Reviewerly/exHarmony huggingface dataset:
Comparison between Poisson, Quasi-Poisson, and negative binomial regression in analyzing under-five children malnutrition cases in East Java

The most straightforward counted data regression is Poisson regression. The problem often discovered in Poisson regression is overdispersion. Some alternatives regression that can be used in an overdispersed counted data are quasi- Poisson and negative binomial regression. This study will identify the most appropriate and suitable regression in modelling the number of under-five children malnutrition cases in East Java as an overdispersed counted data. The data was obtained from 2018th East Java Health Profile Book. Comparison between Poisson, quasi-Poisson, and negative binomial regression will be made based on a prediction plot, a mean-variance plot, and a comparison plot of observation weight in IWLS algorithm. The comparison shows that quasi-Poisson regression is more suitable for modeling the number of under-five children malnutrition cases in East Java. Hypothesis testing result in 10% significance level shows that the percentage of under-five children who receive exclusive breastfeeding, the percentage of under-five children who receive health services at least 8 times, and the percentage of population with proper sanitation access are factors that significantly affect the number of under-five children malnutrition cases in East Java. Based on the three significant factors, 37 regions in East Java later clustered into three clusters with their characteristics.
Financial Management of Muhammadiyah Senior Secondary School

This research was conducted on the basis of the importance of school financial management in carrying out the wheels of education which can have an impact on improving the quality of the school itself. Therefore, the research was conducted to identify and provide analysis of the implementation of education financial management, which includes planning, organizing, implementing, controlling and accountability at SMA Muhammadiyah 1 Taman and the factors that influence its implementation. The research method used is qualitative by using a descriptive approach to describe the phenomena that occur in this institution. SMA Muhammadiyah 1 Taman is the object of research because it has implemented financial management well, marked by the rapid fulfillment of learning facilities and the dynamics of ongoing school activities. From the research results it is known that: 1) management of education financial management, SMA Muhammadiyah 1 Taman has utilized information technology, utilizes bank service products, and financial management software. the implementation of financial management is in accordance with the stages, starting from: a) planning to compile activities, planning budgets, and determining sources of financial revenue; b) the organization includes the principal, treasurer, staff treasurer of revenue, staff of expenditure treasurers, treasurer of BOS, treasurer of BPOPP, wakasek as executor of activities; c) the implementation of activities has been carried out in accordance with the planned activity program, while the realization of the budget for budget planning is less than 100% due to the arrears of tuition fees and student activity funds that have not been paid 100% by the parents of students; d) financial management control is carried out by i) the principal of the school treasurer and budget users, ii) the government to the principal regarding the use of BOS and BPOPP funds, iii) the Muhammadiyah organization to the school principal in relation to the supervision and guidance of financial management sourced from parents students, and; e) accountability for the use of the budget is carried out by i) the treasurer and executor of activities to the principal, ii) the principal to the government regarding the use of BOS and BPOPP funds, and the principal to the Muhammadiyah organization related to funds sourced from the parents of students.
Bio-inspired Neuron Model for Motion Detection on Base Signal Accumulation

The article presents a new model of the MT neuron (neuron of the middle temporal region), which allows motion detecting and determining its direction and speed without the use of recurrent communication. The model is based on signal accumulation and is organized using a space-time vector that sets the weighting coefficients. The space-time vector is formed using the product of the Gaussian, which defines the spatial component, and the "Mexican hat" wavelet, which sets the time vector of the change in the receptive field. This configuration allows not only to motion detect, but also to make the model not sensitive to uniform or textural fill. The model is presented in variants for determining linear and rotational motion. Motion, in this case, is the sequential activation of several edge selection neurons located in the same direction in a certain neighborhood over time i.e. with a change of frame. To assess the motion, the models were tested on the MPI Sintel dataset. The model developed by us shows results better than Spatio-Temporal Gabor. The best accuracy of determining the direction of movement can be obtained with the size of the space-time vector (7*7, 7).
Bit-depth enhancement detection for compressed video

