Reviewerly/exHarmony huggingface dataset:
Study and application of improved level set method with prior graph cut in PCB image segmentation

Purpose The segmentation of printed circuit board (PCB) images is an important process in PCB inspection. The circuit traces, pads and vias in a PCB are dense and curved, and the PCB image obtained using different cameras or in different conditions may exhibit a large image gradient, which leads to inaccuracy and inefficiency in the PCB image segmentation. This paper aims to propose an improved local binary fitting level set method with prior graph cut, aiming to improve the accuracy and efficiency of the segmentation of PCB images obtained using different cameras or in different environments. Design/methodology/approach First, the paper constructs a 4-connected undirected graph using a given PCB image and classifies it based on the graph cut. Second, an adaptive initialization level set is implemented using the priori information obtained from the graph cut. Finally, the paper constructs a priori energy term using the prior information and introduces it into the energy function of the level set. Findings The approach results in an improved accuracy of segmentation in the context of a large gradient within the image. Experimental results demonstrate that the method can solve the deviation of artificially initialized level set from targets and improve the efficiency and accuracy of segmentation. Research limitations/implications This study only considers level set method as the research object. Iteration of the level set method takes a long time for a given huge PCB picture, which makes it impossible to apply to scenes with high real-time requirements. Practical implications PCB image segmentation is an important process in the PCB inspection. Since template matching and morphology techniques are well-established, image segmentation quality has a significant impact on the accuracy of detection. Originality/value This paper studies the segmentation of PCB images, improves the efficiency and accuracy of segmentation and facilitates the subsequent applications, such as in the nondestructive testing of PCB.
Nepali POS Tagging Using Deep Learning Approaches

Deep Learning approaches are being extensively used in Part of Speech (POS) tagging. POS tagging is one of the important step in Natural Language Processing (NLP) including Machine Translation, Retrieval of Information, developing question answering system, word sense disambiguation, text summarization, Named Entity Recognition, text to speech conversion and classification. The efficiency of POS tagging heavily rely on syntactic, contextual information and morphology of the language. POS tagging in Nepali Language is very difficult as it is morphologically rich. This research paper focuses on implementing and comparing various deep learning approaches for POS tagging in Nepali Language. Recurrent Neural Network (RNN), Gated Recurrent Unit (GRU), Long Short Term Memory (LSTM) and Bidirectional LSTM were implemented in tagged Nepali corpus. The result of Bidirectional LSTM (Bi-LSTM) was better than other approaches. Keywords: POS, NLP, RNN, GRU, LSTM, Bi-LSTM, Nepali corpus
Design of Textile Knitted Stretch Sensors for Dance Movement Sensing

This paper presents research about design a textile-based stretch sensor for making a reliable sensing system for dance movement sensing. We examined sixteen textile-based conductive stretch sensors that were made with commercially produced conductive materials. Through the analysis of their sensitivity, linearity, hysteresis, responsiveness, and fatigue, a silver-plated conductive fabric, Technik-tex P130B, shows the best performance for dance movement sensing. Then, we tested the bonding technique, and washability of Technik-tex P130B-made sensors. The finding shows that bonding makes a noticeable impact on sensor performance. Both bonding and washing increase the sensor&rsquo;s resistance. Technik-tex P130B on one side bonding has the best performance for dance movement sensing. This reliable and washable textile-based stretch sensor is utilised for dance bodysuit making for further development.
Large-Scale Spectral Clustering Based on Representative Points

Spectral clustering (SC) has attracted more and more attention due to its effectiveness in machine learning. However, most traditional spectral clustering methods still face challenges in the successful application of large-scale spectral clustering problems mainly due to their high computational complexity <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M1"><mml:mrow><mml:mi>ο</mml:mi><mml:mfenced open="(" close=")" separators="|"><mml:mrow><mml:msup><mml:mrow><mml:mi>n</mml:mi></mml:mrow><mml:mrow><mml:mn>3</mml:mn></mml:mrow></mml:msup></mml:mrow></mml:mfenced></mml:mrow></mml:math>, where n is the number of samples. In order to achieve fast spectral clustering, we propose a novel approach, called representative point-based spectral clustering (RPSC), to efficiently deal with the large-scale spectral clustering problem. The proposed method first generates two-layer representative points successively by BKHK (balanced k-means-based hierarchical k-means). Then it constructs the hierarchical bipartite graph and performs spectral analysis on the graph. Specifically, we construct the similarity matrix using the parameter-free neighbor assignment method, which avoids the need to tune the extra parameters. Furthermore, we perform the coclustering on the final similarity matrix. The coclustering mechanism takes advantage of the cooccurring cluster structure among the representative points and the original data to strengthen the clustering performance. As a result, the computational complexity can be significantly reduced and the clustering accuracy can be improved. Extensive experiments on several large-scale data sets show the effectiveness, efficiency, and stability of the proposed method.
A Hue-Preserving Tone Mapping Scheme Based on Constant-Hue Plane Without Gamut Problem

