Reviewerly/exHarmony huggingface dataset:
Cyber Physical System Based Smart Healthcare System with Federated Deep Learning Architectures with Data Analytics

Data shared between hospitals and patients using mobile and wearable Internet of Medical Things (IoMT) devices raises privacy concerns due to the methods used in training. the development of the Internet of Medical Things (IoMT) and related technologies and the most current advances in these areas The Internet of Medical Things and other recent technological advancements have transformed the traditional healthcare system into a smart one. improvement in computing power and the spread of information have transformed the healthcare system into a high-tech, data-driven operation. On the other hand, mobile and wearable IoMT devices present privacy concerns regarding the data transmitted between hospitals and end users because of the way in which artificial intelligence is trained (AI-centralized). In terms of machine learning (AI-centralized). Devices connected to the IoMT network transmit highly confidential information that could be intercepted by adversaries. Due to the portability of electronic health record data for clinical research made possible by medical cyber-physical systems, the rate at which new scientific discoveries can be made has increased. While AI helps improve medical informatics, the current methods of centralised data training and insecure data storage management risk exposing private medical information to unapproved foreign organisations. New avenues for protecting users' privacy in IoMT without requiring access to their data have been opened by the federated learning (FL) distributive AI paradigm. FL safeguards user privacy by concealing all but gradients during training. DeepFed is a novel Federated Deep Learning approach presented in this research for the purpose of detecting cyber threats to intelligent healthcare CPSs.
Fractal modeling of multivariate principal component of contaminating toxic elements in Irankooh Pb-Zn mining area

Abstract The primary purpose of this investigation is contamination mapping in surrounding areas of Irankuh Pb–Zn mine, located in central Iran, using an integrated approach of principal component analysis (PCA) with the Concentration-Area (C-A) and Power Spectrum-Area (S-A) fractal models. PCA categorized the 45 elements into eight principal components. Component 2, containing the toxic elements of Pb, Zn, As, Mn, Cd, and Ba, was identified as contamination factor. This multivariate contamination factor was modeled using the C-A and S-A fractal methods (in spatial and frequency domains) to delineate pollution areas. Modeling of PCA data using the C-A fractal method showed four main populations for contamination factors. Two populations with higher fractal dimensions are associated with contamination from mining activities or anthropogenic effects. Low fractal dimensions are considered the background population which has not been affected or is less affected by these activities. Five geochemical populations were obtained for contamination factors using the S-A fractal modeling of PCA in the frequency domain. Therefore, various geochemical populations were achieved using geochemical filtering and two-dimensional inverse Fourier transformation. The geochemical populations related to classes 2, 3, and 4 containing intermediate frequency signals showed the pollution anomaly. The spatial distribution of pollutant geochemical signals exhibits excellent conformity with the mining operation limit and tailing dam location as pollutant sources. The results indicate that the elements of Pb, Zn, Cd, and As have significant values in the surrounding soils rather than their concentrations in the earth’s crust. The results demonstrate that the S-A fractal models can more precisely delineate the environmental anomaly than the C-A fractal model, especially in intermediate frequency populations.
Implicit Feature Refinement for Instance Segmentation

We propose a novel implicit feature refinement module for high-quality instance segmentation. Existing image/video instance segmentation methods rely on explicitly stacked convolutions to refine instance features before the final prediction. In this paper, we first give an empirical comparison of different refinement strategies,which reveals that the widely-used four consecutive convolutions are not necessary. As an alternative, weight-sharing convolution blocks provides competitive performance. When such block is iterated for infinite times, the block output will eventually convergeto an equilibrium state. Based on this observation, the implicit feature refinement (IFR) is developed by constructing an implicit function. The equilibrium state of instance features can be obtained by fixed-point iteration via a simulated infinite-depth network. Our IFR enjoys several advantages: 1) simulates an infinite-depth refinement network while only requiring parameters of single residual block; 2) produces high-level equilibrium instance features of global receptive field; 3) serves as a plug-and-play general module easily extended to most object recognition frameworks. Experiments on the COCO and YouTube-VIS benchmarks show that our IFR achieves improved performance on state-of-the-art image/video instance segmentation frameworks, while reducing the parameter burden (e.g.1% AP improvement on Mask R-CNN with only 30.0% parameters in mask head). Code is made available at https://github.com/lufanma/IFR.git
Diverse, Global and Amortised Counterfactual Explanations for Uncertainty Estimates