In recent years, display intensity and contrast have increased considerably. Many displays support high dynamic range (HDR) and 10-bit color depth. Since high bit-depth is an emerging technology, video content is still largely shot and transmitted with a bit depth of 8 bits or less per color component. Insufficient bit-depths produce distortions called false contours or banding, and they are visible on high contrast screens. To deal with such distortions, researchers have proposed algorithms for bit-depth enhancement (dequantization). Such techniques convert videos with low bit-depth (LBD) to videos with high bit-depth (HBD). The quality of converted LBD video, however, is usually lower than that of the original HBD video, and many consumers prefer to keep the original HBD versions. In this paper, we propose an algorithm to determine whether a video has undergone conversion before compression. This problem is complex; it involves detecting outcomes of different dequantization algorithms in the presence of compression that strongly affects the least-significant bits (LSBs) in the video frames. Our algorithm can detect bit-depth enhancement and demonstrates good generalization capability, as it is able to determine whether a video has undergone processing by dequantization algorithms absent from the training dataset.
Super-Resolved Multi-Temporal Segmentation with Deep Permutation-Invariant Networks

Multi-image super-resolution from multi-temporal satellite acquisitions of a scene has recently enjoyed great success thanks to new deep learning models. In this paper, we go beyond classic image reconstruction at a higher resolution by studying a super-resolved inference problem, namely semantic segmentation at a spatial resolution higher than the one of sensing platform. We expand upon recently proposed models exploiting temporal permutation invariance with a multi-resolution fusion module able to infer the rich semantic information needed by the segmentation task. The model presented in this paper has recently won the AI4EO challenge on Enhanced Sentinel 2 Agriculture.
Geometrically Curved Magnetic Field Sensors for Interactive Electronics

The concept of curvilinear magnetism can be applied to a wide range of materials and targets the applications where the interplay of geometry, shape, and magnetic texture arises. A clear advantage of these deformable magnetic materials is that they can be used for applications that demand flexibility or stretchability, something that conventionally rigid ferromagnets cannot provide. This advantage can be readily exploited in the field of flexible electronics, which aims to create electronic circuits and devices that can be folded or bent upon usage. The firm link between the fundamentals and applications of curved magnetic thin films is given by the fact that magnetic domain walls can be pinned at bends. This fundamental discovery has deep consequences for magnetic field sensors based on geometrically curved magnetic thin films. Indeed, curvature of the structure results in an additional pinning mechanism for domain walls, which can lead to the enhancement of the coercive field and hence lower the sensitivity of magnetic field sensors. These considerations came up very recently and its consequences are still to be explored and confirmed experimentally. Here, we will discuss primary the current advances in the application of flexible magnetic field sensors based on geometrically curved magnetic thin films and multilayers. Based on this technology, we present and foresee a wide range of applications in the fields of eMobility, health, and interactive electronics. The latter is the main focus of this chapter, in particular due to the added value of flexible magnetoelectronics to the fields of human-machine interfaces and virtual or augmented reality.
Extension of Windowing as a Learning Technique in Artificial Noisy Domains

Windowing is a guided sub-sampling method conceived to reduce the number of training examples when inducing decision trees, while preserving accuracy. However, its use has been discouraged when dealing with noisy datasets because of a lack of observed benefits. This work proposes modeling such domains probabilistically to study the performance of Windowing under controlled conditions, i.e., on artificial discrete datasets with binary class affected by different levels of noise. An extension of Windowing that removes all inconsistency in the datasets as a preprocessing step, Windowing Inconsistency Filtering (WIF), is proposed to cope with noise in class values. J48, as implemented in Weka, is adopted as a tree-inductive algorithm. WIF, Windowing, and J48 are then compared on a repeated 10-folds cross-validation process over noisy and clean test sets. Results show that the three algorithms obtain similar predictive performance, as expected, but WIF keeps reducing the number of used training examples despite the presence of noise. Future work includes considering different inductive algorithms, e.g., Naïve Bayes, to establish if the adopted extension generalizes beyond decision trees.
Resource-Restricted Environments Based Memory-Efficient Compressed Convolutional Neural Network Model for Image-Level Object Classification

In the past decade, Convolutional Neural Networks (CNNs) have achieved tremendous success in solving complex classification problems.CNN architectures require an excessive number of computations to achieve high accuracy.However, these models are deficient due to the heavy cost of storage and energy, which prohibits the application of CNNs to resource-constrained edge-devices.Hence, developing aggressive optimization schemes for efficient deployment of CNNs on edge devices has become the most important requirement.To find the optimal approach, we present a resource-limited environment based memoryefficient network compression model for image-level object classification.The main aim is to compress CNN architecture by achieving low computational cost and memory requirements without dropping system's accuracy.To achieve the said goal, we propose a network compression strategy, that works in a collaborative manner, where Soft Filter Pruning is first applied to reduce the computational cost of the model.In the next step, the model is divided into No-Pruning Layers (NP-Layers) and Pruning Layers (P-Layers).Incremental Quantization is applied to P-Layers due to irregular weights distribution, while for NP-Layers, we propose a novel Optimized Quantization algorithm for the quantization of weights up to optimal levels obtained from the Optimizer.This scheme is designed to achieve the best trade-off between compression ratio and accuracy of the model.Our proposed system is validated for image-level object classification on LeNet-5, CIFARquick, and VGG-16 networks using MNIST, CIFAR-10, and ImageNet ILSVRC2012 datasets respectively.We have achieved high compression ratio with negligible accuracy drop, outperforming the state-of-the-art methods.
A Core Industrial Maintenance Ontology Development Process

