Reviewerly/exHarmony huggingface dataset:
Pix2pix network for fingerprint texture image synthesis

GANs (Generative Adversarial Networks) were proposed to generate realistic synthetic images. In this work, we will discuss the use of GANs as alternative reconstruction of different fingerprint images from the original ones. The samples result in the same person fingerprint but obtained with other textures. Thus, it is intended to contribute to improving the method to increase databases with new samples, incorporating textures, when the quantities are insufficient for any purpose. To verify the similarity of the synthesized images with the original ones, a convolutional Xception network and the RMSE metric are used. The results obtained with fingerprint images of 3 persons, 20 of each finger, and 4 different textures, showed the tradeoff between similarity, recognizability, and the number of epochs of the Pix2pix training.
Genetic-Cuckoo fusion algorithm

The genetic algorithm is an intelligent optimization algorithm derived from the law of biological evolution. Due to the weak local optimization ability of the algorithm and the increase in the number of iterations, it is easy to cause the lack of diversity of the entire population to affect the search effect. The Cuckoo search algorithm uses the Levy flight to update the position and status. The Levy flight can help the Cuckoo search algorithm avoid the shortcomings of local optimality and insufficient population diversity in the optimization process, so this paper proposes an improvement that integrates Levy operator genetic algorithm. The test function is used to test the improved genetic algorithm optimization ability and robustness and compare it with the genetic algorithm, Cuckoo search algorithm, and particle swarm optimization algorithm. The simulation results show that the algorithm has good optimization ability and robustness, and the algorithm is significantly better than the original genetic algorithm in optimization accuracy and robustness.
A hybrid Chinese named entity recognition method for Internet of Things

In order to facilitate government departments to assess security risks and prevent infiltration, it’s necessary to recognize the IoT device from open data by the method of Named entity recognition (NER). In this study, a hybrid method of chinese NER which contains neural networks and templates is proposed to extract IoT device information from open web. The model first initiates characters of input through BERT, which can create character embedding while reduce dependency with external datasets. Then, the initiated vectors are fed into a multi-layers BiLSTM model to encode the contextual representation of each character, and a linear CRF layer after BiLSTM is used to assign the scores to every character for entity annotation. In addition, a dictionary and rule base is constructed based on the characteristics of IoT devices to correct the annotation results. We experimented the methods on the dataset of IoT, the results prove the proposed model achieves better recognition effect than other models, and the F-scores value is 88.9%.
Withdrawal: “Research on the application and innovation of big data in agricultural development”

The above article, published online on 25 March 2022 in Wiley Online Library (wileyonlinelibrary.com) as an Accepted Article, has been withdrawn by agreement between the journal Editor, Silvia Pampana, the American Society of Agronomy, and Wiley Periodicals, Inc. The withdrawal has been agreed due to plagiarism.
Diversifying Neural Dialogue Generation via Negative Distillation

Generative dialogue models suffer badly from the generic response problem, limiting their applications to a few toy scenarios.Recently, an interesting approach, namely negative training, has been proposed to alleviate this problem by reminding the model not to generate high-frequency responses during training.However, its performance is hindered by two issues, ignoring low-frequency but generic responses and bringing low-frequency but meaningless responses.In this paper, we propose a novel negative training paradigm, called negative distillation, to keep the model away from the undesirable generic responses while avoiding the above problems.First, we introduce a negative teacher model that can produce querywise generic responses, and then the student model is required to maximize the distance with multi-level negative knowledge.Empirical results show that our method outperforms previous negative training methods significantly. 1
Crow search algorithm with dynamic progressive strategy

Crow search algorithm is a kind of swarm intelligence optimization algorithm, which simulates the behavior of crows tracking each other. In order to improve the convergence accuracy of the algorithm and better balance the local and global search strategies, this paper proposes the crow search algorithm based on dynamic progressive strategy (DPCSA). The DPCSA uses a reverse search mechanism to improve the convergence speed, introduces the dynamic progressive strategy into the key parameters: awareness probability and flight length, adds the global optimal position information to the position updating dynamically. These improved strategies make the simulation of optimization process more reasonable. In order to evaluate the effectiveness of DPCSA, a series of test functions are used for experiments and the results show that the improved algorithm has good performance.
Grandfather effects in Laoling disyllabic tone sandhi

In Optimality Theory, grandfather effects refer to the phenomena in which a structure that already exist in the input is allowed to surface while the same structure that is derived (i.e. not in the input) is forbidden. Most studies on this issue focus on segmental processes, and only few on tonal processes. This study presents the data of Laoling regular disyllabic tone sandhi, which demonstrates a grandfathering effect where some tones are allowed in non-derived contexts but banned in derived contexts. We propose a constraint-based analysis adopting Comparative Markedness (McCarthy 2003) constraints.
DESIGN AND DEVELOPMENT OF ACCOUNTING INFORMATION SYSTEM FOR CASH SALES OF MILD STEEL

