchmark, irrespective of different baseline models of varied capacities. We hope our simple yet effective approach will shed some light on future research of attention-based models.
Simple and Effective Stochastic Neural Networks

Stochastic neural networks (SNNs) are currently topical, with several paradigms being actively investigated including dropout, Bayesian neural networks, variational information bottleneck (VIB) and noise regularized learning. These neural network variants impact several major considerations, including generalization, network compression, robustness against adversarial attack and label noise, and model calibration. However, many existing networks are complicated and expensive to train, and/or only address one or two of these practical considerations. In this paper we propose a simple and effective stochastic neural network (SE-SNN) architecture for discriminative learning by directly modeling activation uncertainty and encouraging high activation variability. Compared to existing SNNs, our SE-SNN is simpler to implement and faster to train, and produces state of the art results on network compression by pruning, adversarial defense, learning with label noise, and model calibration.
Mega-Reward: Achieving Human-Level Play without Extrinsic Rewards

Intrinsic rewards were introduced to simulate how human intelligence works; they are usually evaluated by intrinsically-motivated play, i.e., playing games without extrinsic rewards but evaluated with extrinsic rewards. However, none of the existing intrinsic reward approaches can achieve human-level performance under this very challenging setting of intrinsically-motivated play. In this work, we propose a novel megalomania-driven intrinsic reward (called mega-reward), which, to our knowledge, is the first approach that achieves human-level performance in intrinsically-motivated play. Intuitively, mega-reward comes from the observation that infants' intelligence develops when they try to gain more control on entities in an environment; therefore, mega-reward aims to maximize the control capabilities of agents on given entities in a given environment. To formalize mega-reward, a relational transition model is proposed to bridge the gaps between direct and latent control. Experimental studies show that mega-reward (i) can greatly outperform all state-of-the-art intrinsic reward approaches, (ii) generally achieves the same level of performance as Ex-PPO and professional human-level scores, and (iii) has also a superior performance when it is incorporated with extrinsic rewards.
The application of game-based AR learning model in English sentence learning

In recent years, the application of augmented reality (AR) technology has been explored in numerous disciplines. This research focused on English sentence learning and utilized the activity theory as the basis to structure the game-based AR learning model (GBARLM). The aim of the investigation was to describe the model and analyze the following questions: 1) How did the change of teacher’s role influence the effects of English sentence learning in this model? 2) How did the game-based AR learning environment affect English collaborative learning? The subjects of the research were seventy vocational college students. The results of the investigation revealed that the change of teacher’ role has a positive influence on the students' English learning outcomes. The game-based AR learning environment enhanced the effect of the collaborative learning. In addition to that, the change of teacher’ role and the game-based AR learning environment both boosted the learning enthusiasm among students.
The connection between subjective well-being and ideas of happiness (the direction of values, intensity of the motivation and ideas about responsibility) among university students

УДК 159
Restructuring of Object-Oriented Software System Using Clustering Techniques

Producing a well-designed software system is always required, as a result of that largely maintenance cost of software is reduced and reusability of the software is improved. On the other hand, the internal structure of software system is deteriorating because of prolonged maintenance activities. In these circumstances, one technique to get the better overall internal structure of the system without changing its external behavior is restructuring. One technique to carry out restructuring is to use refactoring on the existing source code. Code refactoring is a strong approach in software development in turn to enhance the internal structure of the software without changing its external behavior. The purpose of refactoring is to increase the cohesion of existing code and reducing coupling in the existing code of software system. Different methods have been proposed to increase the cohesion of the system. Clustering is one of the techniques used for improving the cohesion of the system. Clustering, a data mining technique, is used for grouping things based on the characteristics, so that it can be used to overhaul the class structure of the software system. Clustering algorithms are used to find the refactoring opportunity. In this paper, we propose a new method for software restructuring using clustering with multiple refactoring methods. The proposed method utilizes multiple refactoring methods to increase the cohesion of the part with no change in the meaning of software system. The benefit of the proposed method is likely to increase the effectiveness of the software system by improving its cohesion.
Integrated Application of Blockchain in the Electric Information Management System

Blockchain technology has been applied in many fields to improve the management and data security of the information systems. In this paper, we describe the applications of blockchain technology in Electric Management Information System. First, the components and structure of the blockchain framework are introduced. Then the blockchain based authentication application is studied to integrate with existing IT infrastructure. Finally, the benefit and limitations of the proposed integrated framework are analyzed. As industrial application research, this paper could give a reference example for the applications of blockchain in the data management system.
Short term solar irradiance forecasting using artificial neural network for a semi-arid climate in Morocco

