Reviewerly/exHarmony huggingface dataset:
Deep Learning on Facial Expression Detection : Artificial Neural Network Model Implementation

The moods, emotions, and even medical issues of a person can frequently be seen directly reflected in their facial expressions. The fields of social science and human-computer interaction have recently begun to pay more attention to facial emotion detection as a result of this. The primary focus of this study is on the automatic recognition of human facial expressions using an artificial neural network (ANN) model and a technique based on straightforward convolution. The dataset utilized is a self-mined dataset that was obtained by utilizing the web scraping approach on Google Image with the help of the Selenium package for Python. A dataset containing six categories of fundamental human expressions that are likely to be met on a daily basis, namely anger, confusion, contempt, crying, sadness, disgust, and happiness, with a total of 6,016 photos being used. The goal of this research is to determine how accurate the model of artificial neural networks can be in predicting.
Peningkatan Kompetensi SMK dengan Keterampilan Computer Aided Design

Human capitals in the computer field are required in the Industrial Era 4.0. Vocational schools (SMK) are aimed to produce graduates who are ready-to-use to work in industries. However, SMK graduates are one of the major contributors to high unemployment in Indonesia. Teachers with skills in the field of computer technology are scarce. In addition, the facilities and infrastructure are limited in many SMK schools, especially in rural areas. This community service activity was carried out at SMK Muhammadiyah Tirtayasa. The school is located in the northern coastal rural area of Serang Regency. This community service activity provided assistance to Computer Aided Design (CAD) skills by targeting teachers and students. CAD skills can provide skills for vocational graduates who will have careers in industry. The result showed an increase in the skills of participants in the field of 2D/3D CAD drawing. The activity was very fruitful and can be further followed up by both expanding the partner schools or integrating school curriculum which adopts CAD skills with the aim of developing further the CAD knowledge.
Correlation Between Vocabulary Achievement of Computer Engineering Network (TKJ) Students and Their Learning Habit Using Computer at SMKN 1 Pulubala

The objective of this research is to obtain the empirical data about the correlation between vocabulary achievement of Computer Engineering Network (TKJ) students and their learning habit using computer at SMKN 1 Pulubala. This research use correlation method and quantitative descriptive design to measure the correlation. This research was conducted to 67 Computer Engineering Network (TKJ) students at SMKN 1 Pulubala. After doing the research, the researcher concluded that there is a correlation between vocabulary achievement of Computer Engineering Network (TKJ) students and their learning habit using computer. It can be seen through the result of the correlation calculate using SPSS Computer for Windows show a value of 0.507. Based on the interpretation of the correlation result, it can be concluded that correlation between vocabulary achievement of Computer Engineering Network (TKJ) students and their learning habit using computer has medium correlation. The researcher has also measured the influence of correlation between vocabulary achievement of Computer Engineering Network (TKJ) students and their learning habit using computer found 91.04% in the frequency of learning habit and 80.59% in verb.&#x0D; &#x0D; Keyword: vocabulary, learning habit, correlation
Evaluation of Bozem and Drainage Channels of Jade Hamlet Residential in Gresik

Drainage refers to a vital component for water disposal so that a region or area can hinder potential puddle due to excessive water on the land surface. Therefore, a good plan adjusting the land condition of residential area belongs to a fundamental matter. Jade Hamlet Residence is located on Hulaan highway, Hulaan Urban Village, Menganti District, Gresik Regency, covering an area ±20 hectares and consisting of housing area, park, public facility, and catchment area. The drainage channel at this residence serves as the drainage for rain water and household dirty water disposal. During the rainy season in 2018 and 2019, the channel and bozem or drainage basin of this residence encountered overflowing and eventually caused puddle. To overcome this problem, the capacities of bozem and channel are in need of analysis and evaluation so as to figure out the runoff volume that can be accommodated in the bozem and investigate the discharge planned in the drainage channel by employing Log Person III method, chi-square distribution fit test, Kolmogorov-Smirnov test, rational formula, and exponential method. The results of analysis and evaluation demonstrated that the bozem capacity could not afford the runoff discharge (298.35 m3 309.55 m3), thereby demanding additional length and depth against the existing bozem. Meanwhile, the calculation of flood discharge planned for the drainage channels of S4.Ry3, S6.Ry1, S6.D1, S6.I2, S6.Cs1, S6.Rc1, S6.Rc2 indicate that they need redesigning by altering the channel dimension as the discharge capacity exceeds the channel.
Teachers’ Role in Managing Online Listening Class during Covid-19 Pandemic: An Investigation of Students’ Perception

