Reviewerly/exHarmony huggingface dataset:
Active Contours with Term of Smoothing: Application in Medical Imaging

The segmentation of images is to group the different pixels of the image into classes, each classgrouping similar pixels in the sense of a given criteria. The segmentation of images is, in general, verydifficult to achieve because the natural images are various, complex and the way of perceiving them variesaccording to the individuals. Because of this difficulty, several approaches have been proposed. Theapproach addressed in this article is the technique of active contours. We focus on implicit active contours;we propose an improvement for the model of Chan&amp;Vese. The application of our method on medicalimages shown they can be a tool for the image segmentation, and thus provide an aid to diagnosis for thepractitioner.
Efficient and Secure Energy Trading in Internet of Electric Vehicles Using IOTA Blockchain

The Internet of Electric Vehicle (IoEV) energy trading is where the Electric Vehicles (EVs) provide energy to vehicles, grids, community, and buildings. A scalable, efficient, secure, and best price selection scheme is needed that supports the IoEV energy trading transaction. The traditional blockchain is used in the existing researches to achieve these needs. This paper proposes an efficient and secure energy trading scheme in IoEV energy trading using the IOTA blockchain. EVs privacy protection algorithm is proposed in which somebody cannot track EV's exact position. The Stackelberg game theory technique is used to select the best seller at a given time slot and to perform the negotiation between buyers and sellers on the energy price in an off-blockchain manner. The empirical analysis shows that the proposed scheme performs better than the traditional techniques in terms of efficiency, privacy, and energy price.
Professional Self-Awareness as a Factor of Personal and Professional Self-Development of University Students

Introduction. The article analyzes the problem of professional self-awareness, and the influence of this phenomenon on the process of personal and professional self-development of students. A theoretical study of this problem has shown that professional self-awareness is one of the most important prerequisites for the self-development and self-improvement of university students. Materials and Methods. The article presents the results of an empirical study of the characteristics of self-awareness and self-development of students of the Mordovian State University. A set of methods was used: questionnaire “Ability for self-development” I. V. Zvereva, methodology “Readiness for self-knowledge and self-development” T. A. Ratanova, N. F. Shlyakhta, test “Determination of the level of development of reflexivity” by A. V. Karpov, “Questionnaire of self-attitude” by V. V. Stolin, S. R. Pantileev, methods of researching self-assessment by S. A. Budassi, test “Assessment of self-control in communication” by M. Snyder, questionnaire “The style of self-regulation of students’ behavior” by V. I. Morosanova. Results. Statistically significant correlations between the reflexive, affective and regulatory components of students’ self-awareness and the level of their personal and professional self-development were revealed. It has been found that a high level of development of students’ reflective qualities is associated with their desire for self-knowledge. The connection between self-regulation and readiness for self-knowledge and self-development determines the ability of students to carry out systematic and purposeful work on themselves. It is shown that students with high and above average levels of self-development are aware of the changes taking place in the structure of their personality in the process of vocational training. These students are characterized by higher indicators of reflexivity, self-attitude and self-esteem compared to students with low and below average levels of self-development. Discussion and Conclusion. The results obtained confirm the assumption that personal and professional self-development is conditioned by the students’ ability to reflect and realize their personal and professional qualities.
Design of Patrol Unmanned Aerial Vehicle Based on MPU9250

Abstract To improve the attitude control accuracy of quadcopter UAVs, a patrol UAV with STM32 as the main controller and MPU9250 as the attitude detection module is designed in this paper. For attitude control part, a kind of attitude control algorithm which preprocesses by extended Kalman filter algorithm is designed and then a cascade PID control algorithm is adopted. The test flight results show that the UAV can achieve functions of lifting, rolling, pitching, yawing and hovering.
Obstacle avoidance behavior of an autonomous mobile robot in a radioactive environment based on fuzzy logic

Five years after the Fukushima nuclear disaster, many robots have been developed to enter areas that are too radioactive for humans, in the event of a nuclear accident, measuring the level of radioactivity can be difficult and risky. A new detection system has been developed to come to the aid of plant operators and managers in certain emergency situations. While navigating in a radioactive environment and looking for sources.to facilitate this operation and make it safer, we have to assess the autonomy of this system, which is a robot operating in a radioactive environment based and to guarantee autonomy and intelligence for obstacle avoidance behavior, we will use a tool such as Fuzzy Logic (LF). In this article, we present an obstacle avoidance approach using this tool. The approach is validated by a virtual simulation on Matlab Simulink.
Block Chain Based Supply Chain Management for Counterfeit Drugs in Pharmaceutical Industry

