Reviewerly/exHarmony huggingface dataset:
Weakly supervised deep learning for cervical histopathology images analysis

Cervical cancer is the second most common malignancy in women, while is prevented through diagnosing and treating cervical precancerous lesions. Clinically, histopathological image analysis is recognized as the gold standard for diagnosis. However, the diagnosis of cervical precancerous lesions is challenging due to the massive size of whole slide images and subjective grading without precise quantification criteria. Most existing computer aided diagnosis approaches are patches-based, first learning patch-wise features and then aggregating these local features to infer the final prediction. Cropping pathology images into patches restrains the contextual information available to those networks, causing failing to learn clinically relevant structural representations. To address the above problems, this paper proposes a novel weakly supervised learning method called general attention network (GANet) for grading cervical precancerous lesions. A bag-of-instances pattern is introduced to overcome the limitation of the high resolution of whole slide images. Moreover, based on two transformer blocks, the proposed model is able to encode the dependencies among bags and instances that are beneficial to capture much more informative contexts, and thus produce more discriminative WSI descriptors. Finally, extensive experiments are conducted on a public cervical histology dataset and the results show that GANet achieves the state-of-the-art performance.
On Sense Making and the Generation of Knowledge in Visual Analytics

Interactive visual tools and related visualization technologies, built to support explorative data analysis, ultimately lead to sense making and knowledge discovery from large volumes of raw data. These processes namely rely on human visual perception and cognition, in which human analysts perceive external representations (system structure, dataset, integral data visualizations) and form respective internal representations (internal cognitive imprints of external systems) that enable deeper comprehension of the employed system and the underlying data features. These internal representations further evolve through continuous interaction with external representations. They also depend on the individual’s own cognitive pathways. Currently, there has been insufficient work on understanding how these internal cognitive mechanisms form and function. Hence, we aim to offer our own interpretations of such processes observed through our daily data exploration workflows. This is accomplished by following specific explorative data science tasks while working with diverse interactive visual systems and related notebook style environments that have different organizational structures and thus may entail different approaches to thinking and shaping sense making and knowledge generation. In this paper, we deliberate on the cognitive implications for human analysists when interacting with such a diverse organizational structure of tools and approaches when performing the essential steps of an explorative visual analysis.
SAR target image edge detection based on CNN

Aiming at the problems that the classical edge detection method is easily affected by noise and has low detection accuracy when applied to SAR target images, this paper studies the detection performance of the classical edge detection method Canny, CNN-based edge detection methods Holistically Nested Edge Detection (HED) and Richer Convolutional Features (RCF) when applied to SAR target images for the first time. The detection performance is evaluated using the MSTAR dataset, and the detection results of each method are compared based on the common evaluation indicators of image edge detection: F-measure, PR curve, and FPS. Canny's F-measure (ODS) is 0.611 and FPS is 43. The F-measure (ODS) of HED is 0.758 and the FPS is 18. The F-measure (ODS) of RCF is 0.729 and the FPS is 24. The F-measure (ODS) of RCF-MS is 0.753 and the FPS is 6. On the MSTAR dataset, the F-measure of HED is the best, which is 24.06% higher than Canny. RCF and RCF-MS also performed well, which were 19.31% and 23.24% higher than Canny respectively. The edge detection method based on CNN has higher F-measure, is less affected by noise, and has less loss of edge details. When applied to SAR images affected by speckle noise, the performance is much better than Canny, but there is still a shortage of slightly worse computing speed.
Predicting good features using a hybrid feature for visual geolocation system

We address the problem of accurately geolocating an image on a large city scale. Image geolocation is the process of distinguishing a place in an image through geotagged reference images depicting the same place. This is a challenging task due to the appearance changes in large outdoor environments. In particular, the limitation on using large geotagged images effectively for training. To overcome this limitation, we propose to select and predict good hybrid features, and cast the prediction score as a classification task. To this end, we generate training features and learn the classifier offline. For the image representation phase, we propose a new method called hybrid feature to make image representation robust against geometric and photometric changes and have a high discriminative level as well. By doing this, we achieve competitive results compared with other baseline methods. Also, our results show a significant improvement while using hybrid features compared to using handcrafted models or deep learning methods individually.
Improvement of attention modules for image captioning using pixel-wise semantic information

