Reviewerly/exHarmony huggingface dataset:
Observer Design for Large-scale Polynomial T-S Fuzzy System: SOS Approach

This paper proposes a new method to design an observer for estimating the unmeasurable states variables of the large-scale nonlinear system. The large-scale nonlinear system in this study is represented in term of a set of polynomial T-S fuzzy subsystems with interconnection terms. The interconnection parts of this paper are arbitrary. The unknown input method is applied to design observer for both estimating unknown states and eliminating the effects of the interconnection terms. Based on the Lyapunov methodology and Sum-Of-Square (SOS) technique, the conditions for observer synthesis are derived in the main theorems. Finally, an illustrative example is provided to prove the success of the proposed method.
МЕТОДЫ РАЗВИТИЯ ОРГАНИЗАТОРСКИХ СПОСОБНОСТЕЙ

&#x0D; &#x0D; &#x0D; This article describes the organizational abilities of the individual as a set of qualities that help to organize the work of the team. Without which it is impossible to normal interaction within the team, even with the high professionalism of the head. The qualities of organizational abilities and skills and what they affect are discussed in more detail. This will allow us to understand what is paid more attention to, starting to self-development. It should be noted that it is impossible to improve the quality in one day, even if you make every effort.&#x0D; &#x0D; &#x0D;
A Comparative Study on Questionnaire Design for Categorization Based on Fuzzy Co-clustering Concept and Multi-view Possibilistic Partition

This paper considers the problem of designing a questionnaire for categorization of subjects. In order to well-organize the categorical structures of subjects, co-cluster information is utilized based on two different co-clustering approaches under multi-view situations. A comparative experiment studies the characteristics of fuzzy co-clustering-based categorization, where all categories are simultaneously extracted in a batch process, and possibilistic partition-based categorization, where each co-cluster is independently extracted under each view of categories.
Performance Analysis for Cloud Query Encryption

The clouds computing comprises of information inquiry benefits that is versatile and adaptable in costing. Cloud purchasers ordinarily put away secret or fragile information that required to be sheltered and verified. Accordingly, delicate information is encoded for security and classification. A few contemplates have proposed Advanced Encryption Standard (AES) as well as Rivest Shamir Adleman (RSA) encryption techniques to protect cloud information wellbeing and secureness. However, every encryption strategy gives a particular degree of security which accompanies a contrary degree of proficiency. Henceforth, this exploration will examinations the presentation for both encryption techniques. The exploration arrangement includes Microsoft SQL Server as cloud database reenactment, and a Visual Studio stage to recreate the neighborhood questions. The examination is accomplished for encryption and unscrambling mystery quality. In general, the outcomes showed up are not noteworthy as every strategy has its own advantages.
An Image Watermarking Algorithm for Medical Computerized Tomography Images

The increasing number of medical images and the need to share them among different treatment clinical centers lead to the widespread importance of medical image watermarking. However, it should maintain the quality of the image and extract the patient information from the encoded image properly. In this paper, an image watermarking algorithm for medical computerized tomography (CT) images is proposed, which consists of a combination of discrete wavelet transform (DWT) and singular value decomposition (SVD) methods. The proposed scheme is validated using CT images from different human organs that are collected from a clinical imaging center. To evaluate the invisibility of the watermarked image, the mean square error (MSE), peak signal-to-noise ratio (PSNR), and structural similarity index (SSIM) metrics are computed. Also, the correlation coefficient (CRC) and the similarity measure (SIM) are utilized to assess the robustness of the proposed method for the extraction of the patient information from the watermarked image. The experimental results show that the proposed approach can achieve acceptable performance for encoding and decoding watermarking CT medical images.
Comparison of Geophysical and Geochemical Parameters of the Belovo Zinc Facility Dumps (Kemerovo Region) in the Process of Recultivation

Abstract The article presents the results of geochemical and geophysical studies of the Belovsky Zinc Plant reclaimed dumps (Kemerovo Region), which is located in the residential zone of the Belovo city. The relationship between the degree of neutralization of waste and electrical resistivity is established and described. For the first time at this facility, measurements were made by the induced polarization method. When analyzing the polarizability distribution (C, mV/V), it was found that not so much wet and warm zones with a high level of acidity are distinguished on the profiles of electrotomography as they are saturated with minerals with high electrical conductivity. This means that low resistivity values are associated with electronically conductive minerals with metallic conductivity. Thus, the assumption was made that the anomalies are associated with the introduction of coke into the dump, that is, an increased carbon content, which affects not only the resistivity profiles, but also the IP profiles.
Towards Achieving Keyword Search over Dynamic Encrypted Cloud Data with Symmetric-Key Based Verification