We propose a novel hue-preserving tone mapping scheme. Various tone mapping operations have been studied so far, but there are very few works on color distortion caused in image tone mapping. First, LDR images produced from HDR ones by using conventional tone mapping operators (TMOs) are pointed out to have some distortion in hue values due to clipping and rounding quantization processing. Next,we propose a novel method which allows LDR images to have the same maximally saturated color values as those of HDR ones. Generated LDR images by the proposed method have smaller hue degradation than LDR ones generated by conventional TMOs. Moreover, the proposed method is applicable to any TMOs. In an experiment, the proposed method is demonstrated not only to produce images with small hue degradation but also to maintain well-mapped luminance, in terms of three objective metrics: TMQI, hue value in CIEDE2000, and the maximally saturated color on the constant-hue plane in the RGB color space.
Development of mathematical models of R-1 reactor hydrotreatment unit using available information of various types

Abstract The structures and model parameters of the hydrotreating reactor of a catalytic reforming unit were identified based on available information of various types. The structural identification of the developed reactor models was carried out on the basis of the ideas of the sequential switching of regressors method, and the parameters were identified using the least squares method modified for working in a fuzzy environment. The developed models have the structure of nonlinear regression models (for output, i.e. hydrogenated) and fuzzy multiple regression equations (for qualitative indicators of hydrogenated). When developing the models with the source information, we used available statistical and experimental data describing the operation of the reactor and fuzzy information representing the knowledge, experience and judgment of experts about the hydrotreatment process. Based on the simulation of the hydrotreatment reactor, a graph of the temperature dependence of the hydrogenate yield of the reactor is constructed. A linguistic model has been constructed that estimates the dependence of the temperature of the hydrotreating process on the quality of the feedstock. This model allows to determine the optimal temperature value in a fuzzy environment depending on the quality of the raw material being cleaned.
Group Structure of Special Parabola and Its Application in Cryptography

Public key cryptography is one of the most important research contents in modern cryptography.Curve-based public key cryptosystems have attracted widespread attention in recent years because they have more obvious advantages in speed and key length than general public key cryptosystems.People have done a lot of research on elliptic cryptosystem, among which the realization of elliptic cryptosystem is a key content.In this paper, the definition of special parabola in algebraic closed domain is proposed, the group structure of special parabola in finite field is studied, and several forms of public key cryptosystem based on this parabola are given.The results show that the parabola, together with the additive operations defined above, form an Abelian group.The radix of this parabola can be easily determined, so that the factors it contains can be large prime.The security of its public key cryptosystem is based on the difficulty of solving the discrete logarithm problem on this parabola.Moreover, these parabolic public key cryptosystems are easy to code and decode in plaintext, and easier to design and implement than elliptic curve public key cryptosystems.
Artificial Synapse with Mnemonic Functionality using GSST-based Photonic Integrated Memory

Here we present a multi-level discrete-state nonvolatile photonic memory based on an ultra-compact (&lt;4μm) hybrid phase change material GSST-silicon Mach Zehnder modulator, with low insertion losses (3dB), to serve as node in a photonic neural network. Emulating an opportunely trained 100 × 100 fully connected multilayered perceptron neural network with this weighting functionality embedded as photonic memory, shows up to 92% inference accuracy and robustness towards noise when performing predictions of unseen data.
Some approach to data masking as means to counteract the inference threat

The goal of the article is to reveal the essence of some approach to data masking stored in the database as a means to counteract the inference threat. This approach is based on the principles of random permutation of the elements of a data field of the row column of the production database table data and dynamic masking. A distinctive feature of the proposed solution is the approach to the process of data shuffling, namely, shuffling data value elements within the demanded row field. It is possible to mask both an entire value of the field of the table row column and its part using this solution. The proposed approach differs from most of the typical commercial tools for masking sensitive data in that a preliminary physical change of sensitive data is made in the production database, and a user who has the appropriate rights can cancel these changes if it is necessary. The legitimate user in the proposed approach gets access to sensitive data due to the ability to transform (rewrite) the query “on the fly”, and the attacker can only read the previously modified data that is stored in the database. The proposed approach to data masking can be used in both production and non-production databases, expanding the possibilities of so-called static data masking.
Smart Age Detection for Social Media