To interpret uncertainty estimates from differentiable probabilistic models, recent work has proposed generating a single Counterfactual Latent Uncertainty Explanation (CLUE) for a given data point where the model is uncertain, identifying a single, on-manifold change to the input such that the model becomes more certain in its prediction. We broaden the exploration to examine $\delta$-CLUE, the set of potential CLUEs within a $\delta$ ball of the original input in latent space. We study the diversity of such sets and find that many CLUEs are redundant; as such, we propose DIVerse CLUE ($\nabla$-CLUE), a set of CLUEs which each propose a distinct explanation as to how one can decrease the uncertainty associated with an input. We then further propose GLobal AMortised CLUE (GLAM-CLUE), a distinct and novel method which learns amortised mappings on specific groups of uncertain inputs, taking them and efficiently transforming them in a single function call into inputs for which a model will be certain. Our experiments show that $\delta$-CLUE, $\nabla$-CLUE, and GLAM-CLUE all address shortcomings of CLUE and provide beneficial explanations of uncertainty estimates to practitioners.
Counter-Strike Deathmatch with Large-Scale Behavioural Cloning

This paper describes an AI agent that plays the popular first-person-shooter (FPS) video game `Counter-Strike; Global Offensive' (CSGO) from pixel input. The agent, a deep neural network, matches the performance of the medium difficulty built-in AI on the deathmatch game mode, whilst adopting a humanlike play style. Unlike much prior work in games, no API is available for CSGO, so algorithms must train and run in real-time. This limits the quantity of on-policy data that can be generated, precluding many reinforcement learning algorithms. Our solution uses behavioural cloning - training on a large noisy dataset scraped from human play on online servers (4 million frames, comparable in size to ImageNet), and a smaller dataset of high-quality expert demonstrations. This scale is an order of magnitude larger than prior work on imitation learning in FPS games.
Permutations of counters on a table

We consider a game in which a blindfolded player attempts to set $n$ counters lying on the vertices of a rotating regular $n$-gon table simultaneously to $0$. When the counters count$\pmod{m}$ we simplify the argument of Bar Yehuda, Etzion, and Moran (1993) showing that the player can win if and only if $n = 1$, $m = 1$, or $(n, m) = (p^a, p^b)$ for some prime $p$ and $a, b \in \mathbb{N}$. We broadly generalize the result to the setting where the counters can be permuted by any element of a subset of the symmetric group $S \subseteq S_n$, with the original formulation corresponding to $S = \mathbb{Z}_n$ (rotations of the table).
Pose-guided Generative Adversarial Net for Novel View Action Synthesis

We focus on the problem of novel-view human action synthesis. Given an action video, the goal is to generate the same action from an unseen viewpoint. Naturally, novel view video synthesis is more challenging than image synthesis. It requires the synthesis of a sequence of realistic frames with temporal coherency. Besides, transferring the different actions to a novel target view requires awareness of action category and viewpoint change simultaneously. To address these challenges, we propose a novel framework named Pose-guided Action Separable Generative Adversarial Net (PAS-GAN), which utilizes pose to alleviate the difficulty of this task. First, we propose a recurrent pose-transformation module which transforms actions from the source view to the target view and generates novel view pose sequence in 2D coordinate space. Second, a well-transformed pose sequence enables us to separatethe action and background in the target view. We employ a novel local-global spatial transformation module to effectively generate sequential video features in the target view using these action and background features. Finally, the generated video features are used to synthesize human action with the help of a 3D decoder. Moreover, to focus on dynamic action in the video, we propose a novel multi-scale action-separable loss which further improves the video quality. We conduct extensive experiments on two large-scale multi-view human action datasets, NTU-RGBD and PKU-MMD, demonstrating the effectiveness of PAS-GAN which outperforms existing approaches.
Learning Auxiliary Monocular Contexts Helps Monocular 3D Object Detection

Monocular 3D object detection aims to localize 3D bounding boxes in an input single 2D image. It is a highly challenging problem and remains open, especially when no extra information (e.g., depth, lidar and/or multi-frames) can be leveraged in training and/or inference. This paper proposes a simple yet effective formulation for monocular 3D object detection without exploiting any extra information. It presents the MonoCon method which learns Monocular Contexts, as auxiliary tasks in training, to help monocular 3D object detection. The key idea is that with the annotated 3D bounding boxes of objects in an image, there is a rich set of well-posed projected 2D supervision signals available in training, such as the projected corner keypoints and their associated offset vectors with respect to the center of 2D bounding box, which should be exploited as auxiliary tasks in training. The proposed MonoCon is motivated by the Cramer-Wold theorem in measure theory at a high level. In implementation, it utilizes a very simple end-to-end design to justify the effectiveness of learning auxiliary monocular contexts, which consists of three components: a Deep Neural Network (DNN) based feature backbone, a number of regression head branches for learning the essential parameters used in the 3D bounding box prediction, and a number of regression head branches for learning auxiliary contexts. After training, the auxiliary context regression branches are discarded for better inference efficiency. In experiments, the proposed MonoCon is tested in the KITTI benchmark (car, pedestrain and cyclist). It outperforms all prior arts in the leaderboard on car category and obtains comparable performance on pedestrian and cyclist in terms of accuracy. Thanks to the simple design, the proposed MonoCon method obtains the fastest inference speed with 38.7 fps in comparisons
Strategies in JPEG compression using Convolutional Neural Network (CNN)

