Reviewerly/exHarmony huggingface dataset:
Neural network input feature selection using structured l2 − norm penalization

Abstract Artificial neural networks are referred to as universal approximators due to their inherent ability to reconstruct complex linear and nonlinear output maps conceived as input-output relationships from data sets. This can be done by reducing large networks via regularization in order to establish compact models containing fewer parameters aimed at describing vital dependencies in data sets. In situations where the data sets contain non-informative input features, devising a continuous, optimal input feature selection technique can lead to improved prediction or classification. We propose a continuous input selection technique through a dimensional reduction mechanism using a ‘structured’ l 2 − norm regularization. The implementation is done by identifying the most informative feature subsets from a given data set via an adaptive training mechanism. The adaptation involves introducing a novel, modified gradient approach during training to deal with the non-differentiability associated with the gradient of the structured norm penalty. When the method is applied to process data sets, results indicate that the most informative inputs of artificial neural networks can be selected using a structured l 2 − norm penalization.
Preliminary study regarding the effect of season on haematological parameters in Syrian Awassi rams

Abstract Four apparently healthy Syrian Awassi rams were used for one year. Jugular blood samples were collected from the rams once a week to determine some essential blood components (13) using a Veterinary Haematology Analyzer. The overall means were: 8.92 × 10 3 /mm 3 for White Blood Cells (WBC), 5.91% for Lymphocytes (LYM), 1.00 % for Monocytes (MON), 2.62% for Granulocytes (GRA), 7.94 × 10 3 /mm 3 for Red Blood Cells (RBC), 10.24 g/dL for Haemoglobin (Hb), 30.93% for Haematocrit (HCT), 39.12fL for Mean Corpuscular Volume (MCV), 12.57 pg for Mean Cell Haemoglobin (MCH), 33.10g/dL for Mean Corpuscular Haemoglobin Concentration (MCHC), 16.34 % for Red Cell Distribution Width (RDW), 577.3 ×10 3 /mm 3 for Platelet Counts (PLT) and 3.99 fL for Mean Platelet Volume (MPV), with variations in the values among individuals. There were differences in the value means among the months of the year in some components, where the overall means of WBC, LYM, MON, GRA, HGB, MCH and MCHC were significantly (P&lt;0.05) higher in winter as compared to summer months, whereas, MCV means were significantly (P&lt;0.05) higher in summer as compared to winter months, whereas there were no significant (P&gt;0.05) differences in the means of RBC, RDW, PLT or MPV among summer and winter months.
STRUCTURAL-FUNCTIONAL MODEL OF A 0 TEACHER-DEFECTOLOGIST’S PROFESSIONAL COMPETENCE FORMING IN CONDITIONS OF POSTGRADUATE EDUCATION

In the article a structural and functional model of the system of formation of professional competence of special education teachers in postgraduate education was worked out.Peculiarities of the structural-functional model of formation of professional competence of special education teachers in postgraduate education are: 1) by scope -research, 2) by form -informational, 3) by structure -network, 4) by level of detail -detailed, 5 ) according to the object of research -the model of the specialist, 6) according to the development over time -dynamic, 7) according to the degree of reproduction of the main features -functional, 8) according to the breadth of specialists' training issues -nationwide.The interconnected structural blocks of the model of the system of formation of professional competence of special education teachers are singled out: target, semantic, result.The content of the model is subordinated to the leading, strategic idea -the order of society for the professional competence of a specialist who works with children with SEN and their families, as well as the organization of correctional and educational process in inclusive resource centers as a new structure and preschool, general secondary, professional (vocational-technical), higher education of professionally competent specialists who have good knowledge, skills, abilities, personal qualities during training, education and development of children with SEN in educational institutions.
A methodology to assess and improve the physics consistency of an artificial neural network regression model for engineering applications

Abstract In recent times, artificial neural networks (ANNs) have become the popular choice of model for researchers while performing regression analysis between inputs and output. However; in scientific and engineering applications, developed ANN regression model is often found to be inconsistent with the physical laws. This is due to the fact that ANNs are purely based on data and do not have any understanding of underlying physical laws. Alternate ANN frameworks like PGNN (Physics guided neural network) has been proposed in literature which incorporate physics loss function in the overall loss function to partially alleviate this issue. However, these frameworks don’t evaluate the physics consistency of relationship between inputs and output mapped by the ANN model which is the source of all physics inconsistencies. Hence, the present paper presents a methodology to assess and improve the physics consistency of the input output relationship mapped by the ANN regression model. The developed methodology can therefore be used to develop physics consistent ANN regression model. The heart of the methodology is an inferencing algorithm which interprets the input output relationship mapped by the ANN regression model. The inferencing algorithm is based on Taylor series and decomposes the ANN regression model into several region-wise polynomial models. Moreover, the inferencing algorithm can also find regions of singular zones in the ANN model predictions. The region-wise polynomial from inferencing algorithm can be used to assess the physics consistency of the ANN model. In the region of physics inconsistency, additional data points can be added and the ANN model can be re-trained. In cases, where the addition of data points is not possible, a physics based loss function can be used. The developed methodology is illustrated using several datasets. The developed methodology will help engineers and researchers built physics consistent ANN regression models.
EPIDEMIOLOGY AND ASSOCIATED FACTORS OF DEPRESSION IN THE ELDERLY DURING COVID-19

