Reviewerly/exHarmony huggingface dataset:
Image segmentation method based on RGB - D fusion

There is too little information to use in the existing image segmentation algorithms, and it can not achieve the ideal segmentation effect. Kinect depth cameras could obtain surrounding RGB image and depth image in real-time, providing a new approach for image segmentation. In this paper, based on fusion image segmentation algorithm with RGB image and the depth image by Kinect, during the time of image segmentation in the fusion of color and depth information, first of all to align the depth image and RGB image, then to segment the image in advance with significant weighted method of the depth image, finally to make four-valued logic image segmentation. Contrast with other methods, this is simpler and more effective, and could provide accurate criterion for subsequent region merging and image recognition, and it is robuster.
Implementation of support for a multi-type flow of requests in the payment card processing server model in the form of a petri net and its validation

In this paper we are considering the implementation of support for modeling of the input flow of differently typed requests in the model of the payment card processing system server in form of a hierarchical coloured Petri net. Also, the validation of the extended model was performed using data from a real processing system. Based on its results, we made a conclusion on the acceptability of using the model in further research and in practical application.
Information Retrieval Techniques for Automated Policy Review

In this paper we adapt standard information retrieval techniques to a novel task, the mandatory regulatory review of public comments on proposed rule changes. The vast number of public comments exceeds the responsible agency’s ability to manually review in the time allowed. Therefore, the agency requires an automated approach to efficiently sort and process the comments. To rank the public comments’ relevance to rule sections, we implement a vector space model and compare the results to experts’ reviews. We perform experiments over several indexing techniques to improve semantic relevance, splitting the regulatory document based on textual formatting, text length, and a hybrid method combining these two techniques. To improve the accuracy of our predictions, we test various synonym lists generated from a domain-specific ontology, as well as variations of standard stopword lists. By applying the relevance search as a multi-class classification problem, we find the method that most closely matches human reviews, achieving respective normalized discounted cumulative gain and mean average precision scores of 0.83 and 0.75 on our test data set.
Genericide of Trademark: An overview vis-à-vis common parlance

Trademark rights in India are statutorily protected by the Trademark Act,1999 and also under the common law remedy of passing off. The administration of such protection under the Act is done by the Controller General of Patents, Designs and Trademarks. The Trademark Act, 1999 deals with the protection, registration and prevention of fraudulent use of trademarks. It also deals with the rights of the holder of the trademark, penalties for infringement, remedies for the damaged as well as modes of transference of the trademark.This research paper is designed in a respect of genericide of trademark. A brief description regarding genericide of trademark and its related issues has been provided herein. Certain research problems like what is trademark, genericide, how does it occur, how a trademark can be prevented from getting generic and what is the effect of genericide on trademark has been clearly dealt with.Hence, the basic design of this research work is to provide an idea and related information regarding genericide of trademark. Each and every mentioned aspect of this research paper has been laid down with great emphasis and has been clearly dealt with.
Formalized assessment of the sensitivity of economic sectors to the use of blockchain technologies (on the example of the Russian Federation)

Distributed data storage technologies are becoming an integral part of the modern economy and are beginning to have an increasing impact on the prospects and competitiveness of its development. This, in turn, predetermines the high growth rates of the blockchain technology market in the world. These dynamics will form changes not only in the IT markets, but also in the entire financial and economic systems, penetrated with information, digital decisionmaking processes. In this regard, relying on the functional capabilities of distributed data storage technologies, as well as taking into account the high dynamics of the processes of their penetration into the economic environment, it seems to be a very important and urgent task to develop methodological approaches to assess the risks and opportunities for the national economic system in the context of future changes. It is important to note, that the solution of the problem should, in order to objectify the results, rely on the methods of formalized analysis using the tools of economic and mathematical modeling. Based on this approach to the study of the academic and research problem, the work proposes an algorithm for assessing the influence of blockchain technologies on the dynamics of transformation of key development parameters of separate sectors of the economy. Methodologically, the calculations are based mainly on the construction of a cointegration model, that allows to determine the main effects and potential impact of possible changes in certain areas of economic activity as part of the penetration of blockchain technologies into the economic environment.
High Utility Mining of Streaming Itemsets in Data Streams

