Reviewerly/exHarmony huggingface dataset:
Severity Grade Recognition for Nasal Cavity Tumours Using D閏or CNN

Nasal cavity and paranasal sinus tumours that occur in the respiratory tract are the most life-threatening disease in the world. The human respiratory tract has many sites which has different mucosal lining like frontal, parred, sphenoid and ethmoid sinuses. Nasal cavity tumours can occur at any different mucosal linings and chances of prognosis possibility from one nasal cavity site to another site is very high. The paranasal sinus tumours can metastases to oral cavity and digestive tracts may lead to excessive survival complications. Grading the respiratory tract tumours with dysplasia cases are more challenging using manual pathological procedures. Manual microscopic biopsy tissue analysis is time consuming and chances of occurring interobserver variability problems are very high. To avoid nasal cavity tumour grade misclassification a State-Of-The-Art Automated Décor CNN model is proposed. The automated nasal cavity tumour grading classification model is designed to troubleshoot the stain artifact present in the biopsy tissue samples. Major nasal cavity sites are analysed to find the abnormal epithelium. The automated morphological characteristic findings with décor CNN model in biopsy images are promising. The model achieves the classification accuracy of 98% with less training, testing and validation errors compared to other transfer learning convolution neural network models.
A Fast Heterogeneous Approach to Enhanced Blockchain Attack Resilience and Mitigation

We are introducing a new computational model for heterogeneous architectures for performing enhanced security validations and processing of transactions on-chain, a model applicable to both permissioned and permissionless blockchains. By proposing an efficient storage model for the Merkle trees involved in the transaction and security validation processing, and combining it with an efficient heterogeneous approach for processing it, we are outlining the improved computational advantages to ensuring attack resilience on-chain, which surpasses the performance of traditional CPU-based approaches by a significant factor.
Recommender for Channel Pruning with Weakly Supervised Meta Learning

The switchable neural network makes it feasible to deploy a deep neural network to a specific hardware platform by adjusting architectures but without retraining parameters. However, searching out the suitable architecture to adjust is time consuming and computation-tense. To accelerate searching architectures, this paper designs a recommender which takes one round to search and evaluate without repeating. In training stages, the recommender is alternately trained with a meta network which offers the recommender gradients as weakly supervision. In searching stages, the trained recommender extracts features from a specified layer’s outputs, and generates suggested channels to prune for every layer. The recommender is deployed and evaluated on three common classification neural networks, including ResNet, MobileNet-v1 and MobileNet-v2. Experimental results validate that the proposed recommender is able to obtain compressed architectures whose accuracies surpass the switchable neural network’s accuracy at a speed faster than evolutionary algorithm based searching methods.
A Dynamic Multi-criteria Multi-engine Approach for Text Simplification

In this work we present a multi-criteria multi-engine approach for text simplification. The main goal is to demonstrate a way to take advantage of a pool of systems, since in the literature several systems have been proposed for the task, and the results have been improving considerably. Note though, that such systems can behave differently, better or worse than the other ones, according to the input. For this reason, in this work we investigate the benefits of exploiting multiple systems at once, in a single-engine, in order to select the most appropriate simplification output from a pool of candidate outputs. In such an engine, a multi-critera decision making approach selects the final output considering simplicity and similarity scores, by comparing the candidates with the input. Results on both the Turk and WikiSmall corpora indicate that the proposed framework is able to balance the trade-off between bilingual evaluation understudy (BLEU), system output against references and against the input sentence (SARI), and Flesch reading ease scores for existing state-of-the art models.
Dynamic Tuning and Weighting of Meta-learning for NMT Domain Adaptation

Neural machine translation (NMT) systems fall short when training data is insufficient. For low-resource domain adaptation, meta-learning has proven to be an effective training scheme. It aims to find an optimal initialization that is easily adaptable to new domains. However, it is assumed that samples contribute equally in tasks and tasks contribute equally in the training task distribution, which deteriorates the performance of the meta-model. In the inner loop, we propose the dynamic tuning strategy to distinguish the tasks' adapting abilities and weight the loss according to the representativeness to discriminate tasks from the same domain. In the outer loop, to measure effects of each task on meta parameters, we calculate uncertainty-aware confidence and assign weights on meta-updating steps. Experiments show that the proposed approaches gain stable improvements in all domains (+1.35 BLEU points in maximum). We also analyze the ability of our strategies to alleviate domain imbalance in non-ideal settings.
ORGANIZATION OF REMOTE ONLINE TRAINING IN PHYSICAL EDUCATION UNIVERSITIES OF YAKUTIA DURING THE PANDEMIC

