Issue Of Recognition Research Articles

Issues of Authors’ Rights Protection of the Service Objects of Patent Rights

Most of the objects of patent law are created as service objects. At the same time, despite a rather detailed regulation of the legal regime of service inventions and other objects of intellectual rights in the Civil Code of the Russian Federation and clarification in judicial practice, problems both in terms of legal regulation and in terms of law enforcement in this area remain unsettled. The paper discusses the reasons for the refusal to recognize and regulate as service objects the objects of related rights, the problems of the relationship between the regime of service secrets of production (know-how) and objects of patent law, in respect of which the employer decided to keep information secret, the issues concerning the legal regime of service objects created with the help of artificial intelligence, as well as ensuring the author’s right for remuneration for a service object of patent law based on the amendments to Federal Law No. 456-FZ dated 22 December 2020 «On Amendments to Parts Two and Four of the Civil Code of the Russian Federation and the Invalidation of Legislative Acts (Certain Provisions of Legislative Acts) of the Russian Federation», etc. Conclusions are aimed at protecting the rights of authors-employees, in particular at the need to recognize authorship for an employee who created artificial intelligence, etc. Taking into account different points of view and the law enforcement practice, the author draws special attention to the issues of recognition of the official result of intellectual work created by the person superior to the employer operating on the basis of an employment contract (in the absence of a task, notification of the employer, etc.), which is established on the basis of the relationship between the work carried out by the director of the work and the creation of this object, as well as co-ownership of the exclusive right to a service object created by employees and authors who are not employees.

Mammographic mass recognition using feature reuse and channel attention mechanism

AbstractMass recognition is one of the important steps in early diagnosis for breast cancer. However, one of the major issues in mass recognition is low sensitivity. In this study, a novel convolutional neural network using feature reuse and channel attention mechanism is proposed and evaluated for mass recognition in mammograms. To improve representational power of network, our model takes advantage of two advanced techniques: one is feature reuse for exploiting the potential of the network through directing connections, and the other is the channel attention mechanism for modelling the interdependencies between channels by integrating the channel‐wise information. The performance of our model is evaluated on DDSM and the values of the evaluation are 92.05%, 91.25%, 91.25%, 92.71%, 0.91 and 0.96 in terms of accuracy, precision, sensitivity, specificity, F1_score and AUC, respectively. The experimental results demonstrate the effectiveness of the proposed model to mass recognition task in mammograms.

Automatic recognition of sonar targets using feature selection in micro-Doppler signature

Currently, the use of intelligent systems for the automatic recognition of targets in the fields of defence and military has increased significantly. The primary advantage of these systems is that they do not need human participation in target recognition processes. This paper uses the particle swarm optimization (PSO) algorithm to select the optimal features in the micro-Doppler signature of sonar targets. The micro-Doppler effect is referred to amplitude/phase modulation on the received signal by rotating parts of a target such as propellers. Since different targets' geometric and physical properties are not the same, their micro-Doppler signature is different. This Inconsistency can be considered a practical issue (especially in the frequency domain) for sonar target recognition. Despite using 128-point fast Fourier transform (FFT) for the feature extraction step, not all extracted features contain helpful information. As a result, PSO selects the most optimum and valuable features. To evaluate the micro-Doppler signature of sonar targets and the effect of feature selection on sonar target recognition, the simplest and most popular machine learning algorithm, k-nearest neighbor (k-NN), is used, which is called k-PSO in this paper because of the use of PSO for feature selection. The parameters measured are the correct recognition rate, reliability rate, and processing time. The simulation results show that k-PSO achieved a 100% correct recognition rate and reliability rate at 19.35 s when using simulated data at a 15 dB signal-to-noise ratio (SNR) angle of 40°. Also, for the experimental dataset obtained from the cavitation tunnel, the correct recognition rate is 98.26%, and the reliability rate is 99.69% at 18.46s. Therefore, the k-PSO has an encouraging performance in automatically recognizing sonar targets when using experimental datasets and for real-world use.

