Reviewerly/exHarmony huggingface dataset:
Hardware Implementation of Multiplication over Quartic Extension Fields

New Number Field Sieves (NFS) attacks on the discrete logarithm problem have led to increasing the key size of pairing-based cryptography on most popular curves like BN. To ensure 128-bit security level, recent cost estimations between several pairing friendly elliptic curves recommend to switch for BLS24 curves. However, implementing a pairing on a BLS24 curve requires arithmetic operation over $$\mathbb {F}_{p^4}$$ . In this paper, we transpose previous work on multiplication over extension fields using Newton’s interpolation to construct a new formula for multiplication in $$\mathbb {F}_{p^4}$$ and propose efficient hardware implementation of this operation. Our Arithmetic Logic Unit (ALU) is implemented on Kintex-7 Xilinx $$^{\text{\textregistered }}$$ FPGA. The efficiency of our design in terms of $$\boldsymbol{time\times area}$$ is almost 3 times better than previous specific architectures for multiplication in $$\mathbb {F}_{p^4}$$ . We also used this new architecture to estimate full pairing implementations on BLS24 and KSS16 for 128 and 192-bit security level. These estimations highlight that the new $$\mathbb {F}_{p^4}$$ formula allows a performance increase between 5 and 10%. And especially for KSS16 which appears to provide the best performance at the 128-bit level.
STRATEGI JARINGAN PENGGIAT LITERASI DIGITAL DALAM KAMPANYE MELAWAN HOAKS PANDEMI COVID-19

The Ministry of Communication and Information (Kominfo) recorded and labeled 1,733 hoaxes related to Covid-19 and vaccines until May 2021. Mainstream media have not shown satisfactory results in educating the public in Indonesia in the midst of the infodemic. The Digital Literacy Activist Network (Japelidi) is one of the communities in Indonesia that has a great concentration on digital literacy. This community has a movement to educate the public who experience digital stuttering in the midst of the information wave of the Covid-19 pandemic. This article aims to explore the strategies used by Japelidi in mobilizing volunteers from various academics, communities to collaborations carried out during the Pandemic. The method used in. This study uses an analysis of the Culture Participatory Theory, which was initiated by Henry Jenkins. The findings are that Japelidi uses three clusters as a strategy in literacy development. This strategy consists of 3 circular clusters, namely the main cluster in the form of a single narrative, the meso cluster containing the description of the Single Narrative, and the outermost stakeholder cluster. While the digital literacy campaign strategies carried out are agenda setting, key message delivery, media distribution and target reach.
Signal Constellations with Algebraic Properties

In this chapter, we present new code-construction methods, suboptimal detection and set-partitioning techniques and analyze the error performance on the AWGN channel. The application of signal constellations with algebraic properties to SM is treated in Chapter 4 for two-dimensional and Chapter 5 for multidimensional signal constellations.
Implementation of Intellectual Property Education and the Effect in Anticipation of Application to Patent Contest

Patent and intellectual property rights have now become very important to strengthen Japan′s international competitiveness of products. Against that background, the cabinet office held a strategic development and utilization of intellectual property human resources as a significant issue in 2015. Because so, people pay more attention to intellectual property education. And the report of practical examples is on the increase. In this paper, we have introduced the lecture′s contents and will be reporting on the considered effectiveness of the education. We conducted intellectual property education as a lecture. Furthermore, we have participated in a related patent contest.
Blockchain-based Test Management in Full Vehicle Testing

As an important extension and modern methodology to fully digitize test management in full vehicle testing, we present the possible advantages of using modern blockchain technology and contrast classical methods. In doing so, we present some of the various requirements for the stakeholders and how these challenges can be met with the help of the new properties of distributed ledgers. This technology has all the properties of a decentralized database and can thus be used as a neutral storage medium, which would ensure the highest level of manipulation resistance and access security. The integration of such a solution requires a fully digitized, modern test environment that can be developed through consistent process digitization. The following four core characteristics of such a system would be:
LTWNN: A Novel Approach Using Sentence Embeddings for Extracting Diverse Concepts in MOOCs