Introduction: Most deaths caused by Covid-19 are of people over 60 years old, in Brazil, in 2021, this population represents 67.9% of the dead, due to complications from the virus. Faced with this, people in isolation suffered several effects on their health, especially with regard to mental health, such as sleep disorders, concentration on daily tasks, the appearance of intrusive thoughts, feelings of hopelessness, boredom, loneliness, anxiety and depression. Thus, the elderly stand out as the group most affected by mental disorders, with emphasis on depression, which is an aggravating factor for health, triggered by the consequences of social withdrawal, quarantine, the economic crisis, domestic accidents such as falls and high numbers of deaths caused by covid. The objective of this study was to identify scientific evidence in the literature about the impacts of social isolation on the mental health of the elderly during the pandemic, as well as recommendations to prevent depression. Method: This is a descriptive and exploratory study, of the integrative literature review type, carried out in the following databases: LILACS, BRISA AND SCIELO. Results and Discussions: With an analysis of the literature, it was evidenced that depression was one of the biggest impacts caused by the pandemic on the health of the elderly, resulting from sequelae caused by social isolation and the pandemic. Allied to this, domestic accidents, such as falls, are also classified as a predisposing factor for depression during social isolation, given that many elderly people live alone or spend more time exposed to risky situations in their homes, being more likely to fall into their homes and cause fractures. In this aspect, psychic disorders were intensified especially in these individuals in the current pandemic moment, the multimorbidities present in the elderly have a strong impact on the increase of mental health problems, causing emotional distress in these individuals. In this process, recommendations to support the elderly are essential with regard to predisposing factors for depression. Final Considerations: The study highlights the association of depression and the worsening of psychiatric pathologies related to the process of social isolation resulting from the pandemic, associated with the high rate of falls present in this population.
From “Dynamics on Graphs” to “Dynamics of Graphs”: An Adaptive Echo-State Network Solution (Student Abstract)

Many real-world networks evolve over time, which results in dynamic graphs such as human mobility networks and brain networks. Usually, the “dynamics on graphs” (e.g., node attribute values evolving) are observable, and may be related to and indicative of the underlying “dynamics of graphs” (e.g., evolving of the graph topology). Traditional RNN-based methods are not adaptive or scalable for learn- ing the unknown mappings between two types of dynamic graph data. This study presents a AD-ESN, and adaptive echo state network that can automatically learn the best neural net- work architecture for certain data while keeping the efficiency advantage of echo state networks. We show that AD-ESN can successfully discover the underlying pre-defined map- ping function and unknown nonlinear map-ping between time series and graphs.
IMPLEMENTASI MEDIA MAJALAH CERITA SEBAGAI UPAYA MENINGKATKAN KEMAMPUAN MEMBACA SISWA KELAS 3 MIN 10 NGAWI

This study aims to describe the impact of implementing children's story magazine media in class 3 learning at MIN 10 Ngawi. The method used in this research is descriptive method and uses a qualitative research approach. The technique used in this research is the technique of observation, interviews, documentation with data collectors, observation guides, interviews and field notes. The results of data analysis showed that the learning media of children's story magazines can improve student learning outcomes, it can be seen from the learning outcomes before and after using children's story magazine learning media. Before using the children's story magazine media, the average grade 3 MIN 10 Ngawi students was 64, after using the children's story magazine learning media the average grade 3 MIN 10 Ngawi students was 78
Iterative Processes: Notating, Composing, and Performing as Journey Forms

This essay posits that the practices of notation, composition, and performance are not creatively distinct from each other when observed, in particular, in radical works of graphic or text notation that make no or little reference to the western tradition of music notation, and whose composers offer no or few indications of the methods by which they should be interpreted or their intended performance outcomes. In such works of experimental music, performer choice and creativity are highlighted, but issues of composer choice and creativity may be equally observed, alongside the performative practices of notation. Understanding these practices as iterations of the same process means that the activities of notating, composing and performing can be considered as equal without the need to posit the performance of experimental scores as a compositional practice in and of itself. These ideas will be explored in examples from the works ijereja (2015–2016) and glíwmæden (2016)
Cyber Security Protection of Power System Equipment Based on Chip-Level Trusted Computing