Technological developments are now experiencing very rapid development, so many entrepreneurs are competing to produce new companies. At this time the light steel business is growing rapidly. Researchers try to make research on the cash sales system of mild steel sales, most companies do not yet have a computerized recording system, so that the invoice input process is hampered, as well as sales notes that are still written manually, and sometimes errors still occur in reading the names and quantities of goods. ordered, so the report generation will take a long time. As a form of solving the problems encountered by researchers, a computerized financial recording system is better than a manual system.Sales System, Cash Sales System, Information System
Design and Implementation of Solar Based Electric Wheelchair

This paper concentration on the reducing expenditure for the mechanically setup &amp; designation of a solar based electric wheelchair with solid structure design and control system for the handicapped persons. In a country like India, most of the people distressed of temporary or permanent disabilities due to illnesses or accidents there are some person can’t afford to be expensive electric wheelchair. That’s why a developed of electric wheel chair is proposed in this paper which contains more features and a developed control system along with the reduced of manufacturing price &amp; design about two to three times of recent market cost.
Contradiction Detection and Repair in a Large Theory

As with any software, the challenges of developing large andmanually-created axiomatizations in an expressive logic suchas first order logic with equality can be very different fromthose found in comparatively small theories. We present someof the tools and practices that have supported development ofa logical theories with tens of thousands of statements, andensured that they are free of logical contradiction, and suit-able for automated theorem reasoning.
E- Notice Board System

A University with its cutting edge state of the art infrastructure heavily relies on communication and information processing from one key location to another. This research paper focuses on Rashtrasant Tukdoji Maharaj Nagpur University. As its case study, and appears at the development of an records processing infrastructure containing hardware and software program modules that are set up and ported on a number key locations within an institute. Although with such excessive precedence of communication, most of the universities nonetheless use a timber word board striking on a wall to show daily announcements or paper primarily based attendance system. The over-reliance of this practice in a college is still now not adequate to skip applicable statistics around. The foremost electricity of the lookup work is its usability and facts processing in the course of the tutorial or business vicinity. The modules include a large spread touch display appearing as a digital notice board showing facts to students. An admin panel that would handle network communication, software interface created via web technology and teacher interface that would upload information to be displayed on the notice board.
Structural Parametric Identiﬁcation of Linear Dynamic Objects

The problem of structural-parametric identification of linear stationary dynamic objects under conditions of active experiments is considered. The question of the adequacy of dynamic models based on the analysis of the object’s response to a typical test impact is investigated. These studies allow us to formulate an important methodological aspect that is recommended to be taken into account in new developments of active identification algorithms. In this paper it is shown that in real conditions of interference, the classical methodology of constructing the transfer function of an object by means of the analysis of data single experiment, i.e. the response to a test effect can give incorrect results. In this regard, another approach is proposed — to form the base of the experimental data starting from a series of identification experiments, and because of its to investigate the dynamic structure of free and forced movements of the object. The ideological basis of this approach consists of the following solutions: the principle of " probing" the space of states of an object in order to determine its dynamic order; the mechanism of excitation of free movements in the object by means of finite control actions; the integral-operator form of representation of the dynamics of the object, which avoids the need for differentiation of input and output signals; the apparatus of linear regression for parametric identification of the transfer function of the object. One the base the given approach a new method of structural-parametric identification of linear stationary dynamic objects is developed and investigated. The presented results and computer approbation in the MATLAB environment have shown the operability and effectiveness of this identification method.
Virtual Spy (Automating the Role of Third Umpire in the Game of Gully Cricket)

A fair decision is important in any game to give justice to the game. Any wrong decision due to a person’s misunderstanding can be caused by the outcome of the game. Computer vision and image processing techniques have been cited in book reviews that have used multiple cameras to display. This paper focuses on a system that helps the umpire to make decisions such as no ball, Running, out, etc. with the help of a good quality smartphone camera. The Decision Review System (DRS) aims to provide decisions such as run-out and stump-out. Tkinter is used to develop the GUI of DRS. Object classification and object recognition is implemented using Histogram of Gradients (HOG) and Support Vector Machine (SVM). To detect the cricket ball from the video we optimized and used frame subtraction, contour detection and minimum enclosing circle algorithms using OpenCV library. Linear regression and quadratic regression are used to track and predict the motion of the ball from video source. VPython is used for the visual representation.
INFORMATION TECHNOLOGIES FOR INDEPENDENT PRESENTATION OF NATURAL LANGUAGES