Interests in digital image processing are growing enormously in recent decades. As a result, different data compression techniques have been proposed which are concerned mostly with the minimization of information used for the representation of images. With the advances of deep neural networks, image compression can be achieved to a higher degree. This paper describes an overview of JPEG Compression, Discrete Fourier Transform (DFT), Convolutional Neural Network (CNN), quality metrics to measure the performance of image compression and discuss the advancement of deep learning for image compression mostly focused on JPEG, and suggests that adaptation of model improve the compression.
Siamese Attribute-missing Graph Auto-encoder

Graph representation learning (GRL) on attribute-missing graphs, which is a common yet challenging problem, has recently attracted considerable attention. We observe that existing literature: 1) isolates the learning of attribute and structure embedding thus fails to take full advantages of the two types of information; 2) imposes too strict distribution assumption on the latent space variables, leading to less discriminative feature representations. In this paper, based on the idea of introducing intimate information interaction between the two information sources, we propose our Siamese Attribute-missing Graph Auto-encoder (SAGA). Specifically, three strategies have been conducted. First, we entangle the attribute embedding and structure embedding by introducing a siamese network structure to share the parameters learned by both processes, which allows the network training to benefit from more abundant and diverse information. Second, we introduce a K-nearest neighbor (KNN) and structural constraint enhanced learning mechanism to improve the quality of latent features of the missing attributes by filtering unreliable connections. Third, we manually mask the connections on multiple adjacent matrices and force the structural information embedding sub-network to recover the true adjacent matrix, thus enforcing the resulting network to be able to selectively exploit more high-order discriminative features for data completion. Extensive experiments on six benchmark datasets demonstrate the superiority of our SAGA against the state-of-the-art methods.
Explainability of the Implications of Supervised and Unsupervised Face Image Quality Estimations Through Activation Map Variation Analyses in Face Recognition Models

It is challenging to derive explainability for unsupervised or statistical-based face image quality assessment (FIQA) methods. In this work, we propose a novel set of explainability tools to derive reasoning for different FIQA decisions and their face recognition (FR) performance implications. We avoid limiting the deployment of our tools to certain FIQA methods by basing our analyses on the behavior of FR models when processing samples with different FIQA decisions. This leads to explainability tools that can be applied for any FIQA method with any CNN-based FR solution using activation mapping to exhibit the network's activation derived from the face embedding. To avoid the low discrimination between the general spatial activation mapping of low and high-quality images in FR models, we build our explainability tools in a higher derivative space by analyzing the variation of the FR activation maps of image sets with different quality decisions. We demonstrate our tools and analyze the findings on four FIQA methods, by presenting inter and intra-FIQA method analyses. Our proposed tools and the analyses based on them point out, among other conclusions, that high-quality images typically cause consistent low activation on the areas outside of the central face region, while low-quality images, despite general low activation, have high variations of activation in such areas. Our explainability tools also extend to analyzing single images where we show that low-quality images tend to have an FR model spatial activation that strongly differs from what is expected from a high-quality image where this difference also tends to appear more in areas outside of the central face region and does correspond to issues like extreme poses and facial occlusions. The implementation of the proposed tools is accessible here [link].
PEMBUATAN MEDIA PEMBELAJARAN MATEMATIKA INTERAKTIF DENGAN MENGGUNAKAN MACROMEDIA FLASH

The use of learning media can improve the effectiveness and efficiency of the learning process. The solutions offered to partners are; help partners illustrate something abstract in mathematics that requires visualization or animation in writing, especially in the form of images. The methods used for solutions to problems faced by partners and to support the success of the program are in the form of lectures, questions and answers, demonstrations and exercises/practices. Participants who have attended the training will put into practice directly the results of the training they received and create learning media with the guidance of the trainer so that participants can create interactive mathematics learning media using Macromedia Flash, and can develop their work according to their imagination. The results of the implementation of PKM activities were felt by the service team and the training participants were very satisfying. With an indicator of the arrival of participants in each meeting and the enthusiasm of the participants. The results of the practice carried out were successful or maximal.Keywords: training; Practicum methods; Chemistry learning; SMA PGRI Jayapura
Desain Poster Promosi Jaket Wild