Although an attention mechanism is reasonable for generating image captions, how to obtain ideal image regions within the mechanism is a problem in practice due to the difficulty of its calculation between image and text data. In order to improve the attention modules for image captioning, we propose an algorithm for handling a pixel-wise semantic information, which is obtained as the outputs of semantic segmentation. The proposed method puts the pixel-wise semantic information into the attention modules for image captioning together with input text data and image features. We conducted evaluation experiments and confirmed that our method could obtain more reasonable weighted image features and better image captions with a BLEU-4 score of 0.306 than its original attention model with a BLEU-4 score of 0.243.
Face Generation from Skull Photo Using GAN and 3D Face Models

Generating face images from skull images have many applications in fields such as archaeology, anthropology and especially forensics, etc. However, face/skull images generation remain a challenging problem due to the fact that face image and skull image have different characteristics and the data on skull images is also limited. Therefore, we consider this transformation as an unpaired image-to-image translation problem and research the recently popular generative models (GANs) to generate face images from skull images. To this end, we use a novel synthesis framework called U-GAT-IT, a new framework for unsupervised image-to-image translation. This framework use AdaLIN (Adaptive Layer-Instance Normalization), which a new normalization function to focus on more important regions between source and target domains. Furthermore, to visualize the generated face in many other aspects, we use an additional 3D facial generation model called DECA (Detailed Expression Capture and Animation), which is a model for 3D facial reconstruction that is trained to robustly produce a UV displacement map from a low-dimensional latent representation. Experimental results show that the proposed method achieves positive results compared to the current unpaired image-to-image translation models.
Design and Development of a Correspondence Management System (CMS)

In recent years, the methodology of recording customer’s correspondence data and inquiries has been developed depending on the needs of a business. Modern information systems technology proposes various alternative systems for recording and managing clients’ data, feedback, and storing all of the transactions and communication between corporations and clients in a single database. Most of these systems are focused on customer service, while their satisfaction rates and measurements of the responses are rather limited. This is mostly clarified by the absence of methods and techniques exclusively oriented to client satisfaction measurement, and the availability of numerous statistical modules and information analysis systems which resolve these problems. Apart from that, the evaluation of the responses and measurement, including but not limited to the response time and quality, are important factors which need to be recorded and used to evaluate the mechanism itself, even to evaluate the employee responses and reactions to the data received. There is no doubt that corresponding management systems are an extension of CRM. A five-organization study by Campbell settles that cross-function cooperation is required to improve deeper customer-related knowledge on which customer relation management (CRM) is based. The ultimate goal of this article is to deliver insights into the relationship between organizational values and CMS implementation outcomes, including a successful example of a CMS created, implemented and explained in this study.
Alternate Approach to GAN Model for Colorization of Grayscale Images: Deeper U-Net + GAN

Image colorization refers to applying appropriate colors in a given grayscale image, such that the viewer can accept the results as close to reality. By analyzing existing colorization algorithms based on AutoEncoder and VGG-16, this paper showed that they are not able to provide an accurate result in most cases, and are inefficient in terms of computation time. In contrast to these models, we suggested a new model developed from an established GAN model. By reforming the generator part and adding 1x1 convolutional layers based on VGG-11, we were able to create a deeper model where we could also apply nonlinear functions such as ReLU, and Leaky-ReLU. Comparing the results printed by our new model and the conventional model, we proved that our model produced better results in terms of the accuracy and clarity of colors and computation time. However, there is still room for further research in which one can investigate the optimal number of convolution layers and depth that maximizes accuracy and minimizes computation time. Still, this research holds value in that it successfully provides an alternate algorithm with better performance, and opens a path toward further development for colorization algorithms.
Learner Attention Quantification Using Eye Tracking and EEG Signals

We adapted an application called Non-Intrusive Classroom Attention Tracking System (NiCATS) that quantifies and generates statistical data based on a student’s attention level while performing various tasks like coding, browsing through websites, or reading lecture notes on computers. This research is focused on understanding how student attentiveness can be measured using eye-tracking (e.g. gaze points) and Electroencephalogram (EEG) signals data. By leveraging the existing NiCATS with new integration of BCI devices we explore the possibilities of identifying correlations of EEG signals with students’ attention during classroom. Two Eye metrics (number of saccades and total fixations) have slight positive correlation with all the EEG bands (theta, alpha, etc.) except the delta band. The result of this analysis is an additional step toward providing instructors feedback on the effectiveness of instructional design as measured by attentiveness of students in their classroom.
Research on Diverse Feature Fusion Network Based on Video Action Recognition

