Reviewerly/exHarmony huggingface dataset:
Adopting Sustainable Farming: Implications of Renewable Energy and ICT

Abstract Contemporary sustainable farming balances higher productivity with a focus on green technological intervention. The green intervention such as renewable energy and information communication technology (ICT) though have been widely accepted by other sectors, farming community yet to yield the dividend from these two tools. In this paper, a modified technology acceptance model has been presented with a focus towards the adoptability of actual sustainable farming. Further, a success story of sustainable farming with the intervention of ICT is discussed. Finally, interdependency of renewable energy and ICT is elaborated with a focus to formulate a generalized theme towards feasibility of actual sustainability farming is presented.
MESING – a new method of organizing the joint work of neural networks and its metrology

Abstract A description of a new method for the collective solution of local problems – the method of evolutionary coordination of solutions based on the original use of genetic algorithms – is given. Interaction rules, developed on their basis, coordinate the work of intelligent agents (actors). Based on the Rasch model, an absolute scale for measuring the intellectual power of actors and the costs of intellectual labor when solving local problems with a given probability of their correct solution is introduced. The unit of measurement for these values is introduced and justified – 1 INT. A number of theorems are presented that make it possible to substantiate a new procedure for obtaining a collective solution (mesing) both to increase the intellectual power of a committee of neural networks by 150 times in comparison with a single neural network as part of a committee, and to reduce the probability of an erroneous decision to zero under certain conditions. As a result of the committee’s work, either the correct decision is formed, or the answer “no solution has been found” with a low probability of an erroneous decision.
Design of intelligent distribution network based on Geographic Information Technology

Abstract According to the business requirements of distribution network planning, this paper deeply studies the existing simplified display algorithm of distribution network in GIS, constructs the distribution network model based on geographic information technology, develops the functions of supporting storage, display and display, and the function module of professional map editing for distribution network planning. The concept of spatiotemporal data and its application in GIS are innovatively put forward. The display research based on the status quo and the analysis and management technology of distribution network structure under planning state are carried out. The spatiotemporal representation, spatiotemporal evolution, and comparison of power grid are realized. The geographic image elements are matched and associated with equipment parameters to realize the integrated modeling of map and power grid, realize the penetration function from the map to equipment parameters and power grid indicators, and support the diagnosis and analysis of power grid-related indicators. Finally, a three-dimensional power map with time dimension, space dimension, and attribute dimension is constructed to provide geographic environment and equipment parameter data for power grid planning, site selection, and line selection.
Motion Flow Feature Algorithm for Action Recognition in Videos

Motion representation becomes the critical factor since more and more trimmed video action recognition tasks rely on machine learning. In this paper, we proposed a new motion representation whose hint is from optical flow algorithms which have been proved to be effective and efficient in the aspect of video action recognition. Our motion flow is one kind of modality which is different from RGB, RGB diff, and the most popular optical flow, although the methodology is derived from the optical flow, it is faster and more accurate than optical flow algorithms. Furthermore, we introduced the most excellent convolutional neural network framework named densely connected convolutional networks (DenseNet) to optimize the networks and we use the motion flow as the inputs of the framework. We achieve experimental evaluations, when the proposed motion representation is plugged into the DenseNet framework, the accuracy on the UCF-101 and HMDB-51 is 96% and 74.2% respectively, which turn out to be our proposed methodology is satisfactory and 15 times faster in speed.
Multi-view Feature Fusion Recommendation Algorithm based on Representation Learning

With the rapid development of representation learning, more and more external side-information like users `comment on item is introduced into the recommendation system to alleviate the problem of data sparseness. Recommendation algorithm based on multi-view learning considers those external side-information as independent views feature which is used to deal with data sparsity. However, these views are interdependent. By assuming that the view feature is independent, reducing the computational complexity, but resulting in poor recommendation performance. To solve this problem,this paper proposes a multi-view feature fusion recommendation algorithm based on representation learning,namely MVF. First, the algorithm uses an automatic encoder to extract the features of each view, and constructs second-order and third-order interactive features based on those features. Then, the singular value decomposition algorithm is used to compress the second-order interaction feature to extract the main interaction features, and the Tucker tensor decomposition algorithm is used to compress the third-order interaction feature to extract the main interaction features. After getting the main interaction feature,using the attention mechanism to fuse those features to get the representation of item. Considering that users have different preferences for different items, the attention mechanism is used to fuse user's items to obtain the user's preference model. Finally, Extensive experiments on real data sets from Amaza and compared with multiple baseline algorithms to verify the effectiveness of the proposed algorithm.
A Novel Agent-Based Methodology for Job Scheduling in Cyber Physical Systems

