Reviewerly/exHarmony huggingface dataset:
Randpay: The Technology for Blockchain Micropayments and Transactions Which Require Recipient's Consent

Randpay is a technology developed in Emercoin for blockchain micropayments that can be more effective in some scenarios than the Lightning Network, as seen in the paper. The protocol is based on the concept of Ronald L. Rivest published in the paper ‘Electronic Lottery Tickets as Micropayments’ (1997). The “lottery ticket” was designed for centralized systems where a trusted third party is required to provide payments, and in some scenarios, is also a lottery facilitator. The existing blockchain protocol cannot accommodate peer-to-peer “lottery” micropayments at least, without creating payment channels. Therefore, the implementation requires the development of an upgrade to the blockchain core. In the result, RandpayUTXO was introduced – an infinitely spendable zero output that requires the payee’s signature to be published in the blockchain. Randpay is considered to be the first blockchain protocol to require the payee to sign the transaction by their private key. This is a significant feature to improve, not only microtransactions but also extend the use of the blockchain for legal deeds that require a payee’s consent to be recognized in legal applications. The second important innovation of this research is the implementation of Blum’s ‘coin flipping by telephone’ problem to design a ‘lottery ticket’ that does not require any third party to facilitate the lottery. The paper offers an analysis of the mathematical model, and proof of how ‘lottery’ can be beneficial, an API description is also added. There is also an attack analysis and an overview of existing solutions.
Bayesian Nonparametric State-Space Model for System Identification with Distinguishable Multimodal Dynamics

The goal of system identification is to learn about underlying physics dynamics behind the time-series data. To model the probabilistic and nonparametric dynamics, Gaussian process (GP) has been widely used; GP can estimate the uncertainty of prediction. Traditional GP state-space models, however, are based on the Gaussian transition model, and thus they often have difficulty in describing more complex transition models, e.g., aircraft motions. To resolve the challenge, this paper proposes a framework using multiple GP transition models that is capable of describing multimodal dynamics. Furthermore, the model is extended to an information-theoretic framework, the so-called InfoSSM, by introducing a mutual information regularizer helping the model to learn interpretable and distinguishable multiple dynamics models. Two illustrative numerical experiments in a simple Dubins vehicle and high-fidelity flight simulator are presented to demonstrate the performance and interpretability of the proposed model. Finally, this paper further provides a use case of InfoSSM with Bayesian filtering for air traffic control tracking.
Forgery Detection For High-Resolution Digital Images Using FCM And PBFOAAlgorithm

Image forgery detection is the area of research in the field of biometric and forensics. Digital pictures are the resource of data. In the present world of technology, image processing software tools have developed to generate and modify digital images from one location to another. With the current technology, it is simple to establish image forgery by addition and subtraction of the components from the pictures that lead to image interfering. Copy-move image forgery is created by copying and pasting the element in a similar image. Hence, copy-move forgery has become an area of research in the image forensic unit. Various methods have been implemented to detect digital image forgery. Some issues still required to resolve like time complexity, fake, and blurred image. In existing research, the block and feature-based approach used to remove a forged area from the image using SIFT and RANSAC algorithm. The forgery dataset of the 80 pictures collected to achieve accuracy of up to 95%. In the research work, the PBFOA method has been implemented to optimize and extract the features using the component analysis method. FCM is used for image segmentation in the input image. PBFOA is based on an optimization process to select valuable features based on the calculation of the fitness function. In this method, two steps are used to re-verify the instance, features (i) Slower and faster condition. BFOA steps are described in detail in this research paper. Initial steps, Spread the feature set in the whole system. In the rapid condition selected and to eliminate the valuable features one at a time, then reproduction phase is implemented with the help of the fitness function to recover the feature values and detect the forgery information in the uploaded image. The simulation setup using MATLAB 2016a version and improve the accuracy rate and image quality parameter. Performance analysis depends on the proposed metrics FAR, FRR, ACC, Precision, Recall, and compared with the existing methods.
Parametric Genetic Programming

