Reviewerly/exHarmony huggingface dataset:
Seeing Under the Cover: A Physics Guided Learning Approach for In-bed Pose Estimation

Human in-bed pose estimation has huge practical values in medical and healthcare applications yet still mainly relies on expensive pressure mapping (PM) solutions. In this paper, we introduce our novel physics inspired vision-based approach that addresses the challenging issues associated with the in-bed pose estimation problem including monitoring a fully covered person in complete darkness. We reformulated this problem using our proposed Under the Cover Imaging via Thermal Diffusion (UCITD) method to capture the high resolution pose information of the body even when it is fully covered by using a long wavelength IR technique. We proposed a physical hyperparameter concept through which we achieved high quality groundtruth pose labels in different modalities. A fully annotated in-bed pose dataset called Simultaneously-collected multimodal Lying Pose (SLP) is also formed/released with the same order of magnitude as most existing large-scale human pose datasets to support complex models’ training and evaluation. A network trained from scratch on it and tested on two diverse settings, one in a living room and the other in a hospital room showed pose estimation performance of 98.0% and 96.0% in PCK0.2 standard, respectively. Moreover, in a multi-factor comparison with a state-of-the art in-bed pose monitoring solution based on PM, our solution showed significant superiority in all practical aspects by being 60 times cheaper, 300 times smaller, while having higher pose recognition granularity and accuracy.
Variational Bayesian IMM-Filter for JMSs With Unknown Noise Covariances

In this paper, we derive a modified interacting multiple model filter for jump Markov systems with unknown process and measurement noise covariances. Using the inverse-Wishart distribution as the conjugate prior of noise covariances, the system state together with the noise parameters for each mode are inferred by the variational Bayesian method. The mixing and output estimates are calculated according to the weighted Kullback-Leibler average of mode-conditioned estimates. Simulation results show the effectiveness of the proposed algorithm.
Peningkatan Pemahaman Akuntansi Dengan Menggunakan Software Zahir

STMIK IKMI have a student academic test when the students enter to STMIK IKMI, the result of the test is low. Low in this case will explains by categories the students get D 25% C 37% B 0% and all the students can not get A. The research uses kuasi eksperiments method with time series design that collaborate classroom action research. The result of research shows about pre test 1 until 4, the score of pre test 1 about 54,129, the score of pre test 2 about 55,548, the score of pre test 3 about 56,032, the score of pre test 4 about 56,097. Based on Kruskall Walls Test shows Asymp Sig score about 0,986 it means there is no significance differences between student perception for the first time and students understanding the materials. In second part of research the students learn accounting that use zahir accounting software for 6 meetings. In third part of research the students has a post test for 4 meetings and the results are the score of post test 1 62,6456, the results are the score of post test 2 70,065, the results are the score of post test 3 80,032, the results are the score of post test 4 86,742. The research analyze statistic test focus on pre test and post test by Kruskall Wall Test that shows Asymp Sig score about 0,000 it means there is a differences between the result of pre test and post test. This reality shows accounting learning by Zahir software gives the positive effect for improving (upgrading) student understanding about accounting.
E-Learning Satisfaction Menggunakan Metode Auto Model

E-Learning just like learning media in general need to be evaluated to find out and measure much effectiveness, efficiency and user satisfaction with the quality of the overall learning process. One effort that can be done to find out and evaluate the quality of a learning is to use satisfaction evaluation. Measurement of satisfaction requires data derived from questionnaires that are presented using a Likert scale. The data illustrates the perception of users who have uncertainty because it is very subjective so that it has the potential to cause misinterpretation. The auto model method can be used to evaluate e-Learning satisfaction because the auto model method has the advantage of solving a problem with the various models produced, which in this case are in accordance with the context of the satisfaction problem that is often presented in natural language that has uncertainty, such as how satisfied? , How efficient? And much is user satisfaction. Based on the auto model method, the results of the satisfaction scores of each respondent, shown in the table above, are summed and the average is calculated. With the auto model, the results show that SVM is the best performance method with an acceleration rate of 90% and best gains with a value of 38.
Improved White Blood Cells Classification based on Pre-trained Deep Learning Models

