Reviewerly/exHarmony huggingface dataset:
Gated-ViGAT: Efficient Bottom-Up Event Recognition and Explanation Using a New Frame Selection Policy and Gating Mechanism

In this paper, Gated-ViGAT, an efficient approach for video event recognition, utilizing bottom-up (object) information, a new frame sampling policy and a gating mechanism is proposed. Specifically, the frame sampling policy uses weighted in-degrees (WiDs), derived from the adjacency matrices of graph attention networks (GATs), and a dissimilarity measure to select the most salient and at the same time diverse frames representing the event in the video. Additionally, the proposed gating mechanism fetches the selected frames sequentially, and commits early-exiting when an adequately confident decision is achieved. In this way, only a few frames are processed by the computationally expensive branch of our network that is responsible for the bottom-up information extraction. The experimental evaluation on two large, publicly available video datasets (MiniKinetics, ActivityNet) demonstrates that Gated-ViGAT provides a large computational complexity reduction in comparison to our previous approach (ViGAT), while maintaining the excellent event recognition and explainability performance. Gated-ViGAT source code is made publicly available at https://github.com/bmezaris/Gated-ViGAT
Maybe, Maybe Not: A Survey on Uncertainty in Visualization

Understanding and evaluating uncertainty play a key role in decision-making. When a viewer studies a visualization that demands inference, it is necessary that uncertainty is portrayed in it. This paper showcases the importance of representing uncertainty in visualizations. It provides an overview of uncertainty visualization and the challenges authors and viewers face when working with such charts. I divide the visualization pipeline into four parts, namely data collection, preprocessing, visualization, and inference, to evaluate how uncertainty impacts them. Next, I investigate the authors' methodologies to process and design uncertainty. Finally, I contribute by exploring future paths for uncertainty visualization.
Enhancement of CNN-based Probability Modeling by Locally Trained Adaptive Prediction for Efficient Lossless Image Coding

An autoregressive image generative model that estimates the conditional probability distributions of image signals pel-by-pel is a promising tool for lossless image coding. In this paper, a generative model based on a convolutional neural network (CNN) was combined with a locally trained adaptive predictor to improve its accuracy. Furthermore, sets of parameters that adjust the estimated probability distribution were numerically optimized for each image to minimize the resulting coding rate. Simulation results indicate that the proposed method improves the coding efficiency obtained by the CNN-based model for most of the tested images.
Technological Challenges to Practicing 3D Ethics in Archaeology

Digital technologies require that we transform archaeological practice, and 3D technologies bring specific conundrums of data access and representation. Archaeologists can now collect an overwhelming number of x, y, z data points from the scale of objects to vast landscapes. But to what extent are these high resolution 3D data actually accessible, who can actually access them, and who should make decisions about their accessibility? Open data is essential to data sustainability and avoiding digital colonialism; however, there are technological and ethical challenges to sharing 3D archaeological data. While 3D raises many ethical questions related to practicing digital archaeology, the scope of this chapter is 3D ethics that primarily stem from technological affordances. It addresses three key questions: (1) How do we make archaeological 3D data accessible to wide and diverse (including the Global South) audiences; (2) What 3D data and versions of data (i.e., representations) can we ethically make accessible? Who gets to decide, and; (3) How can we represent uncertainty and interpretation in our 3D models/depictions? While technology offers some potential solutions to these challenges, it is essential that we focus on human-centered solutions that foster dialog among diverse communities.
Relation of spatial grasp paradigm to philosophical and psychological concepts

The paper follows practical works on the creation of the first citywide computer networks in Kyiv (Ukraine) which have been integrating different institutes of the National Academy of Sciences and other organizations from the end of the sixties, well before the internet. These works resulted in a new management concept and distributed control methodology and technology, originally called WAVE, which were further developed and demonstrated in different countries in the areas like network management, industry, social systems, collective robotics, military command and control, crisis management, national and international security, defense, distributed simulation, space-based systems, and many others. The current paper analyses the relation of the developed Spatial Grasp Model, resultant Spatial Grasp Language (SGL), and Spatial Grasp Technology (SGT) to some higher-level psychological and philosophical concepts. By providing the basics of SGL and SGT and details of their implementation, it discusses the possible relation of these concepts to some gestalt theory laws like the Law of Proximity, Law of Good Gestalt, and Law of Figure and Ground. The paper also shows how to organize a sort of distributed and global awareness under SGT on an example of a dynamic swarm of chasing units, which can provide the increased operational capability of the swarm and be practically used for the organization of collective behaviour of multiple robot units exploring unknown and harsh environments. The paper mentions how SGT may relate to higher mental concepts like perception, consciousness, and even soul. It also shows relations of SG concept and its implementation to the pattern theory, with pattern considered as a fundamental and universal concept in many areas of human activity, which actually stands in opposition to the terms of logic.
Secure Electronic Healthcare Record based on Distributed Global Database and Schnorr Signcryption