The article presents the organization of remote online training for students of the Institute of Physical Culture and Sports of the North-Eastern Federal University (IFCiS NEFU) and the Churapchinsky State Institute of Physical Culture and Sports (CHIPCIS) by means and technologies of online distance learning for the period of the worldwide quarantine due to COVID-19 (April 2020-April 2021). These events affected all areas of higher education in conditions of self-isolation and quarantine measures in the use of distance learning at all levels of education. The experiment involved 220 students from the two above-mentioned physical education universities in Yakutia. The study was conducted using the survey method developed by one of the authors of the article, taking into account the specifics of the organization of online learning in the regional education system with statistical processing of the data of the research results. The authors substantiate the practical significance of the study in the context of the organization of online distance learning using software tools and a distance learning platform in the regional system of physical education. The materials of the article are of practical value for teachers and researchers on the problems of training teachers and teachers in the field of e-learning implementation, taking into account the scientific and methodological support of the educational process
Image Enhancement by Gain-Limited Histogram Equalization

This study is devoted to the problem of improving digital image and video in real-time applications. This paper looks at the task of enhancing images in automatic mode without distortions and loss of information. This work addresses the challenge of improving the effectiveness of image enhancement based on the use of the technique of clipped histogram equalization. To that end, we offer a new technique to image enhancement by gain-limited histogram equalization (GLHE). The proposed GLHE technique is based on a new approach to normalizing the result of clipped histogram equalization. This approach improves the efficiency of improving complex images without the appearance of undesirable distortions and artifacts. The proposed GLHE technique is intended to be used in real-time applications to normalize and improve video content.
A Comprehensive Attention-Based Model for Image Captioning

Kumar, Vinod Dahiya, Abhishek Saini, Geetanjali Sheokand, SahilImage captioning/automatic image annotation is referred to as description of image in text according to the contents and properties observed in a picture. It has numerous implementations such as its utilisation in virtual assistants for people with visual impairment, for social media and several other applications in computer vision and deep learning. Another interesting application is that a video can be explained frame by frame by image captioning (considering it to be carousel of images). In this paper, we have used an encoder–decoder architecture along with attention mechanism for captioning the images. We have used layers of CNN in the form of an encoder and that of RNN as decoder. We used Adam optimiser which gave the best results for our architecture. We have used Beam Search and Greedy Search for evaluating the captions. BLEU score was calculated to estimate the proximity of the generated captions to the real captions.
WORDA: A Winograd Offline-Runtime Decomposition Algorithm for Faster CNN Inference

Convolutional Neural Networks (CNNs) have demonstrated impressive performance in recent times and have shown a wide range of applicability. The deployment of CNNs on resource-constrained edge devices for inference still proves challenging due to the computation, memory, energy, and band-width requirements of CNNs. To address these issues, FPGAs are commonly used to implement CNNs because of their high flexibility and low power consumption. The Winograd convolution algorithm can be used to further reduce the computation requirements of a convolution operation. This paper proposes the Winograd Offline-Runtime Decomposition Algorithm (WORDA), which provides an efficient approach to performing Winograd convolution to achieve low computation latency. In this work, WORDA is used to design convolution layers for CNN accelerators on FPGA for two CNN architectures, namely LeNet and AlexNet, using Vivado HLS (High Level Synthesis). The state-of-the-art comparison shows a 58.3% decrease in latency while only incurring a constant increase in BRAMs, no change in DSPs, and a 122% increase in flip-flops (FFs) and lookup tables (LUTs) usage when using filters of size 5 × 5.
Multi-objective Approach for Deep Learning in Classification Problems

We study the possibility of using a distributed multi-objective differential evolutionary algorithms with tabu mutation (DMD+) to optimize classification deep learning models. We discuss conflicts between three metrics used for evaluating the quality of some classifiers: an accuracy, F1-score, and Area Under the ROC Curve. Because a smart city is a place where economic mechanisms are intensively supported by modern computational methods, including deep learning, we select some datasets to verify the quality of designed classifiers. As a result, we develop an effective approach for determining the key parameters of Convolution Neural Networks and Long Short Term Memory models with using computing cloud.
Dempster-Shafer Approximations and Probabilistic Bounds in Statistical Matching

Many economic applications require to integrate information coming from different data sources. In this work we consider a specific integration problem called statistical matching, referring to probabilistic distributions of Y|X, Z|X and X, where X, Y, Z are categorical (possibly multi-dimensional) random variables. Here, we restrict to the case of no logical relations among random variables X, Y, Z. The non-uniqueness of the conditional distribution of (Y, Z)|X suggests to deal with sets of probabilities. For that we consider different strategies to get a conditional belief function for (Y, Z)|X that approximates the initial assessment in a reasonable way. In turn, such conditional belief function, together with the marginal probability distribution of X, gives rise to a joint belief function for the distribution of \(V = (X,Y,Z)\).
Sequencing Universal Quantum Gates for Arbitrary 2-Qubit Computations