The application of video action recognition in various fields makes it a hot issue. Network is proposed in this paper a new kind of third-rate fusion, called new third-rate fusion network, the network will come from multiple features of multi-layer flow of time and space with a diverse and compact double linear fusion module integration. After the feature fusion stage, the channel-Spatial module, In UCF101 data sets and HMDB51 experiments on data sets show that compared with the latest model, the network has the best performance.
A General Framework of Particle Swarm Optimization

Particle swarm optimization (PSO) is an effective algorithm to solve the optimization problem in case that derivative of target function is inexistent or difficult to be determined. Because PSO has many parameters and variants, we propose a general framework of PSO called GPSO which aggregates important parameters and generalizes important variants so that researchers can customize PSO easily. Moreover, two main properties of PSO are exploration and exploitation. The exploration property aims to avoid premature converging so as to reach global optimal solution whereas the exploitation property aims to motivate PSO to converge as fast as possible. These two aspects are equally important. Therefore, GPSO also aims to balance the exploration and the exploitation. It is expected that GPSO supports users to tune parameters for not only solving premature problem but also fast convergence.
Utilizing Machine Learning to Predict Breast Cancer: One Step Closer to Bridging the Gap Between the Nature Versus Nurture Debate

with breast cancer, scientists have been trying to find the most effective solutions and treatments. Moreover, studies on genes through machine learning were conducted. By identifying the factor that influences breast cancer the most, this knowledge can be used to prevent or treat patients with breast cancer appropriately. Furthermore, the result of this experiment extends onto the debate of nature versus nurture. If the result concludes that Only Gene or Only Mutation has a stronger effect on tumors, then it weighs nature more in this debate. Likewise, if Only Others is the dominant factor, then this emphasizes the nurture more in this debate. Gathered data was processed and went through eight different machine learning algorithms to predict the tumor size and stage. The ‘Others’ was concluded as the most influential factor for the tumor. Among the ‘Others’, the type of breast surgery and the number of chemotherapy received were identified with the highest correlation with tumor size and stage. In conclusion, this solidifies the nurture’s stance on the debate. The data on the external effects and the usage of a developed machine learning model can be adopted to improve the experiment because they would increase the accuracy of the result.
Feature Extraction and Nuclei Classification in Tissue Samples of Colorectal Cancer

Cancer is considered to be a major health risk and ranked third most important cause of death in the USA. The American Cancer Society (ACS) predicted that by the end of 2020, there will be close to 2 millions new cases and over half million deaths in the USA. In particular, colorectal, breast, lung, and prostate cancers are the most dangerous cancers. This paper aims at providing new solutions for Computer-Aided Diagnosis (CAD) of colorectal cancer, using feature extraction and machine learning algorithms. in this paper, four well-known machine learning techniques have been compared to classify tissue categories. That is, Random Forest, Naive Bayes, Multi-layer Perceptron and Support Vector Machine. In order to measure the performances of these algorithms, we have used Precision, recall and F1-Score. In particular, we have focused on the colors and morphological characteristics in the images and, how they can be useful to improve the classification and diagnosis of colorectal cancer. We believe that such an improvement represents a significant contribution to the state-of-the-art, in both quantitative and qualitative ways.
Implementing Style Transfer with Korean Artworks via VGG16: For Introducing Shin Saimdang and Hongdo KIM’S Paintings

By introducing genre painting to the artists of the time, artist Kim Hongdo is a man known for opening the Renaissance era of Joseon’s art history. Not only did he introduce new genres of art, but he also combined his delicate techniques with his own unique art styles and thus completed over 130 art pieces during his lifetime. Shin Saimdang is another prominent artist of the Joseon Dynasty. Despite the Confucianism beliefs that limited women at that time, Shin Saimdang managed to introduce her meticulous art styles to the public and receive acknowledgment from many officials of the time. This research aimed at a deep learning-based algorithm to recreate original photos by implementing Kim Hongdo’s and Shin Saimdang’s art styles. Unlike previous research which utilized western paintings for the target of the style transfer, this paper proposed the traditional Korean artwork; such difference contributes to making this research meaningful. Furthermore, this paper suggests a novel method that is based on the VGG16 model, in order to reduce the computation speed compared to the VGG19 model. The model implemented style transfer to five original photos which created successful results, capable of introducing Kim Hongdo’s and Shin Saimdang’s art styles and techniques to the general public. The brush strokes and color themes of each artist are successfully recreated in the new images. Despite such drastic changes, the overall structure of the original photo is well maintained and expressed. The five examples can become a helpful guideline for a better understanding of Kim Hongdo’s and Shin Saimdang’s art styles which can further stretch to the understanding of Joseon’s art history as a whole.
How Artificial Intelligence and Videogames Drive Each Other Forward