In conventional Genetic Programming for regression problems, the whole population evolves to adapt to the training data. However, individuals cannot adjust themselves to provided data, which results in slow convergence. On the other hand, gradient-based methods such as gradient descent quickly converge toward the solution by using local gradient information. Local search methods can be applied in Genetic Programming at an individual level, thus introducing local adaptations. In this paper, we propose to use the inner product of the input data with the parameter vector, which is updated using the Gradient Descent method. This approach is beneficial for two reasons: (i) gradient descent optimizes models created by the evolutionary process, thus decreases the search space of the GP, and (ii) inner product reduces the dimension of the input data, thus allows GP to efficiently handle high-dimensional data. Additionally, we propose an efficient algorithm to obtain information on the gradient. The proposed method is tested for regression problems on datasets with 10 or more dimensions. Results show that the proposed method converges to solutions faster than the conventional GP, at the same time providing models comparable to models created by SVM.
TAMIZHİ: Historical Tamil Brahmi Handwritten Dataset

This study focuses on creating offline benchmarks for handwritten characters in the local Tamil Brahmi language in India. This study has generated corpus of 1 lakh and 92000 character of brahmi script by getting offline handwritten scripts from 1600 writters. Hough transform and contour techniques are used to segment and extract scripts from the sheet. The offline data is also stored in csv format. The database is, however, intended to be useful for the identification of linguistic tasks. It will encourage the research work on Tamil Brahmi script handwriting recognition by providing free access to researchers. The designed dataset consists of 9 vowels, 18 consonants, and 209 classes so that the researchers can use machine learning algorithms to perform an extended research analysis. Handwritten data is collected and applied to create any linguistic frameworks for script analyzer.
RESEARCH LIBRARY IN THE SYSTEM OF KNOWLEDGE MANAGEMENT (BASED ON PUBLICATIONS)

System of knowledge management stability under the conditions of dynamic changes of social medium objectively depends on the so-called “code of values” or “culture”. Goals of the system functioning, priorities of development, criteria of quality and effectiveness of activities in achieving these objectives depend on the prevailing paradigm in realizing tasks facing the object. The ever-growing stream of information makes the process of taking decisions more and more complicated. Modern research library is a very important structural element of the knowledge society because, on the one hand, it provides free access to information, achievements of science and arts, on the other hand – actively takes part in developing informational and communicational infrastructure as well as science and education. However, to be a direct productive force information has to transform into knowledge. This problem becomes more urgent in the context of the global paradigm – the economy based on knowledge. In this connection on the new stage of social evolution significance of social institutions, including libraries, engaged in producing, storing, processing and disseminating knowledge for their subsequent practical application is growing. The aim of this article is to give a brief review of publications, devoted to building up the system of knowledge management and the place of library in it. Methods, used in the research are: content analyses and generalization of native and foreign publications on the above mentioned problem.
Recognition algorithm of handwritten symbol based on neural network

It has become a trend for computers to automatically mark examination papers. This paper presents an algorithm for automatically correcting blank filling questions. We first use edge detection algorithm to find the location of the answer, and cut the storage. Then, the connected domain is used to cut out the characters in the answer and store them as MAT files. Finally, we send these characters to the improved lenet-5 convolutional neural network for recognition. Experiments show that the algorithm can correctly review the blank questions.
An e-Voting Model to Preserve Vote Integrity Employing SHA3 Algorithm

Democracy is a government “of the people, by the people, and for the people.” But, in reality, the democratic processes are not trustworthy. One of the crucial democracy-oriented progression is an act of voting. When voting comes into consideration, it brings many feelings in reflection, like fraud, threatening, and cheating. But at some level, e-voting can introduce transparency in the process. Digitalization of any process always leads some security and privacy issues which further leads to democracy tempering. Voter authentication, privacy preservation, and integrity are the three essential principles of e-voting which needs to be indispensably implemented along with. Integrity plays a vital role to increase firm belief in such democratic processes. This research has been carried out to bring integrity in e-voting system to achieve security, trustworthiness, and coherence toward the system. To achieve the expressed objective, secure hash algorithm of family 3 has been implemented to generate message authentication code before final vote transmitted.
On Computer Assisted Language Learning (CALL) and Change of Teachers’ Role

Abstract In recent years, being a fast developing method in language learning area, the Computer Assisted Language Learning (CALL) is widely used in language learning in the world. CALL provides a totally different new mode for language learning. Hence, it increases quality and efficiency of language learning. Through perfect combination of contents, process and computer-assisted, CALL pays attention to stimulate students to learn languages with the assist of computer and pursues best learning results. With the brief introduction of CALL, the thesis fully explores teachers’ roles in the Computer-Assisted Language Learning. With the new achievements in Computer Science, Information Technology and Psychology, CALL will be closely connected with Computational linguistics to increase the intelligence of language learning system.
Forensic analysis of ReFS journaling