Learning attention-guided pyramidal features for few-shot fine-grained recognition

Few-shot fine-grained recognition (FS-FGR) aims to distinguish several highly similar objects from different sub-categories with limited supervision. However, traditional few-shot learning solutions typically exploit image-level features and are committed to capturing global silhouettes while accidentally ignore to exploring local details, resulting in an inevitable problem of inconspicuous but distinguishable information loss. Thus, how to effectively address the fine-grained recognition issue given limited samples still remains a major challenging. In this article, we tend to propose an effective bidirectional pyramid architecture to enhance internal representations of features to cater to fine-grained image recognition task in the few-shot learning scenario. Specifically, we deploy a multi-scale feature pyramid and a multi-level attention pyramid on the backbone network, and progressively aggregated features from different granular spaces via both of them. We then further present an attention-guided refinement strategy in collaboration with a multi-level attention pyramid to reduce the uncertainty brought by backgrounds conditioned by limited samples. In addition, the proposed method is trained with the meta-learning framework in an end-to-end fashion without any extra supervision. Extensive experimental results on four challenging and widely-used fine-grained benchmarks show that the proposed method performs favorably against state-of-the-arts, especially in the one-shot scenarios.

A Novel Approach of BRELU RESNET Based Cyber Attack Detection System with BAIT Based Approach for Mitigation

Purpose: Industrial Control Systems become more vulnerable to digital attacks by merging communication groups and the Internet of Things, which could have severe implications. An Intrusion Detection System is essential in IoT businesses for identifying and stopping assaults. To ensure data privacy and security in the face of digital attacks, legislation and large enterprises should develop network security policies today. As people-based full frameworks have become more vital in today's society, they've also become targets for hostile activities, compelling both industry and research to concentrate more on dealing with local area disruption recognition issues. Contraption reviewing techniques have shown to be effective tools for resolving in-network interruption location issues. Design/Methodology/Approach: This investigation yielded a very unique strategy for tackling hub moderation utilizing a Classification and Encryption method. The UNSW-NB15 dataset is acquired and divided into Data for preparation and testing from the start. The information is pre-handled and included are eliminated right away within the preparation time frame. The TWM Algorithm is then used to determine the relevant highlights from that moment onward. The BRELU-RESNET classifier then sorts the input into went after and non-went after categories. The compromised information is then saved in the security log record, and the typical data is encrypted using the ESHP-ECC computation. The shortest path distance is then calculated using Euclidean distance. Finally, the data is available. Finally, using the DSHP-ECC computation, the information is decrypted. If the information is available in the log document during testing, it is regarded as the sought-after data and is prevented from the transmission. If it is not present, then the process of digital assault recognition begins. Findings/Result: The research is based on the UNSW-NB 15 dataset, which shows that the proposed method achieves an unreasonable awareness level of 98.34 percent, particularity level of 77.54 percent, exactness level of 96.6 percent, Precision level of 97.96 percent, review level of 98.34 percent, F-proportion of 98.15 percent, False Positive Rate of 22.46 percent, False Negative Rate of 1.66 percent, and Matthew's connection coefficient of 77.38 Originality/Value: This experimental-based research article examines the malicious activities in the cyberspace using BRELU-RESNET approach and mitigated by using BAIT based approach mechanism. Paper Type: Research Analysis.

Computational English Online Teaching Monitoring System Based on Deep Learning Algorithm

In order to improve the monitoring effect of English online teaching, this paper combines the deep learning algorithm to construct the English online teaching monitoring system and conduct real-time supervision of the English online teaching process. Furthermore, by altering the original DCGAN, this research seeks to apply the approach of constructing a deep convolutional adversarial network to tackle the issue of small-sample target recognition in a given scene and develop an appropriate learning model. According to the results of the experimental research, the English online teaching monitoring system proposed in this paper, which is based on the deep learning algorithm, can play an important role in online English teaching monitoring and effectively improve the efficiency of online English management.

