Reviewerly/exHarmony huggingface dataset:
State Estimation for Human Motion and Humanoid Locomotion

The future where the industrial shop-floors witness humans and robots working in unison and the domestic households becoming a shared space for both these agents is not very far. The scientific community has been accelerating towards that future by extending their research efforts in human-robot interaction towards human-robot collaboration. It is possible that the anthropomorphic nature of the humanoid robots could deem the most suitable for such collaborations in semi-structured, human-centered environments. Wearable sensing technologies for human agents and efficient human-aware control strategies for the humanoid robot will be key in achieving a seamless human-humanoid collaboration. This is where reliable state estimation strategies become crucial in making sense of the information coming from multiple distributed sensors attached to the human and those on the robot to augment the feedback controllers designed for the humanoid robot to aid their human counterparts. In this context, this thesis investigates the theory of Lie groups for designing state estimation techniques aimed towards humanoid locomotion and human motion estimation. [continued]
A Bio-inspired Virtual Machine Placement towards Sustainable Cloud Resource Management

&lt;p&gt;A Bio-VMP scheme is presented for allocating user's $VM$ based on maximum resource utilization, minimum power consumption as well as carbon emission. To the best of the author's knowledge, this is the first work that combines the ideas of FPO along with NSGA-II to solve the VMP problem.&lt;/p&gt;
Towards the development of RSU augmented campus: An augmented reality campus guide for Romblon State University

In this era of emerging technologies, the application of the new trends has been accepted and applied in the fields of medication, education, sports, and infrastructure. Along with current innovations comes the birth of Augmented Reality (AR), a new technology that imposes 3D images and visuals into the real world. This study aims to automate and augment the process of locating campus buildings and offices by providing information as well navigation by scanning the infrastructures' image trackers using the process of augmentation. The application will elevate the in-campus experience of the students and visitors by providing an entertaining and efficient way of navigating through augmentation. To assess the user acceptability of the application, survey questionnaires were distributed through Google forms. As for the respondents, 450 students and 50 faculty personnel were considered as the sample size. Results reveal that users believe that the application will be able to provide helpful information on the location of buildings and offices for the visitors. Moreover, results showed that the application received a high acceptability rating and in fact is needed by the users.
Towards Improving the Generation Quality of Autoregressive Slot VAEs

Unconditional scene inference and generation are challenging to learn jointly with a single compositional model. Despite encouraging progress on models that extract object-centric representations (''slots'') from images, unconditional generation of scenes from slots has received less attention. This is primarily because learning the multi-object relations necessary to imagine coherent scenes is difficult. We hypothesize that most existing slot-based models have a limited ability to learn object correlations. We propose two improvements that strengthen object correlation learning. The first is to condition the slots on a global, scene-level variable that captures higher-order correlations between slots. Second, we address the fundamental lack of a canonical order for objects in images by proposing to learn a consistent order to use for the autoregressive generation of scene objects. Specifically, we train an autoregressive slot prior to sequentially generate scene objects following a learned order. Ordered slot inference entails first estimating a randomly ordered set of slots using existing approaches for extracting slots from images, then aligning those slots to ordered slots generated autoregressively with the slot prior. Our experiments across three multi-object environments demonstrate clear gains in unconditional scene generation quality. Detailed ablation studies are also provided that validate the two proposed improvements.
EDUCATION OF THE FUTURE// International Journal of Educational and Scientific Research 1(14), 2022/ Chief Editor D. Sidorkin /OEAPS OU - Tallinn, Estonia (Open European Academy of Public Sciences). Marseille, France 30.04.2022: OEAPS OU., 2022 - P. 113-120.

This article is written about the future styles of education. And future trends in education and their types are described. Described about their effectiveness in the future.
Anomalous Event Detection in Surveillance Videos using Spatio-temporal Autoencoders

Surveillance cameras are proliferating, and millions of devices are being used to capture endless footage of surveillance videos. With the advancements in computer vision and deep learning, we can now contemplate these videos to detect anomalies. In this paper, we propose to identify anomalies by using Spatiotemporal autoencoders. The autoencoders based on 3-D convolutional networks will identify anomalies in surveillance footage based on spatiotemporal features. Our architecture consists of two components, an encoder for spatial feature extraction, and a decoder for the reconstruction of frames. Then, abnormal events are identified based on reconstruction loss. We used the Avenue dataset and UCSD dataset for training and evaluation.
Drawing out of Distribution with Neuro-Symbolic Generative Models