Since the analysis of file system is a fundamental step in forensic investigation, file system forensics has been steadily researched. Especially, NTFS forensics has been mainstream research as it is used by Windows, a globally most-used operating system. When investigating NTFS, journaling analysis is an important procedure as it can identify which files are created, modified, and deleted. Meanwhile, Microsoft developed the Resilient File System (ReFS), which is also used in Windows, to maximize data availability; ReFS is also expected to be a popular file system. Similar to the $Logfile and the $UsnJrnl of NTFS, there are artifacts in ReFS: the Logfile and the Change Journal that document information regarding changes to the system. In this paper, we present the structure and operation of the Logfile and the Change Journal. By kernel reverse engineering, we identify that the ReFS artifacts related to journaling are quite different from the NTFS artifacts; the ReFS artifacts use new record formats, named Log Record and USN_RECORD_V3, and the metadata of ReFS handling journaling files is distinct from that of NTFS. Through experiments, we identify logging patterns of transaction record and examine the mechanism of ReFS journaling. In this process, we enhance the knowledge of the metadata and structure of ReFS presented by previous research. Based on the result of our research, we also propose a forensic methodology of ReFS journaling and develop a tool, Awesome ReFS Investigation tool (ARIN), which is an open-source for analyzing the ReFS journal. These outcomes may provide considerable assistance to a forensic examiner trying to investigate ReFS volumes.
Invariance, Encodings, and Generalization: Learning Identity Effects With Neural Networks

Often in language and other areas of cognition, whether two components of an object are identical or not determines if it is well formed. We call such constraints identity effects. When developing a system to learn well-formedness from examples, it is easy enough to build in an identity effect. But can identity effects be learned from the data without explicit guidance? We provide a framework in which we can rigorously prove that algorithms satisfying simple criteria cannot make the correct inference. We then show that a broad class of learning algorithms, including deep feedforward neural networks trained via gradient-based algorithms (such as stochastic gradient descent or the Adam method), satisfies our criteria, dependent on the encoding of inputs. In some broader circumstances, we are able to provide adversarial examples that the network necessarily classifies incorrectly. Finally, we demonstrate our theory with computational experiments in which we explore the effect of different input encodings on the ability of algorithms to generalize to novel inputs. This allows us to show similar effects to those predicted by theory for more realistic methods that violate some of the conditions of our theoretical results.
Teaching Demonstration of Reducer Based on Augmented Reality Technology

Augmented reality technology is developed from virtual reality technology and has more mature applications in the fields of tourism, military, engineering, and education. The traditional form of classroom teaching is relatively simple, and has improved in terms of realism and visualization. This article proposes the application of Augmented Reality (AR) technology to classroom teaching, making full use of the advantages of the combination of virtual and real AR technology, interactive operation and immersive experience, and developed the “Mechanical Design” course including standard parts, transmission structure, A series of virtual product objects including example machinery, etc., and packaged as an application, to achieve the simultaneous three-dimensional display and operation of virtual objects and printed drawings. In the classroom, students can use Ipad, Android mobile phones and other electronic devices to install and use, to achieve the principle analysis, example display, content expansion, etc. of the teaching content, so as to achieve the effect of vivid and three-dimensional classroom teaching, rapid understanding and mastering of the course content.
Challenges in Mineral Exploration

No terrain, province and even continents are endowed with full spectrum of mineral resources. The non-secular variations in distribution pattern of ore deposits compels all nations for a prudent management of the available resources; and strive for the best exploration practices that are in consonance with the geological framework of their territories. The challenges in mineral exploration mainly emerge from steady declining rate of new mineral discoveries at global level. This chapter deals with the geological and technological aspects of the challenges that need consideration to offset the declining rate of new mineral discoveries of economic significance.
Verifiable Secret Sharing Scheme Using Merkle Tree

In Shamir's (k,n) secret sharing scheme, the distributor splits a secret into n shares(shadows), and sends the shares to n participants. Each participant has a different share. In the phase of secret reconstruction, only K or more participants with their shares can reconstruct the secret together, less than K participants can't reconstruct the secret, and know nothing about the secret. Shamir's scheme is unconditionally secure in theory. However, this scheme can't prevent adversaries from cheating. In asynchronous communication, a dishonest participant or a foreign adversary sends a fake share to the honest participants after he gets the shares from other participants and will reconstruct the secret alone while the other honest participants cannot reconstruct the secret. This scheme does not verify the share and identify the participants. In this paper, an efficient share verification method based on Merkel tree will be discussed, in which the root and authentication paths of a Merkel tree are used to verify shares between the participants, so that they can reconstruct secrets correctly after verifying and eliminating the fake shares. This method does not need complex algorithms and estimating the number of cheaters in advance, and will not increase the size of each share.
Noise Intensity Estimation Method Based on PCA and Weak Textured Block Selection for Neutron Image