Cyber physical systems (CPS) is the most extensive inventions in the field of information and computer technology. CPS utilizes the computers to perform tasks efficiently in connection to physical world. CPS can be expressed as an association of computers and networks with the physical process, also tightly integrated with internet and uses cloud for storage and computation. This paper proposes a software agent architecture for efficient job request scheduling in a cloud and improved response in a CPS environment. Intelligent agent architecture for scheduling interconnects various software agents and work without any human interference; acts on behalf of others to make the cyber physical system operate in an intelligent fashion. As it is well known that cloud is an essential paradigm widely used all over the world to handle multiple concurrent jobs and also to bring down their computational and operational expenditure, there is always a need for a highly functional scheduling architecture aimed at timely job completion, which is detailed in this paper.
Personalization Based on Learning Preference

The concept of personalized adaptive learner model (PALM), learning style diagnostic module (LSDM) and behavior monitoring module (BMM) is introduced. Learning objects are identified, concept values of each of learning objects are provided, and interconnections are loaded with fuzzy weights. For the sake of human-related data analysis, soft computing techniques, i.e., fuzzy cognitive maps (FCMs) are used as a combination of neural networks and fuzzy logics. FCMs support dynamic modeling, updated learning materials constantly, enable to capture huge data, extract hidden patterns and established relationship among different parameters suitably. They tackle uncertainties and fuzziness of the enormously generated data log files. So finally, in this chapter, factors detected and variables affected on the learning style are found and how increasing and decreasing these values will affect changing the strengths of learning styles.
Secure and Internet-Less Connectivity to a Blockchain Network for Limited Connectivity Bank Users

Over the past few years, we have seen the emergence of a wide range of banking architectures, technologies, and applications made possible by the significant improvements in hardware, software, and networking technologies. Nowadays, innovative solutions are being developed by banks to leverage the benefits of blockchain, to improve their business agility and performance, and to make their business operations more efficient and secure. However, there are still cases where regular access to Internet is impossible or unreliable due to saturated networks or harsh environments, hence limiting the deployment of typical blockchain based solutions. In this context, an approach using a new connectivity technology is needed in order to increase mobile Internet services for any device to reach nearly 95% of the world population, instantly, simply by drawing on existing mobile phone networks, with no additional infrastructure development. We aim to give the user full bank access from their device, even if the device is not a smart one, using ordinary mobile phone networks. However, providing efficient and secure communications over lossy and low bandwidth networks remains a challenge. The main objective of this paper will be to design an end-to-end and low overhead secure solution for the communications between mobile devices and their corresponding remote application servers that using blockchain via ordinary mobile networks .
Improving Model Performance

In order improve the performance of a neural network model, a number of ways have been studied. In this chapter, minibatch and k-fold cross-validation are explained. The basic idea of these two methods is on controlling the dataset, since repeated usage of the same dataset for training and validation might result in overfitting. Furthermore, regularization of the neural network model training by L-norm regularization and dropout of hidden nodes are explained in this chapter to avoid overfitting.
Comparing Naïve Bayes, Decision Tree and Logistic Regression Methods in Fraudulent Credit Card Transactions

Data mining is utilized to explore banks' data to unravel any hidden scams and detect potential frauds. The aim of this paper is to compare between the Naïve Bayes, Decision Tree and Logistic Regression in fraudulent credit card transactions. Cross-Industry Standard Process for Data Mining (CRISP-DM) is followed to achieve the aim of this research. In terms of accuracy, the best classification model was Logistic Regression with 94.6% accuracy, compared with the Decision Tree and Naïve Bayes that showed accuracy of 89.1% and 90.9% respectively. Other measures were also calculated like time needed to build the model among others.
Comparative Study Of Efficient Data Hiding Techniques

Abstract Data security is one of the fundamental concerns when it comes to modern networking and information sharing as any breach or data loss can end up causing major losses in terms of credibility, personal data and/or law suites. This paper studies data encryption and decryption techniques using algorithms like DES algorithm, 3 DES algorithm, AES algorithm and RSA algorithm. Cryptography was introduced for data security while transmitting data. However, only cryptography cannot give a robust security approach; as a result, a disorganized message can still be snooped [9]. This leads to a desire for information security. There are several techniques available, among those have taken a few of them and will review them considering cryptography measures. We will analyse the algorithm mentioned above, and try to find out better among them.
A novel family of 1-D robust chaotic maps

