Reviewerly/exHarmony huggingface dataset:
Dijital Görüntüler Kullanılarak Kan Grubunun Görüntü İşleme Tabanlı Tespiti

Blood grouping is the first and most important requirement for most basic medical procedures. The expert in the conventional art used to detect blood type monitors the color and shape change in the blood by mixing anti-serum substance into the blood sample and determines the blood group and Rh factor. This is done by the expert throughout the day and is open to errors due to tiredness and oversight. In this study, it was aimed to determine blood group automatically by using image processing techniques. For this purpose, a series of image processing techniques have been applied on digital images of real patient blood. After the blood samples were divided into three parts, firstly RGB-Gray level-Black/White conversion was performed. Subsequently, morphological procedures were performed, and the blood region was segmented and border lines were marked. In the last stage, the white pixel density, the number of objects in the region, and the number of edge pixels were determined and blood group and Rh factor were determined.
Understanding Knowledge Distillation

Knowledge distillation (KD), transferring knowledge from a cumbersome teacher model to a lightweight student model, has been investigated to design efficient neural architectures with high accuracy with a few parameters. However, there is a very limited understanding of why and when KD works well. This paper reveals KD's intriguing behaviors, which we believe useful in a better understanding of KD. We first investigate the role of the temperature scaling hyperparameter in KD. It is theoretically shown that the KD loss focuses on the logit vector matching rather than the label matching between the teacher and the student as the temperature grows up. We also find that KD with a sufficiently large temperature outperforms any other recently modified KD methods from extensive experiments. Based on this observation, we conjecture that the logit vector matching is more important than the label matching. To verify this conjecture, we test an extreme logit learning model, where the KD is implemented with Mean Squared Error (MSE) between the student's logit and the teacher's logit. The KD with MSE consistently shows the best accuracy for various environments. We analyze the different learning behavior of KD with respect to the temperature using a new data uncertainty estimator, coined as Top Logit Difference (TLD). We then study the KD performances for various data sizes. When there are a few data or a few labels, very interestingly, the incapacious teacher with a shallow depth structure facilitates better generalization than teachers having wider and deeper structures.
Implications of Implementation of Artificial Intelligence in the Banking Business with Correlation to the Human Factor

Banks daily interact with a vast number of customers and are still depending on a legacy system.With today's advances in technology, regarding lifting almost all processes to automation, from start of production to finish, there is a need for revolution in archaic monetary management institutes.By not being in tune with the contemporary trends and times, banks are losing on an opportunity to transform some of their business models and relieve humans of repetitive work, prevent frauds, make better decisions and consequently gain losses.Banks can engage in implementation of new Virtual Assistants and Artificial Intelligence (A.I.) machine learning technologies, just as the other industries have engaged in modernizing i.e. medical checks, medical reports and evaluations, and this research paper will elaborate and emphasize the impact of artificial intelligence implementation on the banking sector processes.This research is based on both quantitative and model-based proofs of system performance by using several analytical tools, such as SPSS.The automation process helps institutions to enhance profitability, performance and to reduce human dependency.In a nutshell, Virtual Assistants powered with Artificial Intelligence improve the business process performance in every sector of business, especially the banking sector making it fast, reliable and not human dependent.
Assessing the Influencing Factors on the Accuracy of Underage Facial Age Estimation

Swift response to the detection of endangered minors is an ongoing concern for law enforcement. Many child-focused investigations hinge on digital evidence discovery and analysis. Automated age estimation techniques are needed to aid in these investigations to expedite this evidence discovery process, and decrease investigator exposure to traumatic material. Automated techniques also show promise in decreasing the overflowing backlog of evidence obtained from increasing numbers of devices and online services. A lack of sufficient training data combined with natural human variance has been long hindering accurate automated age estimation - especially for underage subjects. This paper presented a comprehensive evaluation of the performance of two cloud age estimation services (Amazon Web Service's Rekognition service and Microsoft Azure's Face API) against a dataset of over 21,800 underage subjects. The objective of this work is to evaluate the influence that certain human biometric factors, facial expressions, and image quality (i.e. blur, noise, exposure and resolution) have on the outcome of automated age estimation services. A thorough evaluation allows us to identify the most influential factors to be overcome in future age estimation systems.
MODEL HUBUNGAN JUMLAH PENGANGGURAN DAN INDEKS KEDALAMAN KEMISKINAN DI PULAU SUMATERA TAHUN 2019 MENGGUNAKAN REGRESI NONPARAMETRIK SPLINES

