Reviewerly/exHarmony huggingface dataset:
Federated Learning for Digital Pathology: A Pilot Study

Over the last few years, there have been many significant advances in the use of deep learning in digital pathology. Deep learning has been reported to assist with registration, segmentation, classification, and diagnosis tasks in digital pathology. However, the development of scalable, adaptable, and accurate deep learning-based models often relies on collecting large amounts of high-quality annotated training data from various sources or from different sites. Given that the medical data are normally linked to a site and/or within an organization, assembling large-scale datasets has historically required data transfer between them. Patient privacy could be an issue by such transfers, especially if data are to be shared between countries. This poses ethical and legal issues. Therefore, privacy and ownership issues affecting multi-institutional collaborations focused on centrally shared patient data, could affect the translation of deep learning techniques for real-world application. Federated learning has recently emerged as a new paradigm for data-private multi-institutional collaborations, in which model-learning uses all available data without exchanging data between institutions by distributing model training to data-owners and aggregating their outcomes. This paper aims to investigate the possible use of the federated learning method in digital pathology and examine the advantages of using federated learning for real world digital pathology workflow. We have performed our study on the Breast Cancer Histopathological Database, which consists of data from different sites. The case study results presented in this paper have demonstrated that federated learning can be used effectively in the digital pathology area.
Digital educational track formation methodology

For the modern socio-economic system, the provision of organizations with highly qualified personnel with the proper level of soft and hard competencies is becoming the most urgent problem and one of the priorities for the development and increasing the competitiveness of the organization. In these conditions, many organizations prefer to develop their own tools for assessing the level of competencies, determining the lack of knowledge and building an educational track. The greatest demand is for digital educational technologies, which make it possible to build trajectories for the development of necessary and demanded competencies. There is a need to create an integrated professional space for the development of scarce (demanded) soft competencies to increase labor productivity. Improving the efficiency of labor activity presupposes self-determination of the individual in relation to the tasks of his professional activity, a conscious choice of directions for the development of competencies and advanced training.
Attention-Based Unsupervised Sketch Colorization of Anime Avatar

In recent years, when it comes to the task of applying painting style to animation sketch, the method of deep learning has become a research hotspot in the field of animation sketch coloring. Although the effect of some previous coloring methods is effective, it is easy to mix colors in the facial area and dye the upper skin color of the surrounding color blocks, which affects the performance of the final effect. In the method of this paper, the attention mechanism module is added, combined with the unsupervised generated attention network with adaptive layer instance normalization, which can make the model learn the key coloring in the facial features area through the feature map, so as to reduce the surrounding color mixing and help to improve the coloring effect. At the same time, due to the use of unsupervised attention generation network, the whole process is fast and automatic. This method can be used for quick coloring of user-defined animation avatars. It is very friendly to novice users who don't know how to color at all. The final experiment also proves the superiority of this method in facial region coloring.
The Construction and Application of (Related-Key) Conditional Differential Neural Distinguishers on KATAN

At CRYPTO 2019, Ghor applied deep learning to the cryptanalysis of block ciphers and presented neural distinguishers instead of purely differential distinguishers, which improved key recovery attacks of Speck32/64 using Bayesian optimization. In this paper, the authors attempt to improve the performance of neural distinguishers (NDs) and apply new NDs to present practical key recovery attacks on KATAN ciphers. First, with the help of MILP model, we present a (related-key) conditional differential neural distinguishers ((RK)CDNDs) of KATAN ciphers. The (RK)CDNDs use a new data format, combining with conditions and multiple differences. Compared to previous work, we greatly improve the number of rounds and the accuracy of NDs in both single-key and related-key scenarios. Moreover, a related-key conditional differential cryptanalysis framework based on deep learning is proposed with the RKCDNDs, resulting in a significant improvement from the previous. We present a practical key recovery attack on the 125-round KATAN32. The data complexity is $$2^{15.7}$$ and the time complexity is $$2^{19.9}$$ . We also present 106-round KATAN48 and 95-round KATAN64 practical key recovery attacks. The extension of key recovery attack improves the results for two more rounds by calculating the wrong key response profile in parallel. Our work not only increases the number of attack rounds and the recoverable key bits, but also reduces the computational complexity.
Personalized Recommendation of Study Materials Based on Automatic Short Answer Scoring Results