Clothing that is used ereyday lif ca display various function and presentation. Even though one of them is the from of communication in the clothes used can be a means to convey nonverbal messages or convey information without using words. One part of delivering this information is the use of jacket for daily activities and one of them is the Wild Jacket. The purpose the promotional design is to describe the unknown Wild jacket because the conditions on the internet are not focuse and are not specifically discussed on the Wild brand. Meanwhile, what the author describe is that there is a promotion regarding the Wild jacket by having digital design work to provide interesting information on the poster. However, there are still many, especially among cimbers who do not know the creation of the Wild jacket. So from the design, the work of Wild jacket is produced by designing digital poster media to promote or advertise the work of the brand jacket, so that later the work of Wild jacket is better known to the public, especially among cimbing children.
Desain Video Animasi 2 Dimensi Kisah Perjuangan Martha Khristina Tiahahu

Hero is a term for anyone who is extraordinarily meritorious to his nation. Through the figure of a hero, a symbol of struggle, defending the truth, and sacrifice, always maintained and passed down from generation to generation. The hero is also one of the important figures that must be known by all the people around him, not only remembering in his heart and thanking him, but also by imitating the attitudes and actions of the heroes. However, heroes who have many values that can be imitated are increasingly being forgotten by the younger generation. Early recognition is needed so that children can recognize and know the heroes of national independence in order to become a better generation, especially teenagers in Indonesia. The purpose of the design is to introduce the history and national independence hero figures from the Maluku Islands, namely Martha Khristina Tiahahu. The media used as a solution to the problem is by using interactive information communication, looks fun, and interesting, also is in demand by the public, especially teenagers, and can be accessed easily so that information about this problem can be spread out to the general public, especially students. Animation is a film that is the result of hand work in the form of moving images.
Verification of the Effectiveness of a Fire Hose Backpack in Areas Fire Trucks have Difficulty Accessing

The research aims to verify the effectiveness of firefighting hose backpacks through a comparative analysis of a firefighting hose backpack and conventional firefighting hose in an area difficult to access with a fire truck. The research, which targets 24 firefighters, was conducted as a random cross experiment. To this end, the firefighters were divided into 2 groups: one group comprised 12 firefighters with firefighting hose backpacks and the other 12 firefighters with conventional firefighting hoses. The purpose was to test the total time required to extinguish a fire and firefighters’ degree of physical tiredness. In the number of steps, the group with firefighting hose backpacks walked 1109.63 steps less than the group with conventional firefighting hoses (p = &lt; .001). Furthermore, in the total time taken to spread out the hoses and extinguish the fire, the group with firefighting hose backpacks performed on average 674.75 s faster than the other group in both tasks, respectively. Regarding heart rates and lactic acid production, the group with firefighting hose backpacks had on average 23 fewer pulses (p = &lt; .002) and produced on average of 3.14 mmol/L less (p = &lt; .049) lactic acid than the other group with conventional firefighting hoses. An additional verification experiment showed that the hose spreading time was shortened by 81% and the speed of discharge doubled. In addition, the operating pressure for an appropriate discharge of water was analyzed to be 0.5 MPa for 100–200 m, 0.8 MPa for 300 m, and 0.95 MPa for 400–600 m. Therefore, firefighting hose backpacks should be encouraged for use in areas difficult to access with a fire truck to rapidly extinguish the fire and reduce the tiredness of firefighters.
SUBJECT BEHAVIOR DETECTION AND ANALYSIS BASED ON COMPUTER VISION TECHNOLOGY

This article discusses the current problem of identifying violations during distance learning during the final certification.&#x0D; The coronavirus (COVID-19) pandemic has served as a stimulus for innovation in the field of education in all countries, including Kazakhstan. Innovative approaches are being taken to ensure the continuity of education and training. Thanks to the rapid response measures taken by governments and partners around the world to ensure the smooth learning process. The ongoing digital transformation of an educational institution requires appropriate information content, suitable methodological models, effective teaching methods and a supportive learning environment. The solution to one of the urgent tasks is to ensure the quality and reliability of assessing the knowledge of students by introducing an online proctoring system. The primary task of the online proctoring system is to recognize faces and identify abnormal behavior of students.&#x0D; The basis for obtaining data is the unified information educational environment of the D. Serikbayev East Kazakhstan Technical University is represented by the SPORTAL hardware and software system, which is an integration of two powerful subsystems: a Web application - the Dales of Knowledges educational portal and the SPORTAL information and software complex.&#x0D; The main theoretical results obtained are aimed at solving practical problems and are being introduced into the educational environment of D. Serikbayev East Kazakhstan Technical University to increase the degree of confidence in the results of students' knowledge in distance learning using an online proctoring system. The article presents the results of studies of one of the Viola-Jones face detection methods, commonly known as Haar cascades. During the study, a technology for identifying faces and detecting violations in real time was developed. Domestic and foreign scientists who have made a significant contribution to the development of methods for processing facial images are noted.