Preserving privacy and security plays a key role in allowing each component in the healthcare system to access control and gain privileges for services and resources. Over recent years, there have been several role-based access control and authentication schemes, but we noticed some drawbacks in target schemes such as failing to resist well-known attacks, leaking privacy-related information, and operational cost. To defeat the weakness, this paper proposes a secure electronic healthcare record scheme based on Schnorr Signcryption, crypto hash function, and Distributed Global Database (DGDB) for the healthcare system. Based on security theories and the Canetti-Krawczyk model (CK), we notice that the proposed scheme has suitable matrices such as scalability, privacy preservation, and mutual authentication. Furthermore, findings from comparisons with comparable schemes reveal that the suggested approach provides greater privacy and security characteristics than the other schemes and has enough efficiency in computational and communicational aspects.
Constrained Expectation-Maximisation for inference of social graphs explaining online user-user interactions

Current network inference algorithms fail to generate graphs with edges that can explain whole sequences of node interactions in a given dataset or trace. To quantify how well an inferred graph can explain a trace, we introduce feasibility, a novel quality criterion, and suggest that it is linked to the result's accuracy. In addition, we propose CEM-*, a network inference method that guarantees 100% feasibility given online social media traces, which is a non-trivial extension of the Expectation-Maximization algorithm developed by Newman (2018). We propose a set of linear optimization updates that incorporate a set of auxiliary variables and a set of feasibility constraints; the latter takes into consideration all the hidden paths that are possible between users based on their timestamps of interaction and guide the inference toward feasibility. We provide two CEM-* variations, that assume either an Erdos Renyi (ER) or a Stochastic Block Model (SBM) prior for the underlying graph's unknown distribution. Extensive experiments on one synthetic and one real-world Twitter dataset show that for both priors CEM-* can generate a posterior distribution of graphs that explains the whole trace while being closer to the ground truth. As an additional benefit, the use of the SBM prior infers and clusters users simultaneously during optimization. CEM-* outperforms baseline and state-of-the-art methods in terms of feasibility, run-time, and precision of the inferred graph and communities. Finally, we propose a heuristic to adapt the inference to lower feasibility requirements and show how it can affect the precision of the result.
Planning Visual Inspection Tours for a 3D Dubins Airplane Model in an Urban Environment

This paper investigates the problem of planning a minimum-length tour for a threedimensional Dubins airplane model to visually inspect a series of targets located on the ground or exterior surface of objects in an urban environment.Objects are 2.5D extruded polygons representing buildings or other structures.A visibility volume defines the set of admissible (occlusion-free) viewing locations for each target that satisfy feasible airspace and imaging constraints.The Dubins traveling salesperson problem with neighborhoods (DTSPN) is extended to three dimensions with visibility volumes that are approximated by triangular meshes.Four sampling algorithms are proposed for sampling vehicle configurations within each visibility volume to define vertices of the underlying DTSPN.Additionally, a heuristic approach is proposed to improve computation time by approximating edge costs of the 3D Dubins airplane with a lower bound that is used to solve for a sequence of viewing locations.The viewing locations are then assigned pitch and heading angles based on their relative geometry.The proposed sampling methods and heuristics are compared through a Monte-Carlo experiment that simulates view planning tours over a realistic urban environment.
Obsesity: a view on treatment

Obesity is a disease stigmatized by society in general. Many tend not to consider it a disease, which makes it difficult to treat and worsens the pandemic generated by this condition. Considering the seriousness with which health professionals must face this problem, a form was applied to 511 Brazilian academic medical students, to understand how these students see this disease and how to treat it. The results showed that there is a tendency for incoming students to opt for diet therapy associated with physical exercises, while students further on consider the use of drugs associated with diet therapy and physical exercises the best strategy. This shows the importance of medical schools providing solid information about obesity and its treatment, showing that in certain cases it is not enough for the patient to follow a diet and exercise. This change in the view of health professionals, therefore, would be a way to fight the pandemic in question.
Ten Simple Principles for an Ecological Approach to Cognitive Science

