Reviewerly/exHarmony huggingface dataset:
Mammary duct detection using self-supervised encoders

Ductal Carcinoma in Situ (DCIS) constitutes 20–25% of all diagnosed breast cancers and is a well known potential precursor for invasive breast cancer.1 The gold standard method for diagnosing DCIS involves the detection of calcifications and abnormal cell proliferation in mammary ducts in Hematoxylin and Eosin (H&E) stained whole-slide images (WSIs). Automatic duct detection may facilitate this task as well as downstream applications that currently require tedious, manual annotation of ducts. Examples of these are grading of DCIS lesions2 and prediction of local recurrence of DCIS.3 Several methods have been developed for object detection in the field of deep learning. Such models are typically initialised using ImageNet transfer-learning features, as the limited availability of annotated medical images has hindered the creation of domain-specific encoders. Novel techniques such as self-supervised learning (SSL) promise to overcome this problem by utilising unlabelled data to learn feature encoders. SSL encoders trained on unlabelled ImageNet have demonstrated SSL’s capacity to produce meaningful representations, scoring higher than supervised features on the ImageNet 1% classification task.4 In the domain of histopathology, feature encoders (Histo encoders) have been developed.5, 6 In classification experiments with linear regression, frozen features of these encoders outperformed those of ImageNet encoders. However, when models initialised with histopathology and ImageNet encoders were fine-tuned on the same classification tasks, there were no differences in performance between the encoders.5, 6 Furthermore, the transferability of SSL encodings to object detection is poorly understood.4 These findings show that more research is needed to develop training strategies for SSL encoders that can enhance performance in relevant downstream tasks. In our study, we investigated whether current state-of-the-art SSL methods can provide model initialisations that outperform ImageNet pre-training on the task of duct detection in WSIs of breast tissue resections. We compared the performance of these SSL-based histopathology encodings (Histo-SSL) with ImageNet pre-training (supervised and self-supervised) and training from scratch. Additionally, we compared the performance of our Histo-SSL encodings with published Histo encoders by Ciga5 and Mormont6 on the same task.
Characters Type Recognition In Moroccan Documents Using CNN

In recent years, the classification and recognition became a hot topic in computer studies. Deep Learning algorithms present the most outstanding performance in classification and recognition issues. In this paper, we focus on applying these techniques to extract the characters types from the Moroccan official legal documents. Because of the variety of many pre-trained models. we designed a system able to Loop over each model available on TensorFlow Keras API, based on Transfer Learning technique, we trained the models on the dataset that we have built. And the outcome was that the DensNet and VGGNet models have achieved the best performance, with a validation accuracy of 98%. In addition to this, we proposed a modified model based on DenseNet201, the result achieved is 99.01% of overall accuracy.
A Friend-or-Foe framework for multi-agent reinforcement learning policy generation in mixing cooperative–competitive scenarios

Although multi-agent deep deterministic policy gradient is a classic deep reinforcement learning algorithm in multi-agent systems. It also has critical problems such as poor training stability and low policy robustness, which significantly limit the capability and application of the algorithm. So this article proposes an improved algorithm called friend-or-foe multi-agent deep deterministic policy gradient for solving the above problems. The main innovations are as follows: (1) inspired by the concept of friend-or-foe game theory, we modified the framework of the original multi-agent deep deterministic policy gradient by using two identical training networks with agents’ optimal and worst actions input, which improves the robustness of training policies, and (2) we propose an action perturbation technique based on gradient-descent to expand the selection range of actions, thereby improving training stability of our proposing algorithm. Finally, we conducted multiple sets of comparative experiments between our friend-or-foe multi-agent deep deterministic policy gradient and original one in four authoritative mixed cooperative–competitive scenarios. The results show that our improving algorithm can simultaneously improve the training stability and the robustness of agents’ generating policies in different complicated environments.
A study on human-robot coordination in augmented reality assisted hand tracking system

Augmented reality environments allow users to interact naturally with 3D objects, including robots. Many robots have been used in the automated sector for painting, picking, packing, and palletizing tasks. The Baxter robot is an example of an industrial robot ideal for research and education. Baxter robots can offer multiple benefits compared with regular robots. In this study, we designed an augmented reality system that makes users intuitively interact in 3D environments by using a Leap Motion controller as a hand tracker and performing a basic human-robot coordination task with the Baxter robot. And the Baxter robot with a stereo camera is connected to a Linux computer, which was programmed with python language. The augmented reality world was programmed in the Unity software. The human robot-coordination task consisted of an augmented reality alignment. We asked the subject to wear the head-mounted display and move the hands. Every hand motion was translated into the robot’s limb motion in real-time. The subject and robot had to align two augmented reality markers. Here are two different experimental conditions: visual information activated and deactivated. The subject performed three trials under each condition. The experimental results showed that the subject under the visual information activated mode improved the average time by 70.63 %.
2PN: A Unified Panoptic Segmentation Network with Attention Module