Leukocytes, or white blood cells (WBCs), are microscopic organisms that fight against infectious disease, bacteria, viruses, and others. The manual method to classify and count WBCs is tedious, time-consuming and may has inaccurate results, whereas the automated methods are costly. The objective of this work is to automatically identify and classify WBCs in a microscopic image into four types with higher accuracy. BCCD is the used dataset in this study, which is a scaled down blood cell detection dataset. BCCD is firstly pre-processed by passing through several processes such as segmentation and augmentation,then it is passed to the proposed model. Our model combines the privilege of deep models in automatically extracting features with the higher classification accuracy of traditional machine learning classifiers.The proposed model consists of two main layers; a shallow tuning pre-trained model and a traditional machine learning classifier on top of it. Here, ten different pretrained models with six different machine learning are used in this study. Moreover, the fully connected network (FCN) of pretrained models is used as a baseline classifier for comparison. The evaluation process shows that the hybrid between MobileNet-224 as feature extractor with logistic regression as classifier has a higher rank-1 accuracy with 97.03%. Besides, the proposed hybrid model outperformed the baseline FCN with 25.78% on average.
A classification-based approach to semi-supervised clustering with pairwise constraints

In this paper, we introduce a neural network framework for semi-supervised clustering with pairwise (must-link or cannot-link) constraints. In contrast to existing approaches, we decompose semi-supervised clustering into two simpler classification tasks: the first stage uses a pair of Siamese neural networks to label the unlabeled pairs of points as must-link or cannot-link; the second stage uses the fully pairwise-labeled dataset produced by the first stage in a supervised neural-network-based clustering method. The proposed approach is motivated by the observation that binary classification (such as assigning pairwise relations) is usually easier than multi-class clustering with partial supervision. On the other hand, being classification-based, our method solves only well-defined classification problems, rather than less well specified clustering tasks. Extensive experiments on various datasets demonstrate the high performance of the proposed method.
Improving Social Awareness Through DANTE: Deep Affinity Network for Clustering Conversational Interactants

We propose a data-driven approach to detect conversational groups by identifying spatial arrangements typical of these focused social encounters. Our approach uses a novel Deep Affinity Network (DANTE) to predict the likelihood that two individuals in a scene are part of the same conversational group, considering their social context. The predicted pair-wise affinities are then used in a graph clustering framework to identify both small (e.g., dyads) and large groups. The results from our evaluation on multiple, established benchmarks suggest that combining powerful deep learning methods with classical clustering techniques can improve the detection of conversational groups in comparison to prior approaches. Finally, we demonstrate the practicality of our approach in a human-robot interaction scenario. Our efforts show that our work advances group detection not only in theory, but also in practice.
Philosophy of education in a new key: Who remembers Greta Thunberg? Education and environment after the coronavirus

This paper explores relationships between environment and education after the Covid-19 pandemic through the lens of philosophy of education in a new key developed by Michael Peters and the Philosophy of Education Society of Australasia (PESA).The paper is collectively written by 15 authors who responded to the question: Who remembers Greta Thunberg?Their answers are classified into four main themes and corresponding sections.The first section, 'As we bake the earth, let's try and bake it from scratch', gathers wider philosophical considerations about the intersection between environment, education, and the pandemic.The second section, 'Bump in the road or a catalyst for structural change?',looks more closely into issues pertaining to education.The third section, 'If you choose to fail us, we will never forgive you', focuses to Greta Thunberg's messages and their responses.The last section, 'Towards a new (educational) normal', explores future scenarios and develops recommendations for critical emancipatory action.The concluding part brings these insights together, showing that resulting synergy between the answers offers much more then the sum of articles' parts.With its ethos of collectivity, interconnectedness, and solidarity, philosophy of education in a new key is a crucial tool for development of post-pandemic (philosophy of) education.
Computational Social Simulation With E-CARGO: Comparison Between Collectivism and Individualism

Computational social simulation is a long-term, cutting-edge topic in the interdisciplinary field where information technology, computer science, social science, and sociology overlap. In this article, we establish the fundamental requirements for social simulation and demonstrate that the Environments-Classes, Agents, Roles, Groups, and Objects (E-CARGO) model for role-based collaboration (RBC) and the subsequent group role assignment (GRA) optimization model are highly qualified to meet these requirements. Based on E-CARGO and GRA, we propose a new approach to social simulation and conduct a case study to verify this approach. This case study involves a comparison between collectivism and individualism. The contribution of this work is a novel approach to social simulation using E-CARGO and GRA. This approach reveals the exciting results that explain social phenomena, e.g., collectivism is better than individualism if the team manager is perfect in the evaluation process, and individualism can beat collectivism without much difficulty if the team manager is not perfect.
Pengaruh Kata Cashback Terhadap Peningkatan Penjualan Menggunakan Data Mining