This paper provides a detailed overview of the block chain technology and how it can be used to build a foolproof system in eliminating counterfeit products in pharmaceutical industries. From the last decade, pharmaceutical companies are facing difficulties in tracking their products during the supply chain process, allowing the counterfeiters to add their fake medicines into the market. Counterfeit drugs are analysed as a very big challenge for the pharmaceutical industry worldwide. As indicated by the statistics, yearly business loss of around $200 billion is reported by US pharmaceutical companies due to these counterfeit drugs. These drugs may not help the patients to recover the disease but have many other dangerous side effects. According to the World Health Organization (WHO) survey report, in under-developed countries every 10th drug use by the consumers is counterfeit and has low quality. Hence, a system that can trace and track drug delivery at every phase is needed to solve the counterfeiting problem. The block chain has the full potential to handle and track the supply chain process very efficiently. In this paper, we have proposed and implemented a block chain based drug supply chain management. Our proposed system block chain-based drug supply chain management for consumers. In this drug supply chain management the system is deployed using Hyper ledger fabrics which is capable of continuously monitor and track the drug delivery process in the smart pharmaceutical industry. Finally, we also perform several tests to check the efficiency and usability of our proposed system.
HCI Based In-Cabin Monitoring System for Irregular Situations with Occupants Facial Anonymization

Absence of human in-charge in fully autonomous vehicles (FAVs) impose multi-pronged in-cabin monitoring in real-time to monitor any irregular situation. Moreover, occupant’s visual information (such as video, images) transmission from in-cabin of vehicle to the data center and control-room is required for various in-cabin monitoring tasks. However, this information transfer may cause a substantial threat to the occupants. Though applying some patch on face of the occupants protects personal information however, simultaneously it deteriorates the important facial information which is very crucial in monitoring various tasks such as emotion detection. Therefore, a human-computer-interaction (HCI) is required to manage this trade-off between facial information and facial anonymization without any important information loss. Also, this HCI based in-cabin monitoring system should be implicit to retain the users trust in the intelligent transportation system (ITS). In this paper, we have proposed generative adversarial network (GAN) based in-cabin monitoring approach for emotion detection in FAV. In this method, we proposed to generate an artificial (virtual) face having real-facial expressions to provide facial anonymity to the occupants while monitoring FAV cabin. Therefore, this method provides anonymous facial information which is essentially required in detection and monitoring tasks to avoid irregular situations. We have used both publicly available and our own in-cabin dataset to assess our proposed approach. Our experiments show satisfactory results.
Binary Modal Companions for Subintuitionistic Logics

The weak subintuitionistic logicSub-intuitionistic logics WF, for which no standard unary modal companionModal companion is known, is found to have a strict implicationStrict implication logic as its binary modal companionModal companion. It is also shown that for all modal logicsModal logic extending the weak logic EN, classical modal logicClassical modal logic with necessitation, a strict implicationStrict implication logic exists which is essentially equivalent to it. This logic extends a basic strict implicationStrict implication logic plus an axiom U, and conversely each such logic corresponds to a modal logicModal logic extending EN. Among other things this means that any subintuitionistic logicSub-intuitionistic logics which has a modal companionModal companion has a strict implicationStrict implication companion as well.
Robust semi-supervised non-negative matrix factorization for binary subspace learning

Abstract Non-negative matrix factorization and its extensions were applied to various areas (i.e., dimensionality reduction, clustering, etc.). When the original data are corrupted by outliers and noise, most of non-negative matrix factorization methods cannot achieve robust factorization and learn a subspace with binary codes. This paper puts forward a robust semi-supervised non-negative matrix factorization method for binary subspace learning, called RSNMF, for image clustering. For better clustering performance on the dataset contaminated by outliers and noise, we propose a weighted constraint on the noise matrix and impose manifold learning into non-negative matrix factorization. Moreover, we utilize the discrete hashing learning method to constrain the learned subspace, which can achieve a binary subspace from the original data. Experimental results validate the robustness and effectiveness of RSNMF in binary subspace learning and image clustering on the face dataset corrupted by Salt and Pepper noise and Contiguous Occlusion.
S2NOW: Secure Social Network Ontology Using WhatsApp

Communicating and sharing the ideas and feelings between human beings form the basis of social network. With the advent of different social network tools and applications, the task of networking between people has reached to next level, crossing bridges and boundaries. However, is there a connection or linkage between the frequent and sometime just random exchange of text messages between people using social network-based tools and applications and also are these messages secure? Or are they vulnerable to threat and attacks? Literature survey shows that very little work has been done in this direction. With this intention, in the current research work, a real-time case study has been taken identifying the creation of the social network between a group of persons who frequently chat using the WhatsApp Messenger application based on their common hobbies, choices, and areas of interest and applying cryptographic techniques to ensure the security of data (chats). The proposed study uses the Caesar Cipher Cryptographic Technique and newly proposed Block Quadra Crypto Technique for encrypting the chats and showcases a comparison between the two techniques. The results are encouraging and exceed the expectation.
An Integration Convolutional Neural Network for Nuclei Instance Segmentation