In everyday life we carry out many tasks that are apparently easy and simple but are in fact underpinned by surprisingly sophisticated cognitive and neural mechanisms. The impressive adaptability of human perception and cognition is starkly revealed in Artificial Intelligence and robotics where everyday tasks have turned out to be huge challenges.To understand how we can have a tremendous human advantage for these sorts of skills, yet “superhuman AI” for others, we would need to understand how the human mind and brain cope with the complexity and ambiguity of real-world tasks. This can only be achieved by taking a properly ecological approach to how we do research in cognitive science, both at the level of theory and methodology. But what does an “ecological” approach amount to? What counts (anddoes not count) as an “ecological” approach to Cognitive Science?This paper gives you ten simple principles to evaluate the “ecological” credentials of empirical research questions and results in the cognitive sciences – be it a single experiment, a theory, a broader conceptual framework, or a computational model etc. We present these principles as rules, “ten axioms” of Ecological Cognitive Science (ECS), discuss their philosophical rationale, and draw from them as “corollaries” methodological and theoretical recommendations that show whatEcological Cognitive Science would look like. We also derive prohibitions to show what a non-ecological approach looks like, and thereby how we researchers all too often violate the good rules of ECS.
Public Cloud to Cloud Repatriation Trend

Cloud repatriation is the process of moving a workload or application from a public cloud to an on-premises or private cloud. The statistics of cloud repatriation, its possible effects, and the justifications for company adoption are all included in the paper. According to recent estimates, in 2021, over 80% of US businesses will pull some workloads from the public cloud. The statistic of the Public Cloud to Cloud Repatriation Trend initially seems to indicate a significant return to on-premises systems.
Clustering Analysis of Tweets About COVID-19 Using the K-Means Algorithm

One of the trending topics in 2020 to 2022 is tweets about Coronavirus Disease 2019 (COVID-19). A large number of tweets regarding COVID-19 that have appeared have been mixed and not grouped properly, making it difficult for Twitter users to read and sort them based on the information they want. One solution that can be applied to overcome the problems described is through clustering of tweets information about COVID-19. In this study, researchers used quantitative research with the K-Means method, which is one of the clustering methods used in grouping data. The data used in this study is a dataset taken from Kaggle, namely Omicron-Covid-19 Variant Tweets, and also taken through a scraping process with Bright Data with a total of 4,103 datasets. The results showed that determining the best cluster using the Elbow method on the dataset produced empirical evidence that the best cluster was k = 5. The results of grouping tweets regarding COVID-19 using the K-Means Clustering method with k = 5 resulted in the largest number of cluster members being cluster 4 with 1,185 tweets, the second largest was cluster 1 with 1,047 tweets, the third largest was cluster 2 with 757 tweets, the fourth largest was cluster 3 as many as 744 tweets, and the smallest number of cluster members is cluster 5 as many as 370 tweets.
Capturing the complete language environment: What could children learn by 'listening in'?

Inspired by qualitative studies typically limited to child-directed speech, we develop a coding scheme designed to characterize all utterances accessible to two English-learning children in terms of their relative utility for word-learning. We focus in particular on contributors to referential transparency as a well-established and meaningful dimension of language learnability in context. These include the spatial positions of the caregivers and children, the caregivers' use of gaze or gesture to illustrate their meaning, the child's visual access to the caregiver or to the referent of the utterance, and the caregiver's use of modified prosody. As a proof of concept, we apply this coding scheme to existing naturalistic video corpora for one English-learning child whose language development is well-documented. We find that both speech directed to the child and speech overhearable by her are highly variable along the qualitative dimensions we coded, and identify the heterogeneity of overheard speech as a source of noise in previous investigations. While irrelevant as a referential cue, our results suggest caregivers' prosodic modification may play a functional role in marking speech intended for the child — especially given the significant qualitative overlap between overhead and child-directed speech along other dimensions. In spite of the frequent similarity between overheard and child-directed speech, overheard utterances were significantly less associated with child attention. Taken together, our results shed light on how adults and children co-structure the early language environment, and promise to provide similar insights when applied to naturalistic video corpora for children across the world.
NORA – HealthCare Voice Based Chatbot

A health care chatbot is a computer program that uses artificial intelligence to interact with people through a chat interface. The chatbot is able to answer questions about health and provide medical advice by using a decision tree algorithm to determine the best response based on the information provided
Image Enhancement by Using Fuzzy Firefly Optimization and Fuzzy Perceptron Neural Network

The image enhancement methods play an important role in digital image processing. And by using a different kinds of image enhancement techniques, such as artificial intelligence techniques methods. The aim of this methods is to enhance the visual appearance of the digital image and to reduce image noise. In this paper, to enhance the corrupted image and de-noise image, we used swarm optimization algorithms such as a firefly algorithm (FA) and also used neural network such as the perceptron neural network algorithm (PNN). And then after we added the fuzzy membership function to these two algorithms, we obtained to a new method called a fuzzy firefly algorithm (FFA) and fuzzy perceptron neural network algorithm (FPNN). And was computed the performance and efficiency measures for all methods, such as RMSE and PSNR. And the FFA method was the best among the other methods used in this paper.
Computational analysis of adjuncts in ASD-STE100 for the NLP parser ARTEMIS

