Reviewerly/exHarmony huggingface dataset:
MonitDB: a Customizable API for Monitoring Heterogeneous Databases

NoSQL technology provides specialized solutions for specific data models and is as such often combined in polyglot persistence implementations. The technological heterogeneity in monitoring APIs and inspection capabilities complicates the management of such storage architectures, and this is further exacerbated by their distributed deployment. More specifically, (i) the use of a combination, e.g., in a multi-cloud context, requires extensive knowledge of these technologies and their APIs, which results in increased application complexity and further leads to technology or vendor lock-in, and (ii) relying solely on a generalistic approach for monitoring different databases falls short in providing in-depth inspection of an individual database.In this paper, we present MonitDB, a uniform API that uses a hybrid approach (a combination of both generalistic and specialistic) for monitoring different types of databases. The approach adopted by MonitDB covers a broad range of databases in terms of a set of common monitoring metrics and also gives more detailed visibility and insights into key metrics for each specific database. At its core, MonitDB is (i) extensible both in terms of new database technologies and monitoring metrics, (ii) configurable (through policies) in scheduling specific monitoring metrics from each of the supported databases, and (iii) customizable in terms of the type of data monitored (i.e. monitoring metrics) and also a performance by simply changing parameters, and as such without storing the measurements in the underlying database of MonitDB.We have validated MonitDB in a working prototype implementation and conducted an extensive evaluation, the results of which confirm the extensibility and configurability benefits, which are achieved with an acceptable performance overhead (around 1% for insert and 9% for read operations).
Does Corporate Social Responsibility Initiative Dissuade the Increasing Electoral Violence in sub-Saharan Africa? Evidence from Nigeria’s Oil Producing Region

Purpose – The purpose of this paper is to critically examine the multinational oil companies’ (MOCs) corporate social responsibility (CSR) initiatives in Nigeria. Its special focus is to investigate the impact of the global memorandum of understanding (GMoU) on reducing incidents of electoral violence in the oil-producing communities.Design/methodology/approach – This paper adopts a survey technique, aimed at gathering information from a representative sample of the population, as it is essentially cross-sectional, describing and interpreting the current situation. A total of 1200 households were sampled across the Niger Delta region of Nigeria.Findings – The results from the use of a combined propensity score matching and logit model indicate that GMoU model made significant impact in deterring occurrences of electoral violence, when interventions on cluster development boards (CDBs) are designed to mitigate the intricate of political clashes in the region.Practical implication – This implies that CSR interventions of MOCs play a vital role in reducing incidents of electoral violence in Nigeria’s oil producing region.Social implication – Reducing the increasing electoral violence in the oil host communities, will in turn create an enabling environment for more extensive and responsible business of Multinational Corporation in sub-Saharan Africa.Originality/value –This paper extends and contributes to the literature on CSR initiatives of multinational enterprises in developing countries and rationale for demands for social projects by host communities. It concludes that business has an obligation to help in solving problems of public concern.
Leveraging Expert Demonstrations in Robot Cooperation with Multi-Agent Reinforcement Learning

While deep reinforcement learning (DRL) enhances the flexibility and intelligence of a single robot, it has proven challenging to solve the cooperatively of even basic tasks. And robotic manipulation is cumbersome and can easily yield getting trapped in local optima with reward shaping. As such sparse rewards are an attractive alternative. In this paper, we demonstrate how teams of robots are able to solve cooperative tasks. Additionally, we provide insights on how to facilitate exploration and faster learning in collaborative systems. First, we increased the amount of effective data samples in the replay buffer by leveraging virtual targets. Secondly, we introduce a small number of expert demonstrations to guide the robot during training via an additional loss that forces the policy network to learn the expert data faster. Finally, to improve the quality of behavior cloning, we propose a Judge mechanism that updates the strategy by selecting optimal action while training. Furthermore, our algorithms were tested in simulation using both dual arms and teams of two robots with single arms.
System Foundations of Natural Classification

For many years, the problem of natural classification is an actual problem of classic, scientific, and theoretical systematics. The attempts to create a rational reproducible scientific method for solving this problem have not been successful until recently. New opportunities have arisen from the creation of physics of open systems that has met the challenges of natural classification through the development of ontological prerequisites and operational definition for taxon. This paper provides an overview of the results that allow to overcome complexity of the problem, and to propose a rational reproducible proven method for solving the system-wide task of natural classification on the basis of multidimensional knowledge-centric analytics of physics of open systems. The method is intended for using in different subject areas with regard to open natural, social, anthropogenic, cyber-physical, as well as complex, technical systems with hundreds and thousands of variables. It should be pointed out that initially these systems are taken in their natural scales and real complexity by using huge amount of polymodal heterogeneous empirical data.
COVID 19 PANDEMIC: EFFECT ON ACADEMIC PERFORMANCE OF 4P’S GRADE 7 STUDENTS