Comprehensive and accurate surveillance of the environment forms the basis of secure Internet of things (IoTs), the threats can be observed, and the AI services of IoT systems can be preserved. Panoptic segmentation is an efficient and popular approach for environmental surveillance based on images captured by smart sensing devices. This approach can jointly detect stuffs and things within an image and feed subsequent tasks like image detection. So far, there are many methods for panoptic segmentation which focus on extracting sophisticated visual features for segmentation. However, these efforts are both heavy on their workload and cannot clearly distinguish essential features useful for surveillance in an open environment. Therefore, this paper proposes a novel deep learning model 2PN for panoptic segmentation. The model includes a 2-way pyramid network and an attention module to learn in a more concentrated and reasonable way which enhances the feature extraction part. It strikes a balance between the computing complexity and the power of model capability. Finally, 2PN (2-way pyramid network) results are reflected on the Cityscapes dataset.
Research on the Differences in the Value Correlation of Accounting Data Under the Background of COVID-19

In today's highly developed world of financial globalization, international capital flows in my country and the entire Asia-Pacific region are gradually increasing. The stock market is an important part of the capital market. The stock market has gradually improved its capital liquidity. With the improvement of the investment environment in the international capital market and the gradual relaxation of capital controls, with the development of the "Belt and Road" development concept, Thailand has gradually chosen to buy and sell shares of Chinese investors. What investors need to consider is how to achieve capital appreciation. Investors need to consider how to achieve capital appreciation. However, a sudden new crown pneumonia epidemic broke the peace and excitement that the New Year should have, and also brought a great impact on the stock market. Of course, the stock market is affected by many factors. There are some problems here, such as the experience of the Chinese stock market.
Hexacopter Drones Overview

Unmanned Aerial Vehicles, or drones, are a topic of interest to many academic and industrial research organisations. They find applications in several fields, ranging from military functions to civilian functions. Consequently, the UAVs are expected to perform a wide range of missions, which necessitates a certain level of autonomy. An autonomous UAV relies on an efficient control architecture to perform various tasks and make appropriate decisions. In robotics, control architectures are categorized under six classifications, as we list: deliberative, reactive, hybrid, behaviour, hybrid behaviour, and lastly subsumption architecture. Through this paper, we introduce a short review on a particular type of UAV called Hexacopter or Hexarotor, in which we discuss the items that compose the hexarotor from the very fundamental (framework, sensors …) to the most elaborated parts (modelling, control…). The document covers the flight mechanism, the avionics sensors, the dynamic modelling, and the various control techniques used on a hexacopter. The vision system, the different localisation, and navigation techniques are also explored. At last, but not at least, the focus is directed toward the investigation of the robotics control architectures.
Assessment of Addition-Chain-Based Masked S-Box Using Deep-Learning-Based Side-Channel Attacks

Masking schemes are considered to be effective countermeasures to protect Internet-of-Things devices from side-channel attacks. Deep-learning-based side-channel attacks (DL-SCAs) have been demonstrated to be very effective targeting on masked implementations. In this paper, we investigate the resistance of a popular computation-based masking scheme against DL-SCAs, that is, the addition-chain-based one. We find that addition chain introduces computations of intermediate monomials over <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <msub> <mrow> <mi mathvariant="double-struck">F</mi> </mrow> <mrow> <msup> <mrow> <mn>2</mn> </mrow> <mrow> <mi>n</mi> </mrow> </msup> </mrow> </msub> </math> with smaller output sizes, which decreases its resistance against DL-SCAs. Specifically, we first use mutual information metric to evaluate the side-channel resistance of different monomials from an information theory point of view. Next, we further propose the Kullback–Leibler divergence ratio as an evaluation metric to analyze the impact of monomial output size on DL-SCAs. The measurement values show that the monomial with smaller output size is less-resistant against DL-SCAs. Then we conduct simulated and practical experiments respectively to verify it. In simulated experiments, we perform DL-SCAs on first-order masked implementations with different noise levels and training trace numbers. The results demonstrate that monomials with smaller output size are more vulnerable. Moreover, with the increase (resp. decrease) in noise level (resp. training trace number), the resistance difference of these monomials becomes more significant. In addition, we obtain similar results through simulated experiments on second-order masked scenario. In practical experiments based on an ARM Cortex-M4 architecture, we collect power and electromagnetic traces in consideration of low and high noise levels. The results show that the number of required traces for targeting the S-Box output is at least three times as much that for targeting the weakest monomial.
Improving the efficiency of sparse matrix class processing by using the SPM-CSR parallel algorithm and OpenMP technology