Abstract Noise intensity estimation has a very important application in image denoising. In image processing, the denoising method can achieve an ideal denoising effect under the assumption that the Gaussian noise intensity in the image is known. But in real denoising applications, especially the neutron image, the noise level is unknown, which will greatly affect the denoising effect of neutron image processing. In this paper, a method which combined the principal component analysis with weak texture block selection is proposed for noise intensity estimation of neutron images. The experimental results show that the proposed method can accurately estimate the Gaussian noise in the neutron image. Compared with the existing noise intensity estimation methods, the qualitative and quantitative results show that the proposed method has higher accuracy and stability.
Feature Quantization for Many-to-many Voice Conversion

In recent years, Voice Conversion (VC) has made great strides with the application of deep learning. Many works on many-to-many Voice Conversion task require to have parallel training data and multiple generative adversarial networks. One of the successful models for VC is the Star generative adversarial network (StarGAN-VC), which does not require parallel training data and can use a single generator and discriminator for all pairs of speakers domains. However, training a high quality StarGAN is not an easy task. Using a single generator and discriminator with mini-batch statistics to perform feature mapping will result in an insurmountable gap between the converted distribution and the real distribution. In this paper, we proposed a Feature Quantization model plugged into the discriminator of StarGAN-VC2, which can quantize the continuous feature into a discrete embedding space to solve the feature mapping problem and improve the quality of converted speech. Experiments show that our proposed model can improve the MCD scores of baseline methods which confirm the efficiency of the proposed method.
Multi-grained Fusion for Conditional Image Retrieval

We tackle a new task conditional image retrieval, where the query includes an image and a corresponding additional text, with the aim to search the most similar target images in the whole gallery database. The additional text describes the additional conditions for the query image. Previous methods always represent the query pairs by fusing features at the deeper layer, which neglects the fine-grained relationships between the image and the different stages of the sentence. In this paper, we propose a Multi-Grained Fusion (MGF) module to mine the multi-grained relationships in the query pairs to fuse features more effectively. To further improve the performance, we propose an unsupervised Online Groups Matching (OGM) loss to make the feature include more identity information. Extensive experiments show that our method outperforms other state-of-the-art approaches.
A Matching Network Focusing on the Relation between Samples

Deep learning plays an important role in the field of artificial intelligence, but in most situation, deep learning requires a larger number of training samples to complete high-precision learning. Few-shot learning, which only uses a small dataset to learn different tasks, has been extensively researched. It can be summarized into three methods: metric learning, optimization-based and model-based. In this paper, we develop a novel method based on Matching Networks: focusing on the relation between samples. We have proposed a simple method based on the idea that we can aggregate intra-class samples and separate inter-class samples. Our experiments on the Omniglot dataset and miniImageNet dataset show that our method outperforms the baseline networks.
Development of Creative Thinking Support Method through Text Mining for Inspiration

In this study, for multiple keywords, we collect text data on those keywords from cyberspace by crawling and scraping. Then, natural language processing is used to reduce the text data to word groups of nouns that are considered to be important, and the importance of each word is calculated from the transition probability between words, and a network is generated. In addition, we construct a system to support idea generation by drawing 3D directed graphs to check the strength of word associations from arbitrary viewpoints.
Facial Emotion Recognition to Examine Human Face Using Hybridization Method

Emotion recognition is a method used to inspect the ones internal feelings on a human face by using a technique Facial recognition technology can be used for authentification, recognition, and verification across various industries, including KYC, payment verification, and access control. Moreover, its aptitude is first to inspect facial expressions for emotions like happiness, sadness, surprise, anger, etc., Facial expression recognition is progressively receiving attention now a days. A person will express feelings through their emotions. The expressions are the exterior signals communicate the internal feelings of a person. Face appearances are divided into 7 feeling states (neutral, happy, sad, disgust, surprise, fear, and anger). The present paper is about hybrid method for face expression or emotion recognition. LBP and PCA are two methods individually used to recognize face. LBP method is used for local feature extraction. LBP method is incredibly sensitive to noise and can’t distinguish between a powerful and a weak pattern. PCA used for global feature extraction. An Hybridization method can be used for face expression recognition to improve the authentication process.