As a global online education platform, Massive Open Online Courses (MOOCs) provide high-quality learning content. It is a challenging issue to design a key course concept for students with different backgrounds. Even though much work concerned with course concept extraction in MOOC has been done, those related works simply utilize external knowledge to get the relatedness of two different candidate concepts. Furthermore, they require the input to belong to multi-document and severely rely on seed sets, in which their model shows poor performance when input is a single document. Addressing these drawbacks, we tackle concept extraction from a single document using LTWNN, a novel method Learning to Weight with Neural Network for Course Concept Extraction in MOOCs. With LTWNN, we make full use of external knowledge via making relatedness between each candidate concept and document by introducing an embedding-based maximal marginal relevance (MMR), which explicitly increases diversity among selected concepts. Moreover, we combine the inner statistical information and external knowledge, in which the neural network automatically learns to allocate weight for them. Experiments on different course corpus show that our method outperforms alternative methods.
A Study on Randomness Used in Signature Generation of UOV

Multivariate public key cryptography is a leading candidate for post-quantum cryptography (PQC). In 1999, Kipnis et al. proposed a multivariate signature scheme called unbalanced oil and vinegar (UOV). It has not been broken for two decades and is thus an important scheme in PQC. In particular, Rainbow, an efficient variant of UOV, was selected as one of the finalists for National Institute of Standards and Technology (NIST) PQC standardization. The signature generation of UOV is performed by substituting a random value (randomness) into quadratic equations and solving an associated linear system. In this paper, we study the randomness used in the signature generation of UOV. In particular, we consider the case in which the range of randomness is restricted and analyze the security against such a case.
Research on Hybrid Code Networks Based on Time Perception Enhancement

The Hybrid Coding Networks (HCN) model is an end-to-end task-oriented dialogue system that takes learning RNN as the training core and allows developers to express domain knowledge through software and operating templates. It is a significant advancement in task-oriented dialogue systems. However, due to the lack of training samples for those niche and selective domains, the traditional HCN model has limitations. Such limitations include interpreting users’ accurate semantic meaning and their original intention. Moreover, since the traditional HCN model require a significant level of artificial manipulation, it’s challenging to transfer the adaption from an existing domain to another with the associated data set, especially for which shows diverse characteristics and various forms of dialogues. In this paper, we build an attention mechanism of user intention time perception on top of the traditional HCN to solve the above problems. The so-called Time-Perception Hybrid Code Networks (TP-HCN) model dynamically captures the users’ intention over time by implementing user intention integration (UII) gates. Furthermore, we conduct experiment and the experiment yield competitive results that agree with our hypothesis of the effectiveness of the TP-HCN model.
Soil Toxic Elements Determination Using Integration of Sentinel-2 and Landsat-8 Images: Effect of Fusion Techniques on Model Performance

Finding an appropriate satellite image as simultaneous as possible with the sampling time campaigns is challenging. Fusion can be considered as a method of integrating images and obtaining more pixels with higher spatial, spectral and temporal resolutions. This paper investigated the impact of Landsat 8-OLI and Sentinel-2A data fusion on prediction of several toxic elements at a mine waste dump. The 30 m spatial resolution Landsat 8- OLI bands were fused with the 10 m Sentinel-2A bands using various fusion techniques namely hue-saturation-value, Brovey, principal component analysis, Gram-Schmidt, wavelet, and area-to-point regression kriging (ATPRK). ATPRK was the best method preserving both spectral and spatial features of Landsat 8-OLI and Sentinel-2A after fusion. Furthermore, the partial least square regression (PLSR) model developed on genetic algorithm (GA)- selected laboratory visible-near infrared-shortwave infrared (VNIR–SWIR) spectra yielded more accurate prediction results compared to the PLSR model calibrated on the entire spectra. It was hence, applied to both individual sensors and their ATPRK-fused image. In case of the individual sensors, except for As, Sentinel-2A provided more robust prediction models than Landsat 8-OLI. However, the best performances were obtained using the fused images, highlighting the potential of data fusion to enhance the toxic elements’ prediction models.
Key Management

Managing cryptographic keys is one of the most difficult parts of a cryptographic system. It is particularly difficult if only symmetric key cryptography is available. The advent of asymmetric key cryptography greatly simplified key management and makes it much more secure. Public Key Infrastructure addresses only management of public keys. Private keys must be managed and protected with other mechanisms. A particularly thorny issue is providing escrow of private keys (in the event of a key being lost or the key owner leaving the organization). This can easily be abused by the cryptographic system owner (or the government). Some systems, such as TLS and S/MIME, provide very secure mechanisms for exchange of a symmetric session key (one used only for a single session). With S/MIME it is important to provide a central shared address book with all participant’s digital certificates, since anyone sending an encrypted message must have the recipient’s certificate at the time they send the message.
Secure Optical Image Encryption and Data Hiding Technique in Compression Domain Using Dual Key-Based Bit Swapping