The development of computationally efficient algorithms and the improvement of their software implementation are urgent issues that require continuous attention due to the ongoing development of computer system architecture. In multithreading systems bad coordination cause a big problems and give unwilling results. The purpose of the work is to improve performance of the parallel algorithm for sparse matrix multiplication in CSR format by using vector (SpMV-CSR) using OpenMP technology. Using OpenMP technology and reduction directive to solve sparse matrix time execution and bad coordination problem leads to execution time reduction and speed-up increase. Results obtained an acceleration increase of 14&#x0025; when using reduction.
The Brief Analysis on Recommender Systems and Similarity Algorithms

This paper presents a brief analysis of the most commonly used approaches in the field of recommender systems, which are content-based filtering, collaborative filtering, and matrix factorization improvement, a hybrid approach that mixes the previous two. Then four algorithms are introduced to calculate similarities between objects to find similar items to recommend. This paper also discusses the advantages and limitations of each recommendation approach and similarity algorithm.
The Design of Multi Level Closed Loop Repair System for Enterprise Self Service Big Data

Due to the poor ability to identify abnormal data in the process of enterprise data repair, the integrity of data repair is poor and the data carrying capacity is low. Therefore, it is necessary to design a new enterprise self-service big data multi-level closed-loop repair system. Optimize the abnormal data recognition module, set the data calculation threshold, and realize the abnormal data recognition according to the smooth estimation threshold algorithm. According to the data partition calculation process, the repair level is set according to the data category in the data repair. Combined with the traditional data repair technology, the multi-level closed-loop repair process is completed. The experimental results show that the system has good data integrity, high data carrying capacity and good performance.
PRIMORSKY CENTRE FOR LOCAL ELEMENTAL AND ISOTOPIC ANALYSES OF FEGI FEB RAS: EXPERIENCE OF THE FUNCTIONING AND MAIN AREAS OF RESEARCH

The Shared Research Facilities "Primorsky Centre for Local Elemental and Isotopic Analyses" was created based on the instrumental base of the Far Eastern Geological Institute of the Far Eastern Branch of the Russian Academy of Sciences. The Shared Research Facilities conducts its own (for the needs of FEGI FEB RAS) fundamental and applied scientific research to establish the elemental, isotopic composition and structure of both natural and artificial objects, as well as jointly with other scientific Russian and foreign institutions and universities, industrial enterprises, state and private companies that require modern, precision methods for analyzing various substances and materials, diagnosing and monitoring the condition and changes in the environment.
Adjustable Load Interactive Blockchain Interoperability Strategy Based on Reputation Value Promotion

In order to solve the security and credibility problems of contract signing and transactions in the electricity market, a solution based on the combination of blockchain technology and distributed load interaction is proposed. The matching characteristics of load interaction transaction and blockchain, and the design of node reputation value evaluation mechanism, based on this research on the user-side adjustable load distributed interaction interoperability strategy, three algorithms are used to compare the results of the calculation examples, and the algorithm is proved by the simulation results Reasonableness.
Predicting the unpredictable

This study investigates the use of the perfective aspect marker –le in Chinese serial verb constructions through an analysis of 386 serial verb constructions selected from the Sinica Corpus. The analysis takes into account the position of –le, the form of the object co-occurring with –le, and the semantic relationships between VP1 and VP2. The results indicate that –le is used predominately with VP1, that the primary semantic relationship between VP1 and VP2 is “consecutive”, and that quantified objects occur more frequently than definite objects or bare noun objects in VPs containing –le. Understanding the semantic relationship between VP1 and VP2 can enable Chinese learners to predict where and when to use –le correctly in an SVC.
Face Detection on Thermal Infrared Images Combined with Visible Images

Due to COVID-19, intelligent thermal imagers are widely used all over the world. Since intelligent thermal imagers usually require real-time temperature measurement, it is significant to find a method to quickly and accurately detect human faces in thermal infrared images. This paper mainly proposes two different methods. One is to use image processing methods and face detected from visible images to determine the position of the face in the infrared image, while the other is to use target detection algorithms on infrared images, including YOLOv3 and Faster R-CNN. This paper uses the two methods on a self-collected dataset containing 944 pairs of visible and infrared images and observes the robustness of methods by adding random noise to images. Experiments show that the first one has much lower latency and the latter one has higher accuracy in both cases.
Optimized Linguistic Feature Selection Through Deep Learning Approach for Forensic Applications