Learning general-purpose representations from perceptual inputs is a hallmark of human intelligence. For example, people can write out numbers or characters, or even draw doodles, by characterizing these tasks as different instantiations of the same generic underlying process -- compositional arrangements of different forms of pen strokes. Crucially, learning to do one task, say writing, implies reasonable competence at another, say drawing, on account of this shared process. We present Drawing out of Distribution (DooD), a neuro-symbolic generative model of stroke-based drawing that can learn such general-purpose representations. In contrast to prior work, DooD operates directly on images, requires no supervision or expensive test-time inference, and performs unsupervised amortised inference with a symbolic stroke model that better enables both interpretability and generalization. We evaluate DooD on its ability to generalise across both data and tasks. We first perform zero-shot transfer from one dataset (e.g. MNIST) to another (e.g. Quickdraw), across five different datasets, and show that DooD clearly outperforms different baselines. An analysis of the learnt representations further highlights the benefits of adopting a symbolic stroke model. We then adopt a subset of the Omniglot challenge tasks, and evaluate its ability to generate new exemplars (both unconditionally and conditionally), and perform one-shot classification, showing that DooD matches the state of the art. Taken together, we demonstrate that DooD does indeed capture general-purpose representations across both data and task, and takes a further step towards building general and robust concept-learning systems.
DUOLINGGO AS AN ATTRACTIVE APPLICATION TO UPGRADE STUDENT’S MOTIVATION IN LEARNING ENGLISH INDEPENDENTLY

As one of the modern, simple and easy to use application in learning English, Duolinggo is popular to help facilitation students or English learner in acquiring foreign language. This research purposes to investigate the students’s motivation in learning English indenpendently by using educational tools like Duolinggo everywhere. Quantitative research was used to explore the phenomenon. There are 16 students participated in this resaech from SMP Muhammadiyah Padangpanjang. The techniques of collectiong data are by using quitionaire and after they use Duolinggo outside the classroom and learn by themselves. The result of this research are 75% students agree percentage that Duolinggo really encourage them in learning English indenpendently, 62,5% students agree that Duolinggo can give opportunities to them to learn English better, while 43,75% students agree that Duolinggo encourage them to spend more time learning English outside of the classroom. It has the same percentage with another situation like Duolinggo make English learning more enjoyable and interesting and It is more fun to practice Duolinggo in the smartphone than in the classrrom. It can be conclude that students can be more motivated in learning English independently by using Duolinggo as one of the suppoting educational learning tool.Keywords: Duolinggo, Gamification, Student’s motivation, Learning Independently
Securing Cross-Chain Asset Transfers on Permissioned Blockchains

&lt;p&gt;Blockchains currently exist in silos, competing when they could be cooperating. Interoperability is essential to allow for communication between them and motivate mass adoption. In permissioned blockchains, interoperability is harder given their opaqueness. The solutions proposed so far to address interoperability require a trusted private third party, which may be insecure and is not ideal. We propose T-ODAP, a secure multi-layered protocol that enables a trustless solution for permissioned blockchain interoperability, eliminating the need for trust in the protocol's participants. It provides a Decentralized View Storage, a connector that connects permissioned blockchains to the latter, and a trustless version of the ODAP protocol. T-ODAP models the participants as rational agents using game theory techniques and is implemented using \textit{Polkadot} and \textit{Hyperledger Cactus}. We tested the implemented solution, evaluated the system's robustness in face of attacks, and concluded that the system is \textit{(k,t)-weak-robust}.&lt;/p&gt;
Are classical neural networks quantum?

Neural networks are being used to improve the probing of the state spaces of many particle systems as approximations to wavefunctions and in order to avoid the recurring sign problem of quantum monte-carlo. One may ask whether the usual classical neural networks have some actual hidden quantum properties that make them such suitable tools for a highly coupled quantum problem. I discuss here what makes a system quantum and to what extent we can interpret a neural network as having quantum remnants.
SEPARATION OF DOMESTIC WASTE WITH DEEP LEARNING TECHNIQUES

Thanks to the rapid development of deep learning technology, smart systems used in almost every part of our daily life are being developed. Developed applications not only made our lives easier, but also contributed positively to nature. Traditional waste separation methods fall short in terms of efficiency and accuracy. In addition to its high cost, it can also cause problems in terms of environmental risks. In recent years, artificial intelligence, machine learning and the deep learning techniques it brings have become a popular method for solving complex problems such as organic, household and packaging waste sorting. In this study, the problem of separation of domestic wastes, which is of great importance in terms of both human and living life and the protection of nature, is discussed. In the artificial intelligence cluster; Classification performances were compared by using popular conventional neural network (CNN) based ResNet-50, DenseNet-121, Inception-V3, VGG16 architectures to detect and sort household waste with deep learning, a sub-branch of machine learning.
Dyna-DM: Dynamic Object-aware Self-supervised Monocular Depth Maps