Poverty does not only focus on decreasing the number of poor people. There is an important thing that must also be considered, namely the Poverty Gap Index (P1). From year to year, the poverty gap index for all regencies/cities in Sumatra tends to stagnate. While the island of Sumatra is the second island with the largest population in Indonesia. This should be a serious concern for the government. One of the factors that influence the poverty gap index is unemployment. The more people who are unemployed can increase the poverty gap index. Therefore we need to model the relationship between the number of unemployment and poverty gap index. The approach used is nonparametric regression modeling where the residual value is not normally distributed. The model is smoothing splines regression and quantile splines regression (median, τ = 0, 5). Meanwhile, to see the best model performance by looking at the RMSE values of both models. From the results of the study, it was found that the quantile regression smoothing splines model was better because the RMSE value was lower than the regression smoothing splines. Keywords: poverty gap , unemployment , quantile regression JEL Classification : I32, J64, C21
Ambient assisted living and European funds: a bibliometric approach

Purpose The authors analyse the impact of European funding research programmes on the topic of Ambient Assisted Living by considering its status, future context, and the implications for prospective knowledge management. Design/methodology/approach The authors apply our variation of classical Systematic Literature Review – Systematic Literature Network Analysis, which also includes bibliographic networks – to identify the readership cliques of the associated technological publication outputs. Findings The authors’ main conclusion suggests that there was an increase in scientific production on AAL fields just after the start of the two EU funding programmes (2008 and 2014). Three main research directions were identified: activity and vital sign recognition, human-computer interaction and technology acceptance. Originality/value To date, previous reviews on Ambient Assisted Livig focus on specific aspects, such as the study of technology. The present review provides a complete overview of Ambient Assisted living technology and it grasps how the European funds have impacted on the development of this technology.
Self-Supervised Transformers for Activity Classification using Ambient Sensors

Providing care for ageing populations is an onerous task, and as life expectancy estimates continue to rise, the number of people that require senior care is growing rapidly. This paper proposes a methodology based on Transformer Neural Networks to classify the activities of a resident within an ambient sensor based environment. We also propose a methodology to pre-train Transformers in a self-supervised manner, as a hybrid autoencoder-classifier model instead of using contrastive loss. The social impact of the research is considered with wider benefits of the approach and next steps for identifying transitions in human behaviour. In recent years there has been an increasing drive for integrating sensor based technologies within care facilities for data collection. This allows for employing machine learning for many aspects including activity recognition and anomaly detection. Due to the sensitivity of healthcare environments, some methods of data collection used in current research are considered to be intrusive within the senior care industry, including cameras for image based activity recognition, and wearables for activity tracking, but recent studies have shown that using these methods commonly result in poor data quality due to the lack of resident interest in participating in data gathering. This has led to a focus on ambient sensors, such as binary PIR motion, connected domestic appliances, and electricity and water metering. By having consistency in ambient data collection, the quality of data is considerably more reliable, presenting the opportunity to perform classification with enhanced accuracy. Therefore, in this research we looked to find an optimal way of using deep learning to classify human activity with ambient sensor data.
Automated methods of coherence evaluation of Ukrainian texts using machine learning techniques

The main methods of coherence evaluation of texts with the usage of different machine learning techniques have been analyzed. The principles of methods with the usage of recurrent and convolutional neural networks have been described in details. The advantages of a semantic similarity graph method have been considered. Other approaches to perform the vector representation of sentences for the estimation of semantic similarity between the elements of a text have been suggested to use. The experimental examination of methods has been performed on the set of Ukrainian scientific articles. The training of recurrent and convolutional networks with the usage of early stopping has been performed. The accuracy of the solving of document discrimination and insertion tasks has been calculated. The comparative analysis of the results obtained has been performed.
A Social Collaborative Filtering Method to Alleviate Data Sparsity Based on Graph Convolutional Networks

Nowadays recommender systems (RS) keep drawing attention from academia, and collaborative filtering (CF) is the most successful technique for building RS. To overcome the inherent limitation, which is referred to as data sparsity in CF, various solutions are proposed to incorporate additional social information into recommendation processes, such as trust networks. However, existing methods suffer from multi-source data integration (i.e., fusion of social information and ratings), which is the basis for similarity calculation of user preferences. To this end, we propose a social collaborative filtering method based on novel trust metrics. Firstly, we use Graph Convolutional Networks (GCNs) to learn the associations between social information and user ratings while considering the underlying social network structures. Secondly, we measure the direct-trust values between neighbors by representing multi-source data as user ratings on popular items, and then calculate the indirect-trust values based on trust propagations. Thirdly, we employ all trust values to create a social regularization in user-item rating matrix factorization in order to avoid overfittings. The experiments on real datasets show that our approach outperforms the other state-of-the-art methods on usage of multi-source data to alleviate data sparsity.
Developing Innovative Cross-platform Web-based Virtual Reality Services