Teachers' roles are critical in the teaching and learning process for students. This study aims to determine students' perceptions of teachers' roles in managing online listening classes. The qualitative descriptive method was used to design this study. This research focused on 2018 English Department students who have completed their listening for academic purposes. Questionnaires and interviews were used to collect data. This study showed that teachers took on the following roles when teaching: controller, organizer, assessor, prompter, participant, resource, tutor, and observer. Teachers also got better at running online listening classes.
Pauses and Repetition on Students' Proposal Presentation

Difficulties in delivering and understanding presentations can be due to several factors, especially in the psycholinguistics aspect, and due to errors. This research aimed to investigate the types of pauses and repetition and to describe the factors affecting pauses and repetition produced by English Department students during their proposal presentations. It uses a qualitative descriptive design by applying Dardjowidjojo theories. This research was completed with two data collection instruments: audio-visual material and structured interviews. The types of pauses and repetition obtained by audio-visual material and the factors affecting the pauses and repetition gained by structured interviews. By random purposive sampling, five (5) students were selected as the participants of this research. Some aspects are found in this research the first is Pauses which are divided into silent expiration, silent respiration, fillers, and discourse markers. The second is repetition, divided into repetition of words and sentences. The data analysis showed that psychological constraints, word choice, language interference, and limited English proficiency are the factor affecting pauses and repetition. Briefly, pauses and repetitions were made by all the participants during their proposal presentation.
THE ISSUE OF THE DEVELOPMENT OF DIGITAL COMPETENCE OF MASTERS OF PRODUCTION EDUCATION

The article presents the content of the training of masters of industrial education, the tasks of their positions and services, the level of digital competence, the issue of regular professional development on the basis of professional educational institutions, the necessary knowledge and practical skills to be acquired, as well as feedback on their mastery.
PEDAGOGICAL ESSENCE AND MODEL OF TECHNOLOGICAL COMPETENCE DEVELOPMENT IN FUTURE ENGINEERS

Pedagogical factors of development of technological competence of students of higher educational institutions with the help of electronic educational tools are considered in this article. The components of the didactic support system for electronic educational tools are highlighted. The model of the advantages of organizing education based on electronic educational tools and the main problems of organizing the educational process with the help of electronic educational tools are shown.
Analysis of the Footprint of Uncertainty of a Parallelogram Membership Function

The general effectiveness of fuzzy representation depends heavily on the membership functions. The bestfootprint of uncertainty for a specific fuzzy system is highly dependent on the nature of the problem that the fuzzysystem is supposed to solve. As a result, a proper footprint of uncertainty size selection is needed to improve the fuzzysystem’s efficiency. This research intends to investigate the impact of a unique linear parallelogram membershipfunction’s footprint of uncertainty on nonlinear system modeling and control. The proposed type-2 membershipfunction has a crisp membership degree at the end points of the footprint of uncertainty and uncertain values inbetween. When dealing with data whose membership degree is certain at the boundary but uncertain in-between, theproposed membership function having its highest uncertainty at the midpoint of the membership function width isadvantageous. Tuning the parameters of the proposed MF will provide a variety of triangular and quadrilateralfootprints of uncertainty shapes that will better capture the training data’s uncertainties. The gradient descent learningalgorithm was used to tune the consequent parameters of the evaluated interval type-2 fuzzy system. The performanceresults demonstrated the effect of the footprint of uncertainty on linear parallelogram membership function-based fuzzysystem’s capability in prediction, identification, and control tasks.
CONTENT AND THEORETICAL ISSUES OF THE INTEGRATIVE APPROACH

In the article, a system of modern educational technologies has been created today, and its main task is to ensure the continuity and coherence of the educational content in the educational system, to improve the methodology of teaching subjects, to introduce pedagogical and information technologies into the educational process, to integrate higher education with science and production, and to develop theoretical and practical professional Harmonization of education requires the formation of professional competences of future specialists, despite the fact that a lot of scientific and research work is carried out on the development of professional knowledge and skills of students, in a situation where market relations are being resolved, those who have thoroughly mastered the specialized sciences that can withstand the vital competition that occurs in the labor market, correctly adapt to certain situations It is necessary to ensure the necessary and sufficient level of professional training and the integration of disciplines in order to educate the evaluator as a person who can perform labor activities in various sectors of the economy. the necessity of formation in ita is thoroughly analyzed.
Young university students, COVID-19 and vulnerabilities diferenciales

