Reviewerly/exHarmony huggingface dataset:
On Convolutional Neural Networks and Transfer Learning for Classifying Breast Cancer on Histopathological Images Using GPU

This paper presents a study about transfer learning using convolutional neural networks for detecting breast cancer in histopathological images. Transfer learning is a deep learning technique that reuses pre-trained neural network models to perform a particular task, which in this paper is to detect breast cancer in the referred images. Three convolutional architectures were tested: ResNet-18, ResNet-152, and GoogLeNet. The architectures were implemented in Python using PyTorch and trained using GPUs in the Google Colaboratory environment. Moreover, a random image processing stage was used to avoid overfitting, as well. Results indicate that ResNet-152 presents the best results reaching a mean accuracy of 84%, a precision of 90%, and F1 Score of 88%.
Generative Adversarial Imitation Learning to Search in Branch-and-Bound Algorithms

Recent studies have shown that reinforcement learning (RL) can provide state-of-the-art performance at learning sophisticated heuristics by exploiting the shared internal structure combinatorial optimization instances in the data. However, existing RL-based methods require too much trial-and-error reliant on sophisticated reward engineering, which is laborious and inefficient for practical applications. This paper proposes a novel framework (RAIL) that combines RL and generative adversarial imitation learning (GAIL) to meet the challenge by searching in branch-and-bound algorithms. RAIL has a policy architecture with dual decoders, corresponding to the sequence decoding of RL and the edge decoding of GAIL, respectively. The two complement each other and restrict each other to improve the learned policy and reward function iteratively.
Study of Classification Algorithms for Handwritten Character Recognition

Recognizing handwritten characters automatically has a lot of applications and is not an easy task given the number of ways a character is written by people across the world. To automate, this process involves intelligent machine learning algorithms. In this work, the main focus is on using convolutional neural networks (CNN) for efficient character identification. The other traditional algorithms like Naive Bayes and decision tree were also fitted after proper scaling. The CNN model makes use of convolutional, max-pooling, dense, and dropout layers. ReLU was used as activation for all layers except for the last dense layer which uses the softmax activation function. CNN has an advantage over its prior models that is to detect the important features without human supervision. The user input image for the English alphabets was preprocessed by grayscaling, reshaping, sharpening, and resizing. The model takes preprocessed input and gives its prediction for the characters. Experimental results proved that the CNN model provided an accuracy of 93% compared with the other models.
Face inpainting via Learnable Structure Knowledge of Fusion Network

With the development of deep learning, face inpainting has been significantly enhanced in the past few years.Although image inpainting framework integrated with generative adversarial network or attention mechanism enhanced the semantic understanding among facial components, the issues of reconstruction on corrupted regions are still worthy to explore, such as blurred edge structure, excessive smoothness, unreasonable semantic understanding and visual artifacts, etc.To address these issues, we propose a Learnable Structure Knowledge of Fusion Network (LSK-FNet), which learns a prior knowledge by edge generation network for image inpainting.The architecture involves two steps: Firstly, structure information obtained by edge generation network is used as the prior knowledge for face inpainting network.Secondly, both the generated prior knowledge and the incomplete image are fed into the face inpainting network together to get the fusion information.To improve the accuracy of inpainting, both of gated convolution and region normalization are applied in our proposed model.We evaluate our LSK-FNet qualitatively and quantitatively on the CelebA-HQ dataset.The experimental results demonstrate that the edge structure and details of facial images can be improved by using LSK-FNet.Our model surpasses the compared models on L1, PSNR and SSIM metrics.When the masked region is less than 20%, L1 loss reduce by more than 4.3%.
Enhance Both Text and Label: Combination Strategies for Improving the Generalization Ability of Medical Entity Extraction

This paper describes our approach for the Chinese medical Named Entity Recognition (NER) and event extraction tasks organized by the China Conference on Knowledge Graph and Semantic Computing (CCKS) 2021. For the NER task, we need to identify the entity boundaries and category labels of six types of entities from Chinese electronic medical records (EMR). And for the Event Extraction task, we need to recognizes a type of tumor event from Chinese EMR, which contains three tumor-related attributes. This medical entity and event extraction task has two main challenges: 1) How to build a unified modeling framework for entity and event extraction. 2) How to improve the generalization ability of medical entity extraction. For these two challenges, we use a sequence labeling framework based on entity extraction to unify the above two tasks. Based on the pre-trained model, we propose a combined strategy of unsupervised text mode enhancement and label mode enhancement. In the end, it ranked second without any post-processing.
Applications of ST distributions to neural networks and regression models