Abstract Chaotic dynamics of various continuous and discrete-time mathematical models are used frequently in many practical applications. Many of these applications demand the chaotic behavior of the model to be robust. Therefore, it has been always a challenge to find mathematical models which exhibit robust chaotic dynamics. In the existing literature there exist a very few studies of robust chaos generators based on simple 1-D mathematical models. In this paper, we have proposed an infinite family consisting of simple one-dimensional piecewise smooth maps which can be effectively used to generate robust chaotic signals over a wide range of the parameter values.
Selective pumping of a nonlinear quantum oscillator

Abstract Selective pumping of a quantum system under the influence of unmodulated short-range pulses of large amplitude is studied using the example of a weakly nonlinear Josephson oscillator. An exact solution is obtained in the case of small parameter anharmonicity for changes in the system energy and population of levels after impact. It is shown that due to the interference of state amplitudes, it is possible to stabilize the system and prevent undesirable excitations (energy growth). When anharmonicity is taken into account, the system stability conditions are studied numerically. The obtained results are important when analyzing leakage to high-lying levels in a transmon-qubit under the influence of short unipolar control pulses.
Weakly Supervised Semantic Segmentation Using Constrained Dominant Sets

The availability of large-scale data sets is an essential pre-requisite for deep learning based semantic segmentation schemes. Since obtaining pixel-level labels is extremely expensive, supervising deep semantic segmentation networks using low-cost weak annotations has been an attractive research problem in recent years. In this work, we explore the potential of Constrained Dominant Sets (CDS) for generating multi-labeled full mask predictions to train a fully convolutional network (FCN) for semantic segmentation. Our experimental results show that using CDS's yields higher-quality mask predictions compared to methods that have been adopted in the literature for the same purpose.
Serverless and Containerization Models and Methods in Challenger Banks Software

The article describes the perks of the serverless and containerization models and methods in challenger banks software. Data sovereignty in the context of microservices means that the stored model of the domain may differ same between subsystems. This approach to data modeling creates an easier way to write a query that merges data from various sources. Microservices intercommunication methods shows that typical microservices-based application is a distributed backend running as multiple processes, in most cases on multiple hosts in the same network. Service in the simplest case is just a process which can interact with other parts of the system. Methods of identification of the domain boundaries for each service imply the goal which should be to achieve the most meaningful separation leaded by your (or some expert’s in your team) domain knowledge. The main idea here is it’s not the size, that matters, but business capabilities and concerts separation. Methods for handling transactions in asynchronous distributed system show that each service is usually an isolated system apart with its own dedicated database. This means you can no longer take advantage of the 2PC to maintain the consistency of the whole system. Dealing with transient errors, eventual (in)consistency between microservices, isolations, and version rollbacks are scenarios that should be considered during the design phase.
Keyed Non-parametric Hypothesis Tests

The recent popularity of machine learning calls for a deeper understanding of AI security. Amongst the numerous AI threats published so far, poisoning attacks currently attract considerable attention. In a poisoning attack the opponent partially tampers the dataset used for learning to mislead the classifier during the testing phase. This paper proposes a new protection strategy against poisoning attacks. The technique relies on a new primitive called keyed non-parametric hypothesis tests allowing to evaluate under adversarial conditions the training input's conformance with a previously learned distribution $\mathfrak{D}$. To do so we use a secret key $\kappa$ unknown to the opponent. Keyed non-parametric hypothesis tests differs from classical tests in that the secrecy of $\kappa$ prevents the opponent from misleading the keyed test into concluding that a (significantly) tampered dataset belongs to $\mathfrak{D}$.
Eventually consistent distributed ledger despite degraded atomic broadcast

Abstract The distributed ledger or blockchain technologies originated from the Bitcoin have been rapidly widespread in recent years. However, it also gives incentive to malicious users who would like to break the system or take advantage of it (steal money, hide some information stored in the ledger, isolate a particular node from the rest of the network, and so forth). Thus, research focusing on overcoming potential attacks to distributed ledgers is required. In this article, we focus on attacks that damage underlying networks of distributed ledgers. Underlying networks offer useful communication primitives such as an atomic broadcast, however, such attacks may degrade the property of the primitives and make distributed ledgers relying on the primitives no longer work. Hence we should design algorithms to make the distributed ledgers still work even when some attacks degrade the primitives. As the first study for such situations, we consider a problem to implement distributed ledgers tolerating the degradation of an underlying atomic broadcast service that distributed ledgers are relying on. We consider the case where the uniform agreement property of the atomic broadcast is degraded, and propose new algorithms that could ensure to reach eventual consistency despite degraded atomic broadcast.
Mining Overlapping Communities in Real-World Health Data