Abstract The traditional models for mining frequent itemsets mainly focus on the frequency of the items listed in the respective dataset. However, market basket analysis and other domains generally prefer utility obtained from items regardless of their frequencies in the transactions. One of the main options of utility in these domains could be profit. Therefore, it is significant to extract items that generate more profit than items that occurs more frequently in the dataset. Thus, mining high utility itemset has emerged recently as a prominent research topic in the field of data mining. Many of the existing researches have been proposed for mining high utility itemset from static data. However, with the recent advanced technologies, streaming data has become a good source for data in many applications. Mining high utility itemset over data streams is a more challenging task because of the uncertainty in data streams, processing time, and many more. Although some works have been proposed for mining high utility itemset over data streams, many of these works require multiple database scans and they require long processing time. In respect to this, we proposed a single-pass fast-search model in which we introduced a utility factor known as utility stream level for tracing the utility value of itemsets from data streams. The simulation study shows that the performance of the proposed model is more significant compared with the contemporary method. The comparison has been performed based on metrics like process-completion time and utilized search space.
Handwritten Recognition for Tamil Language: Overview, Challenges and Future

We define offline handwriting recognition method that presently supports Tamil language, which is focus on high accuracy and efficiency of the system. The optical handwriting recognition system developed for the Tamil language carry a very poor recognition rate due to its various writing styles and large no of characters.The goal of this system is to find a digital format for Tamil handwritten document. It is the most challenging task even though, various researches have been proposed for printed Tamil text. The modern research in this domain has used the deep learning algorithm to upturn the accuracy for the input document. This survey grants an exhaustive review of the Tamil Handwritten Character Recognition, challenges and numerous skills.
The Peculiarities of Computer Technologies Application in Informatics Training in Conditions of the Economy and Education Digitalization

The widespread adoption of digital technologies has led to a radical change in the trajectory of economic and educational development. New transformations require new skills from specialists. The relevance of the study is dictated by new requirements for specialists in the digital society; special socio-psychological characteristics of the digital generation; new digital technologies that form the digital environment and develop in it. The main idea of the work is to substantiate the choice of computer technologies that ensure the training of a competitively capable specialist in digital society in computer science training. Federal strategic documents and scientific and pedagogical literature were analyzed. It was revealed that 75–85% of success in professional activity today depends on the level of formation of communicative and digital skills. The directions of training a modern specialist in the field of informatics are determined: the formation of digital skills and knowledge; formation of skills in search, processing and analysis of information; development of high communication skills related to teamwork, cooperation, business negotiations. Highlighted computer technologies, the knowledge of which will provide professional competitiveness in the near future: cloud technologies, search and reference systems, the basics of artificial intelligence and programming. The choice of forms of organization of the educational process is substantiated: individual, collective, work in small groups #CSOC1120.
Image recognition and detection based on fast area convolutional neural network

Abstract Nowadays, image recognition and detection technologies based on traditional artificial neural networks and convolutional neural networks are slightly inadequate in terms of training and recognition time and accuracy, and are difficult to deploy on devices with limited hardware resources. Therefore, this article proposes a recognition and detection technology based on fast regional convolutional neural networks. We use RPN (Region Proposal Network) instead of Selective Search method to rebuild the network, and add a new ROI pooling layer before the fully connected layer of CNN. Determine the category. The average detection accuracy on our data set can reach 83.8%, and the training time is only 0.34 hours.
Circular Encryption Algorithms to Provide a Secure Channel for Storing and Transmitting Information in Mobile Robot Groups

Information interaction in teams of robots, is currently a hot topic in group robotics. The high relevance of the problem was achieved by using robot collectives in solving technically complex tasks, they found themselves in solving tasks of monitoring, working with radioactive substances, search operations, moving large-sized bodies, etc. Fulfillment of all these purposes requires a high level of team interaction, taking into account dynamically changing environment and limited radius of data transfer, as a consequence, the question of team information interaction, storage and protection of transmitted information from unauthorized access becomes acute. In article the approach of realization of information interaction by means of anonymous cryptoprotected algorithms which use method of ciphering of the sender of the information from unauthorized access, allowing to receive data on an environment group without personalization of a source is considered. The existing algorithms of anonymous decentralized data transfer based on blockchain are also briefly considered.
Comparison of Various Data Center Frameworks