Operational safety is at the center of all companies' concerns because increasing productivity is the top priority. Moreover, the high cost of breakdowns invests in a maintenance strategy, one of the most critical aspects of a manufacturing system. In the digital age, e-maintenance platforms can interoperate technically but fail to achieve semantic interoperability. The results are ambiguities and misunderstandings that can lead to equipment shutdowns or inefficient use of material resources. However, as formal knowledge specifications, ontologies have successfully integrated heterogeneous systems for some time. Therefore, the authors developed a core industrial maintenance ontology that is both generic and capable of extending and refining to meet semantic information repository requirements. Ontologies can unite and merge different datasets without adapting new ontologies. Only the core ontology needs to be modified according to the new semantics. A detailed description of the development process is provided, beginning with the definition of requirements and ending with the evaluation.
Don't Share My Face: Privacy Preserving Inpainting for Visual Localization

Visual localization is an important task for many robotic and augmented reality applications. As localizing within large scale maps can be memory and computationally de-manding, cloud-based localization services are appealing for developers. However, such services raise important privacy concerns for both passive and active users. In particular, some sensitive information might be revealed by an attacker who intercepts data during the data-sharing process. Therefore, the sensitive data in the image should be concealed before it is shared. As a motivating case, we demonstrated the exposure generated by the common feature descriptor SIFT when attempting to recover private content. In this paper, we propose a pipeline to effectively conceal privacy-sensitive image regions from possible attacks to the transmission or localization services, by making use of learning-based image inpainting techniques while preserving, and even boosting, the localization performance. We tested our pipeline with two off-the-shelf localization services based on deep neural networks on the publicly available Oxford Robotcar dataset, showing that the localization performance on our generated private concealed images is on par with the non-private baseline. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>
A Rapid Dual Feature Tracking Method for Medical Equipments Assembly and Disassembly in Markerless Augmented Reality

Markerless Augmented Reality (MAR) is a superior technology that is currently used by the medical device assembler with aid in design, assembly, disassembly and maintenance operations. The medical assembler assembles the medical equipment based on the doctors requirement, they also maintains quality and sanitation of the equipment. The major research challenges in MAR are as follows: establish automatic registration parts, find and track the orientation of parts, and lack of depth and visual features. This work proposes a rapid dual feature tracking method i.e., combination of Visual Simultaneous Localization and Mapping (SLAM) and Matched Pairs Selection (MAPSEL). The main idea of this work is to attain high tracking accuracy using the combined method. To get a good depth image map, a Graph-Based Joint Bilateral with Sharpening Filter (GRB-JBF with SF) is proposed since depth images are noisy due to the dynamic change of environmental factors that affects tracking accuracy. Then, the best feature points are obtained for matching using Oriented Fast and Rotated Brief (ORB) as a feature detector, Fast Retina Key point with Histogram of Gradients (FREAK-HoG) as a feature descriptor, and Feature Matching using Rajsk’s distance. Finally, the virtual object is rendered based on 3D affine and projection transformation. This work computes the performance in terms of tracking accuracy, tracking time, and rotation error for different distances using MATLAB R2017b. From the observed results, it is perceived that the proposed method attained the least position error value about 0.1 cm to 0.3 cm. Also, rotation error is observed as minimal between 2.40 (Deg) to 3.10 and its average scale is observed as 2.7140. Further, the proposed combination consumes less time against frames compared with other combinations and obtained a higher tracking accuracy of about 95.14% for 180 tracked points. The witnessed outcomes from the proposed scheme display superior performance compared with existing methods.
The possibilities of using virtual reality when introducing students to scientific activities in physics

The article is devoted to the prospects of using virtual and mixed reality systems as a tool for introducing students into scientific activity. Attention is paid to the development of content for such systems in condensed matter physics based on the method of molecular dynamics. In this study we suggested one of the possible ways to simplify the making of 3D models for a course in solid state physics. A survey was conducted among students about the complexity of developing such models. The results obtained testify in favor of the use of such methods both in teaching physics itself and in forming a base of models. In addition, the use of these technologies increases the interest of students in the subject of study. The results of this study indicate the feasibility of using this technology as an educational tool when introduced to scientific activity.
Lightweight Wearable AR System using Head-mounted Projector for Work Support