The first months of 2020 surprised us all with the appearance of the SARS-Cov-2 virus. Its impact was global, affecting the different areas of the social, cultural, economic, and political life of citizens, in addition to modifying our personal habits and our daily lives. In the area of ??education, the teaching-learning processes at all levels had to substantially modify their dynamics. At the University of Colima, Mexico, as of March 18, face-to-face classes were suspended, and after a brief period of adjustments, they were virtually resumed through “technological mediation”. The challenge was enormous for teachers and students, the curricular contents were reorganized, an academic continuity program was designed, and the use of materials and technological tools was fully incorporated into the school dynamics, both for classes, tutorials, teacher meetings, extracurricular activities, and so on. This unprecedented situation made visible a basic problem: the differential vulnerabilities of teachers and students who did not have the basic conditions and adequate technological equipment in their homes. In this article, from a phenomenological theoretical and methodological perspective, we reflect on the differential vulnerabilities that are visible in students when starting another semester from home through technological mediation. Which allowed the unveiling of two scenarios: an adverse one full of emotional, educational, and economic challenges that young people have had to overcome, and another encouraging scenario, anchored to positive expectations associated with new learning, in the hope of getting ahead of the pandemic. by COVID-19 and return to the classroom again.
V.I. VERNADSKY AND MODERNITY

The features of V.I. Vernadsky's works, which have preserved their significance to the present time, are considered. The data showing that V.I. Vernadsky's works on the transformation of human activity to global Earth transformation preceded and correspond to the modern identification of a new geological epoch – the Anthropocene. The connection between V.I. Vernadsky's analysis of the complexity of human creative activity and the fallacy of modern simplification of ideas about the leading factors of scientific research is emphasized. The uniqueness of V.I.'s system approach to the objects of scientific research, including the evolutionary aspect, the interaction between the cosmic component, inert and living substances, the natural emergence of the human mind, necessary for the development of methods to increase the stability of the biosphere is discussed.
Deceptive Reinforcement Learning in Model-Free Domains

This paper investigates deceptive reinforcement learning for privacy preservation in model-free and continuous action space domains. In reinforcement learning, the reward function defines the agent's objective. In adversarial scenarios, an agent may need to both maximise rewards and keep its reward function private from observers. Recent research presented the ambiguity model (AM), which selects actions that are ambiguous over a set of possible reward functions, via pre-trained $Q$-functions. Despite promising results in model-based domains, our investigation shows that AM is ineffective in model-free domains due to misdirected state space exploration. It is also inefficient to train and inapplicable in continuous action space domains. We propose the deceptive exploration ambiguity model (DEAM), which learns using the deceptive policy during training, leading to targeted exploration of the state space. DEAM is also applicable in continuous action spaces. We evaluate DEAM in discrete and continuous action space path planning environments. DEAM achieves similar performance to an optimal model-based version of AM and outperforms a model-free version of AM in terms of path cost, deceptiveness and training efficiency. These results extend to the continuous domain.
Hardware-Aware Graph Neural Network Automated Design for Edge Computing Platforms

Graph neural networks (GNNs) have emerged as a popular strategy for handling non-Euclidean data due to their state-of-the-art performance. However, most of the current GNN model designs mainly focus on task accuracy, lacking in considering hardware resources limitation and real-time requirements of edge application scenarios. Comprehensive profiling of typical GNN models indicates that their execution characteristics are significantly affected across different computing platforms, which demands hardware awareness for efficient GNN designs. In this work, HGNAS is proposed as the first Hardware-aware Graph Neural Architecture Search framework targeting resource constraint edge devices. By decoupling the GNN paradigm, HGNAS constructs a fine-grained design space and leverages an efficient multi-stage search strategy to explore optimal architectures within a few GPU hours. Moreover, HGNAS achieves hardware awareness during the GNN architecture design by leveraging a hardware performance predictor, which could balance the GNN model accuracy and efficiency corresponding to the characteristics of targeted devices. Experimental results show that HGNAS can achieve about $10.6\times$ speedup and $88.2\%$ peak memory reduction with a negligible accuracy loss compared to DGCNN on various edge devices, including Nvidia RTX3080, Jetson TX2, Intel i7-8700K and Raspberry Pi 3B+.
EqMotion: Equivariant Multi-agent Motion Prediction with Invariant Interaction Reasoning

Learning to predict agent motions with relationship reasoning is important for many applications. In motion prediction tasks, maintaining motion equivariance under Euclidean geometric transformations and invariance of agent interaction is a critical and fundamental principle. However, such equivariance and invariance properties are overlooked by most existing methods. To fill this gap, we propose EqMotion, an efficient equivariant motion prediction model with invariant interaction reasoning. To achieve motion equivariance, we propose an equivariant geometric feature learning module to learn a Euclidean transformable feature through dedicated designs of equivariant operations. To reason agent's interactions, we propose an invariant interaction reasoning module to achieve a more stable interaction modeling. To further promote more comprehensive motion features, we propose an invariant pattern feature learning module to learn an invariant pattern feature, which cooperates with the equivariant geometric feature to enhance network expressiveness. We conduct experiments for the proposed model on four distinct scenarios: particle dynamics, molecule dynamics, human skeleton motion prediction and pedestrian trajectory prediction. Experimental results show that our method is not only generally applicable, but also achieves state-of-the-art prediction performances on all the four tasks, improving by 24.0/30.1/8.6/9.2%. Code is available at https://github.com/MediaBrain-SJTU/EqMotion.
Boosting Weakly Supervised Object Detection using Fusion and Priors from Hallucinated Depth