Automated detection and segmentation of every nucleus in microscopy images is a fundamental task in biomedical research and clinical practice, including nuclear morphology analysis, cancer grading and prognosis predicting. However, this task is still challenging because of the nuclear atypical in shape, size and internal organisation. In this paper, we propose a nuclei instance segmentation method with the aim of jointing detection and segmentation simultaneously. The method builds on a two-stream Convolutional Neural Network(CNN) architecture that explicit utilize a single shot multi-box detector with Feature Pyramid Network in one stream for obtaining the nuclear location information and keep an U-net in the other stream for nuclei instance segmentation. Furthermore, the recurrent, residual and attention mechanisms are integrated for focusing on the useful information. The approach utilizes the strengths of the Recurrent Convolutional Neural Network (RCNN), the Attention Network and the Residual Network. The experiments demonstrate that the proposed nuclei instance segmentation approach outperforms prior state-of-the-art methods, and could be generalized across variety of nuclear type, magnification and imaging modality.
Will You Be My Quarantine

The quarantine situation inflicted by the COVID-19 pandemic has left many people around the world isolated at home. Despite the large variety of mobile device-based self exercise tools for training plans, activity recognition or repetition counts, it remains challenging for an inexperienced person to perform fitness workouts or learn a new sport with the correct movements at home. As a proof of concept, a home exercise system has been developed in this contribution. The system takes computer vision and inertial sensor data recorded for the same type of exercise as two independent inputs, and processes the data from both sources into the same representations on the levels of raw inertial measurement unit (IMU) data and 3D movement trajectories. Moreover, a Key Performance Indicator (KPI) dashboard was developed for data import and visualization. The usability of the system was investigated with an example use case where the learner equipped with IMUs performed a kick movement and was able to compare it to that from a coach in the video.
Application of An Improved Artificial Bee Colony Algorithm

Abstract An improved artificial bee colony algorithm which overcome the shortcomings, including slow convergence speed, easily falling into local optimum value, Mechanism of other bionic intelligent optimization algorithms is put forward. The algorithm with number theory of uniform distribution of good point set, first established algorithm initialization model, in order to maintain the diversity of the population; Then, the location is updated iteratively to maintain the diversity of the population and prevent the algorithm from falling into the local optimum. Finally, the seven standard test functions are selected to carry out the experiment and simulation. The results show that compared with the ABC algorithm, GABC algorithm and so on. The algorithm (GCABC) the convergence speed and accuracy have been improved significantly, and to improve the overall searching ability, effectively avoid the population into a local optimum, to solve the problem of multi-modal optimization.
Focus-and-Context Skeleton-based Image Simplification using Saliency Maps

Medial descriptors offer a promising way for representing, simplifying, manipulating, and compressing images. However, to date, these have been applied in a global manner that is oblivious to salient features. In this paper, we adapt medial descriptors to use the information provided by saliency maps to selectively simplify and encode an image while preserving its salient regions. This allows us to improve the trade-off between compression ratio and image quality as compared to the standard dense-skeleton method while keeping perceptually salient features, in a focus-and-context manner. We show how our method can be combined with JPEG to increase overall compression rates at the cost of a slightly lower image quality. We demonstrate our method on a benchmark composed of a broad set of images.
Cybermigration: Connections, Human Mobility and Media Culture. An Italian Case Study

The migration process, dependent on international interconnections and the symbolic contents of the media, now sees a new protagonist, a new social subject-nomadic, cosmopolitan, capable of redefining himself, crossing geographical and cultural barriers, sustained by the power of electronic (and digital) media, able to imagine the future and himself within new environments (online and offline), exploiting opportunities and running significant risks (fake news or digital trafficking of migrants, surveillance, for example).This individual is the cybermigrant.Cybermigration is a new form of human mobility in the digital environment and focuses in particular on two aspects: social-digital capital and online security (Buoncompagni, 2020).This research starts from Bordieu's line of thought and highlights several theories of particular importance, focusing in particular on a new form of "social-digital capital" (Boyd & Ellison, 2007;De Feo & Pitzalis, 2015) that can be applied to migration and the various forms of communication involved.Furthermore, the social use of media by migrants before and after their journey to Italy is investigated.The methodology mainly used is that of the focus group.
An Ensemble Deep Learning Architecture for Multilabel Classification on TI-RADS