This paper details multiple areas in which artificial intelligence and video games interact, discusses how these fields can continue to grow together, and highlights some of the other fields that benefit by the combination. Artificial intelligence has a positive effect, not just on video games, but can be used in combination for the benefit of such fields as education, military, healthcare, and aerospace by use of simulations. In turn, video games foster an environment for which artificial intelligence can grow, be that a competition to create the best artificial intelligence player, the use of artificially intelligent characters in video games, or the production of video games.
Distending Function-based Data-Driven Type2 Fuzzy Inference System

Some challenges arise when applying the existing fuzzy type2 modeling techniques. A large number of rules are required to complete cover the whole input space. A large of parameters associated with type2 membership functions have to be determined. The identified fuzzy model is usually difficult to interpret due to the large number of rules. Designing a fuzzy type2 controller using these models is a computationally expensive task. To overcome these limitations, a procedure is proposed here to identify the fuzzy type2 model directly from the data. This model is called the Distending Function-based Fuzzy Inference System (DFIS). The proposed procedure is used to model the altitude controller of a quadcopter. The DFIS model performance is compared with various fuzzy models. The performance of this controller is compared with type1 and type2 fuzzy controllers.
The effects of sense of community, satisfaction about early childhood educational institution, and cultural disposition of parents on the partnership with teachers in the COVID-19 situation

This purpose of this study is to investigate effects of sense of community, satisfaction about early childhood educational institution, and cultural disposition of parents on the partnership with teachers in the COVID-19 situation. To do this, data were collected from 330 early childhood parents using scales for checking four factors and analyzed using descriptive statistics, correlation analysis, and multiple regression analysis. The results of this study are follows. First, parents tended to perceive four factors at a higher level than normal. Second, the partnership with teachers showed a significant positive correlation with factors of sense of community of early childhood educational institution, early childhood educational institution satisfaction and cultural disposition. And, there was a significant positive correlation between the factors of sense of community of early childhood educational institution, early childhood educational institution satisfaction and cultural disposition. Third, parents' sense of community of early childhood educational institution, early childhood educational institution satisfaction and cultural disposition were a influencing variable to the teacher-parent cooperation.&#x0D; Particularly, it was found that the sub-factors for satisfaction about teachers had the greatest influence among the factors for early childhood educational institution satisfaction. The findings of this study imply that diverse on-off line education and counseling in improving sense of community, satisfaction of early childhood educational institution of parents reflecting their cultural disposition in order to increase parent-teacher partnerships in the COVID-19 situation.
An efficient image encryption scheme based on variable row-columns scrambling and selective block diffusion

With the continuous development of multimedia technology and shooting hardware, more and more high-quality images appear in life and production. At present, there are many image encryption methods to ensure image security, but most of them cannot meet the real-time requirements of large-size image encryption, which causes great obstacles to the application and popularization of image encryption. In this paper, an efficient image encryption method based on variable row-columns scrambling and dynamic threshold selective block diffusion is designed. The pixels are operated in batches row by column and block, and the length of chaotic sequence required is reduced without reducing the security, thus reducing the time consuming of the encryption system. In addition, the modified five-point sampling is adopted to select chaotic sequence, which improves the utilization rate of chaotic sequence and further improves the encryption efficiency. Experimental results show that the proposed method has a higher encryption efficiency than the existing methods with similar security, and has strong practicability.
Analysis and research on the Algorithm of university book recommendation

This paper analyzes the performance of user-based collaborative filtering algorithm and item-based collaborative filtering algorithm in university library lending data and the applicable environment of different algorithms through accuracy and recall rate. The borrowing data of university library are processed by different methods and the processed data are run by different algorithms. The running results of different cutting ratios of training sets and test sets, different recommended quantities and different data processing are recorded. And then look for variables that are relevant. Finally, the best running environment of different recommendation algorithms is found by comparing the data.