With recognition of quantum computer's enormous computational ability, it is of paramount importance to develop fault-tolerant quantum computing systems for their practical use. Recently, it has been shown that fault-tolerant systems can be achieved using a small set of basic quantum operations. This, however, incurs technical difficulties in finding an optimal sequence of basic operations toward a specific target computation and may limit possible quantum computations. In this work, we aim to achieve arbitrary target quantum computations under the restriction of four universal quantum gates of Pauli-X, -Y, -Z and SWAP. We develop two gate-sequence search methods based on the fidelity measure and deep neural networks. We verify the performance of our proposed methods through numerical results comparing total search space and the number of searched nodes.
MMNet: Multi-granularity Multi-mode Network for Item-Level Share Rate Prediction

Item-level share rate prediction (ISRP) aims to predict the future share rates for each item according to the meta information and historical share rate sequences. It can help us to quickly select high-quality items that users are willing to share from millions of item candidates, which is widely used in real-world large-scale recommendation systems for efficiency. However, there are several technical challenges to be addressed for improving ISRP's performance: (1) There is data uncertainty in items' share rate sequences caused by insufficient item clicks, especially in the early stages of item release. These noisy or even incomplete share rate sequences strongly restrict the historical information modeling. (2) There are multiple modes in the share rate data, including normal mode, cold-start mode and noisy mode. It is challenging for models to jointly deal with all three modes especially with the cold-start and noisy scenarios. In this work, we propose a multi-granularity multi-mode network (MMNet) for item-level share rate prediction, which mainly consists of a fine-granularity module, a coarse-granularity module and a meta-info modeling module. Specifically, in the fine-granularity module, a multi-mode modeling strategy with dual disturbance blocks is designed to balance multi-mode data. In the coarse-granularity module, we generalize the historical information via item taxonomies to alleviate noises and uncertainty at the item level. In the meta-info modeling module, we utilize multiple attributes such as meta info, contexts and images to learn effective item representations as supplements. In experiments, we conduct both offline and online evaluations on a real-world recommendation system in WeChat Top Stories. The significant improvements confirm the effectiveness and robustness of MMNet. Currently, MMNet has been deployed on WeChat Top Stories.
A Coarse-to-Fine Object Tracking Based on Attention Mechanism

Abstract In the visual object tracking task, although the convolutional neural network has excellent performance, it is difficult to learn the global and long-range semantic information interaction due to the inherent locality of convolution. Simultaneously, the existing template updating methods focus on the target position of the next frame, which will integrate more irrelevant background and cause tracking instability. Therefore, to address this issue, inspired by Transformer, in this paper, we propose a coarse-to-fine tracking method based on the attention mechanism (CTFT). First, employing swin transformer block instead of the original convolutional network backbone to achieve self-attention from local to global. Second, using attention to effectively combine template and search region features in the part of feature fusion, and explore the possibility of using attention mechanism as the backbone in object tracking for the first time. Finally, a tracking strategy from coarse to fine is proposed. In the offline coarse tracking stage, the initial estimation of the target object is generated to obtain the coarse regress bounding box of the target. In the online fine tracking stage, we make use of the coarse regress bounding box to expand the corresponding object frame, and use the segmentation to get the fine regress bounding box of the object. Experiments on challenging benchmarks including GOT-10k, LaSOT, VOT2018, VOT2019, UAV123 and OTB-100 demonstrate that the proposed CTFT outperforms many state-of-the-art trackers and achieves leading performance.
Synergy-Net: Artificial Intelligence at the Service of Oncological Prevention