The results of automatic short answer scoring (ASAS) can be used to provide learning recommendations. However, previous studies on ASAS are focused on the quality of the scoring method, without considering giving feedback on the student performance in the exam sessions. Moreover, research on feedback in the learning activity is more concentrated on creating an adaptive learning process. Therefore, this study aims to combine the Automatic Short Answer Scoring method and an Automatic Recommendation System to create an adaptive feedback system that has the ability to personalize the recommendation of study materials for the student based on their exam result. This study uses a dataset of ten questions, including one reference answer per question. The experiment result shows the recommendation system gives the maximum similarity score at 0.8972, with the maximum average score for all questions is 0.801. Future studies may continue to examine a technique for automatically generating study materials from various resources.
Privacy and Surveillance Conflict: A Comparative Analysis of the laws in the USA and India

The Right to Privacy and the need for Surveillance has always remained a contentious issue between citizens and law enforcement agencies.This paper attempts to analyse the various laws relating to Surveillance in the largest and oldest democracies of the world, India and the United States of America.Regardless of vast variances in socio-economic and political realities, these two countries qualify as intriguing focuses for study.Though the Right to Privacy is generally accepted as a fundamental right throughout the nations of the world, the primacy given to 'National Security' and simultaneously balancing it with individual liberties seems to be a recognised phenomenon in both these jurisdictions.
Accuracy Enhancement During Sentiment Analysis in Twitter Using CNN

In order to determine user sentiment, researchers are utilizing sentiment analysis on a Twitter data set that includes both graphical and textual user comments. The world is fast changing as a result of contemporary technological breakthroughs. In today's world, the Internet has become a need for everyone. People are using social network applications to voice their opinions on common issues because of the rapid rise of these platforms. Obtaining and analyzing people's answers to purchasing a product, utilizing public services, and so on is essential. Opinion mining (also known as sentiment analysis) is a common practice for getting ready for a discussion in which the sentiments that underlie various points of view are sought to be discovered. Recent years have seen the rise of sentiment analysis as a tool for determining how consumers are feeling. Textual and graphical tweets posted by users are the primary focus of current study. CNN-based technique is used for graphical content; neural networks are used for textual content. An increasing amount of attention has been paid in recent years to “tweets” (comments) and the content of these expressions in opinion research utilizing Twitter data. Because of this, the goal of this research is to find out how various sentiment analyses on Twitter data turn out. The suggested research uses a neural network machine learning method to detect feelings based on graphical input.
Variational Reinforcement Learning for Hyper-Parameter Tuning of Adaptive Evolutionary Algorithm

The performance of an evolutionary algorithm (EA) is deeply affected by its control parameter's setting. It has become a trend in recent studies to treat the control parameter as a random variable. In these studies, the associated distribution of the control parameter is updated at each generation and new parameter setting is sampled from the distribution. The distribution's parameter (called hyper-parameter) is thus critical to the algorithmic performance. In this paper, we propose a variational learning framework to tune the hyper-parameters of EA, in which the expectation-maximization (EM) algorithm and a reinforcement learning algorithm are combined. To verify the effectiveness of the proposed method which is named Reinforcement EM (REM), we apply it to tune the hyper-parameters of the distributions of two important parameters, i.e. the scaling parameter ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$F$</tex-math></inline-formula> ) and crossover rate ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$CR$</tex-math></inline-formula> ), of differential evolution (DE) and an adaptive DE algorithm. In addition, we propose to use the meta-learning technique to learn good initial distributions for the hyper-parameters of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$F$</tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$CR$</tex-math></inline-formula> so that the REM can effectively adapt to a new optimization problem. Experimental results obtained on the CEC 2018 test suite show that with the tuned hyper-parameters, DE and the adaptive DE can achieve significantly better performance than their counterparts with empirical parameter settings and with parameters tuned by some widely-used tuning methods, including ParamILS, F-Race and Bayesian optimization algorithm.
Investigating Color-Object Associations With Eye Tracking

Humans are able to associate colors with certain objects. However, if the color is missing like in grayscale images it is questionable if such associations can be built the same way. To investigate this aspect we conducted an eye tracking study. We found out that the visual attention behavior for colored objects is different from grayscale objects.
Organization of independent work among scientific and pedagogical personnel in multilevel education system