In recent years, thyroid nodule is being one of the most common nodular lesions. Ultrasonography is widely used in the clinical diagnosis of thyroid nodules. With the development of artificial intelligence, there emerge great progress in medical image diagnosis. Clinically, physician diagnose malignant or benign by many pathological features. In this work, we propose an ensemble architecture to resolve multi-label problem, which integrate three methods to extract features on thyroid nodule for ultrasound images. They are EfficientNet, feature engineering and feature pyramid network. We consider five kinds of pathological features quantified by Thyroid Imaging Report and Data System (TI-RADS). In the experiments, we use two datasets. One includes 587 original ultrasound images on thyroid nodules collected from the local health physical center of a 3A hospital. The other is a public dataset in the MICCAI 2020 competition. It contains 3644 ultrasound images of thyroid images. We use receiver operating characteristic curve (ROC) to evaluate the model, the area under curve (AUC) on every kind of features. The experimental results show that the proposed method can effectively assist doctors in diagnosing ultrasound images of thyroid nodules.
Drone-Based Monitoring and Redirecting System

Drones are discussed since the early 1990s. Nowadays, there is a wide range of applications. State of the artwork states mobility, positioning, propagation models, pattern movements, data collection, federated learning etc. features in the present scenario. This work presents case studies for the traffic monitoring prototype. In traffic monitoring, different traffic engineering equipment will be explored that can be integrated with drones through the Internet of Things (IoTs). Drones will be enabled through multiple means for traffic monitoring. Here, drones can collect data from sensors placed alongside the roads or they can use a video/image-based system. Opportunities for both types of systems will be explored in detail. This work also explores the possibilities to measure on-road vehicles’ features, movement properties, building their profiles, constructing databases, analyzing, and visualization systems. The vehicle’s feature identification starts from identifying the number plates, reading system, and sharing the collected information to the centralized database through drones. The possibilities of parallel processing in drone-based infrastructure are explored to shred the load over drones and efficient use of cloud infrastructure for experimental results that show significant accuracy, reliability, and performances. Further, the possibilities of integrating security primitives and protocols are explored. These primitives and protocols can be identified based on drones’ resources. Lightweight primitives and protocols will be explored that reduces the hardware cost and give an efficient performance with high-security standards. The primitives and protocols take care of security properties like confidentiality, integrity, availability, authentication, and non-repudiation. Additionally, security measures with all primitives will be taken care of at data storage, processing, and propagation points. To control the complete infrastructure, a control room strategy can be designed. This strategy narrates the man-power requirements, operation, and duties.
Illuminating tremors in the deep

Telecommunication cables on the seafloor monitor seismic activity
Blind Tone-mapped Image Quality Assessment and Enhancement via Disentangled Representation Learning

For compatibility with existing low dynamic range (LDR) display devices, the tone mapping operator (TMO) is widely applied to the high dynamic range (HDR) image, which inevitably leads to visual quality degradation. Various blind image quality assessment models have been developed to quantity the distortion degrees across different HDR images. However, these models only extract the quality-aware features and serve as a selector for different TMOs, which excludes visual content information and fails to conduct an end-to-end image enhancement towards a desired quality score. In this paper, we propose to jointly conduct blind tone-mapped image quality assessment and enhancement via disentangled representation learning. An encoder is firstly used to map the input image into the general feature space. Then, two branches are separately developed to extract the quality-aware and content-aware latent representations from the general feature, which are supervised with the quality score and image reconstruction constraints, respectively. Meanwhile, these two branches are also coupled with the adaptive instance normalization, which enables our model to flexibly modify the image towards any desired quality score. Extensive experiments confirm the effectiveness of the proposed method.
On the Diophantine Equation (4^n)^x − P^y = Z^2

In this paper, the Diophantine equation (4n ) x − p y = z 2 , where p is an odd prime, n ∈ Z + and x, y, z are non-negative integers, has been investigated to show that the solutions are given by {(x, y, z, p)} = {(k, 1, 2 nk − 1, 2 nk+1 − 1)} ∪ {(0, 0, 0, p)}.
Automatic Classification of NVST Short-exposure Data Based on Deep Learning

Abstract The New Vacuum Solar Telescope is one of the most important solar telescopes in China. However, in the process of reconstructing high-resolution solar data, the data may be distorted by thin film interference fringes. In this paper, an automatic classification method based on deep learning is proposed to distinguish fringe-contained data and fringe-free data, employing the Adaptive Wavelet Transform to construct the sample data set while transfer learning is utilized to train the classification model. The experimental results show that classification accuracy of the proposed method can reach up to 99.3%. This proposed method can make the high-resolution reconstruction pipeline run automatically whether the solar data contains fringes or not.