Today in any business, it has become normal need to use text recognition or optical character recognition (OCR) and text field extraction from the different types of documents. There are several open-source free online OCR tools which are available currently. They are responsible for getting editable text from document images by considering few linguistic features. Once digitized, extracting the information of interest is an obvious task. One of the asset management tools being used by the TPS records the life event of valves, providing status report on valve health, future maintenance planning, and other resource maximizing functionalities. In this context, an OCR-based image retrieval and analysis model has been developed. This work proposes the development of an efficient custom object detection model using deep learning techniques to facilitate the key field extraction from the scanned forensic evidences.
Flipped Classroom Smart Computer System in University Mathematics Guiding under the Background of New Engineering Model

Aiming at the target positioning and needs of talent training in the context of new engineering, the use of information technology, combined with specific examples, explores teaching models, teaching cases, assessment and evaluation, curriculum ideology, etc., and actively carries out university mathematics teaching reform and practice. Discussed the necessity of carrying out mathematical experiments. In view of the powerful mathematical functions of Matlab, a computer system was chosen to assist teaching, and the four mathematics courses of Matlab in “Advanced Mathematics”, “Linear Algebra”, “Probability Theory and Mathematical Statistics” and “Discrete Mathematics” were explained. And application in computer professional courses.
Digital application to display brain shift simulation in tumor resection procedures

The quality of neurosurgical planning can become compromised by soft tissue deformations that occur during surgery (i.e., brain shift). Conventional image guidance systems do not consider these intraoperative changes. In recent efforts, model based strategies have been developed to estimate surgical load displacements and modify the patient’s data intraoperatively to account for brain shift. While the efficacy of the model has been previously established, there is also an opportunity to further assist surgical planning with this pipeline. To address this, a mobile application designed for an Android tablet was developed to display simulated brain shifts that would occur during brain tumor surgery. The application has two primary functions to facilitate planning: a patient positioning mode and a simulation mode. The patient positioning mode allows the neurosurgeon to load the patient’s preoperative MR data and create a surgical plan (i.e., head orientation and craniotomy location) for the procedure. The simulation mode then displays both the preoperative data and the model predicted brain shift as a function of the specified orientation from the patient positioning mode. Additionally, to account for positional variations between planning and procedural implementation, the simulation mode also displays solutions with additional perturbations to the planned positioning to estimate shift possibilities. To assess the simulation mode prototype, practicing neurosurgeons were provided a prototype demonstration and interviews were performed to evaluate efficacy and design. Due to computational rendering and 3D rotation shortcomings based on clinical feedback, the prototype was redesigned into a full mixed reality simulation app on the Microsoft HoloLens. Preliminary survey responses show that the prototype could be an impactful surgical planning tool, especially among neurosurgeons with less experience.
Efficient Handwriting Character Recognition Based on Convolutional Neural Network

The handwriting recognition is becoming a major area of research owing to the increasing usage of digital innovation in all sectors and in practically all day-to-day activities of collecting and sending information. People still demand handwriting copies turned into electronic versions that being spoken and kept electronically, despite the fact that handwriting is always useful. Handwriting recognition is the ability of a computer to recover and analyze legible handwritten inputs from smart phones, pictures, paper documents, as well as from other sources. The Handwriting Character Recognition System is existing using a number of approaches and methodologies however, just a few of them are concerned with neural networks and also recognizing the written characters at accurate rate are still complicated. This is mainly because of various individuals have distinct handwriting styles. The proposed methodology provides the creation of a most accurate handwritten character recognition tool which will be used in modern real time techniques. The convolutional neural network approach is used to demonstrate this research. In comparison to other existing techniques and algorithms, the use of neural networks in detecting handwriting letters will be particularly accurate and effective. The proposed work also discusses the handwriting recognition system&#x0027;s operations, architecture, and structure, as well as system testing and outcomes. From the result analysis it is inferred that the accuracy of 93.8&#x0025; accuracy is obtained on using convolutional neural network technique.
A study of convolutional neural networks in face recognition

Face recognition technology has attracted much attention in the past few years because of its potential value of both research and applications in the fields of pattern recognition, image analysis, computer vision and other fields. Meanwhile, as an important branch of the deep learning family, convolutional neural network (CNN) methods has yield remarkable achievements in many large-scale recognition tasks in the field of computer vision, especially face recognition. This is because, compared to classic neural networks, CNN has more hidden layers, a more complicated network topology, and more robust feature learning and feature expression capabilities. This paper gives an overview of the development on face recognition using CNN approaches, including categories of current face recognition technologies, description of the CNN method, and applications and research progress on CNN for face recognition.