Neighborhood function, neighborhood radius and learning rate are terms used in neural networks theory to denote area of dependence between neurons and the degree of adaptation to new information correspondingly.For example, in Kohonen Self-Organizing Maps (SOM) – feed-forward neural networks which apply unsupervised learning - the neuron-winner (Best Matching Unit – BMU or Best Performing Unit - BPU) and his neighbors change their weights in order to be closer to the corresponding input vector. Neighborhood here is defined through neighbor function and neighbor radius. The learning rate is used when changing the weights of the BPU and its neighbors. It allows introducing different degree of change of the weights of the neurons in the neighborhood. In this way, different quantity of new knowledge is ensured for different neighbors.In this article, a new type of neighborhood function and learning rate function is proposed based on Switch-Time (ST) distribution.In neural networks, as well as in non-linear regression (which may be considered as a very simple case of neural network), different kinds of non-linear activation functions are used.In this article a new type of non-linear activation functions is proposed based on Switch-Time (ST) distribution and the term stopit regression is defined.ST distributions are also considered as regime capturing functions in neural networks with a smooth transition regime switching.
IOWN for digital twin enabled societies

Achieving the extreme data volume and velocity requirements of digital twin applications energy-efficiently is challenging. IOWN will address this challenge by making architectural shifts in computing and networking with the evolution of optical technologies.
Evolutionary Design of Reduced Precision Levodopa-Induced Dyskinesia Classifiers

Parkinson’s disease is one of the most common neurological conditions whose symptoms are usually treated with a drug containing levodopa. To minimise levodopa side effects, i.e. levodopa-induced dyskinesia (LID), it is necessary to correctly manage levodopa dosage. This article covers an application of cartesian genetic programming (CGP) to assess LID based on time series collected using accelerators attached to the patient’s body. Evolutionary design of reduced precision classifiers of LID is investigated in order to find a hardware-efficient classifier together with classification accuracy as close as possible to a baseline software implementation. CGP equipped with the coevolution of adaptive size fitness predictors (coASFP) is used to design LID-classifiers working with fixed-point arithmetics with reduced precision, which is suitable for implementation in application-specific integrated circuits. In this particular task, we achieved a significant evolutionary design computational cost reduction in comparison with the original CGP. Moreover, coASFP effectively prevented overfitting in this task. Experiments with reduced precision LID-classifier design show that evolved classifiers working with 8-bit unsigned integer data representation, together with the input data scaling using the logical right shift, not only significantly outperformed hardware characteristics of all other investigated solutions but also achieved a better classifier accuracy in comparison with classifiers working with the floating-point numbers.
Experimental Results Regarding the Efficiency of Business Activities Through the Use of Chatbots

In this article we take into consideration the chatbots, describing some models used in different fields of activity comparing how to achieve them, the estimation effort given by the specialists who worked on their implementation, the benefits, and efficiency at work. A chatbot is a computer program that has the role of connecting a verbal or written dialogue with a person. Over time, chatbots became necessary in all areas. They are used in healthcare, education, nutrition, transportation, finance, tourism, relaxation, and rest. The trend of companies is to replace the customer service department with chatbots that are efficient, do not mean very high costs, and work 24 h a day helping employees with personalized explanations, sending messages, or emails regardless of location, with possibility to upload some documents in the application. Chatbots are secure, and for a user to log into the chatbot, he/she must go through several steps. In this article we present the way of working, original solutions, and experimental results analyzed in the development of three chatbots from different fields. Conversational skills are described, but the emphasis are on the four dimensions of the chatbot development in the chosen areas of activity: interaction, integration, testing, and analysis. Moreover, we want to highlight the concern of business people for using the chatbot in any field of activity, streamlining economic activities, and achieving the success of the chatbot.
Construction of an Intelligent Fuzzy System for English Teaching Based on Data Mining Algorithms

With the improvement of my country's international status and closer exchanges with other countries, English is the universal language of the world. The country and society urgently need compound talents with comprehensive qualities and pay more attention to the practical skills of English. In this case, an intelligent fuzzy system(IFS) for English teaching was created. The system uses data mining technology to analyze the learning effects of students and optimize the teaching mode. This paper uses interviews to understand the students' English learning situation, constructs an English teaching IFS, and uses the online recommendation algorithm in data mining to analyze the data of the students' English learning situation in the system. The analysis results show that the majority of students learn English by watching English movies in their spare time, accounting for 37%, and the least number of students learning English by other methods, accounting for 11%.
A TSX-Based KASLR Break: Bypassing UMIP and Descriptor-Table Exiting