This study was conducted to determine the Effect of Covid19 pandemic to 4P’s Grade 7 students in Paaralang Sekundarya ng Lucban Integrated School towards in Araling Panlipunan subject.Specifically, it aimed to answer the following questions; (1)  What is the demographic profile of the students in terms of; Age ,Gender, Proximity, Grades in First and Second Grading, Family Size ,Family Income ,Financial Aid and Availability in Resources(2)  What is the mean level of 4P’s Grade 7 students in Paaralang Sekundarya ng Lucban Integrated School towards their performance in Araling Panlipunan subject?(3)   Is there a significant effect on 4P’s Grade 7 students profile on their Academic Performance in Araling Panlipunan subject during the time of Covid19 Pandemic...
A Mobile Application for a Smart Home Ecosystem

Smart home ecosystems aim to adapt to users' needs and preferences and provide comfort and improved quality of life. Users need access to efficient and effective applications in order to interact with such environments. The target of this paper is to present a mobile application designed and created to support end user interaction with smart home ecosystems; this application is capable of adapting to any smart home, regardless of this architecture and the devices used. We followed a user first design thinking approach to design the user experience of this application and we deployed it on the infrastructure of the Ambient Assisted Living Laboratory, AAL House. Users via our application can monitor sensors and devices, define their routine preferences and view data on energy consumption. In order to study the acceptance and performance of our application, a survey was conducted using a questionnaire.
Developing quadrature themes

Readers will most likely be aware of the works of Johann Sebastian Bach in the field of music, particularly of his Goldberg variations and the changes that can be rung, wherein aesthetically appealing alterations to structure produce a raft of colourful sounding themes. Similarly in the field of quadrature it is possible to revisit and re-establish well-known formulae by developing variations on the theme of a three-point interpolating quadratic formed to represent a function that is to be integrated.
ACCOUNTING OF THE THEORY OF GENERATIONS WHEN PLANNING DEVELOPMENT OF TOURISM INDUSTRY OF BELARUS

The development of the tourism industry is one of the strategic directions of development of many countries, the contribution of this industry to the world GDP is growing every year. Over the past two decades, the Republic of Belarus has set itself such a task, which is characterized by global trends in the development of this industry. However, despite the significant tourism potential of Belarus, there is a significant lag in the share of the contribution of this sector of the economy to the country's GDP, which continues to remain a negative balance of foreign trade in tourist services of Belarus. In order to improve the use of the existing potential of Belarus in this area, the article discusses the possibility of using the theory of generations in forecasting trends in demand for tourist services.
An Abnormal Behavior Recognition Method Based on Fusion Features

The human action recognition technology has developed rapidly in recent years. The technologies of RNN and 3D convolution based on posture information and video frame information respectively have achieved high accuracy using various data sets, however, both of them have shortcomings in the field of abnormal behavior recognition. The definition of abnormal behavior needs to consider not only the action type simply, but also the environmental information comprehensively, so there are limitations in using RNN only based on posture information. Due to the input characteristics, action recognition technology based on 3D convolution is more related to environmental information and group behavior information, it cannot locate the action time accurately. This paper proposed an abnormal behavior recognition framework based on P3D and LSTM. The framework used pre-trained P3D to extract environmental features, and adopted pre-trained LSTM to extract individual action features to help system for time positioning, finally apply ranking model to classify abnormal behaviors after combining environmental features with action features. When training LSTM model, a regression network was added to enhance its time positioning ability. The experiment showed that the proposed framework based on P3D and LSTM has a greater improvement in the recognition accuracy and time positioning than only using 3D convolution technology or LSTM technology, and can accurately recognize abnormal behaviors.
Object-centric Video Prediction without Annotation