Our world is now becoming more attached to IT technologies. With the growing use of the Internet, there is a store and process exponential data. Data centers gained importance due to the emergence of Internet services. Data centers are responsible for the storage, management, and dissemination of data. Since the data is exponentially increasing, the data centers are more overloaded with data and their size is increasing. Since the data centers are more overloaded to fulfill the growing demands of the customers, this in turn is indirectly contributing to global pollution in terms of heavy consumption of power. Data centers consume a lot of power that is why many organizations want to design their data center their operations as green as possible. Thus, an efficient green data center framework is needed for the efficient design of data centers. To achieve this, the comparison of green data centers frameworks is being done which evaluates each of the data centers. The contrast inferred that the frameworks have the chances for enhancement in terms of components, attributes, energy-effective metrics, and implementation procedure. A new data center framework is proposed which is implemented considering the disadvantages of the existing data center frameworks.
Fuzzy Run Theory and Its Application in Performance Evaluation of Automatic Target Recognition

The fuzzy comprehensive evaluation has played an important role in performance evaluation, unfortunately, however, it may lose information of the system being evaluated. We establish the theory of fuzzy run which can give more objective conclusion in fuzzy comprehensive evaluation, as can help the latter in building more practical evaluation models. There are three components brought to fuzzy mathematics in this theory: (i) the "bottom run" to show the difference of current output and the lowest limitation in n characteristic dimensions; (ii) the "limitation of the bottom run" to assure the variance of the former quantity; and (iii) the "run between dimensions" indicate the difference between n dimensions. . Subsequently, aiming at performance evaluation of Automatic Target Recognition (PE-ATR, ATR), a general idea is developed; the evaluation indexes and their measurement method are presented; then, a performance evaluation model is formed. Furthermore, simulation is carried out with test data, and the results show the practicability of this newly proposed facility. This new method is beneficial for evaluating an ATR system in: (i) it can take many different kinds of performance characters into account in the evaluation course; (ii) it is easy to be configured; it is an open model, as users can easily change the evaluation set and the comment set.
Optimization of Privacy-Preserving in Agricultural Census by Value-Inserting and Reconstruction Algorithm

As a major national survey, the National Agricultural Census provides an important basis for researching and formulating rural economic and social development policies and plans. As statistical data analysis methods become diverse, it is necessary to protect personal privacy when processing data. We proposed both value-inserting and reconstruction algorithm (VIR) to ensure the privacy of personal information attributes contained in data. Firstly, a class of composite attributes is defined based on the concepts of QID and sensitive attributes. Afterwards, in order to conduct anonymization, several attribute values are randomly inserted into each sensitive data for every record through an anonymization method based on t-closeness, the randomization parameters of the value-inserting procedure will be optimized simultaneously by solving the minimization problem of L2 distance. Finally, according to the Bayesian method, the distribution of the target combination of attributes is reconstructed. The simulation results show that the utility and run-time of algorithm VIR are optimal when compared with the algorithm UAD and algorithm BUREL.
Mathematics Content of an Undergraduate Course on Deep Learning

Deep learning, also known as neural networks, is a key AI technology that is increasingly important in science and engineering applications.Most of the undergraduate curriculum in science and engineering places emphasize on analytical models, often derived from first principles.In contrast, deep learning is based on empirical models learned from a large number of training examples.These empirical models are currently being used to solve challenging problems such as autonomous driving in unstructured environments or enabling robots to grasp arbitrary objects.With the invention and widespread use of computers, the applied mathematics curriculum has evolved to include courses like numerical analysis that supplement analytical modeling with simulation and numerical solutions to complex problems.Even introductory STEM courses such as calculus, differential equations, matrix algebra and statistics are turning to computer-based simulation and solutions.The bootstrap method, for example, has become a main topic in some introductory statistics courses.Widespread adoption of deep learning will likely usher in another analogous evolution in the undergraduate mathematics curriculum.Engineering students are familiar with core mathematical concepts used in deep learning such as linear functions, vector spaces, matrix arithmetic and the gradient vector.In this paper, the author summarizes his experience teaching an undergraduate course in deep learning in a mathematics department.Also presented are data that suggest that weak programming skills may not be as significant an obstacle for STEM majors as the author originally feared.
Variational Autoencoders Versus Denoising Autoencoders for Recommendations