Despite recent attention and exploration of depth for various tasks, it is still an unexplored modality for weakly-supervised object detection (WSOD). We propose an amplifier method for enhancing the performance of WSOD by integrating depth information. Our approach can be applied to any WSOD method based on multiple-instance learning, without necessitating additional annotations or inducing large computational expenses. Our proposed method employs a monocular depth estimation technique to obtain hallucinated depth information, which is then incorporated into a Siamese WSOD network using contrastive loss and fusion. By analyzing the relationship between language context and depth, we calculate depth priors to identify the bounding box proposals that may contain an object of interest. These depth priors are then utilized to update the list of pseudo ground-truth boxes, or adjust the confidence of per-box predictions. Our proposed method is evaluated on six datasets (COCO, PASCAL VOC, Conceptual Captions, Clipart1k, Watercolor2k, and Comic2k) by implementing it on top of two state-of-the-art WSOD methods, and we demonstrate a substantial enhancement in performance.
Attention Disturbance and Dual-Path Constraint Network for Occluded Person Re-Identification

Occluded person re-identification (Re-ID) aims to address the potential occlusion problem when matching occluded or holistic pedestrians from different camera views. Many methods use the background as artificial occlusion and rely on attention networks to exclude noisy interference. However, the significant discrepancy between simple background occlusion and realistic occlusion can negatively impact the generalization of the network. To address this issue, we propose a novel transformer-based Attention Disturbance and Dual-Path Constraint Network (ADP) to enhance the generalization of attention networks. Firstly, to imitate real-world obstacles, we introduce an Attention Disturbance Mask (ADM) module that generates an offensive noise, which can distract attention like a realistic occluder, as a more complex form of occlusion. Secondly, to fully exploit these complex occluded images, we develop a Dual-Path Constraint Module (DPC) that can obtain preferable supervision information from holistic images through dual-path interaction. With our proposed method, the network can effectively circumvent a wide variety of occlusions using the basic ViT baseline. Comprehensive experimental evaluations conducted on person re-ID benchmarks demonstrate the superiority of ADP over state-of-the-art methods.
Testing distributional equality for functional random variables

In this article, we present a nonparametric method for the general two-sample problem involving functional random variables modelled as elements of a separable Hilbert space ${\cal H}$. First, we present a general recipe based on linear projections to construct a measure of dissimilarity between two probability distributions on ${\cal H}$. In particular, we consider a measure based on the energy statistic and present some of its nice theoretical properties. A plug-in estimator of this measure is used as the test statistic to construct a general two-sample test. Large sample distribution of this statistic is derived both under null and alternative hypotheses. However, since the quantiles of the limiting null distribution are analytically intractable, the test is calibrated using the permutation method. We prove the large sample consistency of the resulting permutation test under fairly general assumptions. We also study the efficiency of the proposed test by establishing a new local asymptotic normality result for functional random variables. Using that result, we derive the asymptotic distribution of the permuted test statistic and the asymptotic power of the permutation test under local contiguous alternatives. This establishes that the permutation test is statistically efficient in the Pitman sense. Extensive simulation studies are carried out and a real data set is analyzed to compare the performance of our proposed test with some state-of-the-art methods.
Hospitalization Length of Stay Prediction using Patient Event Sequences

Predicting patients hospital length of stay (LOS) is essential for improving resource allocation and supporting decision-making in healthcare organizations. This paper proposes a novel approach for predicting LOS by modeling patient information as sequences of events. Specifically, we present a transformer-based model, termed Medic-BERT (M-BERT), for LOS prediction using the unique features describing patients medical event sequences. We performed empirical experiments on a cohort of more than 45k emergency care patients from a large Danish hospital. Experimental results show that M-BERT can achieve high accuracy on a variety of LOS problems and outperforms traditional nonsequence-based machine learning approaches.
Quantile and moment neural networks for learning functionals of distributions

We study news neural networks to approximate function of distributions in a probability space. Two classes of neural networks based on quantile and moment approximation are proposed to learn these functions and are theoretically supported by universal approximation theorems. By mixing the quantile and moment features in other new networks, we develop schemes that outperform existing networks on numerical test cases involving univariate distributions. For bivariate distributions, the moment neural network outperforms all other networks.