Irrefutable Searchable Symmetric Encryption, as a significant cloud security method, enables clients to recover the encoded information from the cloud through catchphrases and check the legitimacy of the returned outcomes. Dynamic update for cloud information is one of the most well-known and principal prerequisites for information proprietors in such plans. As far as we could possibly know, the existing irrefutable SSE plans supporting information dynamic update are altogether founded on hilter kilter key cryptography confirmation, which includes tedious activities. The overhead of check may turn into a huge weight because of the sheer measure of cloud information. Along these lines, how to accomplish catchphrase search over dynamic scrambled cloud information with productive confirmation is a basic unsolved issue. To address this issue, we investigate accomplishing catchphrase search over unique scrambled cloud information with symmetric-key based confirmation and propose a down to earth plot in this paper. So as to help the productive check of dynamic information, we structure a novel Accumulative Authentication Tag in view of the symmetric-key cryptography to create a validation tag for every catchphrase. Profiting by the aggregation property of our structured AAT, the validation tag can be helpfully refreshed when dynamic tasks on cloud information happen. So as to accomplish productive information update, we plan another safe record made by a pursuit table ST based on the symmetrical rundown and a check list VL containing AAT. Attributable to the availability and the adaptability of ST, the update proficiency can be altogether improved. The security investigation what's more, the presentation assessment results show that the proposed plot is secure and productive.
Optimized Bit Matrix based Power Aware Load Distribution Policy for Federated Cloud

Federated Cloud is a next generation cloud computing model which works towards a new dynamic scenario in smart ecosystems. It consolidates individual clouds together to form single large cloud to satisfy all on-demand requirements of the users. Federated cloud is currently facing many challenges including: monitoring of individual cloud services, maintaining service level agreement, fair load distribution, power usage etc. Hence, to address these challenges this paper presents a brokerage model "Optimized Bit Matrix based Power Aware Load Distribution Policy for Federated Cloud (OBMPLP)". Presented model includes two novel concepts: Bit Matrix and Load Distribution Factor. Bit Matrix constructed by individual cloud members representing resource availability status and environment in executing the user's request. Load Distribution Factor representing load distribution level at individual cloud. OBMPLP policy dispenses the incoming requests among multiple clouds by analyzing bits pattern and load distribution factor that not only performs fair load distribution among multiple clouds, but also improves response time, alleviate power consumption at each cloud and achieve better quality service. The performance of the proposed policy is evaluated and results demonstrate reduction in response time, optimized power consumption and fair distribution of load compared to existing approaches.
Multi-Resolutional Image Format Using Stochastic Numbers and Its Hardware Implementation

The popularization of IoT devices made image processing very common for users. Image formats used in hardware abound since there are varieties of IoT devices. Conversion of image formats in hardware is relatively complicated compared with other calculation. This paper focuses on conversion of image resolution, especially image reduction. By expressing images with stochastic numbers, this paper proposes an image format which can be treated to be in several resolution with one data. From experimental evaluations, we found that the proposed image format enables image reduction by pixel average to be implemented into hardware with lower costs compared with conventional pixel average using binary numbers. Also, image magnification using the proposed image format can restore the original image, while conventional image magnification cannot.
Research on Desensitization Algorithm of Power Consumption Data Based on Anonymity and Differential Privacy Technology

Abstract With the development of information technology, the problem of privacy disclosure has become increasingly prominent. State Grid Corporation has a large number of sensitive data such as power marketing data, power customer data, personal electricity information, which has disclosure dangers during the production, transmission, storage, processing and sharing stage. This paper mainly studies the desensitization algorithm model of power consumption data based on anonymity and differential privacy technology, builds a general algorithm model and then realizes data desensitization according to each application scenario. The evaluation and analysis results show that the scheme can effectively protect the massive power marketing data, power customer data and personal power information from invasion and disclosure.
Novel Adaptive Statistical Method-CNN Synergism Based Two-Step WCE Image Segmentation