Modern era has many advantages for us one of the advantages is selling and buying process as we known is online shopping. There is one word which is loved by seller and buyer is cashback recently. Cashback became a favorite thing in online shopping, every seller using cashback for developing their selling, and every buyer using cashback for getting many benefits. In this research researcher analyzed the using of cashback by beauty product company which is used cashback as their program in special program last year. The aim of research is describing the important of cashback word in selling and buying process. The researcher used language theory as communication by George Yule. The researcher also used Data Mining for analyzing the data and giving a provement about the influencing of cashback in selling and buying. The researcher used Algoritma K-Means as a method of research and used Rapid Minner application for analyzing te data. The result of this research showed the word of cashback increased double selling rate for beauty product company.
Efficient Attention Mechanism for Visual Dialog that Can Handle All the Interactions Between Multiple Inputs

It has been a primary concern in recent studies of vision and language tasks to design an effective attention mechanism dealing with interactions between the two modalities. The Transformer has recently been extended and applied to several bi-modal tasks, yielding promising results. For visual dialog, it becomes necessary to consider interactions between three or more inputs, i.e., an image, a question, and a dialog history, or even its individual dialog components. In this paper, we present a neural architecture named Light-weight Transformer for Many Inputs (LTMI) that can efficiently deal with all the interactions between multiple such inputs in visual dialog. It has a block structure similar to the Transformer and employs the same design of attention computation, whereas it has only a small number of parameters, yet has sufficient representational power for the purpose. Assuming a standard setting of visual dialog, a layer built upon the proposed attention block has less than one-tenth of parameters as compared with its counterpart, a natural Transformer extension. The experimental results on the VisDial datasets validate the effectiveness of the proposed approach, showing improvements of the best NDCG score on the VisDial v1.0 dataset from 57.59 to 60.92 with a single model, from 64.47 to 66.53 with ensemble models, and even to 74.88 with additional finetuning.
Deferred neural lighting

We present deferred neural lighting, a novel method for free-viewpoint relighting from unstructured photographs of a scene captured with handheld devices. Our method leverages a scene-dependent neural rendering network for relighting a rough geometric proxy with learnable neural textures. Key to making the rendering network lighting aware are radiance cues: global illumination renderings of a rough proxy geometry of the scene for a small set of basis materials and lit by the target lighting. As such, the light transport through the scene is never explicitely modeled, but resolved at rendering time by a neural rendering network. We demonstrate that the neural textures and neural renderer can be trained end-to-end from unstructured photographs captured with a double hand-held camera setup that concurrently captures the scene while being lit by only one of the cameras' flash lights. In addition, we propose a novel augmentation refinement strategy that exploits the linearity of light transport to extend the relighting capabilities of the neural rendering network to support other lighting types (e.g., environment lighting) beyond the lighting used during acquisition (i.e., flash lighting). We demonstrate our deferred neural lighting solution on a variety of real-world and synthetic scenes exhibiting a wide range of material properties, light transport effects, and geometrical complexity.
An Improved Whale Optimization Algorithm for the Traveling Salesman Problem

The whale optimization algorithm is a new type of swarm intelligence bionic optimization algorithm, which has achieved good optimization results in solving continuous optimization problems. However, it has less application in discrete optimization problems. A variable neighborhood discrete whale optimization algorithm for the traveling salesman problem (TSP) is studied in this paper. The discrete code is designed first, and then the adaptive weight, Gaussian disturbance, and variable neighborhood search strategy are introduced, so that the population diversity and the global search ability of the algorithm are improved. The proposed algorithm is tested by 12 classic problems of the Traveling Salesman Problem Library (TSPLIB). Experiment results show that the proposed algorithm has better optimization performance and higher efficiency compared with other popular algorithms and relevant literature.
A Novel Short-Term Photovoltaic Power Forecasting Approach based on Deep Convolutional Neural Network

