Reviewerly/exHarmony huggingface dataset:
Detection and Classification of White Blood Cells through Deep Learning Techniques

Leukemia is one of the deadliest diseases in human life, it is a type of cancer that hits blood cells. The task of diagnosing Leukemia is time consuming and tedious for doctors; it is also challenging to determine the level and type of Leukemia. The diagnoses of Leukemia are achieved through identifying the changes on the White blood Cells (WBC). WBCs are divided into five types: Neutrophils, Eosinophils, Basophils, Monocytes, and Lymphocytes. In this paper, the authors propose a Convolutional Neural Network to detect and classify normal white blood cells. The program will learn about the shape and type of normal WBC by performing the following two tasks. The first task is identifying high level features of a normal white blood cell. The second task is classifying the normal white blood cell according to its type. Using a Convolutional Neural Network CNN, the system will be able to detect normal WBCs by comparing them with the high-level features of normal WBC. This process of identifying and classifying WBC can be vital for doctors and medical staff to make a decision. The proposed network achieves an accuracy up to 96.78% with a dataset including 10,000 blood cell images.
Heritage education through serious games. A web-based proposal for primary schools to cope with distance learning

&lt;p dir="ltr"&gt;&lt;span&gt;In recent years a growing amount of research has shown interest in studying how virtual reality (VR) could be relevant in many fields. In this respect, VR has gained consideration throughout many applications such as education. Among other aims for its use in education, serious games based on VR were used to promote heritage and make students experience either far or inaccessible scenarios. Until now, VR-based applications have been mainly implemented using head mounted displays (HMD), which actually reduced their circulation. This gap is particularly remarkable in the current Sars-CoV19 pandemic because students, being at home or being at school without sharing equipment, cannot exploit educational programs based on this technology. The current paper proposes a web-based platform on which VR applications could be accessed on any device, either desktop- or mobile-based. The serious game was initially set up on a computer with a specialized software using a HMD, while the process of turning it into a web-based platform is described so that the used methodology could be available to those, who would like to follow it. This project is probably also able to cope with the general aim of making inaccessible objects available to students and, thus, to make the application useful even beyond the current pandemic emergency.&lt;/span&gt;&lt;/p&gt;&lt;p dir="ltr"&gt;&lt;span&gt;&lt;br /&gt;&lt;/span&gt;&lt;/p&gt;
The Organization of Hacking Community: Criminological and Criminal Law Aspects

Digital technology is an integral part of our daily lives. Regardless of whether we have a computer at home, whether we use the possibilities of obtaining state and municipal services in digital form or simply operate electronic gadgets, society's dependence on technology is increasing. A secure digital environment enhances trust and contributes to the creation of a stable and prosperous nation. Government and the business community are also taking advantage of the technological revolution through greater adoption and use of digital technologies. Traditional forms of crime have also evolved, as criminal associations increasingly use the information and telecommunications network - the Internet - to commit cybercrimes and increase their profits. Digital crime is developing at an incredibly fast pace, and new types of criminal acts are constantly emerging. So we need to keep up with digital technologies, understand the opportunities they create for cybercriminals, and how they can be used as a tool to combat cybercrime. The active use of digital technologies in all spheres of social life in the last three decades formed a background for the emergence of a special type of criminals - the so-called hackers. Criminal groups of hackers pose a public danger because, if they unite, they are capable of planning a large-scale computer attack which could target, among other things, critically important information infrastructure objects. Besides, hacker groups have become a real danger for both governments, large corporations, the military, and for private persons. The trend for blurring the boundaries between hacker groups and organized crime, that the experts predicted a few years ago, has now become a reality. In fact, it is possible to say that a new independent type of organized crime has emerged - the hacking community. These circumstances make it necessary to develop a special norm that provides for the liability for organizing hacking community or participating in it. Such a norm will allow for a complex approach to the criminal law counteraction against such criminal groups by ensuring an adequate criminal law assessment of the actions of the organizers and coordinators of hackers organizations.
Towards Knowledge-Augmented Visual Question Answering