The new notion: independent interactive computer presentation of an object introduced by the au-thors is developed in the paper with application to natural languages (the user is not required to know other similar objects; knowledge of any other language is not required during learning a language). Then the user’s thoughts appear in the learned language immediately. The main feather of the method proposed is presentation of verbs naturally, as actions by the user. The hypothesis of the investigation: modern capacities of computer equipment connected with feedback are sufficient to learn base of a natural language by the user. Definitions of permanently instable (affectable) object and of language, of mathematical and computer models of a notion, ways to introduce new notions and using of “media” and of “relations” are given. The results obtained are used to develop new types of softs.
Performance comparison between Arduino based dual axis solar tracker and fixed module

The purpose of this study is to create an overview of sun on track system that can enhance the power of photovoltaic panel on a tracker. Its functioning idea is to maintain the solar panel aligned every time, maximising the solar cell exposure to the Sun rays and allowing it to create more output power. The project’s work includes hardware implementation, as well as Arduino based programming for the solar tracker’s Arduino unit. An audio was used to control the action of two step up motors that rotates the solar panel in two different directions. The Arduino calculates the sum of motion based on data collected from four photo sensors near the solar panel.A working solar tracking system is designed and implemented at the end of this project. It can also maintain the solar cell combined with the sun. The solar tracker made from this research serves as a model and a beginning point for the developments in the upcoming future.
Enabling Dynamic Multi-Client and Boolean Query in Searchable Symmetric Encryption Scheme for Cloud Storage System

Searchable symmetric encryption (SSE) provides a safe and effective solution for retrieving encrypted data on cloud servers.However, the existing SSE schemes mainly focus on single keyword search in single client, which is inefficient for multiple keywords and cannot meet the needs for multiple clients.Considering the above drawbacks, we propose a scheme enabling dynamic multi-client and Boolean query in searchable symmetric encryption for cloud storage system (DMC-SSE).DMC-SSE realizes the fine-grained access control of multiclient in SSE by attribute-based encryption (ABE) and novel access control list (ACL), and supports Boolean query of multiple keywords.In addition, DMC-SSE realizes the full dynamic update of client and file.Compared with the existing multi-client schemes, our scheme has the following advantages: 1) Dynamic.DMC-SSE not only supports the dynamic addition or deletion of multiple clients, but also realizes the dynamic update of files.2) Non-interactivity.After being authorized, the client can query keywords without the help of the data owner and the data owner can dynamically update client's permissions without requiring the client to stay online.At last, the security analysis and experiments results demonstrate that our scheme is safe and efficient.
Deep Learning-Based Super-Resolution and De-Noising for XMM-Newton  Images

The field of artificial intelligence based image enhancement has been rapidly evolving over the last few years and is able to produce impressive results on non-astronomical images. In this work we present the first application of Machine Learning based super-resolution (SR) and de-noising (DN) to enhance X-ray images from the European Space Agency's XMM-Newton telescope. Using XMM-Newton images in band [0.5, 2] keV from the European Photon Imaging Camera pn detector (EPIC-pn), we develop XMM-SuperRes and XMM-DeNoise deep learning-based models that can generate enhanced SR and DN images from real observations. The models are trained on realistic XMM-Newton simulations such that XMM-SuperRes will output images with two times smaller point-spread function and with improved noise characteristics. The XMM-DeNoise model is trained to produce images with 2.5x the input exposure time from 20 to 50 ks. When tested on real images, DN improves the image quality by 8.2%, as quantified by the global peak-signal-to-noise ratio. These enhanced images allow identification of features that are otherwise hard or impossible to perceive in the original or in filtered/smoothed images with traditional methods. We demonstrate the feasibility of using our deep learning models to enhance XMM-Newton X-ray images to increase their scientific value in a way that could benefit the legacy of the XMM-Newton archive.
Improved sparrow search algorithm combining adaptive mutation and proportion adjustment strategy

Aiming at the problems of low optimization accuracy, slow convergence speed and easy to fall into local optimization of sparrow algorithm, an improved sparrow search algorithm combining adaptive mutation and proportion adjustment strategy(AISSA) is proposed. Firstly, the nonlinear decreasing inertia weight is used to adjust the global and local search ability of SSA. Secondly, the producer-scrounger proportion adjustment strategy is introduced to adjust the proportion of producers and scrounger in the iterative process, so as to improve the optimization accuracy and convergence speed of the algorithm. Finally, Tent chaotic mutation and adaptive t-distribution mutation are carried out for individuals with different fitness values to improve the local optimization ability of SSA and speed up the convergence. The final results show that AISSA has higher optimization accuracy and better stability than SSA.
Threat object detection and analysis for explosive ordnance disposal robot