In order to interact with the world, agents must be able to predict the results of the world’s dynamics. A natural approach to learn about these dynamics is through video prediction, as cameras are ubiquitous and powerful sensors. Direct pixel-to-pixel video prediction is difficult, does not take advantage of known priors, and does not provide an easy interface to utilize the learned dynamics. Object-centric video prediction offers a solution to these problems by taking advantage of the simple prior that the world is made of objects and by providing a more natural interface for control. However, existing object-centric video prediction pipelines require dense object annotations in training video sequences. In this work, we present Object-centric Prediction without Annotation (OPA), an object-centric video prediction method that takes advantage of priors from powerful computer vision models. We validate our method on a dataset comprised of video sequences of stacked objects falling, and demonstrate how to adapt a perception model in an environment through end-to-end video prediction training.
Zero-shot Policy Learning with Spatial Temporal Reward Decomposition on Contingency-aware Observation

It is a long-standing challenge to enable an intelligent agent to learn in one environment and generalize to an unseen environment without further data collection and finetuning. In this paper, we consider a zero shot generalization problem setup that complies with biological intelligent agents’ learning and generalization processes. The agent is first presented with previous experiences in the training environment, along with task description in the form of trajectory-level sparse rewards. Later when it is placed in the new testing environment, it is asked to perform the task without any interaction with the testing environment. We find this setting natural for biological creatures and at the same time, challenging for previous methods. Behavior cloning, state-of-art RL along with other zero-shot learning methods perform poorly on this benchmark. Given a set of experiences in the training environment, our method learns a neural function that decomposes the sparse reward into particular regions in a contingency-aware observation as a per step reward. Based on such decomposed rewards, we further learn a dynamics model and use Model Predictive Control (MPC) to obtain a policy. Since the rewards are decomposed to finer-granularity observations, they are naturally generalizable to new environments that are composed of similar basic elements. We demonstrate our method on a wide range of environments, including a classic video game – Super Mario Bros, as well as a robotic continuous control task. Please refer to the project page for more visualized results. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>
Neural Network Pruning Through Constrained Reinforcement Learning

Network pruning reduces the size of neural networks by removing neurons such that the performance drop is minimal. Traditional pruning approaches focus on designing metrics to quantify the usefulness of a neuron which is often tedious and sub-optimal. More recent methods have instead focused on training auxiliary networks to automatically learn how useful each neuron is however, they often do not take computational limitations into account. We propose a general methodology for pruning neural networks. Our approach can prune neural networks to respect pre-defined computational budgets on arbitrary, possibly non-differentiable, functions. Furthermore, we only assume the ability to be able to evaluate these functions for different inputs, and hence they do not need to be fully specified beforehand. We achieve this by proposing a novel pruning strategy via constrained reinforcement learning algorithms. We prove the effectiveness of our approach via comparison with state-of-the-art methods on standard image classification datasets. Specifically, we reduce 83−92.90% of total parameters on various variants of VGG while achieving comparable or better performance. We also achieved 75.09% reduction in parameters on ResNet18 without incurring any loss in accuracy. The code is also available at our github repository <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
Robot in a China Shop: Using Reinforcement Learning for Location-Specific Navigation Behaviour

Robots need to be able to work in multiple different environments. Even when performing similar tasks, different behaviour should be deployed to best fit the current environment. In this paper, We propose a new approach to navigation, where it is treated as a multi-task learning problem. This enables the robot to learn to behave differently in visual navigation tasks for different environments while also learning shared expertise across environments. We evaluated our approach in both simulated environments as well as real-world data. Our method allows our system to converge with a 26% reduction in training time, while also increasing accuracy.
Diverse Sports Video Classification Using Large Space MPEG Features

Due to the substantial increase in the digital videos, distinguishing videos based on their genres has become the major problem in today’s world. Recognizing and extracting the relevant information from the sports videos is a very challenging task because of the similarities in sports, whether that be background or foreground detail in videos. In this paper, a different approach is presented to classify sports videos based on feature extraction using MPEG-7 feature descriptors and support vector machine. Each video is encapsulated using its frames, and feature vector is computed for these video frames using the feature descriptor. The effectiveness of these extracted features is shown in the experimental section of this paper. In this paper we achieved an accuracy of 83% using Support Vector Machine.
An Object-Extensible Training Framework for Image Captioning