This article proposes a network security protection scheme for power system embedded devices based on chip-level trusted computing, gives the overall architecture of power equipment chip-level trusted computing, introduces the principle of trust chain transfer within power terminals, and proposes the method of using the on-chip SRAM inside the main control chip of power system embedded devices as the PUF to construct the trusted root. Furthermore, we propose a complete trust transfer mechanism from booting trust to the loading that trust on operating system and application, and give the corresponding program design framework and program logic. The chip-level trusted computing program designed in this article was applied in a distribution automation DTU device equipped with the FUXI chip that is developed by the China Southern Power Grid and the Sylix OS operating system. Finally, we carry out the security protection test. The validity of the designed program is verified by running malicious programs to attack the power equipment. The results show that the designed program can effectively detect and intercept illegal programs, and provide an effective solution for the security protection of power equipment in the open network environment.
Efficient Design Guidelines for Innovative Aerial Robot Design

Abstract The field of aerial robotics has advanced rapidly, but the design knowledge has not yet been codified into reusable design guidelines. Design guidelines have been developed for many mechanical design areas to advance the field itself and help novice designers benefit from past expert knowledge more easily. We used an inductive approach and collected 90 aerial robot examples by reviewing recent work in aerial robotics and studying the key motivations, features, functionalities, and potential design contradictions. Then, design guidelines are derived by identifying patterns and grouping them by the problem they solve and the innovation made to solve it iteratively. From this, we find 35 unique design examples that can be grouped into either 14 design guidelines for more sensing, battery, mission, or actuation efficiency; or to improve the desired functionality in an aerial robot such as reducing complexity or improving how the robot can interact with objects or its environment. The derived guidelines are validated for thematic saturation using convergence analysis and its utility through a qualitative design study involving novices and experienced designers working on two design problems. The design guidelines presented in this research can support the design of future innovative aerial robots.
Prediction Method of Public Participation in Decision-making Based on Bayes Theorem

Abstract Public participation is an activity that citizens participate in the decision-making without government and experts. It could create better informed and more creative decision-making. However, it is difficult to invite all stakeholders to participate in the urban design and planning project. So most of projects could just receive a few decisions from the public. This article would introduce a model based on Bayes theorem. This algorithm model can predict the possibility of other stakeholders' decision-making on a proposal based on the feedback results of a small part of the surveyed participants and big data analysis. This article mainly studies how the algorithm model can be displayed in the actual project through a case of local residents' decision-making and prediction of the building height of the planned area in a planning project. Key words: Public participation, Decision-making prediction, Bayes theorem
A Stochastic Momentum Accelerated Quasi-Newton Method for Neural Networks (Student Abstract)

Incorporating curvature information in stochastic methods has been a challenging task. This paper proposes a momentum accelerated BFGS quasi-Newton method in both its full and limited memory forms, for solving stochastic large scale non-convex optimization problems in neural networks (NN).
Multiagent reinforcement learning for strictly constrained tasks based on Reward Recorder

Multiagent reinforcement learning (MARL) has been widely applied in engineering problems. However, many strictly constrained problems such as distributed optimization in engineering applications are still a great challenge to MARL. Especially for strict global constraints of agents' actions, it is very easy to lead to sparse rewards. Besides, existing studies cannot solve the instability caused by partial observability while making the algorithm fully distributed. Algorithms with centralized training may encounter significant obstacles in real-world deployment. For the first time, we provide theoretical analysis for MARL to determine the adverse effects of partial observability on convergence, and a fully distributed and convergent MARL algorithm based on Reward Recorder is proposed. Each agent runs an independent reinforcement learning algorithm and uses the average-consensus protocol to estimate the global state-action value locally to achieve global optimization. To verify the performance of the algorithm, we propose a novel generalized constrained optimization model, which includes local inequality constraints and strict global constraints. The proposed distributed reinforcement learning algorithm is supported by several simulation examples. The results reveal that the proposed algorithm has high stability and excellent decision-making ability.
Reduction of Artifacts and Edge Preservation of Underwater Images Using Deep Convolution Neural Network