Explosive Ordnance Disposal (EOD) robots are useful in military applications like the safe disposal of explosives. However, many of these robots do not have the capability to identify threat objects using their onboard vision system due to data unavailability for training an improvised explosive device (IED) detector. As a solution, this study used image processing and object detection algorithms to detect and analyze threat objects inside the baggage. A threat object detector was developed and composed of two separate modules such as baggage detection and IED detection and analysis modules. The experiments showed that baggage detection achieved 22.82% mean average precision (mAP) using Single Shot Detector (SSD) in the Microsoft Common Objects in Context (COCO) dataset, while IED detection achieved 77.59% mAP using Faster R-CNN in the X-ray dataset. The threat objects from the X-ray image were also analyzed using image processing techniques to get the dimension of the object and the distance from a reference object. Also, the baggage detection module was successfully deployed in Jetson TX2, which runs at a frame rate of 12 frames per second (FPS).
Modified Content-Based Filtering Method Using K-Nearest Neighbors and Percentile Concept

In the age of information, vast amount of data is within the grasp of everyone.The availability and amount of information is absurd that it could lead to information overload.Recommender systems exist so that it could recommend information that are relevant and appropriate based on user's preference.Content-based filtering (CBF) is a recommender system approach that focuses solely on user preference and content of an item.CBF works by recommending items that satisfies user's interest based on user's previously liked items.CBF suffers the problem of overspecialization or also called the serendipity problem.Overspecialization occurs when the items that are being recommended is very similar to the previously liked item of the user, thus, not being able to recommend unexpected recommendations.The researchers used a pure content-based approach in eliminating the overspecialization problem.The researchers' first method is to use K-Nearest Neighbors (KNN) algorithm to find the nearest neighbors of the top recommended items.The researchers' premise is to recommend similar items of similar items.The researchers' second method is to use the percentile concept in the cosine similarity matrix of all the items.This method lets the researchers prevent overspecialization by recommending items that are in the lower percentiles since overspecialization occurs in the higher percentiles.The result of this study shows that the first and second are effective in preventing overspecialization because these methods recommended unexpected yet relevant items.
Real time face recognization attendance management system

Real Time face recognition system which is worked on the scenario of recognition of face detection and further also we used as it for attendance marking purpose. Marking attendance in the classroom or in any institution level during ongoing period of time is not only headache activity but also a time consuming. Due to an unpredictable or an unusually high number of students which are present during the ongoing lecture so there will be high chance of probability of proxy’s attendance if the guide did not connect the class so there are highly chances. Attendance marking system with conventional types of method has been an area of challenges occurs. Technology plays a significant part in today's society. One of the most extensively utilized technologies is face recognition. It can be used for security, authentication, and identity, as well as unlocking phones. It is extensively utilized, despite having low accuracy when compared to iris detection and fingerprint identification. Furthermore, this technology is employed in a variety of settings, including schools, colleges, hospitals, and a variety of others.
Maintaining Argument In Talk Show Interaction: A Pragma-Stylistic Inclination of Michelle Obama In Oprah Interview

This research was taken from online media in the form of a Michelle Obama interview with Oprah Winfrey. First is Oprah's 2020 vision tour talk show. The talk show led by Oprah Winfrey tends to be more serious when discussing something that also affects the style of the conversations. Second, Oprah Winfrey Hold a Conversation on the United States of Women Summit by the white house. . The method used is descriptive qualitative by analyzing several utterances in Michelle utterances. This research accompaniment three research questions by analyzing the Pragmatic feature in Michelle Obama and the stylistic feature in Michelle Obama. Then the common patterns of Michelle Obama's arguments in her interviews. The results showed that Michelle maintains her argument through a pragmatic feature with adjacency pairs consisting of Q and A, Acceptance, Recognition, Agreement, and Approval. From a stylistic perspective, Oprah uses Hyperbole, Simile, Personification, Anaphora, and Parallelism. After the research compared both Michelle's interviews on Oprah's show, Michelle often uses the Agreement pattern and some of the figures of speech. During the research, researchers found the main idea of maintaining her argument. From maintaining that argument, the researcher also found three important points from Michelle's two interviews with her audience: Maintaining dignity, the importance of focusing on yourself, keep dreaming.
OVERVIEW OF EXISTING E-LEARNING TECHNOLOGIES IN THE KYRGYZ REPUBLIC

The article provides an overview of the existing technologies of distance learning in the Kyrgyz Republic: the history of the development and implementation of DL in the Kyrgyz Republic is described.