Virtual reality (VR), in which users wear a headset fully immersing them inside a computer-generated environment, has been developed to meet various design, marketing, education, training, and retail needs. However, the scenarios and modes of stand-alone systems have certain bottlenecks limiting the use of VR applications. This study begins by applying a panel service design method to analyze the different developments of future cross-platform virtual reality applications. HTML and A-Frame web VR components are used to develop an independent modular cross-platform VR prototype system, which can simultaneously integrate stand-alone HTC VIVE and mobile device operations with digital content for learning purposes. The analysis and evaluation results show that the technologies described in this study can provide a service model for the future development of multi-person cross-platform virtual reality applications. Subsequent studies are planned for exploring technologies to create a mechanism for multi-person interactions in virtual reality applications.
Building of Visual Analysis System for Design of Youth Sports Shoe Products Based on Comment Mining

Abstract This paper proposes a visual analysis system for data collection, pre-preprocessing and sentiment analysis to support whole-process innovative design of youth sports shoes. This system aims to analyze the sales data of sports shoe e-commerce sellers before and after design, provide reference for key design elements in innovative design of sports shoes based on 3D printing and realize dynamic closed design of pre-design data investigation and analysis and post-design feedback data analysis. With dynamic data collection technology based on a crawler, product feature extraction model, sentiment analysis model and big data visualization technology, this system can better satisfy data analysis requirement for innovative design of sports shoes.
Semantic Software Capability Profile Based on Enterprise Architecture for Software Reuse

Open source and software reuse become more and more challenging for companies to save time and development cost. Collecting existing service-oriented solutions and qualifying them helps to reuse them directly or via orchestration. Our objective in this research work targets the consolidation of an advanced service repository able to be directly mapped to end-user requirements for new business application development. In this perspective, we define a model-based capability profile based on Enterprise Architecture offering a wider view qualification for service-oriented software. We propose a meta-model to gather architecture artifacts used to guide the development of existing solutions. The generated capability profiles help to create a repository of software capabilities. Furthermore, an ontology is proposed to exploit the resulted capability profiles to guide the future business needs to reuse the qualified software in an efficient way. This contribution aims to upgrade research on dynamic services consumption and orchestration to the level of end-users’ requirements mapped with advanced service assets as an enabler for accelerating business application development.
3D Guided Weakly Supervised Semantic Segmentation

Pixel-wise clean annotation is necessary for fully-supervised semantic segmentation, which is laborious and expensive to obtain. In this paper, we propose a weakly supervised 2D semantic segmentation model by incorporating sparse bounding box labels with available 3D information, which is much easier to obtain with advanced sensors. We manually labeled a subset of the 2D-3D Semantics(2D-3D-S) dataset with bounding boxes, and introduce our 2D-3D inference module to generate accurate pixel-wise segment proposal masks. Guided by 3D information, we first generate a point cloud of objects and calculate objectness probability score for each point. Then we project the point cloud with objectness probabilities back to 2D images followed by a refinement step to obtain segment proposals, which are treated as pseudo labels to train a semantic segmentation network. Our method works in a recursive manner to gradually refine the above-mentioned segment proposals. Extensive experimental results on the 2D-3D-S dataset show that the proposed method can generate accurate segment proposals when bounding box labels are available on only a small subset of training images. Performance comparison with recent state-of-the-art methods further illustrates the effectiveness of our method.
A novel joint points and silhouette-based method to estimate 3D human pose and shape

This paper presents a novel method for 3D human pose and shape estimation from images with sparse views, using joint points and silhouettes, based on a parametric model. Firstly, the parametric model is fitted to the joint points estimated by deep learning-based human pose estimation. Then, we extract the correspondence between the parametric model of pose fitting and silhouettes on 2D and 3D space. A novel energy function based on the correspondence is built and minimized to fit parametric model to the silhouettes. Our approach uses sufficient shape information because the energy function of silhouettes is built from both 2D and 3D space. This also means that our method only needs images from sparse views, which balances data used and the required prior information. Results on synthetic data and real data demonstrate the competitive performance of our approach on pose and shape estimation of the human body.
Generalization in Overparameterized Models