Visual Question Answering (VQA) remains algorithmically challenging while it is effortless for humans.Humans combine visual observations with general and commonsense knowledge to answer a question about a given image.In this paper, we address the problem of incorporating general knowledge into VQA models while leveraging the visual information.We propose a model that captures the interactions between objects in a visual scene and entities in an external knowledge source.Our model is a graph-based approach that combines scene graphs with concept graphs, which learns a question-adaptive graph representation of related knowledge instances.We use Graph Attention Networks to set higher importance to key knowledge instances that are mostly relevant to each question.We exploit ConceptNet as the source of general knowledge and evaluate the performance of our model on the challenging OK-VQA dataset.
An Enhanced Knowledge Injection Model for Commonsense Generation

Commonsense generation aims at generating plausible everyday scenario description based on a set of provided concepts. Digging the relationship of concepts from scratch is non-trivial, therefore, we retrieve prototypes from external knowledge to assist the understanding of the scenario for better description generation. We integrate two additional modules into the pretrained encoder-decoder model for prototype modeling to enhance the knowledge injection procedure. We conduct experiment on CommonGen benchmark, experimental results show that our method significantly improves the performance on all the metrics.
X-ray image denoising based on wavelet transform and median filter

Abstract This paper mainly proposed and researched based on wavelet transform, and then used the X-map denoising technique of value filter. In other words, the value image was filtered in the spatial domain, and the value filtering was used as the standard pulse (salt) noise, also used as in the wavelet domain. After the filtered image was decomposed by biorthogonal double wavelet transform, a wavelet coefficient matrix was generated, and a soft threshold quantisation process was performed on the wavelet coefficients to produce a new wavelet coefficient matrix. In the end, they used a new wavelet coefficient matrix for image reconstruction. The processing resulted that the denoising method proposed in this paper showed that the X image can be denoised, which not only reduced the X-picture-like noise but also preserved the X-picture-like details as much as possible. It also helped to enhance diagnostic accuracy and reduced the difference in reading.
Topological Insights into Sparse Neural Networks

Sparse neural networks are effective approaches to reduce the resource requirements for the deployment of deep neural networks. Recently, the concept of adaptive sparse connectivity, has emerged to allow training sparse neural networks from scratch by optimizing the sparse structure during training. However, comparing different sparse topologies and determining how sparse topologies evolve during training, especially for the situation in which the sparse structure optimization is involved, remain as challenging open questions. This comparison becomes increasingly complex as the number of possible topological comparisons increases exponentially with the size of networks. In this work, we introduce an approach to understand and compare sparse neural network topologies from the perspective of graph theory. We first propose Neural Network Sparse Topology Distance (NNSTD) to measure the distance between different sparse neural networks. Further, we demonstrate that sparse neural networks can outperform over-parameterized models in terms of performance, even without any further structure optimization. To the end, we also show that adaptive sparse connectivity can always unveil a plenitude of sparse sub-networks with very different topologies which outperform the dense model, by quantifying and comparing their topological evolutionary processes. The latter findings complement the Lottery Ticket Hypothesis by showing that there is a much more efficient and robust way to find "winning tickets". Altogether, our results start enabling a better theoretical understanding of sparse neural networks, and demonstrate the utility of using graph theory to analyze them.
Diamonds are not forever: liveness in reactive programming with guarded recursion

When designing languages for functional reactive programming (FRP) the main challenge is to provide the user with a simple, flexible interface for writing programs on a high level of abstraction while ensuring that all programs can be implemented efficiently in a low-level language. To meet this challenge, a new family of modal FRP languages has been proposed, in which variants of Nakano's guarded fixed point operator are used for writing recursive programs guaranteeing properties such as causality and productivity. As an apparent extension to this it has also been suggested to use Linear Temporal Logic (LTL) as a language for reactive programming through the Curry-Howard isomorphism, allowing properties such as termination, liveness and fairness to be encoded in types. However, these two ideas are in conflict with each other, since the fixed point operator introduces non-termination into the inductive types that are supposed to provide termination guarantees. In this paper we show that by regarding the modal time step operator of LTL a submodality of the one used for guarded recursion (rather than equating them), one can obtain a modal type system capable of expressing liveness properties while retaining the power of the guarded fixed point operator. We introduce the language Lively RaTT, a modal FRP language with a guarded fixed point operator and an `until' type constructor as in LTL, and show how to program with events and fair streams. Using a step-indexed Kripke logical relation we prove operational properties of Lively RaTT including productivity and causality as well as the termination and liveness properties expected of types from LTL. Finally, we prove that the type system of Lively RaTT guarantees the absence of implicit space leaks.
Towards the creation of a unified glossary of Russian biobanks