Accurate automatic segmentation of anomalies i.e. ulcers, bleeding regions, polyps, etc. from Wireless Endoscopy Images (WCE) are absolutely essential for proper diagnosis of diseases. Using a single segmentation method for segmenting all these above-mentioned anomalies is a very challenging task due to their different shapes and characteristics. Mostly, existing WCE anomaly segmentation methods are dedicated to only one type of anomaly, but the method proposed in this paper is capable of segmenting four types of anomalies (bleeding regions, small bowel erosions and ulcers, tapeworm and vascular ectasia) from WCE images despite their varied shapes and characteristics. Here we have proposed a novel adaptive statistical method-Convolution Neural Network (CNN) synergism-based two-step segmentation procedure. Initially, we have removed the labeled regions from the acquired WCE images using a polygonal mask and then converted them from the RGB to HSV color space. Here we have chosen HSV color space to carry out the entire segmentation procedure due to its resemblance with the human vision system. In the first step of the segmentation method we extracted the Region of Interest (ROI) from WCE images using statistical measures and by manipulating the intensities of pixels belonging to the ROI and NON-ROI regions accordingly using two adaptive reinforcement constants. The segmented ROI is obtained from this step itself, but it sometimes gives erroneous results. To overcome this shortcoming, we classified the output images of this stage using AlexNet, GoogLeNet, InceptionV3, and SVM trained with Bag-of-Features to obtain accurate results. This stage finally classifies those segmented images into ROI and NON-ROI images. Finally, we obtained a segmentation accuracy of 95.49% using InceptionV3 and also verified the reliability of the method from professional experts.
Research on Underwater Acoustic Location Algorithm Based on Multilayer Extreme Learning Machine

In view of underwater acoustic positioning model which is complex, highly nonlinear and solving difficult, as well as the low accuracy of underwater acoustic gradient measurement, an underwater acoustic positioning algorithm based on multilayer extreme learning machine(MELM) networks was proposed to achieve the high accuracy of underwater target positioning. In this paper, the first layer ELM network in the algorithm is proposed to realize the initial location of sound source, solve its approximate location, and eliminate invalid data. The second layer ELM network calibrates the underwater acoustic velocity relying on the initial position depth, and uses the filtered data to achieve higher positioning accuracy for the target. Simulation results show that the underwater acoustic positioning algorithm using multilayer ELM networks has higher positioning accuracy and stronger fault tolerance.
Introduction to cognitive science, informatics, and modeling

This chapter introduces the use of cognitive science, cognitive informatics, and computer modeling for cognitive applications. Cognitive science due to its broad scope is used in a variety of applications of science, technology, engineering, and mathematics. For each of the application areas the overview of cognitive science, necessary mathematical background, and a basic knowledge of modeling concept are required, and the chapter attempts to present this in addition to the psychological and philosophical background of the cognition process. Cognition is closely associated with brain, memory, and organization of information using suitable learning mechanism, and thus the concept of learning be introduced and detailed description of learning, various types of learning and their application will be discussed in subsequent chapters.
Simulation and Implementation of an Autonomous Mobile Robot for Outdoor Competitions

In this paper is presented the design, construction and evaluation of an autonomous mobile robot, designed to outdoor competitions. The category of outdoor competition, in Brazil named Trekking Pro, is based on a mobile robot in an open field, which must pass through preset coordinates. A commercial (at first remote controlled) robot was adapted for autonomous operation. The perception of the robot consists of 9-axis Absolute Orientation Sensor and an encoder. At fist, the system was simulated in a ROS (Robotic Operation System) environment integrated with a scene V-REP (Virtual Robot Experimentation Platform). PID controllers, were used in implementation. Afterwards, a prototype was built, that participated in a competition with success, winning the first place. Both simulation and real-world experimental results of the constructed prototype are shown in detail. The robot described in this paper was the champion of the Winter Challenge 2018 (Robocore - Brazil) in the Trekking Pro category.
A Legal Framework for AI Training Data

In response to recent regulatory initiatives at the EU level, this article shows that training data for AI do not only play a key role in the development of AI applications, but are currently only inadequately captured by EU law. In this, I focus on three central risks of AI training data: risks of data quality, discrimination and innovation. Existing EU law, with the new copyright exception for text and data mining, only addresses a part of this risk profile adequately. Therefore, the article develops the foundations for a discrimination-sensitive quality regime for data sets and AI training, which emancipates itself from the controversial question of the applicability of data protection law to AI training data. Furthermore, it spells out concrete guidelines for the re-use of personal data for AI training purposes under the GDPR. Ultimately, the legislative and interpretive task rests in striking an appropriate balance between individual protection and the promotion of innovation. The article finishes with an assessment of the proposal for an Artificial Intelligence Act in this respect.
Actuator Based Throttle Control System for Vehicle: An Assistive Vehicle for Partially Disabled People

Vehicle automation is now a very warm topic of all kinds of automation. Several vehicle brands are researching this issue and they have already made some of the amazing automated vehicles. They are available in the market too. But all the vehicles are not electrically driven. The number of internal combustion engine vehicles is more than electric vehicles available. The main objective of this project is to help those people who are physically challenged or handicapped. The operation of the internal combustion engine will be automated by the use of externally installed electromechanical devices. The entire system is designed so that it can be installed in all kinds of light and four-wheeler vehicles.
Addressing the Conflict of Negative Feedback and Sampling for Online Ad Recommendation in Mobile Social Networks