In this paper, we introduce a reliable method based on Transactional Synchronization Extensions (TSX) side-channel leakage to break the KASLR and reveal the address of the Global Descriptor Table (GDT) and Interrupt Descriptor Table (IDT). We indicate that by detecting these addresses, one could execute instructions to sidestep Intel’s User-Mode Instruction Prevention (UMIP) and the Hypervisor-based mitigation and, consequently, neutralized them. The introduced method is successfully performed after the most recent patches for Meltdown and Spectre. Moreover, we demonstrate that a combination of this method with a call-gate mechanism (available in modern processors) in a chain of events will eventually lead to a system compromise despite the restrictions of a super-secure sandbox in the presence of Windows’s proprietary Virtualization Based Security (VBS). Finally, we suggest software-based mitigation to avoid these issues with an acceptable overhead cost.
Directional Stockwell transform in <i>L</i><sup>2</sup>(ℝ<sup><i>n</i></sup>)

In the present article, we propose a directionally tuned variant of the continuous Stockwell transform in the context of multi-dimensional signal analysis. Owing to the directional selectivity coupled with a frequency adaptable window, the proposed transform serves as a bridge between the directional short-time Fourier and wavelet transforms. Besides the formulation of all the fundamental results, including the orthogonality relation and reconstruction formula, we also obtain several uncertainty inequalities associated with the directional Stockwell transform.
GAN-based Augmentation of Mammograms to Improve Breast Lesion Detection

Mammography is an important part of breast cancer diagnostics, as it allows to inspect the inner breast structure without physically penetrating breast tissue. Commonly, mammograms tend to vary in their visual appearance based on the specific device and the circumstances under which the mammogram is acquired. Such images could cause artificial intelligence algorithms to fail as they can introduce an undesired variation into the data. This study intends to put these images to use by utilizing a cycle-consistent Generative Adversarial Network (GAN) in order to augment the training data by diversifying instances of each visual domain into all the available ones. A publicly available dataset was augmented to train a detection network; the GANs used for the augmentation were trained with an in-house dataset with three visually different domains.Results show that using our augmentation technique consistently increases the detection performance by reaching a mean average precision of up to 0.82 against 0.77 without augmenting the data.
Improved ELBP descriptors for face recognition

In this work, the three novel descriptors are introduced for face recognition (FR) so-called sobel horizontal elliptical local binary pattern (SHELBP), sobel vertical elliptical local binary pattern (SVELBP) and sobel elliptical local binary pattern (SELBP). All three proposed descriptors are the extensions of the work proposed by Nguyen and Caplier (2012). Nguyen and Caplier (2012) proposed three descriptors for FR called as HELBP, VELBP and ELBP. In HELBP and VELBP, the horizontal neighbourhood pixels (aligned elliptically) and vertical neighbourhood pixels (aligned elliptically) are compared with centre pixel to produce their feature sizes and ELBP is the combined histogram extracted from both the descriptors. The performance of these descriptors are not effective under illumination variations (without pre-processing), as it is experimentally proved in this work. To compensate for that sobel operator is applied as image pre-processing before feature extraction is performed. The features extracted from sobel magnitude and directional gradients eliminates this problem very effectively.
Joints of Varieties

Abstract We generalize the Guth–Katz joints theorem from lines to varieties. A special case says that N planes (2-flats) in 6 dimensions (over any field) have $$O(N^{3/2})$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>O</mml:mi> <mml:mo>(</mml:mo> <mml:msup> <mml:mi>N</mml:mi> <mml:mrow> <mml:mn>3</mml:mn> <mml:mo>/</mml:mo> <mml:mn>2</mml:mn> </mml:mrow> </mml:msup> <mml:mo>)</mml:mo> </mml:mrow> </mml:math> joints, where a joint is a point contained in a triple of these planes not all lying in some hyperplane. More generally, we prove the same bound when the set of N planes is replaced by a set of 2-dimensional algebraic varieties of total degree N , and a joint is a point that is regular for three varieties whose tangent planes at that point are not all contained in some hyperplane. Our most general result gives upper bounds, tight up to constant factors, for joints with multiplicities for several sets of varieties of arbitrary dimensions (known as Carbery’s conjecture). Our main innovation is a new way to extend the polynomial method to higher dimensional objects, relating the degree of a polynomial and its orders of vanishing on a given set of points on a variety.
Optimal liquidation through a limit order book: a neural network and simulation approach

We present a learning algorithm based on simulation and neural networks to solve a stochastic optimal control problem with a large state space using dynamic programming. The problem consists in liquidating a given number of shares of a stock through a limit order book (LOB). The state space includes prices and quantities at each level in the LOB. The objective is to maximize the expected liquidation proceeds. Shares are sold by market orders matching the current limit orders in the LOB and have an impact on the future evolution of the LOB. The optimal strategy is obtained using a hybrid form of performance and value iteration procedures based on neural networks. The probability distribution of the LOB is estimated by a deep learning classification task. The model is tested on 12 stocks traded through the NYSE Arcabook, and a numerical implementation shows that the model outperforms the most common optimal liquidation models in the literature by a significant amount.
Baybayin Script Word Recognition and Transliteration Using a Convolutional Neural Network