In this study, a novel photovoltaic power forecasting system that utilizes a deep Convolutional Neural Network (CNN) structure and an input signal decomposition algorithm is proposed. The proposed CNN architecture extracts deep features to forecast short-term power using transfer learning-based AlexNet. The historical power, solar radiation, wind speed, and temperature data are selected as the input. The signal decomposition algorithm called Empirical Mode Decomposition (EMD) is utilized to decompose the historical power signal into sub-components. In order to extract deep features, all input parameters are converted to 2D feature maps and feed to the input of the CNN. The experiments are realized on a grid-tied Photovoltaic Power Plant (PVPP) that has 1000 kW installed capacity located in Turkey. The experiments are performed under four weather conditions as partial cloudy, cloudy-rainy, heavy-rainy, and sunny days to show the effectiveness of the proposed method. The obtained results are compared with the benchmark regression algorithms. When the results are analyzed, the proposed method gives the highest Correlation Coefficient (R) and the lowest Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and SMAPE values under all horizons and weather conditions. For 1-h to 5-h ahead, the average R values of the proposed method are obtained as 97.28%, 95.77%, 94.49%, 93.61%, and 92.62%, respectively. The average RMSE values are observed as 4.90%, 6.30%, 7.50%, 8.00%, and 9.17% for 1-h to 5-h ahead. The experimental results confirm that the proposed method outperforms the conventional regression algorithms and reveals effective results with its competitive performance.
Adaptive Transition Probability Matrix Learning for Multiview Spectral Clustering

Multiview clustering as an important unsupervised method has been gathering a great deal of attention. However, most multiview clustering methods exploit the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">self-representation property</i> to capture the relationship among data, resulting in high computation cost in calculating the self-representation coefficients. In addition, they usually employ different regularizers to learn the representation tensor or matrix from which a transition probability matrix is constructed in a separate step, such as the one proposed by Wu <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al.</i> . Thus, an optimal transition probability matrix cannot be guaranteed. To solve these issues, we propose a unified model for multiview spectral clustering by directly learning an adaptive transition probability matrix (MCA <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> M), rather than an individual representation matrix of each view. Different from the one proposed by Wu <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al.</i> , MCA <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> M utilizes the one-step strategy to directly learn the transition probability matrix under the robust principal component analysis framework. Unlike existing methods using the absolute symmetrization operation to guarantee the nonnegativity and symmetry of the affinity matrix, the transition probability matrix learned from MCA <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> M is nonnegative and symmetric without any postprocessing. An alternating optimization algorithm is designed based on the efficient alternating direction method of multipliers. Extensive experiments on several real-world databases demonstrate that the proposed method outperforms the state-of-the-art methods.
An Expert Annotated Dataset for the Detection of Online Misogyny

Online misogyny is a pernicious social problem that risks making online platforms toxic and unwelcoming to women.We present a new hierarchical taxonomy for online misogyny, as well as an expert labelled dataset to enable automatic classification of misogynistic content.The dataset consists of 6,567 labels for Reddit posts and comments.As previous research has found untrained crowdsourced annotators struggle with identifying misogyny, we hired and trained annotators and provided them with robust annotation guidelines.We report baseline classification performance on the binary classification task, achieving accuracy of 0.93 and F1 of 0.43.The codebook and datasets are made freely available for future researchers.
MUDES: Multilingual Detection of Offensive Spans

The interest in offensive content identification in social media has grown substantially in recent years.Previous work has dealt mostly with post level annotations.However, identifying offensive spans is useful in many ways.To help coping with this important challenge, we present MUDES, a multilingual system to detect offensive spans in texts.MUDES features pre-trained models, a Python API for developers, and a user-friendly web-based interface.A detailed description of MUDES' components is presented in this paper.
Investigating the utility of VR for spatial understanding in surgical planning: evaluation of head-mounted to desktop display

Abstract Recent technological advances have made Virtual Reality (VR) attractive in both research and real world applications such as training, rehabilitation, and gaming. Although these other fields benefited from VR technology, it remains unclear whether VR contributes to better spatial understanding and training in the context of surgical planning. In this study, we evaluated the use of VR by comparing the recall of spatial information in two learning conditions: a head-mounted display (HMD) and a desktop screen (DT). Specifically, we explored (a) a scene understanding and then (b) a direction estimation task using two 3D models (i.e., a liver and a pyramid). In the scene understanding task, participants had to navigate the rendered the 3D models by means of rotation, zoom and transparency in order to substantially identify the spatial relationships among its internal objects. In the subsequent direction estimation task, participants had to point at a previously identified target object, i.e., internal sphere, on a materialized 3D-printed version of the model using a tracked pointing tool. Results showed that the learning condition (HMD or DT) did not influence participants’ memory and confidence ratings of the models. In contrast, the model type, that is, whether the model to be recalled was a liver or a pyramid significantly affected participants’ memory about the internal structure of the model. Furthermore, localizing the internal position of the target sphere was also unaffected by participants’ previous experience of the model via HMD or DT. Overall, results provide novel insights on the use of VR in a surgical planning scenario and have paramount implications in medical learning by shedding light on the mental model we make to recall spatial structures.