Improved cost-sensitive representation of data for solving the imbalanced big data classification problem

Dimension reduction is a preprocessing step in machine learning for eliminating undesirable features and increasing learning accuracy. In order to reduce the redundant features, there are data representation methods, each of which has its own advantages. On the other hand, big data with imbalanced classes is one of the most important issues in pattern recognition and machine learning. In this paper, a method is proposed in the form of a cost-sensitive optimization problem which implements the process of selecting and extracting the features simultaneously. The feature extraction phase is based on reducing error and maintaining geometric relationships between data by solving a manifold learning optimization problem. In the feature selection phase, the cost-sensitive optimization problem is adopted based on minimizing the upper limit of the generalization error. Finally, the optimization problem which is constituted from the above two problems is solved by adding a cost-sensitive term to create a balance between classes without manipulating the data. To evaluate the results of the feature reduction, the multi-class linear SVM classifier is used on the reduced data. The proposed method is compared with some other approaches on 21 datasets from the UCI learning repository, microarrays and high-dimensional datasets, as well as imbalanced datasets from the KEEL repository. The results indicate the significant efficiency of the proposed method compared to some similar approaches.

The Issue of Political Recognition in International Policy: The Case of Kosovo

Due to its geographical and strategic location, the Balkans has become a region where different actors play a role in the historical process. When the physical map of the Balkans is considered, it is seen that Kosovo is also in the center of this region. Therefore, Kosovo is the center of historical events in the Balkans. Kosovo is a young country of today, which is the favorite of the Medieval Serbian State, attaches importance to the ownership and conquest of the Ottoman Empire, has an important place in the disintegration of Yugoslavia and has experienced painful situations during the process of becoming a state. The hypothesis of this study is that although Kosovo has achieved diplomacy with states on the path to recognition, it has not taken an important step towards recognition by international organizations and the entire international community. For this purpose, in the context of current developments, the relations of Kosovo with the states of the region, the strategies to gain the personality of the international community, the diplomatic movements and the reasons for not being recognized by the international community will be discussed.

Finger Print Based ATM System

Our Project is to develop the technique for fingerprint authentication in ATMs. This target can be mainly decomposed into image preprocessing, feature extraction and feature match. For each sub-task, some classical and up-to-date methods in literature are analyzed. Based on the analysis, an integrated solution for fingerprint recognition and authentication is developed for demonstration. Our demonstration program is coded in ASP.NET & MATLAB. For the program, some optimization at coding level and algorithm level are proposed to improve the performance of my fingerprint authentication system. These performance enhancements are shown by experiments conducted upon a variety of fingerprint images. Also, the experiments illustrate the key issues of fingerprint recognition that are consistent with what the available literatures say.

Evaluation Methodology for Object Detection and Tracking in Bounding Box Based Perception Modules

The aim of this work is to formulate a new metric to be used in the automotive industry for the evaluation process of software used to detect vehicles on video data. To achieve this goal, we have formulated a new concept for measuring the degree of matching between rectangles for industrial use. We propose new measure based on three sub-measures focused on the area of the rectangle, its shape, and distance. These sub-measures are merged into a General similarity measure to avoid problems with poor adaptability of the Jaccard index to practical issues of recognition. Additionally, we create method of calculation of detection quality in the sequence of video frames that summarizes the local quality and adds information about possible late detection. Experiments with real and artificial data have confirmed that we have created flexible tools that can reduce time needed to evaluate detection software efficiently, and provide more detailed information about the quality of detection than the Jaccard index. Their use can significantly speed up data analysis and capture the weaknesses and limitations of the detection system under consideration. Our detection quality assessment method can be of interest to all engineers involved in machine recognition of video data.