Recent years have witnessed great progress in image captioning based on deep learning. However, most previous methods are limited to the original training dataset that contains only a fraction of objects in the real world. They lack the ability to describe other objects that are not in the original training dataset. In this paper, we propose an object-extensible training framework that enables a widely-used captioning paradigm to describe objects beyond the original training dataset (i.e., extended objects) by generating high-quality training data for these objects automatically. Specifically, we design a general replacement mechanism, which replaces the object (An object includes the object region in the image, and the corresponding object word in the caption) in the original training dataset with the extended object to generate new training data. The key challenge in the proposed replacement mechanism is that it should be context-aware to get the meaningful result that complies with common knowledge. We introduce the multi-modal context embedding to ensure that the generated object representation is coherent in the visual context and the generated caption is smooth and fluent in the linguistic context. Extensive experiments show that our method improves significantly over the state-of-the-art methods on the held-out MSCOCO in both automatic and human evaluation.
Mental Model of Educational Environments

Modern education requires the formation of thinking in complexness, which allows the cognition and creation of post-non-classical holistic texts of the culture of mankind and to synchronize at various levels of mutual assistance, up to global. The presented mental model of educational environments is based on the description of complexness relationships at two levels of self-implementation of life forms with consciousness: levels of energy-matter-information model of the world (EMI mw) and the model of the world of culture.
The $$\mathbb {K}$$ Vision for the Future of Programming Language Design and Analysis

Formal programming language semantics should be a unique opportunity to give birth to a better language, not a cumbersome post-mortem activity. Moreover, language implementations and analysis tools should be automatically generated from the formal semantics in a correct-by-construction manner, at no additional cost. In this paper, we discuss how we are pursuing this vision of programming language design and analysis within the context of the \(\mathbb {K}\) framework (http://kframework.org), where it is easy and fun to design and deploy new programming languages; where language designers can focus on the desired features and not worry about their implementation; and where the correctness of all auto-generated language implementations and tools is guaranteed on a case-by-case basis, and every individual task, be it parsing, execution, verification, or anything else, is endorsed by its own proof object that can be independently checked by third-party proof checkers, making no compromise to safety or correctness.
SIS-CNN: Semantic Image Segmentation Using Convolutional Neural Networks

Abstract Semantic image segmentation is a vast area of interest for computer vision which has gained exceptional attention from the research community. It is the process of classifying each pixel in respective category. In this paper, we exploit the problem of scene understanding and perform the segmentation by combining different classification models as a feature encoder and segmentation models as a feature decoder. All of the experiments were performed on Camvid dataset. It covers a wide range of real-world applications such as autonomous driving, virtual/augmented reality, indoor navigation, etc.
Penggunaan Model Pembelajaran Jigsaw Dapat Meningkatkan Motivasi Dan Hasil Belajar Faktorisasi Prima Siswa Kelas VI SDN 3 Pule Semester I Tahun Pelajaran 2019/2020

The purpose of this classroom action research is to determine the improvement of the learning process, increase motivation and learning outcomes using the Jigsaw Type Cooperative learning model. To achieve this goal, this research was designed in two cycles. The procedure in each cycle goes through several stages, namely planning, acting, observing and 4) reflecting on the follow-up.The improvement of the learning process is based on each cycle measured from the results of observations and tests of learning outcomes. Observational data are described, interpreted, then reflected to determine corrective actions in the next cycle. The action research conducted in two cycles showed that the average student motivation in the initial conditions was 64.41, the first cycle was 72, and the second cycle was 76.14. Learning outcomes in the initial conditions of 58.57 classical completeness level of 31.82%. In the first cycle, the average value of 67.61 classical completeness level of 58%. In the second cycle, the average value of 76.67 classical completeness level of 77%.Based on the results of the study, it can be concluded that the use of the Jigsaw learning model can increase the learning motivation of Prima Factorization of class VI students of SDN 3 Pule semester 1 of 2019/2020 by 11.73%.
Empirical Study of Propositionalization Approaches

Propositionalization is a promising approach for effectively handling dataset in the field of knowledge discovery. It is the most important part of data mining. Knowledge discovery always motivates data summarization when structured and unstructured datasets are stored in a large database system. Propositionalization leads from relational data and background knowledge to a single table representation. This table is the input to the widespread systems for knowledge discovery. This paper presents different approaches like logical and database-oriented propositional logic in a formal way. Logic-based propositionalization depends on background knowledge of the dataset moreover that is a single relational database. This paper explained relational subgroup discovery (RSD) and relational features (RelF) as a logic-based approach. For both approaches, datasets are required in Prolog language as background knowledge. Experiments are done with PROPER and relational aggregation (RELAGGS) for a huge amount of input data that are mainly stored in a multi-relational database.