Online advertisement (ad) recommendation in the mobile social network (MSN) is an uprising interest of research. Compared to traditional recommendation systems, one of its major difference is the presence of explicit negative feedback from users (e.g., a user does not click an ad, or she/he does not like it). On the other hand, most methods utilize negative sampling (e.g., randomly sampling an item that a user never interacts with to avoid overfitting, that is, she/he is assumed to dislike it) while training conventional recommendation systems. This may lead to a conflict between negative feedback and sampling, as they should be treated differently, but they are considered as the same if traditional methods are directly applied for online ad recommendation. In this paper, we present AdRec, a novel framework of online ad recommendation in MSN to address this conflict. We introduce an auxiliary output and modify the loss function to assign different weights to negative samples and feedbacks. A theoretical analysis is applied to show the efficiency of our design, and experiments on real world datasets demonstrate that our proposed method outperforms several state-of-the-art approaches.
Determination of the Computerized Monitoring System Structure of the Small-Scaly Turbulence of the Water Environment Hydrophysical Fields

Abstract The algorithm definition of system’ optimum structure of monitoring hydrophysical fields of the water environment inhomogeneities that provides a specified measurement uncertainty with the minimum cost of the equipment and the experiment is proposed. The algorithm uses the object-evolutionary model of the information-measuring system and the vertical arrangement model of small-scale turbulence layers.
Authentication of Internet Connected White Goods Using Gestures or Key Sequences

In the context of IoT, more and more white goods (e.g., water boiler, micro wave, washing machine) are connected to the internet for various purposes from the manufacturers side. In many countries, the consumer has the right to access the data generated and transmitted by his own devices. To be compliant with this right, the manufacturer has to guarantee the access of the data to the consumer without violating his privacy. This means, it has to be guaranteed, that the user is able to access his own data only, while no one else is able to access his data. Many methodologies to implement this might be thought of, while in this proposal we introduce an approach of a straight forward implementation covering easy and good user experience and also enabling securing privacy issues.
A Robust and Secured Mechanism for Sharing Encrypted Data in Cloud Systems

Citizens are embracing the major power of cloud computing, make sure that cloud providers can not truly believe in hosting information that is vulnerable to privacy, as access to cloud control is lacking. Due to this, the data owners will share the encrypted data instead of plaintexts to ensure confidentiality. By using the Ciphertext Policy-Attribute based Encryption (CP-ABE), we can share the encrypted files with the other users, for solidified and owner-significant monitoring of access. Through this, we do not get safe enough by means of some unknown attacks. Many existing methods will not allow the cloud provider to check to see if the downloader is able to decrypt. Such files will also be open to anyone usable for storage in the cloud. A malevolent attacker can access hundreds of files to conduct Economic Denial of Sustainability attacks (EDoS) that use the cloud resource to a large extent. For this purpose, the responsibility rests with cloud service payer and the payer has to bear the expenses. The cloud provider simply acts as both the assessor and the technology use tax payee, minimizing data owners’ responsibility. In real-public cloud storage, these issues should be addressed. In this paper, we suggest a solution for protecting Encrypted cloud storage from Economic Denial of Sustainability assaults and providing resource usage transparency. In a black-box approach, it uses CP-ABE methods along with complying with CP-ABE's arbitrary access policy. We have two protocols proposed for a particular environment, performance and safety review are followed.
Asymptotics on the number of walks until no shoes when the number of doors is large

A man has a house with n doors. Initially he places k pairs of walking shoes at each door. For each walk, he chooses one door at random, and puts on a pair of shoes, returns after the walk to a randomly chosen door and takes off the shoes at the door. Let Tn be the first time a door is chosen to walk out but with no shoes available. We show that as n→∞, Tn has the same asymptotic distribution and moments as the number of choices required to choose among n equally likely alternatives repeatedly until any one of the alternatives has appeared k + 1 times. To derive these results, we need to consider a more general setting where the numbers of pairs of shoes initially placed at the doors (initial configuration) are not necessarily equal. We show that Tn increases in the sense of stochastic ordering if the initial configuration is more evenly distributed in the sense of majorization.
Comparison of the complexity of Diffie–Hellman and discrete logarithm problems

The article presents an algorithm for solving the discrete logarithm problem with an oracle, solving the Diffie–Hellman problem. Certified the discrete logarithm problem is considered. The Diffie–Hellman oracle works with elements of the original group, but with new group operations that are compositions of the Diffie–Hellman oracle. In particular, a universal (generic) algorithm can be substituted as the Diffie–Hellman oracle. The result is improved since 1996—the degree of logarithm in the estimation of the complexity of the algorithm presented is reduced to one. Of course, this does not affect the property of polynomial reduction of the considered problems to each other but excludes from the evaluation in a sense unnecessary terms.