To carry out research projects, clinical trials and other studies in the field of personalized medicine, it is necessary to have collections of high-quality biological samples of various types. With the development of biomedical technologies, the need for large collections of biological samples will grow every year, which necessitates the creation of various biobanks for standardized collection, storage and distribution of such samples. One of the goals of the National Association of Biobanks and Biobanking Specialists is the development of a network of Russian biobanks interacting with each other at various levels, as well as the development and implementation of organizational and legal tools for its regulation. It is required not only to standardize the access and exchange of biological samples and data, but also to create a unified terminology that will be used by biobanks throughout Russia. The main aim is to create an accurate, professional and legally correct tool containing information accessible and understandable to a wide range of researchers.
BlockChain Platforms in Financial Services: Current Perspective

Abstract Background BlockChain technology was invented to support bitcoin , currently the most popular virtual currency. Objectives The purpose of this paper is to investigate contemporary BlockChain platforms in financial services. Methods/Approach An unstructured literature review has been used. Results BlockChain in financial services is mostly associated with bitcoin exchange. However, this is a partial view of both BlockChain technology and its possible adoption for financial services: in fact, many BlockChain platforms are now available and many different financial services can be effectively supported by BlockChain platforms, even though they are not based on virtual-money exchange. Furthermore, people are attracted by the concept of smart contract , i.e., a contract that is automatically executed by computer technology, without human intervention. Conclusions The contribution of this paper is twofold: first of all, we introduce the four BlockChain platforms that are now most popular, discussing how they support the smart contract concept; second, we identify some typical categories of financial services, matching each of them with the platform that provides the best support for each category.
Knowledge-Routed Visual Question Reasoning: Challenges for Deep Representation Embedding

Though beneficial for encouraging the visual question answering (VQA) models to discover the underlying knowledge by exploiting the input-output correlation beyond image and text contexts, the existing knowledge VQA data sets are mostly annotated in a crowdsource way, e.g., collecting questions and external reasons from different users via the Internet. In addition to the challenge of knowledge reasoning, how to deal with the annotator bias also remains unsolved, which often leads to superficial overfitted correlations between questions and answers. To address this issue, we propose a novel data set named knowledge-routed visual question reasoning for VQA model evaluation. Considering that a desirable VQA model should correctly perceive the image context, understand the question, and incorporate its learned knowledge, our proposed data set aims to cut off the shortcut learning exploited by the current deep embedding models and push the research boundary of the knowledge-based visual question reasoning. Specifically, we generate the question-answer pair based on both the visual genome scene graph and an external knowledge base with controlled programs to disentangle the knowledge from other biases. The programs can select one or two triplets from the scene graph or knowledge base to push multistep reasoning, avoid answer ambiguity, and balance the answer distribution. In contrast to the existing VQA data sets, we further imply the following two major constraints on the programs to incorporate knowledge reasoning. First, multiple knowledge triplets can be related to the question, but only one knowledge relates to the image object. This can enforce the VQA model to correctly perceive the image instead of guessing the knowledge based on the given question solely. Second, all questions are based on different knowledge, but the candidate answers are the same for both the training and test sets. We make the testing knowledge unused during training to evaluate whether a model can understand question words and handle unseen combinations. Extensive experiments with various baselines and state-of-the-art VQA models are conducted to demonstrate that there still exists a big gap between the model with and without groundtruth supporting triplets when given the embedded knowledge base. This reveals the weakness of the current deep embedding models on the knowledge reasoning problem.
Simulation of classroom student behavior recognition based on PSO-kNN algorithm and emotional image processing

Students’ classroom behavior recognition and emotion recognition effects directly determine the degree of teachers’ control of the classroom teaching process. At present, teachers and students belong to two groups in traditional teaching, and teachers cannot effectively mobilize students’ learning emotions. In order to improve the teaching effect, this paper combines the PSO algorithm and the KNN algorithm to obtain the PSO-KNN joint algorithm, and combines with the emotional image processing algorithm to construct an artificial intelligence-based classroom student behavior recognition model. Moreover, based on the image processing technology, this paper uses key frame detection for feature recognition, and this paper improves the recognition process based on the inter-frame similarity measurement algorithm and initial cluster center selection in the key frame extraction method of clustering. In addition, this paper analyzes the effect of the model constructed on the behavior recognition and emotion recognition of students. The research results show that the joint algorithm constructed in this paper has a high accuracy rate for students’ emotion recognition and behavior recognition, and can meet the actual teaching needs.
Reinforced Multi-task Approach for Multi-hop Question Generation