Independent work has a great role in the training of scientific and pedagogical personnel in a multilevel education system. The research purpose is to consider the problematic aspects of independent work organization among adjuncts; to identify their most preferred learning strategies; to develop methodological recommendations for the organization of independent learning activities for this category of students. The following research methods were used: theoretical (analysis of pedagogical, scientific and methodological literature, normative legal acts, materials of the panel discussion) and empirical (observation, survey, testing). In the course of the research, the main reasons for the inefficiency of organizing and conducting independent work among adjuncts are insufficient material and technical support, didactic imperfection of pedagogical guidance and organizational and methodological support. The adjuncts’ preferences for learning strategies are cognitive (repetition and collaboration) and metacognitive (planning and regulation). Specific methodological recommendations are proposed to solve the problem of organizing and conducting independent work of students in military educational organizations: specifying the content of training modules, developing a point-rating system for evaluating the adjuncts’ achievements, improving educational strategies, variability of training technologies, etc.
Development of Multi-task Models for Emotion-Aware Gender Prediction

With the rise of personalized online services, a huge opportunity for user profiling has developed. Gender plays a very important role for services that rely on information about a user's background. Although, due to anonymity and privacy, the gender information of a user is usually unavailable for other users. Social Networking sites have provided users with a lot of features to express their thoughts and emotions either using pictures or emojis or writing texts. Based on the idea that female and male users have some differences in their post and message contents, social media accounts can be analyzed using their textual posts for finding the user's gender. In this work, we explore different emotion-aided multi-modal gender prediction models. The basic intuition behind our proposed approach is to predict the gender of a user based on the emotional clues present in their multimodal posts, which includes texts as well as images. PAN 2018 dataset is enriched with emotion labels, for the experimentation. Different multi-tasking based architectures have been developed for gender prediction. Obtained results on the benchmark PAN-2018 dataset illustrate that the proposed multimodal emotion-aided system performs better than the single modal (with only text and only image) based models and the state of the art system too.
Main requirements of end-to-end deep learning models for biomedical time series classification in healthcare environments

The use of deep learning models with medical data is becoming more widespread. However, although numerous models have shown high accuracy in medical-related tasks, such as medical image recognition (e.g. radiographs), there are still many problems with seeing these models operating in a real healthcare environment. This article presents a series of basic requirements that must be taken into account when developing deep learning models for biomedical time series classification tasks, with the aim of facilitating the subsequent production of the models in healthcare. These requirements range from the correct collection of data, to the existing techniques for a correct explanation of the results obtained by the models. This is due to the fact that one of the main reasons why the use of deep learning models is not more widespread in healthcare settings is their lack of clarity when it comes to explaining decision making.
Research on the semantic segmentation algorithm for automatic driving with improved HRNet

In automatic driving, semantic segmentation plays a very important role. A proposed image semantic segmentation algorithm based on HRNet joint cross-attention mechanism addresses the problems of low resolution of feature maps extracted by current mainstream image semantic segmentation algorithms, excessive loss of effective semantic information during upsampling, and easy loss of pixel points and relevance of target object regions. A denoising module is incorporated into the segmentation model during the training process to improve training speed and robustness. In comparison with the original segmentation model, this method produces an mIoU of 83.2% in the widely used Cityscapes dataset.
Interpretable linear models for predicting security vulnerabilities in source code

In our increasingly digital and networked society, computer code is responsible for many essential tasks. There are an increasing number of attacks on such code using unpatched security vulnerabilities. Therefore, it is important to create tools that can automatically identify or predict security vulnerabilities in code, in order to prevent such attacks. In this paper we focus on methods for predicting security vulnerabilities based on analysis of the source code as a text file. In recent years many attempts to solve this problem involve natural language processing (NLP) methods which use neural networks-based techniques where tokens in the source code are mapped into a vector in a Euclidean space which has dimension much lower than the dimensionality of the encoding of tokens. Those embedding-type methods were shown effective solving problems like sentence completion, indexing large corpora of texts, classifying & organizing documents and more. However, it is often necessary to extract an interpretation for which features are important for the decision rule of the learned model. A weakness of neural networks-based methods is lack of such interpretability. In this paper we show how L1 regularized linear models can be used with engineered features, in order to supplement neural network embedding features. Our approach yields models which are more interpretable and more accurate than models which only use neural network based feature embeddings. Our empirical results in cross-validation experiments show that the linear models with interpretable features are significantly more accurate than models with neural network embedding features alone. We additionally show that nearly all of the features were used in the learned models, and that trained models generalize to some extent to other data sets.
Anchor graph semi-supervised learning method based on Evidence Theory