Self-supervised monocular depth estimation has been a subject of intense study in recent years, because of its applications in robotics and autonomous driving. Much of the recent work focuses on improving depth estimation by increasing architecture complexity. This paper shows that state-of-the-art performance can also be achieved by improving the learning process rather than increasing model complexity. More specifically, we propose (i) disregarding small potentially dynamic objects when training, and (ii) employing an appearance-based approach to separately estimate object pose for truly dynamic objects. We demonstrate that these simplifications reduce GPU memory usage by 29% and result in qualitatively and quantitatively improved depth maps. The code is available at https://github.com/kieran514/Dyna-DM.
Subject Membership Inference Attacks in Federated Learning

Privacy attacks on Machine Learning (ML) models often focus on inferring the existence of particular data points in the training data. However, what the adversary really wants to know is if a particular individual's (subject's) data was included during training. In such scenarios, the adversary is more likely to have access to the distribution of a particular subject than actual records. Furthermore, in settings like cross-silo Federated Learning (FL), a subject's data can be embodied by multiple data records that are spread across multiple organizations. Nearly all of the existing private FL literature is dedicated to studying privacy at two granularities -- item-level (individual data records), and user-level (participating user in the federation), neither of which apply to data subjects in cross-silo FL. This insight motivates us to shift our attention from the privacy of data records to the privacy of data subjects, also known as subject-level privacy. We propose two novel black-box attacks for subject membership inference, of which one assumes access to a model after each training round. Using these attacks, we estimate subject membership inference risk on real-world data for single-party models as well as FL scenarios. We find our attacks to be extremely potent, even without access to exact training records, and using the knowledge of membership for a handful of subjects. To better understand the various factors that may influence subject privacy risk in cross-silo FL settings, we systematically generate several hundred synthetic federation configurations, varying properties of the data, model design and training, and the federation itself. Finally, we investigate the effectiveness of Differential Privacy in mitigating this threat.
A New Weighted Associative Classifier Based on Nature Language Processing and Its Application in Chemical Data Mining

Associative classification mining (ACM) integrating association rule mining and classification has become a significant tool for knowledge discovery, especially in the chemical domain. Its major advantage is providing high accuracy as well as chemically interpretable models. Additionally, it is able to find associations among features while other traditional methods such as decision tree and naïve Bayesian consider the features independent to each other. In this paper, we propose a new weight framework for ACM based on information gain and graph theory. Combing this new scheme with CBA (classification based on associations), a novel classifier—IGAC (information gain and graph based associative classifier) is implemented and applied to three chemical datasets. In the generated models, the importance of the features related to the observed label classes is considered. The results show that not only IGAC can produce high accuracy (above 90%) but also the resulted models can be relatively easily interpreted by chemical knowledge. In addition, IGAC can discover meaningful rules which cannot be identified by classical associative classification mining (ACM).
Primary interval-valued intuitionistic fuzzy M group

The concept of interval-valued intuitionistic fuzzy M group is extended by introducing primary interval-valued intuitionistic fuzzy M group and primary interval-valued intuitionistic fuzzy anti M group using this concept primary interval-valued intuitionistic fuzzy M group and primary interval-valued intuitionistic fuzzy anti M group is defined and using some properties are established.
Parallel Genetic Algorithm Based on Construction of Gene Pool in the Ordinary Network for TSP

Though using parallel evolutionary algorithm to solve large-scale TSP problems is efficient, the parallel computer costs too much and the algorithm is not easy to expand. To address this issue, I propose a parallel genetic algorithm based on a gene pool under the existing network. To replace the group-genes in the evolutionary algorithm with the genes from the gene pool, the algorithm conducts greedy algorithm. The host process conducts greedy algorithm and improved evolutionary algorithm of Inver-over operator while the child process performs the improved hybrid genetic algorithms. Simulation results demonstrate that this algorithm achieves a better solution.
"Hydrothermal Veins and Not Hydrothermal Exhalations-Responsible for Deposition of Manganese Ores in the Precambrian Sausar Group of Rocks of Madhya Pradesh, India"

After the discovery of Manganese deposits associated with the Precambrian rocks of Sausar Group of Bhandara district by P.N.Dutta in 1893-94, it was Sir Fermor LL [1] who brought out a detailed account of Sausar rocks and the associated manganese ore deposits of Central India.
USE OF MODERN EDUCATIONAL TECHNOLOGIES AND INNOVATIVE TEACHING METHODS IN THE EDUCATIONAL PROCESS OF IT STUDENTS