In this paper, we propose a lightweight wearable AR system for work support in which a user mounts a projector and a monocular camera on his or her head. The system can present information in front of the user's line of sight by superimposing images on a surface in real space. The system uses monocular SLAM to estimate the pose of the user's head, and changes images depending on the head movement, enabling the fixation of virtual objects. We developed a prototype system in which a small projector and a camera are fixed to glasses. The total weight of the prototype system is 84 g, which is small and lightweight enough for work support. We confirmed that a virtual object was fixed on a real surface using the prototype system.
Statistical - Spatial technique for video object detection

Object detection is the most important stage due to its great impact on next stages. Moving objects detections in real environment have several challenges such as dynamic background, illumination changes (gradual, sudden) and noise etc. Traditional techniques of background modeling do not have the ability to face these challenges but can deal with the static background. Based on statistical, spatial and temporal features we have proposed hybrid technique to model background and detecting moving object, it can deal efficiently with challenges in real environment. The proposed system consists of two stages, the first one is construction of statistical model by computing mean and standard deviation for each pixel and spatial model that are created by calculation Center Symmetric Local Binary Patterns which consists of a group of histograms for each pixel. The second one is foreground detection by checking each pixel in current frame with predefined thresholds if the value of pixel raises out of these thresholds then the statistical model will be applied otherwise the spatial model is applied. The contribution of this paper is creating model hybrid between statistical and spatial features which lead speed and accuracy cause statistical features are fast in calculation and spatial features characterized by accuracy. Experimental results proved that our proposed method lead to approximate 2% increase in the accuracy comparing with the traditional benchmark methods by using the exact dataset.
SESS: Saliency Enhancing with Scaling and Sliding

High-quality saliency maps are essential in several machine learning application areas including explainable AI and weakly supervised object detection and segmentation. Many techniques have been developed to generate better saliency using neural networks. However, they are often limited to specific saliency visualisation methods or saliency issues. We propose a novel saliency enhancing approach called SESS (Saliency Enhancing with Scaling and Sliding). It is a method and model agnostic extension to existing saliency map generation methods. With SESS, existing saliency approaches become robust to scale variance, multiple occurrences of target objects, presence of distractors and generate less noisy and more discriminative saliency maps. SESS improves saliency by fusing saliency maps extracted from multiple patches at different scales from different areas, and combines these individual maps using a novel fusion scheme that incorporates channel-wise weights and spatial weighted average. To improve efficiency, we introduce a pre-filtering step that can exclude uninformative saliency maps to improve efficiency while still enhancing overall results. We evaluate SESS on object recognition and detection benchmarks where it achieves significant improvement. The code is released publicly to enable researchers to verify performance and further development. Code is available at https://github.com/neouyghur/SESS .
The Effects of Augmented Reality Remapping on Individual Differences in Task Performance, Workload, and Error Frequency within ICU Nurses

Fast-paced environments require operators who perform in them to use their cognitive abilities such as perception and memory to complete tasks. Individual differences in ability can result in different outcomes. Previous research has shown that common errors in Intensive Care Units (ICUs) can be reduced if individual differences in cognition, such as attentional ability, are addressed. The goal of this research is to understand how cognitive aids in the form of Augmented Reality (AR) can reduce errors and perceived workload, as well as mitigate differences in performance and attentional ability between individuals.
Real-Time Embedded Object Tracking with Discriminative Correlation Filters Using Convolutional Features

Object tracking is an essential element of visual perception systems. It is used in advanced video surveillance systems (AVSS), autonomous vehicles, robotics, and many more. For applications such as autonomous robots, the system must be implemented on some embedded platform with limited computing performance and power. Furthermore, sufficiently fast response is required from the tracking system in order to perform some real-time tasks. Discriminative Correlation Filter (DCF) based tracking algorithms are popular for such applications, as they offer state-of-the-art performance while not being too computationally complex. In this paper, an FPGA implementation of the DCF tracking algorithm using convolutional features is presented. The ZCU104 board is used as a platform, and the performance is evaluated on the VOT2015 dataset. In contrast to other implementations that use HOG (Histogram of Oriented Gradients) features, this implementation achieves better results for $$64 \times 64$$ filter size while being able to potentially operate at higher speeds (over 467 fps per scale).