Invalidation of a Notarial Certificate of the Right to Inheritance and Cancellation of Ownership Rights: Actual Problems of Theory and Judicial Practice

Introduction. In Russia, the right to inherit is guaranteed by the Constitution of the Russian Federation (part 4, article 35). This right includes both the right of the testator to dispose of his property in the event of death, and the right of the heirs to receive it. Art. 1162 of the Civil Code of the Russian Federation establishes the right of the heir who has accepted the inheritance to receive a certificate of the right to inheritance, which can be realized by him at any time after the expiration of the six-month period for accepting the inheritance. However, if the inheritance includes real estate, then a certificate of the right to inheritance is necessary for state registration of the heir's property right. The purpose of this article was to study the issues of recognition of certificates of the right to inheritance, both from the point of view of legislative regulation and from the standpoint of existing judicial practice. The legal consequences of the invalidation of certificates of the right to inheritance, including in relation to the registered ownership of inherited real estate, have also been studied.Materials and methods. This study is based on a combination of such methods of scientifi knowledge as: the dialectical method, which made it possible to connect the theory of inheritance law and judicial practice; the formal legal method made it possible to analyze legislative norms; The systematic method made it possible to consider the certifi of the right to inheritance as a system with internal unity and interconnections with other institutions of law.The results of the study. The study made it possible to analyze the provisions of Section V "Inheritance Law" of the Civil Code of the Russian Federation, the Fundamentals of the Legislation of the Russian Federation on Notaries, which regulate the procedure for issuing certificates of the right to inheritance to heirs (by law, by will), as well as the existing judicial practice on recognizing them as invalid in the force of disputability or nullity, including from the personal practice of the author. It was stated that the contestation of notarial certificates of the right to inheritance expands the range of judicial practice to invalidate notarized transactions, which, in turn, does not contribute to the proper protection of the rights and legitimate interests of citizens (in 2019, there were 18 cases of compensation for losses incurred through the fault of a notary, in 2020 -15). Today, questions remain open about the responsibility of notaries for errors, as a result of which material damage was not caused to citizens or if the damage cannot be calculated and expressed in monetary terms. A number of gaps in the legislation in the studied area were also identified.Discussion and conclusions. The absence in civil law of a clear correlation between the certificate of the right to inheritance and transactions is a legislative gap and does not contribute to the uniformity of judicial practice in inheritance disputes. It is advisable to make appropriate changes to Art. 35 Fundamentals of the legislation of the Russian Federation on notaries, as well as a number of articles of Section V "Inheritance Law" of the Civil Code of the Russian Federation based on the prevailing trends in judicial practice in inheritance cases.

Aircraft Detection in Remote Sensing Imagery Based on Improved YOLOv4

Abstract A method is provided to resolve the issues of false recognition and lower recall of the you only look once version 4 (YOLOv4) algorithm in the aircraft targets prediction of remote sensing imagery. The method is based on the YOLOv4. According to the characteristics of small size and intensive arrangement of aircraft targets in remote sensing images, the backbone is mended to boost the prediction capacity of smaller targets. To strengthen the transmission and reuse of network features, the connection mode of the residual module in YOLOv4 is changed to the dense connection mode, and the five residual blocks in the backbone network are modified to the corresponding dense connection blocks to reduce the network layers and model redundancy. The experiment results reveal that compared with the YOLOv4, our method can enlarge targets features in smaller image regions, thereby availably increasing the prediction precision of aircraft targets.

The Balance Between the Public Order Preservation Policy and the Protection of Children's Interests in Cross-Border Surrogate Parentage

The international community is widely divided on the validity of surrogacy, which has led to the phenomenon of cross-border surrogacy conflicts and triggered the application of public order reservation on the issue of cross-border surrogacy and recognition of foreign surrogacy judgments. In the process of applying public order, the judges of various countries are prone to abuse their discretionary power due to the rigidity of the connotation and theory of public order, and the unclear standard of invoking public order, which interferes with family life and is detrimental to the protection of children. This paper argues that in the parent-child relationship of cross-border surrogacy, public order reservation and protection of children's interests are equally important issues, and cross-border surrogacy judgments should strike a reasonable balance between public order reservation and the principle of the best interests of children. Not only it is necessary to enrich the theory of public order reservation and improve the rules of its application, but the principle of the best interests of the child should be implemented to the greatest extent possible to protect the interests of the surrogate child.