Question generation (QG) attempts to solve the inverse of question answering (QA) problem by generating a natural language question given a document and an answer.While sequence to sequence neural models surpass rule-based systems for QG, they are limited in their capacity to focus on more than one supporting fact.For QG, we often require multiple supporting facts to generate high-quality questions.Inspired by recent works on multi-hop reasoning in QA, we take up Multi-hop question generation, which aims at generating relevant questions based on supporting facts in the context.We employ multitask learning with the auxiliary task of answeraware supporting fact prediction to guide the question generator.In addition, we also proposed a question-aware reward function in a Reinforcement Learning (RL) framework to maximize the utilization of the supporting facts.We demonstrate the effectiveness of our approach through experiments on the multi-hop question answering dataset, HotPotQA.Empirical evaluation shows our model to outperform the single-hop neural question generation models on both automatic evaluation metrics such as BLEU, METEOR, and ROUGE, and human evaluation metrics for quality and coverage of the generated questions.
Chessboard and Chess Piece Recognition With the Support of Neural Networks

Abstract Chessboard and chess piece recognition is a computer vision problem that has not yet been efficiently solved. Digitization of a chess game state from a picture of a chessboard is a task typically performed by humans or with the aid of specialized chessboards and pieces. However, those solutions are neither easy nor convenient. To solve this problem, we propose a novel algorithm for digitizing chessboard configurations. We designed a method of chessboard recognition and pieces detection that is resistant to lighting conditions and the angle at which images are captured, and works correctly with numerous chessboard styles. Detecting the board and recognizing chess pieces are crucial steps of board state digitization. The algorithm achieves 95% accuracy (compared to 60% for the best alternative) for positioning the chessboard in an image, and almost 95% for chess pieces recognition. Furthermore, the sub-process of detecting straight lines and finding lattice points performs extraordinarily well, achieving over 99.5% accuracy (compared to the 74% for the best alternative).
Classifier-Based Data Transmission Reduction in Wearable Sensor Network for Human Activity Monitoring

The recent development of wireless wearable sensor networks offers a spectrum of new applications in fields of healthcare, medicine, activity monitoring, sport, safety, human-machine interfacing, and beyond. Successful use of this technology depends on lifetime of the battery-powered sensor nodes. This paper presents a new method for extending the lifetime of the wearable sensor networks by avoiding unnecessary data transmissions. The introduced method is based on embedded classifiers that allow sensor nodes to decide if current sensor readings have to be transmitted to cluster head or not. In order to train the classifiers, a procedure was elaborated, which takes into account the impact of data selection on accuracy of a recognition system. This approach was implemented in a prototype of wearable sensor network for human activity monitoring. Real-world experiments were conducted to evaluate the new method in terms of network lifetime, energy consumption, and accuracy of human activity recognition. Results of the experimental evaluation have confirmed that, the proposed method enables significant prolongation of the network lifetime, while preserving high accuracy of the activity recognition. The experiments have also revealed advantages of the method in comparison with state-of-the-art algorithms for data transmission reduction.
Development of technology for controlling access to digital portals and platforms based on estimates of user reaction time built into the interface

The paper addresses the development of technology for controlling access to digital portals and platforms based on assessments of personal characteristics of user behavior built into the interface. In distributed digital platforms and portals using personal data, big data is collected and processed using specialized applications using computer networks. In accordance with the law, the data is stored on internal corporate servers and data centers. Special attention is paid to the tasks of differentiation and control of access in modern information systems. Wide availability and mass scale of services should be accompanied by more careful control and user verification. Access control to such systems cannot be ensured only through technologies and information security tools; efficiency can be increased through software and hardware architectural solutions. The paper proposes to expand the currently developing SIEM technology (Security information and event management), which combines the concept of security event management and information security management, with blocks of user behavior analysis. As a characteristic that can be measured without overloading communication channels and is independent of the type of device used, the psychomotor reaction time is proposed, measured as the performance of actions with the interface. A technological solution has been developed for implementation in a wide range of digital platforms: banking, medical, educational, etc. The results of experimental research using a digital platform of mass psychological research are presented. For the research, data from a mass survey were used when answering (in the form of a choice from the available options) to questions about the level of education. Analysis of the reaction time data showed the possibility of standardization and the same indicators of specific users when answering different questions.