Knowledge of irradiance with high accuracy is of paramount importance for monitoring planning and for better exploitation and distribution of photovoltaic (PV) energy. Different methods have been developed to accurately forecast solar irradiance, in this study, we applied multilayer perception to predict GHI (Global Horizontal Irradiance) for a hot semiarid climate in Benguerir, Morocco. Ground measurements of several meteorological variables and the GHI from the meteorological station installed at the green energy park in Benguerir were used to build the database, different model architectures with various inputs were tested to choose the most efficient model. To evaluate the performance of the models we used the nMBE (normalized Mean Bias Error), nRMSE (normalized Root Mean Square Error), and CC (Correlation Coefficient). The results favored the MLP (Multilayer Perceptron) with 3 inputs and 7 neurons in the hidden layer. The final model was experimented to predict GHI for clear and cloudy days, nMBE, nRMSE and CC obtained are respectively (-0.051%, 0.10% and 0.99) for clear days, and (0.14%, 0.39% and 0.96%) for cloudy days.
An Intelligent Combination: Assessing the Impact of harmonized Emotional and Artificial Intelligence for the Success of Industry 4.0

Artificial intelligence (AI) is the simulation of human intelligence. With man-made consciousness and advent of AI, industrial process reengineering is reevaluated now. Machine learning supports predictions using enormous data, at the same time, is independent enough to infer knowledge based on pattern recognition from the data. For this reason, the technology has found its place in new industrial processes. What was previously a single direction road has now turned into a two-way communication. We would now be able to instruct technology to identify with individuals, as much as we train individuals to utilize technology. However, it is pertinent to note that individuals are also driven emotionally. The existing literature talks about getting human-driven artificial structure right, but does not comprehend the greatest factor in the achievement or disappointment of AI-driven changes, by incorporating human Emotional Intelligence (EI). The objective of the study include: a) To understand the role of artificial intelligence in Industry 4.0 b) To comprehend the collective impact of emotional and artificial intelligence in contemporary times and c) To assess the relationship between artificial intelligence, emotional intelligence and industry success. The methodology followed is a focus group data collected from AI experts in Dubai. The results highlight that a right combination of artificial intelligence and emotional intelligence is the recipe for the success of Industry 4.0.
Modeling the Incident Solar Radiation of the City of N’Djamena (Chad) by the Capderou Method

Chad is like many African countries with no meteorological station at the moment to measure solar radiation throughout the country. Thus, theoretical models are used to estimate incident solar radiation. These models are established in correlation form. Our objective was to present a model, which allows the determination of the solar component on two surfaces (horizontal and inclined). This model allowed us to determine, over time, the component of global, direct, and diffuse solar radiation over a period that will cover the different seasons of the year. The calculation is done according to Klein’s days over all the months of the year. The hourly results of the global, direct, and diffuse radiation obtained for all the planes going from January to December are satisfactory compared to the results of the other authors quoted in the literature, which give the maximum and minimum values very close to theirs. These results allowed us to validate the applicability of this model in a climate other than the desert climate.
Hybrid Generative-Discriminative Generalized Dirichlet-Based Hidden Markov Models with Support Vector Machines

Recent years have witnessed tremendous advancement in data modelling. In this paper, we focus on Dynamic textures (DT). We propose the use of Fisher kernels with Generalized Dirichlet based hidden Markov models (HMM) for DT recognition. Experiments demonstrate promising results on the DynTeX Alpha dataset using the proposed generative discriminative approach. This is the first application of Generalized Dirichlet HMMs to DT recognition, to the best of our knowledge.
On web-applications usability testing

The paper discusses the task of organizing the usability testing of web applications that are in pilot or industrial operation. A usability testing technique is described that combines the development, debugging and execution of test scenarios, and analysis of the received test data. The development of test scenarios is based: on the use of the accumulated experience of users who have already worked with this application or with another, similar in functionality; on the use of web analytics, which can provide scenarios of the behavior of user data on the site; and on the use of statistical data on visits to specific pages. Simultaneously with this approach, a different approach is used in the construction of tests. It is based on the development of tests aimed at identifying defects in the program. Debugging and execution of test tasks is carried out in the same way as it is realized when performing functional testing of web applications using testing automation tools. In addition, the analysis of the data obtained during the operation using web analytics makes it possible to form a group of respondent testers, whose capabilities reflect the capabilities of the entire set of probable users of this web application. The approaches outlined in the work were put into practice. As an example, the article provides test data for the page of the admissions committee of MIREA – Russian Technological University – priem.mirea.ru. The obtained experimental data showed that, despite testing the usability of software at the development stages, some errors in the operation of web applications remain undetected. The cost of detecting and eliminating these errors increases significantly. Therefore, it is recommended in the work to increase the level of usability already in the early stages of development. In particular, for the operational prediction of the level of usability, it is desirable to have mathematical tools for modeling the behavior of the designed system and the user.