Texts written in Simplified Technical English present semantic and syntactic restrictions with respect to standard English that should be considered in the implementation of natural language processing systems dealing with controlled natural languages. This paper explores the syntax and semantics of adjuncts in a controlled natural language, namely, the Simplified Technical English (ASD-STE100) used in the Airbus corpus with a view to observing how the peculiarities identified in the behaviour of Airbus adverbials should be reflected in the computational treatment of these constituents. Thus, our main aim is to contribute to the computational implementation of the prototype ARTEMIS by designing the parsing rules and the catalogue of feature-bearing matrixes that encode the grammatical constraints of Airbus adjuncts. The parsing rules reflect the positional preferences of the various peripheral types and capture the semantic variability of adjuncts in the corpus. In addition, they provide a weight factor that predicts the scale of markedness of these constituents with respect to the different positions that they occupy in the clause. On the whole, these properties offer a precise description of the syntactic features of adverbials that will facilitate their automatic processing.
The impact of strategic human resource development on organizational growth

There is a major need to focus on all such aspects to avoid inconsistencies. It is even noticed that managing the workforce during the time of pandemic has become a real challenge for the organizations because it started to create a negative impact over the workers' productivity that eventually has begun to develop the adverse impact over the organizational growth. It is even claimed by that the high focus over the human asset developed is essential to focus to have the competent and retained workforce. The research tends to clarify the relationship between the Strategic Human Resource Development (SHRD) practices and organizational growth. However, the following research has been organized in the context of the Abu Dhabi National Oil Company (ADNOC). This research has specifically investigated determining the key SHRD practices that contribute to the organizational growth and explained how SHRD helps establishments to maximize the organizational growth of ADNOC by embracing organizational learning. The overall study has an approach to focus more on the challenges areas of organizational development within ADNOC company. The outcomes were evaluated effectively to understand the issues and factors faced by the employees who were responsible for reducing the productivity of the company toward organizational growth by focusing over the organizational development.
FORMATION OF A LEARNING COMMUNITY IN PEDAGOGICAL AGROFOREST

This research aimed to investigate the development of participants in a core learning community that uses group dynamics functions integrated with values, through ecological learning in a pedagogical agroforestry with a focus on human relationships and the environment.The study was carried out with 41 participants, the community and students of a municipal elementary school located in the rural area in the countryside of the State of Mato Grosso, during 12 months.The research was guided by the following question: are there changes in human relationships in a learning community that applies values-integrated group dynamics functions?For the methodology, we opted for a qualitative approach, action research (THIOLLENT, 1685), with the use of observation scripts for the data collection process.The theoretical framework was based on studies by Pacheco (2021), Lima (1969), Götsch (1995) among others.The results of the research indicated changes in values in human relationships (autonomy, respect, among others) and showed the beginning of the process of consolidation of the group dynamics functions for the construction of the experience in cooperation.Based on the results of the survey, the participants showed an interest in continuity and prepared an Action Plan -2022, for community training and humanizing learning with the methodology of learning community in pedagogical agroforestry, with the insertion of learning circles.
Using Five Main Criterions in Teaching Public Speaking to UBSI Students in the Faculty of Communication and Language Jakarta

This research focuses on teaching public speaking using Five Main Criterions (FMC) to UBSI Students from the Faculty of Communication and Language. The subjects of this research are twenty students divided into two groups, ten boys and ten girls. Each student has to deliver public speaking in front of the class with different topics in limited time. The method used in this research is a qualitative research that pays attention to the utilization of Five Main Criterion as one of teaching techniques utilized by the researcher in teaching public speaking to UBSI students. Each criterion is interconnected to each other as unity that helps students in presenting their public speaking appropriately. The scoring was obtained according to the points from each criterion. This teaching technique was compiled by the researcher from numerous reliable resources. The researcher uses Five Main Criterions as a solution to enhance the level of confidence and also to improve the presentation skills of UBSI Students when they conduct public speaking task in class. There are a number of theories utilized by researcher in order to support this research to have solid scientific background. The theories were originated from a number of scholars who already conducted researchers in public speaking. The aim of this research is to provide a solution to fellow lecturers in providing suitable method in teaching public speaking to their students.. In addition, three previous researchers upon public speaking were added by the researcher as valuable sources to enrich the quality of this research.