Integration of Fuzzy with Incremental Import Vector Machine for Intrusion Detection

IDM design and implementation remain a difficult undertaking and an unsolved research topic. Multi-dimensional irrelevant characteristics and duplicate information are included in the network dataset. To boost the effectiveness of IDM, a novel hybrid model is developed that combines Fuzzy Genetic Algorithms with Increment Import Vector Machines (FGA-I2VM), which works with huge amounts of both normal and aberrant network data with high detecting accuracy and low false alarm rates. The algorithms chosen for IDM in this stage are machine learning algorithms, which learn, find, and adapt patterns to changing situations over time. Pre-processing is the most essential stage in any IDM, and feature selection is utilized for pre-processing, which is the act of picking a collection or subset of relevant features for the purpose of creating a solution model. Information Gain (IG) is utilized in this FGA-I2VM model to pick features from the dataset for I2VM classification. To train the I2VM classifier, FGA uses three sets of operations to produce a new set of inhabitants with distinct patterns: cross over operation, selection, and finally mutation. The new population is then put into the Import Vector Machine, a strong classifier that has been used to solve a wide range of pattern recognition issues. FGA are quick, especially considering their capacity to discover global optima. Another advantage of FGA is their naturally parallel nature of assessing the individuals within a population. As a classifier, I2VM has self-tuning properties that allow patterns to attain global optimums. The FGA-efficacy I2VM model’s is complemented by information gain, which improves speed and detection accuracy while having a low computing cost

Обязательства автономных учреждений: теоретические и учетные аспекты

To date, in modern economic conditions one of the most difficult and controversial accounting issues can be considered accounting obligations, institutions that are maintained at the expense of budgetary funds, which is largely due to the aggravation of non-payment problems. In the deepening of the payment crisis contributes its share and the lack of regulation of the budget system, which concerns the untimely payment of public obligations, which are carried out by autonomous institutions. In this context, the purpose of the article is to consider from theoretical and accounting points of view the obligations of autonomous institutions. As a result of the study different classification attributes and substantive aspects of the obligations of autonomous institutions are highlighted. Also, special attention is paid to the issues of accounting and recognition of obligations, some features of which, are considered on specific examples, namely: the accounting of obligations arising in the process of operations for the supply of goods, works, services and rent.

Recognition of a status acquired abroad

In the French legal system, recognition of status is a classic issue that attracts diverse responses, depending on the circumstances, from procedural recognition of judgement to conflict of laws and conflict of authorities. In the light of this classic scheme, many foreign statuses are recognised in France without any difficulties, provided that they were legally obtained abroad. However, many obstacles to recognition remains and the current changing legal context in favour of a new subjective right of free movement including the status of persons, has been provoking active academic discussions among French scholars and unprecedented judicial developments. For these reasons, it is important to rethink globally the issue of recognition of status in a broad perspective within the French legal order.

Exploring Educational Designs by Schematic Models: Visualizing Educational Value and Critical Issues of Recognition and Socio-Material Frames

Drawing on experiences from a large-scale Nordic co-design research project, this paper explores the development of cross-border blended education with the aim to strengthen students’ Nordic identity and knowledge of neighboring Nordic languages illuminating the flow of didactical designs students were offered to engage in, i.e. the knowledge content, activities, spaces and resources. The iterative, three-year educational development of designing for learning proved to provide space for shared and collaborative knowledge development involving students in rich, authentic, and goal-oriented inter-Nordic comprehension practices on multiple levels, addressing content-related as well as contextual frames in teaching situations. Schematic models of the didactical designs revealed initial dis-alignment and diverse appropriation of modes and media in parallel cooperative activities combined with collaborative cross-border face-to-face talk promoting students to take perspective, compare, and contrast. Addressing the socio-material frames of teaching gave an opportunity to discover new learning goals and design activities in new adequate arrangements of the physical classroom and the digital space. The analysis demonstrates that students developed linguistic, cultural, critical, and digital competences of varied educational value based on the teams’ divergent approaches to knowledge processes to incorporate digital technologies in teaching practice. The study highlights the critical factors of recognizing digital, multimodal meaning-making and informal skills as means to promote learning and schools’ remaining insufficient provision of digital infrastructure for learning activities in blended learning environments.