In the Philippines, the sudden interest in the Baybayin script through the advocacy of institutions to revive its use leads to the need for it to be preserved and taught for future generations. A learning solution is needed for an easier and faster understanding of the script. This paper presents a Convolutional Neural Network (CNN) based transliteration model that converts Baybayin symbols into a coherent Filipino word. The model was trained using datasets that are specific for Baybayin character classification, word detection, and transliteration and was then evaluated in these individual categories. A VGG16 based classification model was used as a benchmark for the Baybayin character classification. Results show that the proposed and benchmark model has the same accuracy of 97.4% and 97.26% but the models have a significant difference between their predicting and training time. The assessment of word detection and transliteration using a novel dataset has an accuracy score of 96.15% and 91.54% respectively.
Combining Local and Global Features for Quality Assessment of Stitched Images in Virtual Reality

High-quality stitched images, namely wide-angle images stitched from small viewpoint images using stitching algorithm, are a key component for immersive VR experience. There is no stitching algorithm that can achieve perfect stitching for all visual scenes. To design better stitching algorithms, an accurate quality metric for stitched panoramic images is desired. In this paper, we propose a new quality assessment metric that focuses on both global and local distortions in stitched images. Specifically, we have performed the positioning of the distorted area and divided the panoramic image into distorted area and non-distorted area. For local quality, we separately extract the quality features of the distorted and non-distorted regions of the stitched image, and then calculate the distance between the features as a local quality metric of the stitched image. For global quality, we use general image quality evaluation features. The experimental results show that the combination of local and global features delivers significant performance improvement compared to the traditional image quality metrics.
Modeling Data Complexity in Public History and Cultural Heritage

The publication by Galleries, Libraries, Archives and Museums of metadata about their collections is fundamental for the creation of our shared digital cultural heritage. Yet, we argue, these digital collections are, on one hand, of little use to scholars (because of the inconsistent quality of the published records), and, on the other hand, they fail to attract the interest of the general public (because of their dry content). These problems are exacerbated by the current move towards public history, where citizens are no longer just passive actors, but play an active role in contributing, maintaining and curating historical records, leading some to question the trustworthiness of collections in which non-scholars have the ability to contribute. The core issue behind all these problems is, we believe, a (doomed) search for objectivity, often caused by the fact that data models ignore the derivative and stratified nature of cultural objects, and allow only one point of view to be expressed. In turn this forces the publication of bowdlerized records and removes any venue for the expression of disagreement and different opinions. We propose an approach named “contexts” to solve these issues. The adoption of contexts makes it possible to support multiple points of view inside the same dataset, not only allowing multiple scholars to provide their own possibly contrasting points of view, but also making it possible to incorporate additions, corrections and more complex kinds of commentaries from citizens without compromising the trustworthiness of the whole dataset.
Moving Other Way: Exploring Word Mover Distance Extensions

The word mover's distance (WMD) is a popular semantic similarity metric for two texts. This position paper studies several possible extensions of WMD. We experiment with the frequency of words in the corpus as a weighting factor and the geometry of the word vector space. We validate possible extensions of WMD on six document classification datasets. Some proposed extensions show better results in terms of the k-nearest neighbor classification error than WMD.
Accurate and Efficient Salient Object Detection Via Position Prior Attention

The excessive pursuit of accuracy has resulted in complex and huge structures of most existing salient object detection (SOD) models, and the meticulously designed lightweight SOD models cannot accurately detect salient objects. To improve the practicality of SOD, we design a novel position prior attention network (PPANet) for fast and accurate salient object detection in this paper. In detail, we propose a position prior attention module (PPAM), which first assigns different weights to positions based on the prior that objects near the image center are more attractive to people, and then perceives object context information through different receptive fields. In addition, we propose a context fusion module (CFM) to prevent the coarse resolution of high-level features from diluting the salient object boundaries during fusion. We present two PPANet versions: a heavyweight PPANet-R aimed at high accuracy SOD and a lightweight PPANet-M that achieves a good balance between accuracy and efficiency. Besides, we construct a structural polishing loss that gives more attention to object boundary and solves the problem of sample imbalance. Experimental results on 5 popular benchmark datasets demonstrate that the proposed PPANet-R outperforms existing SOD models, and PPANet-M achieves accuracy comparable to the state-of-the-art heavyweight SOD methods with 150 FPS real-time detection speed.