In many practical machine leaning tasks, labeled training samples are expensive to obtain, while unlabeled ones are readily available. Therefore, semi-supervised classification(SSC) have attracted a lot of attention, among which graph based semi-supervised learning is one of the main research hotspots. The Graph semi-supervised learning methods use unlabeled samples and few labeled samples to build a reliable graph model and spread the label information on the graph model. However, the traditional graph semi-supervised learning algorithms are time-consuming and can not build very reliable graph models on high-dimensional data. In order to deal with these problems, this paper propose an anchor graph semi-supervised learning method based on Evidence Theory. The proposed method uses some multiple feature subspaces of original data to train different anchor graph semi-supervised learning models, and combines the different results using Evidence Theory. Experimental results based on benchmark data sets show that the proposed algorithm can achieve better performance compared with the prevailing related algorithms.
Synthesizing Voice User Interface for Augmented Reality Experience Enhancements

Augmented Reality(AR) is used in several fields to make interactive models by combining the virtual and real world. While Voice User Interface(VUI) is used as a form of communication between software and the user via voice. In this research, we experimented with a VUI based experience for AR device(Android Smart Phone) to assist the existing touch-based interaction by employing speech to text(STT) and ARCore SDK. Such experiences will provide a smooth flow for several use cases such as AR shopping, AR classrooms, AR Modeling, AR tool-set navigation etc. The study concluded by successfully executing a model which utilizes the VUI system where the user can provide instructions to the AR system verbally and which will trigger the necessary commands in the AR system.
AI-Based COVID Alert System on Embedded Platform

Corona Virus Disease (COVID-19) has hit the world hard and almost every country has faced its consequences may be the population and number of people affected or economically. Crowd management is incredibly tough for big surroundings and continuous watching manually is troublesome to execute. Vaccinated people are also getting affected by the virus so it is advisable to take Public Health & Social Measures (PHSM) such as wearing a proper mask, sanitization and keeping social distancing in crowded places. The proposed paper presents a machine learning based real-time Covid alert and prevention system to ensure Covid appropriate behavior in public places and social gatherings. There are three modules under this system: (i) Real-time Face mask detection, where persons with masks, improper masks or no mask are detected and classified; (ii) Real-time people counting for ensuring a limit on public meetings and social gatherings and (iii) Real-time social distance monitoring. All these modules are integrated and deployed on embedded hardware, NVidia’s Jetson Nano. The implementation results are presented and analysis of the detection is done in real-time on the edge-AI platform.
A Block-Based Arithmetic Entropy Encoding Scheme for Medical Images

The digitization of human body, especially for treatment of diseases can generate a large volume of data. This generated medical data has a large resolution and bit depth. In the field of medical diagnosis, lossless compression techniques are widely adopted for the efficient archiving and transmission of medical images. This article presents an efficient coding solution based on a predictive coding technique. The proposed technique consists of Resolution Independent Gradient Edge Predictor16 (RIGED16) and Block Based Arithmetic Encoding (BAAE). The objective of this technique is to find universal threshold values for prediction and provide an optimum block size for encoding. The validity of the proposed technique is tested on some real images as well as standard images. The simulation results of the proposed technique are compared with some well-known and existing compression techniques. It is revealed that proposed technique gives a higher coding efficiency rate compared to other techniques.
Large-Capacity Local Multi-Dimensional Information Hiding Method for 6G Networks

A 6G network is not only faster, but also offers a wider range of services than 5G networks, which will lead to an increased number of participants. However, combined with the Internet of Everything, 6G networks face security issues such as man-in-the-middle hijacking and bypass listening. Because covert transmission is inherently highly concealed and difficult to detect, a 6G network based on information hiding has become a new paradigm to solve such security problems. In this article, we propose a large-capacity local multi-dimensional information hiding method based on orthogonal space and spread transform. By extending the quantization index modulation hiding approach, the proposed method hides the multi-layer information in digital signals' multi-dimensional space using orthogonal space and spread transform. In theory, this method can hide information in multiple layers at the same location while allowing the extraction of information from multiple layers independently. This proposed method offers a much higher hiding capacity than prior work. Through performance analysis and comparison against existing advanced methods, we demonstrate the superiority of the proposed scheme.