Modern educational standards for teaching IT students require prototyping skills training. It is proposed to build the curriculum of the course in such a way that during the course students will study teamwork and will be able to develop effective communication skills. To meet all the goals of the educational program and the principle of minimal redundancy, a comparative analysis of prototyping tools is carried out and the StarUML software product is selected for a set of functions and formatting options, as well as supported programming languages for code export.
Condition Monitoring of Substation Equipment Based on Machine Vision

This article aims to reach a global level by selecting a Harr-like function with the participation of bubbles and light and shadow on a digital display, improving the fitness function of the genetic algorithm and the crossover change function. The classifier support vector machine parameters were optimized by an improved genetic algorithm. Finally, compared to other identification algorithms, basic component analysis reduces the number of characters through support vector machines and recognizes the target character. The results of the experiment showed that eight sets of experiments were performed on each character. Experiments have shown that character recognition is best achieved using Harr-like software and then SVM classification based on improved gene algorithms. For small samples, the sorting speed is also fast, which can meet the time requirement. In addition, the historical state data of the transformer are analyzed, which is consistent with the manual monitoring results, but the time is shorter; machine vision has been shown to be effective in monitoring the condition of substation equipment.
Implementation of Machine Learning Algorithms on Multi-Robot Coordination

Occasionally, professional rescue teams encounter issues while rescuing people during earthquake collapses. One such issue is the localization of wounded people from the earthquake. Machines used by rescue teams may cause crucial issues due to misleading localization. Usually, robot technology is utilized to address this problem. Many research papers addressing rescue operations have been published in the last two decades. In the literature, there are few studies on multi-robot coordination. The systems designed with a single robot should also overcome time constraints. A sophisticated algorithm should be developed for multi-robot coordination to solve that problem. Then, a fast rescuing operation could be performed. The distinctive property of this study is that it proposes a multi-robot system using a novel heuristic bat-inspired algorithm for use in search and rescue operations. Bat-inspired techniques gained importance in soft-computing experiments. However, there are only single-robot systems for robot navigation. Another original aspect of this paper is that this heuristic algorithm is employed to coordinate the robots. The study is devised to encourage extended work related to earthquake collapse rescue operations.
Making sense of Raster chart: a computer vision approach

Sea navigation and operations within areas of interest has been a major focus of naval research. Documents such as Raster Navigational Charts (RNC) that help with sea navigation tasks are critically important. A RNC is a copy of a navigational paper chart in image form. Therefore, RNC contains important information such as navigational channels, water depths, rocky areas etc. However, a RNC is hard to interpret by computers and even humans as it contains very dense information due to the different layers of drawings from the information mentioned above. In this paper, we introduce a reverse engineering approach using computer vision to extract features from the RNC image. We use optical character recognition to extract text features and templates matching for symbolic features. With the new approach, we show that RNC will become machine readable, and the features extracted can be used to draw tactical regions of interest.
EXPERIMENTAL STUDY OF THE LEVELS FORMATION WORLD PERCEPTION IN CHILDREN 5–6 YEARS

The article is devoted to the problem of forming world perception in children 5–6 years.&nbsp;The relevance of the study is determined by: the lack of theoretical and experimental research in this area, conducted with children of this age; the importance of this age, when the child has the ability to perceive the world, the primary fragmentary worldview is formed, the subordination of motives, the child is well oriented in the categories of good and evil; demand for pedagogical practice of developing methodological orientation to optimize the process of forming world perception in children 5–6 years in preschool and family. Scientific views on the definition of the essence of the concept of «world perception» as a certain experience of forming cognitive ideas about the world with the help of visual images – perceptions are analyzed. The importance of this process in the life of olderpreschoolers is revealed. The components of world perception, which include the processes of perception (sensory reflection of objective reality), cognition (creative activity of the child to master reality) and reflection (generalized image of objects or phenomena of objective reality) are identified.&nbsp; Features of experimental study of levels of formation of world perception of children at the given age period are detailed. The method of the ascertaining stage of the pedagogical experiment is substantiated, one of the tasks of which was to determine and analyze the levels of formation perception of children aged 5–6. The structure of the studied process is determined, which includes three components: cognitive (a set of knowledge and ideas about the world, acquired on the basis of individual life experience and expressed in the value orientations of the individual); emotional and value (attitude to the surrounding reality and to oneself due to awareness of one’s own feelings about the objects perceived by the child) and behavioral (direct possibility of dialogical interaction with the outside world on the basis of humanism); the level of formation of the child’s world perception depends on the degree of formation each of these components. Levels of formation of world perception at children of 5–6 years are defined and characterized, for instance: high, sufficient, average and low.