The quantity of more seasoned individuals in western nations is continually expanding. The greater part of them like to live freely and are defenceless to fall episodes. Falls frequently lead to genuine lethal wounds which are the main source of ending life for senior individuals. To describe this issue, it is common to create strong fall identification frameworks. Right now, build up an AI structure for fall discovery and day by day living action recognition. The tale system is an AI framework for fall location and action acknowledgment. Right now, have proposed an AI approach for fall location and day by day movement acknowledgment. Calculated Regression is utilized in perceiving the exercises falling, strolling, strolling upstairs, strolling ground floor, sitting, standing, and lying dependent on the quickening and the rakish speed information. All the more especially, we utilize the mean estimation of the triaxial speeding up and accomplish a fall location exactness and accuracy.
THE INFORMATION BASE OF SIBERIAN AND THE FAR EASTERN LIBRARIES’ LOCAL HISTORY ACTIVITY

The article provides a brief overview of library local lore activity with an emphasis on the local lore direction in book studies and bibliography of the Siberian- Far Eastern region; names organizations providing information support for local lore activities of libraries; lists local lore resources on the websites of regional libraries in Siberia and the Far East. Special attention is paid to the resources generated by the State Public Scientific Technological Library of Siberian Branch of the Russian Academy of Sciences, which is the center for information support of local history research in Asian Russia, and obtains unique techniques to create regional databases, as well as highly qualified specialists. It presents a list of databases providing information support of theoretical and practical aspects of local history activity in the regional libraries. Authors give a detailed description of DB “Scientific Sibirika”, which includes regional material on a wide range of local history: history, ethnography, economy, nature and natural resources of certain territories of Siberia and the Far East, Arctic. It is shown that theoretical and methodological research in the field of library local history, individual persons, are presented in the “professional” DB “Librarianship and bibliography in Siberia and the Far East”, which has a special heading “Local lore activities of libraries in Siberia and the Far East”; the bibliologic direction of local lore is implemented in the bibliographic DB “History of books and book business in Siberia and the Far East”. The paper concludes about the importance of library local history in the library activities, and the need to attract information resources of large libraries for more complete implementation of local history research.
Research on Nonintrusive Load Decomposition of Enterprises Based on Bidirectional LSTM

To detect the operating condition of equipment and understand the environmental management situation of enterprises in real-time, this paper studies the non-intrusive load decomposition of enterprises based on bidirectional LSTM. In this paper, we first obtain the load characteristic parameters of different equipment in different states, and then obtain the electrical power measured from the main power meter of decontamination equipment in TOP-5 through the softmax layer of bidirectional LSTM, and then change the softmax layer to decompose the load data from the main power meter.
Face Determination Based on Multi-template and Convolution

A face determination algorithm based on multi-template is proposed, which is combined with convolution and dynamic programming. Firstly, three kinds of templates are synthesized using the experimental face dataset, including face, eyes, and mouth-nose templates, and then obtaining their weights and the optimal threshold. Secondly, the area of face, eyes, mouth and nose is cut from image, then realizing the size and number normalization, and calculating the three convolution values for detected image and the templates, and then the sum convolution values for face detecting are got by a weighted summation of them. Finally, according to the principle of threshold decision to judge whether the image is human face or non-face. Experimental verifications are conducted to evaluate the algorithm's performance on the acquisition, human face and non-face datasets. The results show that the algorithm runs faster, the accuracy is 94%, 84%.
Corner detection of a quadrilateral for strain analysis in sheet metal forming by image processing

Circle Grid Analysis (CGA) and Square Grid Analysis (SGA) are commonly used methods to measure the strain in sheet metal forming operations. In these methods, small diameter circles or squares are printed on a flat sheet and deformed to the required shape. The strain at any location of the formed part is evaluated by measuring the deformed circle (ellipse) or square (quadrilateral). Herein, an attempt has been made to use an image processing technique to detect the corners of a quadrilateral for automatic strain measurement in SGA. For this, different sizes of quadrilaterals are printed on a flat sheet, and images are captured using a USB microscope. These images have dense noise due to the capturing method as well as inherent surface defects on the sheet metal. Different noise reduction algorithms such as Hough Transform (HT), Gaussian filter of varying sizes, and Biorthogonal wavelets have been used to assess their efficiency in noise reduction. Among these, the HT method seems to be efficient in noise reduction. Further, the images are processed with the Harris corner detection method to detect quadrilateral corners. Finally, projective transformations have been used to fit the maximum size ellipse in the deformed quadrilateral for strain measurement.