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Funções wavelet e transformada wavelet contínua: representação simultânea nos domínios do tempo e da frequência

Wavelet functions and continuous wavelet transform: simultaneous representation in time and frequency domains
A Statistical Analysis on In-Built Pseudo Random Number Generators Using NIST Test Suite

All programming languages have a built-in function for Pseudo-Random Number Generators (PRNGs) for the ease of programmers. The algorithms used to generate Pseudo-Random Numbers (PRNs) are mainly focused on time complexity. The question we are interested in is to check if the PRNs generated using these inbuilt functions are good enough for the use in cryptography. Here we analyze the results obtained by standard NIST tests applied on PRNs generated by the functions in some of the commonly used programming languages.
Learning Edge-Preserved Image Stitching from Large-Baseline Deep Homography

Image stitching is a classical and crucial technique in computer vision, which aims to generate the image with a wide field of view. The traditional methods heavily depend on the feature detection and require that scene features be dense and evenly distributed in the image, leading to varying ghosting effects and poor robustness. Learning methods usually suffer from fixed view and input size limitations, showing a lack of generalization ability on other real datasets. In this paper, we propose an image stitching learning framework, which consists of a large-baseline deep homography module and an edge-preserved deformation module. First, we propose a large-baseline deep homography module to estimate the accurate projective transformation between the reference image and the target image in different scales of features. After that, an edge-preserved deformation module is designed to learn the deformation rules of image stitching from edge to content, eliminating the ghosting effects as much as possible. In particular, the proposed learning framework can stitch images of arbitrary views and input sizes, thus contribute to a supervised deep image stitching method with excellent generalization capability in other real images. Experimental results demonstrate that our homography module significantly outperforms the existing deep homography methods in the large baseline scenes. In image stitching, our method is superior to the existing learning method and shows competitive performance with state-of-the-art traditional methods.
ANALISIS PENALARAN PROPORSIONAL PADA SISWA KESULITAN BELAJAR MATEMATIKA DI SEKOLAH INKLUSIF

Many Mathematical concepts require students to use proportional reasoning. Proportional reasoning can be an indication of the level of students' mastery of mathematics on subjects involving comparison problems. Problems solving with proportional reasoning can be even more challenging for students with mathematic learning disabilities. The purpose of this study was to analyze the reasoning of students of difficulty learning mathematics in inclusive schools. Research approach with qualitative methods. The results showed that the three subjects in solving four different types of problems tended to be the same. Proportional reasoning ability of the three subjects is at level 0 to 1 proportional reasoning ability.
Modeling the early accumulation of amyloid using differential equations in WRAP and BLSA

Abstract Background Accumulation of beta‐amyloid plaques, an early hallmark neuropathology of Alzheimer’s disease (AD), begin more than a decade prior to the onset of clinical symptoms. Understanding the temporal progression of early amyloid accumulation is important for predicting who will become amyloid‐positive and when. Based on observations from several longitudinal amyloid positron emission tomography (PET) studies that the rate of overall amyloid burden is closely associated with baseline level of amyloid, we develop a formal mathematical model capturing this phenomenon and use it to (i) align temporal trajectories of amyloid accumulation across individuals, (ii) create a fine‐grained timeline of amyloid accumulation, and (iii) estimate the age at onset of amyloid positivity per individual. Method We used (PiB) PET scans from the Wisconsin Registry for Alzheimer’s Prevention (WRAP) and the Baltimore Longitudinal Study of Aging (BLSA) to assess global amyloid burden cDVR, see Figure 1. A first‐order autonomous model for the slope of cDVR as a function of cDVR was fit to the data. We used Bayesian modeling with a Gaussian process (GP) prior and independent homoscedastic noise. Result The best fit was obtained with a quadratic GP kernel (Figure 2). Since the system is autonomous, the trajectories are parallel and thus can be registered by translation. The progression of the Amyloid burden is thus visualized as a function of the time (in years) from PIB positivity in Figure 3. The estimated age when the PIB cDVR becomes positive is estimated. This age depends on APOE status, with a significant difference between the APOE groups [3,3] and [3,4]. p‐value=.008 in WRAP, p‐value=.054 In BLSA. Conclusion Large studies on aging allow studying the time‐progression of the amyloid burden. We found that an autonomous ordinary differential equation allows for a good explanatory model of the PIB‐DVR data in the WRAP as well as in the BLSA studies. Still, a few participants do not fit the data closely. It remains to understand if this is due to noisy measurements or to specific conditions. Also, alternative dynamical systems, including Hamiltonian, still need to be investigated and compared.