The Application of Integration of EEG Signals for Authorial Classification Algorithms in Implementation for a Mobile Robot Control Using Movement Imagery—Pilot Study

This paper presents a new approach to the issue of recognition and classification of electroencephalographic signals (EEG). A small number of investigations using the Emotiv Epoc Flex sensor set was the reason for searching for original solutions including control of elements of robotics with mental orders given by a user. The signal, measured and archived with a 32-electrode device, was prepared for classification using a new solution consisting of EEG signal integration. The new waveforms modified in this way could be subjected to recognition both by a classic authorial software and an artificial neural network. The properly classified signals made it possible to use them as the signals controlling the LEGO EV3 Mindstorms robot.

Methods for the frugal labeler: Multi-class semantic segmentation on heterogeneous labels.

Deep learning increasingly accelerates biomedical research, deploying neural networks for multiple tasks, such as image classification, object detection, and semantic segmentation. However, neural networks are commonly trained supervised on large-scale, labeled datasets. These prerequisites raise issues in biomedical image recognition, as datasets are generally small-scale, challenging to obtain, expensive to label, and frequently heterogeneously labeled. Furthermore, heterogeneous labels are a challenge for supervised methods. If not all classes are labeled for an individual sample, supervised deep learning approaches can only learn on a subset of the dataset with common labels for each individual sample; consequently, biomedical image recognition engineers need to be frugal concerning their label and ground truth requirements. This paper discusses the effects of frugal labeling and proposes to train neural networks for multi-class semantic segmentation on heterogeneously labeled data based on a novel objective function. The objective function combines a class asymmetric loss with the Dice loss. The approach is demonstrated for training on the sparse ground truth of a heterogeneous labeled dataset, training within a transfer learning setting, and the use-case of merging multiple heterogeneously labeled datasets. For this purpose, a biomedical small-scale, multi-class semantic segmentation dataset is utilized. The heartSeg dataset is based on the medaka fish’s position as a cardiac model system. Automating image recognition and semantic segmentation enables high-throughput experiments and is essential for biomedical research. Our approach and analysis show competitive results in supervised training regimes and encourage frugal labeling within biomedical image recognition.

Multi-Domain Deep Convolutional Neural Network for Ancient Urdu Text Recognition System

Deep learning has achieved magnificent success in the field of pattern recognition. In recent years Urdu character recognition system has significantly benefited from the effectiveness of the deep convolutional neural network. Majority of the research on Urdu text recognition are concentrated on formal handwritten and printed Urdu text document. In this paper, we experimented the Challenging issue of text recognition in Urdu ancient literature documents. Due to its cursiveness, complex word formation (ligatures), and context-sensitivity, and inadequate benchmark dataset, recognition of Urdu text from the literature document is very difficult to process compared to the formal Urdu text document. In this work, first, we generated a dataset by extracting the recurrent ligatures from an ancient Urdu fatawa book. Secondly, we categorized and augment the ligatures to generate batches of augmented images that improvise the training efficiency and classification accuracy. Finally, we proposed a multi-domain deep Convolutional Neural Network which integrates a spatial domain and a frequency domain CNN to learn the modular relations between features originating from the two different domain networks to train and improvise the classification accuracy. The experimental results show that the proposed network with the augmented dataset achieves an averaged accuracy of 97.8% which outperforms the other CNN models in this class. The experimental results also show that for the recognition of ancient Urdu literature, well-known benchmark datasets are not appropriate which is also verified with our prepared dataset.