Recommender systems help users explore new content such as music and news by showing them what they will find potentially interesting. There are many methods and algorithms that can help recommender systems create personalized recommendations. All recommendation approaches can be divided into three categories: Content-based recommendation, Collaborative filtering and Hybrid methods. In this paper, we explore and compare Variational Autoencoders and Denoising Autoencoders for Collaborative Filtering with implicit feedback. A Variational Autoencoders(VAE) is a non-linear model, so it can capture patterns that are more complex in the data and since the forward pass is sufficient to obtain the recommendation of a given user then the query time is fast. A Denoising AutoEncoder is a specific type of AutoEncoder, which is generally classed as a type of deep neural network and is trained to use a hidden layer to reconstruct a particular model based on its inputs. Comparison results between Variational Autoencoders (VAE) and Denoising Autoencoders (DAE) show that VAE has the upper hand when it comes to large datasets while DAE is better when using small datasets. We explore and evaluate both methods on three public datasets and using different metrics.
VLSI Tree-Based Inference Design Applications for Low-Power Learning

Abstract For the decision tree ensemble, this paper suggests a hardware architecture utilizing many feature channels. The proposed work uses the complexity of function channels for rapid identification compared to parallel processing in spatial domain scheduling to achieve conflict-free system memory. The results’ analysis demonstrates that only an FPGA implementation of the new architecture with a pedestrian sensor collated channel feature will conduct 229 thousand pulses per second at an operational value of 100 MHz while providing relatively limited resources. Checking estimation systems’ electricity-accuracy trade-offs has become central. This research evaluates the nature of data sets, investigating the outcomes of design difficulty or intensity of accuracy approximation. We improved the simulations’ precision by up to 6.7 percent by quantizing the inputs to small sizes relative to specific scenarios. The gap in model complexity was more important than source distance in terms of capacity, as we achieved reductions of up to 67 percent by reducing tree depth.
A Fast Computing Model for Despeckling Ultrasound Images

Ultrasound imaging is a highly preferred diagnostic method due to its non- invasive nature; however, the presence of speckle noise degrades the quality of the images captured by this modality. Among the plentiful researches that happened in the field of despeckling, the non-local total variation methods have demonstrated promising results by maintaining relevant details and edges present in images. Nevertheless, the model is computationally expensive as it has to deal with large size matrices in computing the results, which in turn restricts its applicability in real-time environments. This study contributes a fast and numerically stable Non-local Total Variation Model for despeckling Ultrasound images. Multi-core GPU processors are employed for computing the parallelized algorithms developed using a fast converging Split-Bregman iterative scheme. A comprehensive evaluation is performed on the basis of execution time to demonstrate the efficiency of the model.
Respondent Behavior Logging: A Design Science Research Inquiry into Web Survey Paradata

This paper introduces a framework for Respondent Behavior Logging (RBL), consisting of static and dynamic models that conceptualize respondent behavior when filling in online questionnaires, and visualization techniques and measurement constructs for RBL data. Web survey design may benefit from paradata logging as a technique for evaluation, since such data may prove useful during the re-design of questionnaires. Although other aspects of online surveys have attracted considerable attention both in the industry and in literature, it is still underexplored how the Web may leverage new and innovative techniques to support survey design. The RBL framework is evaluated using a focus group and through an experimental survey with 120 participants. We elaborate on implications for research and practice in an informed argument.
Vuln-Check: A Static Analyzer Framework for Security Parameters in Web

In the current day scenario, cyber-attacks are substantially increased in web applications known as “Thick” client-focused mainly on e-retail/e-commerce, government websites, and social media platforms. Over decades, attackers are used to target the web applications upon various modules like system/web application from several sources, vulnerabilities (or) exploits as all phases rapidly involve the manual intervention of attacker, where the obtained information is also not validated, and misinformation will lead to cost more time and resources, and it will eventually fail to reach the target. As more sophisticated technologies are available in the market, due to that information credibility will help the attacker to get promising results, as consequences the attackers managed to manipulate, deface, and information stealing on targeted applications. The proposed vuln-check framework involves the automation of two phases: reconnaissance and static analyzer. Reconnaissance helps us to understand demographic information about the target, and the second is a static analyzer that will help to understand the vulnerabilities of the target. Therefore, the primary purpose of this study is to formulate a vuln-check framework suitable for measuring and improving web application security vulnerabilities. In our framework, core modules are compared to the open-source benchmark tools.Divya Jyothi, P. Lakshmy, K. V.