Underwater exploration has been one of the areas of active research over the last few decades. Image enhancement is difficult for computer vision-based underwater research because of the degradation of the underwater environment’s images. Several types of artifact reduction approaches are already available of value averaging filters to smooth the continuity that artifact reduction approaches appear across image boundaries. While some of these approaches can somewhat reduce these unwanted artifacts’ severity, other approaches have some limitations that can cause blurring to high-contrast edges in the image. Therefore we need novel methods to overcome the theses’ drawbacks. This research work introduced a Deep Convolution Neural Network (DCNN)-based deep learning method to overcome the drawbacks of blurring to low-contrast edges in the image. The proposed system’s performance is validated through simulation, and the simulation results are obtained using Python simulation software. The obtained simulation results demonstrate the superiority of the proposed underwater image enhancement method. The number of different coefficients is compared with the results of the algorithm of the Underwater Image Colorfulness Measure (UICM), Underwater Image Sharpness Measure (UISM), Underwater Image Contrast Measure (UIconM) and Underwater Image Quality Measure (UIQM) values. After that, it was found to be an effective comparison of visualization techniques.
InteractEva: A Simulation-Based Evaluation Framework for Interactive AI Systems

Evaluating interactive AI (IAI) systems is a challenging task, as their output highly depends on the performed user actions. As a result, developers often depend on limited and mostly qualitative data derived from user testing to improve their systems. In this paper, we present InteractEva; a systematic evaluation framework for IAI systems. InteractEva employs (a) a user simulation backend to test the system against different use cases and user interactions at scale with (b) an interactive frontend allowing developers to perform important quantitative evaluation tasks, including acquiring a performance overview, performing error analysis, and conducting what-if studies. The framework has supported the evaluation and improvement of an industrial IAI text extraction system, results of which will be presented during our demonstration.
Distribution Aware VoteNet for 3D Object Detection

Occlusion is common in the actual 3D scenes, causing the boundary ambiguity of the targeted object. This uncertainty brings difficulty for labeling and learning. Current 3D detectors predict the bounding box directly, regarding it as Dirac delta distribution. However, it does not fully consider such ambiguity. To deal with it, distribution learning is used to efficiently represent the boundary ambiguity. In this paper, we revise the common regression method by predicting the distribution of the 3D box and then present a distribution-aware regression (DAR) module for box refinement and localization quality estimation. It contains scale adaptive (SA) encoder and joint localization quality estimator (JLQE). With the adaptive receptive field, SA encoder refines discriminative features for precise distribution learning. JLQE provides a reliable location score by further leveraging the distribution statistics, correlating with the localization quality of the targeted object. Combining DAR module and the baseline VoteNet, we propose a novel 3D detector called DAVNet. Extensive experiments on both ScanNet V2 and SUN RGB-D datasets demonstrate that the proposed DAVNet achieves significant improvement and outperforms state-of-the-art 3D detectors.
A TEXTUAL ANALYSIS OF STUDENTS’ WRITING IN NARRATIVE TEXT

The objective of this study is to analyze cohesion and coherence in students’ narrative texts using critical discourse analysis approach and to identify the implications of applying critical discourse analysis approach to students’ narrative writing. This study used descriptive qualitative method. The object of this research is 6 narrative texts written by 2 nd semester students of English Education Department of STKIP PGRI Bangkalan. Data collection is done through observation, questionnaires, interviews and documentation. The findings show that the students applied their narrative text writing tasks with grammatical cohesion, lexical cohesive, and coherence. Related to student assignment texts, most of them are good at writing cohesion and coherence and some of them need to be improved. In terms of writing, they are good at the reference, conjunction and reiteration or repetition. From student writings, the researcher got 349 reference words collected, and 26 words from substitution, no ellipsis at all, 112 conjunction words collected, and the last 69 words lexical cohesive. They need to learn more about ellipsis and collocation, they are good at reiteration or repetition. About coherence they are good. most sentences are coherence but there is some to learn about coherence. Then, the implications of the critical discourse analysis approach model of Norman Fairclough are also very effective in analyzing a discourse or text because it can identify the content and intent of students’ narrative texts through three stages, namely the description stage, the interpretation stage and the explanation stage.
Socially Intelligent Affective AI

Artificial Intelligence has aimed to give the systems or agents, the ability to learn, perceive, recognize, plan, reason and act. Affective Computing has brought into focus the importance of giving AI systems, the capability to perceive, detect, utilize and generate emotion, affect, sentiment or feelings. To have a meaningful human-computer interaction, we need to design and develop a more socially intelligent and affective AI. My doctoral research goal is to delve deeper into some of these aspects, firstly by surveying computational models implemented in AI that uses emotion in decision-making or behaviour; secondly, by creating new model to predict social event context and affect in group videos; thirdly, to predict the social identities in visual scenes; and lastly to combine information about context, identities, behaviour and emotion in a social interaction scene to predict social incoherence and to recommend appropriate behaviour.