Over the recent years, there has been an immense attraction towards age detection due to its raised implementation in various sectors.Such as government regulations and rules, security control, and human computer interaction.Popular human features such as the face and fingerprints can be modified or changed with time.However, ear has a stable structure that does not change with time and have unique features that satisfies the requirements of a biometric trait.This research presents a detailed analysis extracting the features of the human ear only by applying Deep Learning techniques.In particular, Convolutional Neural Network (CNN) is applied on large datasets which have multiple layers to extract the features and classify them.The proposed methodology increased the number of the dataset by collecting more private children datasets, and consequently achieved high accuracy by 98.75% along with amending the architecture of the selected neural network compared to previous studies.This research can be benefited to control the contents of social media by detecting the age of group whether it is under 18 or above 18.
Permainan Kuda Bisik Untuk Meningkatkan Kemampuan Pembendaharaan Kosakata Anak Usia 5-6 Tahun

This research aims to see results of the development of the Kuda Bisik traditional game to improve the vocabulary of children in group B. The type of development used is to adapt development type of borg gall which has been simplified into 3 main stages namely; needs analysis; product design; and product trials. test subjects using 10 children in limited trials and 22 children from 2 schools with name Ummi Adniyah Pancor Kindergarten and Hamzanwadi Pancor Kindergarten. Data collection techniques used were observation; tests and documentation with instruments used observation sheets; product validation sheet; and test. The analysis technique uses the percentage of test results of learning outcomes with a classical success rate of 75%. The results of his research are in the initial trials of the product in the form of a Kuda Bisik media game well implemented. while the initial field of the 22 samples used from the two schools described the results of observations of the use of Kuda Bisik products to increase vocabulary of children, which began to develop as expected (BSH) by 7 people or with a percentage of 32% and 15 children or 62% is very well developed (BSB), and learning outcomes tests found 8 children or 36% developed as expected, and 14 children or 64% of children developed so well that the Kuda Bisik product was able to improve the vocabulary children age 5-6 years old
A Label Embedding Method for Multi-label Classification via Exploiting Local Label Correlations

Multi-label learning has attracted more attention recently due to many real-world applications (e.g., text categorization and scene annotation). As the dimensionality of label space increases, it becomes more difficult to deal with this kind of applications. Therefore, dimensionality reduction techniques originally for feature space is also applied to label space, one of which is label embedding strategy which converts the high-dimensional label space into a low-dimensional reduced one. So far, existing label embedding methods mainly investigate the global recoverability between original labels and reduced labels, dependency between original features and reduced labels, or both. It is widely recognized that local label correlations could improve multi-label classification performance effectively. In this paper, we construct a trace ratio minimization problem as a novel label embedding criterion, which not only includes the global label recoverability and dependency, but also exploits the local label correlations as a local recoverability factor. Experiments on four benchmark data sets with more than 100 labels demonstrate that our proposed method is superior to four state-of-the-art techniques, according to two performance metrics for high-dimensional label space.
Estimation of Students' Learning States using Bayesian Networks and Log Data of Learning Management System

Over the last decade, learning analytics (LA) and its related fields have rapidly developed. LA has become a robust concept of Institutional Research (IR) or student support. In this study, building a model of learning process by a Bayesian network from a large amount of educational data has been studied. Moreover, we propose the Bayesian network model for the learning process based on the log data accumulated from the learning management system (LMS). From the numerical simulation results, the proposed approach can be used to predict certain learning states of students.
Verifiable Subgraph Matching With Cryptographic Accumulators in Cloud Computing

Due to the rapid development of social networks, bio-informatics, internet-of-things <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">etc.</i> , subgraph matching query can be found in various applications. Meanwhile, the increasing popularity of storing graph data in the cloud drives demands for graph query processing on a remote cloud server. However, the query results in this scenario may not be guaranteed to be correct, especially when the cloud service provider (CSP) is malicious or compromised by some adversaries, for example, a CSP might omit some edges of the graph so that its search cost would be substantially reduced. Besides, various software bugs and unintended errors are also inevitable. All current generic verifiable computation (VC) schemes applied in this scenario are not only too impractical to be implemented but also need a lot of space to locally store their auxiliary data. To that end, we have put forth both public and designated verification schemes which focus on subgraph matching problems for outsourced graph data. They utilize a modified cryptographic primitive called accumulator to realize fast verification and low local storage overhead. In addition to the two main constructions, we have proposed an optimization to make the scheme more applicable, namely, supporting dynamic updates of the graph. At last, rigorous security proofs and efficiency analysis are given, which justify that our proposed schemes are secure and efficient, satisfying the requirements of general verifiable computation protocols.
CONTEMPORARY TRENDS IN STUDIES ON MOBILE LEARNING OF FOREIGN LANGUAGES: A META-ANALYSIS.