In recent years the constant development of diagnostic techniques has contributed to improving the prognosis of many diseases. Among all, oncological diseases remain those in which a correct and early diagnosis can not only significantly improve the patient’s quality of life but also impact the effectiveness of the therapy itself. Artificial Intelligence can provide valuable aid to this need through the development of predictive models to support the physicians in the diagnosis of the disease. The project “Synergy-Net: Research and Digital Solutions in the Fight Against Oncological Diseases”, born from the collaboration between the Department of Medical and Advanced Surgical Sciences of the University of Campania “L. Vanvitelli”, the National Informatics Inter-University Consortium (National Informatics Inter-University Consortium), Lab ITEM “C. Savy” and Bollino IT S.p.A., aims at the realisation of a technological platform to support the early oncological diagnosis based on the integration of an interoperable communication and clinical data management system leveraging AI. The project has a deeply interdisciplinary nature (lung cancer, breast cancer, colorectal cancer, gastrointestinal carcinomas, prostate cancer, thyroid cancer and malignant skin tumours), which requires the collaboration of very different professionals, including general practitioners, specialist doctors, radiologists, surgeons, pathologists, molecular biologists and oncologists, as well as the support of a team of researchers for aspects related to machine learning and expert system development in health care. The core of the project consists in the creation of a Computer-Aided Detection/Diagnosis (Computer-Aided Detection/Diagnosis) system that, based on Machine Learning and Deep Learning techniques, assists the operator in the analysis of screening data such as anamnestic information, blood tests, instrumental and diagnostic images. The assistance to the operator is achieved by suggesting the portions of information (e.g. regions in an X-ray image) on which to focus more attention. The use of the system will help the physician in the development of increasingly personalised diagnostic and therapeutic strategies, meeting the criteria of tailored therapy/surgery, a desirable objective of any cancer prevention program.
THE MODEL OF MOTIVATING THE HIGHER EDUCATION FACULTIES OF UKRAINE BY EMPLOYING IT IN KNOWLEDGE SHARING TO IMPROVE ACADEMIC PUBLICATIONS

Research and development are central to national and civilizational achievements. Research and development in any country is measured by the results of research, such as scientific publications. Scientific publications become the main criterion for evaluating teachers, as well as research and teaching staff of higher education institutions and research institutions. Knowledge is the most important resource in universities. Useful knowledge can improve the performance of employees in the organization. The exchange of knowledge or information exchange between employees is important for scientists and practitioners. Acquisition of knowledge, exchange of knowledge and use of knowledge are three types of knowledge management. Higher education in Ukraine seeks to strengthen research as a key component of the development of the nation and its universities. In recent years, higher education in Ukraine has initiated many projects to improve research on the productivity of research and teaching staff in Ukrainian universities. Ukrainian higher education seeks to automate all research activities to increase the effectiveness of research. This article discusses the impact of knowledge use. Joint use to improve the scientific publications of scientists and teachers of Ukrainian universities. In addition, this study identifies factors that lead to an increase in the level of knowledge exchange in Ukrainian universities. The author conducted an online interview with 350 representatives of the scientific and pedagogical staff of the Taras Shevchenko National University of Kyiv, studied scientific articles and websites of academic institutions to determine important indicators for measuring the productivity of scientific research. The results show that computer- mediated communication and the nature of knowledge are the main factors that positively affect the exchange of knowledge in Ukrainian universities. The author also concluded that scientific publications are a key indicator for measuring research performance. In addition, the exchange of academic knowledge and indirect communication significantly increases the productivity of research in Ukrainian universities.
Better Learning and Fusing Multi-Granularity Context Representations for Relevant Response Generation

In open-domain multi-turn dialogue, capturing related information from conversational history is key to generating relevant responses. In this paper, we propose a method to extract and fuse related context information at different levels of granularity with a word-level encoder, an utterance-level encoder, and a fusion decoder. The word-level encoder aims to obtain a context-aware representation for the last utterance, called a post, by attending the post to the context utterances at the word level. The utterance-level encoder is used to model the correlation among context utterances with the self-matching attention mechanism. Finally, the multi-granularity context representations from the two encoders are fused via the fusion decoder to improve the relevance of generated responses. Experimental results on the Ubuntu Dialog Corpus and Cornell Movie Dialog Corpus show that our methods significantly outperform all the strong baselines.
A Framework for Intelligent Fitness Guiding System

Using fitness equipment for workout is becoming increasingly popular. High expertise and skills were required for the user to manipulate the equipment for exercising. Injuries were commonly generated by applying an awkward posture to exertion due to lack of professional guidance. This study therefore proposed an intelligent guiding system for instructing users for fitness. The system used 3D computer vision technology to identify the body contour of the user and established a video-base of virtual fitness posture model. This model digitized the user’s actions to accurately obtain fitness posture data in real time, and comparison with the standard motion model through the in-depth algorithm calculation to identify whether the fitness posture is standard. Providing feedbacks to the bodybuilder in time, correct the bodybuilder's motions, and effectively ensure that people are efficient and safe in weight training.
Cyber-hate versus Freedom of Speech: is it possible to fight hate speech in the Internet while preserving the users’ freedom of speech?

This article seeks to raise awareness around hate speech and cyber-hatred in particular, pointing out the victims and perpetrators of hate crimes, highlighting the main protective legislation against this practice, and suggesting measures to combat hate speech while guaranteeing the basic principle of freedom of speech.
Approximate fixed point property and unions of convex disks in the digital plane

We use results of [6] to enlarge our knowledge of the approximate fixed point property (AFPP) for digital images in Z2. In particular, we study conditions under which the union of two convex digital disks has the AFPP.