Previous research has provided a review of the literature on the state of research on mobile learning, but the use of m-learning and MALL to formal and informal learning of foreign languages needs further study. The need to review the language learning assisted by mobile devices emerges from a globalized context, in which pedagogy must question itself to plan and design research drawings in which mobile learning assumes a fundamental importance in teaching practice aimed above all at new generations of students.This study adopts a meta-analysis approach to systematically review the literature, thus providing a more complete analysis and synthesis of 83 studies from 2009 to 2018. Previous studies focus on the effectiveness and design of the mobile learning system.The search for databases and citations was performed by limiting the keywords referring to m-learning and to MALL. Compared to existing reviews, the results confirm revealing a skill-based approach to language learning with an emphasis on vocabulary, learning factors that influence students' perceptions and availability to use new cellular technologies.
A new Cryptographic Algorithm AEDS (Advanced Encryption and Decryption Standard) for data security

Nowadays security is very important to protect our sensitive information in computer or over the internet such as in online banking, online shopping, stock market and bill payments etc.Without security our information exchanged over internet are not safe.Encryption Algorithms provides the security to the information which is exchanged over the internet.In this project we are proposing a new cryptographic algorithm AEDS (Advanced Encryption and Decryption Standard) which is developed by combining properties of DES and AES algorithms.Then we compared all these three algorithms and we found that AEDS is more secure and robust for data security.
WAF-A-MoLE: An adversarial tool for assessing ML-based WAFs

Web Application Firewalls (WAFs) are plug-and-play security gateways that promise to enhance the security of a (potentially vulnerable) system with minimal cost and configuration. In recent years, machine learning-based WAFs are catching up with traditional, signature-based ones. They are competitive because they do not require predefined rules; instead, they infer their rules through a learning process. In this paper, we present WAF-A-MoLE, a WAF breaching tool. It uses guided mutational-based fuzzing to generate adversarial examples. The main applications include WAF (i) penetration testing, (ii) benchmarking and (iii) hardening.
On Analysis of the Bitcoin and Prism Backbone Protocols in Synchronous Networks

Bitcoin is a peer-to-peer payment system proposed by Nakamoto in 2008. The bitcoin backbone protocol has been analyzed in some depth: the blockchain growth property quantifies the number of blocks added to the blockchain during any time intervals; the blockchain quality property ensures the honest miners always contribute at least a certain fraction of the blockchain; the common prefix property ensures if a block is deep enough, it must be adopted by all honest miners with high probability. The Prism protocol was recently proposed to dramatically improve the blockchain throughput while maintaining the same level of security. Prior analyses of the bitcoin and Prism backbone protocols assume the lifespan of blockchain is finite. This paper presents a streamlined and strengthened analysis in synchronous networks without the finite lifespan assumption. Specifically, the results include a blockchain growth property, a blockchain quality property, and a common prefix property of the bitcoin backbone protocol, as well as the liveness and persistence of the Prism backbone protocol regardless of whether the blockchains have finite lifespan. The properties take the form of explicit expressions in lieu of order optimal results.
Performance analysis of container-based networking solutions for high-performance computing cloud

Recently, cloud service providers have been gradually changing from virtual machine-based cloud infrastructures to container-based cloud-native infrastructures that consider performance and workload-management issues. Several data network performance issues for virtual instances have arisen, and various networking solutions have been newly developed or utilized. In this paper, we propose a solution suitable for a high-performance computing (HPC) cloud through a performance comparison analysis of container-based networking solutions. We constructed a supercomputer-based test-bed cluster to evaluate the serviceability by executing HPC jobs.
Characterizing and using gullibility, competence, and reciprocity in a very fast and robust trust and distrust inference algorithm for weighted signed social networks

Predicting trust is a classic problem in social networks analysis. Furthermore, while most early approaches ignore distrust, recent works seem to consider it as important, if not more important, than trust itself. In this paper, we present a novel approach to predict both trust and distrust in Weighted Signed Social Networks very efficiently and in a satisfyingly accurate and robust way. Therefore allowing people to have healthier online presence and interactions. Being a local metric that does not rely on trust propagation, the proposed approach does not suffer from some serious limitations like trust decay, opinions conflict, path dependence, and time complexity. Moreover, our experiments on four real-world datasets show that, in addition to its simplicity and extensibility, this algorithm is robust to network sparsity, and provides satisfyingly accurate and very fast predictions.