Over the last twenty years or more, researchers have suggested a significant variety of optical information security systems that take advantage of the inherent benefits of optics to outperform existing digital security methods. Along with encryption, hiding secret data plays added advantages in various applications such as defense, biomedical diagnostics, and other security-related imaging applications. In this work, a new technique is proposed along with secret data hiding in the compressed bitstream. American Standard Code For Information Interchange (ASCII) representation is used directly to hide the secret text into the image using least significant bit (LSB) replacing technique. To evaluate the performance, various performance measures such as correlation coefficient (CC) of 0.99, peak signal to noise ratio (PSNR) of 28.53, mean square errors (MSE) of 0.0056, structural similarity index measure (SSIM) of 0.99, mean absolute errors (MAE) of 0.015, and root mean square error (RMSE) of 0.056 values are calculated. From the acquired result, it is quite obvious that this work delivers 0.2% high performance when compared to existing optical encryption methods.
Probability

The purpose of this chapter is to introduce and examine formal probability concepts and terminology. Specifically, the interpretation of probability as being a long-run relative frequency is discussed along with ideas of discrete versus continuous random variables. The probability of an event is a conditional probability when the likelihood of that event depends upon the likelihood of a precipitating event. Bayes’ theorem makes use of conditional probability to determine the probability of other related conditional probabilities.
Efficient Auto Scaling and Cost-Effective Architecture in Apache Hadoop

In the age of Big Data Analytics, Cloud Computing has been regarded as a feasible and applicable technology to address Big Data Challenges, from storage capacities to distributed processing computations. One of the keys of its success is its high scalability which refers to the ability of the system to increase its performance, resources and functionalities according to the workload. This flexibility has been seen as an appropriate way to decrease datacenters’ energy consumption and thus assures cost-saving and efficiency without effecting performance of the system. In order to handle Big Data operations, Cloud Computing has implemented various platforms and tools such as Apache Hadoop and provides distributed processing of very large data sets across multiple clusters. This paper proposes an auto scaling architecture based on the framework of Hadoop; it adjusts automatically the computation resources depending on the workload. In order to validate the effectiveness of the proposed architecture, a case study about Twitter data analysis in a cloud simulated environment has been implemented to improve the cost-effectiveness and the efficiency of the system.
Multipurpose Visual Positioning of the Underactuated Mobile Robot

The paper is devoted to the problem of the positioning of the mobile robot in front of some external visual marker which is recognized by the video camera mounted on the robot. The task is complicated by the fact that the robot is underactuated. Algorithms which use the visual information directly in the feedback loop are well known as visual servoing approach, but mostly such algorithms consider fully actuated moving plants. Moreover, the considered algorithm uses the mathematical model of the robot’s dynamics to enforce the control quality and also to use the special multipurpose structure of the feedback which allows meeting the set of requirements, particularly—the presence of the external disturbances. The results of the experiments with the computer model are given.
Object Motion Detection in Video by Fusion of RPCA and NMF Decompositions

In this paper two new schemes are proposed for fusion of the results from video decomposition by Robust Principal Component Analysis and Non-negative Matrix Factorization with the aim of detecting moving objects over stationary background. The schemes use the logical OR and AND operators on a pixel basis over the binary outputs of the base decomposition algorithms. Experimental results from testing with videos, containing natural scenes with humans and vehicles, reveal the applicability of both schemes with higher Detection Rate for the OR operator and considerably higher Precision for the AND operator. The latter gets the highest F-measure of 0.8168 and is considered applicable in various systems where higher reliability is sought. Execution times for all tested implementations are practical, although allowing further optimization, which renders the proposed algorithms applicable in a wide set of applications.
The R Prompt and the RStudio Main Menus and Subwindows

This chapter covers the R prompt and the RStudio main menus and subwindows. It starts with descriptions of the three parts of R: objects, operators, and assignments. It continues with a discussion of working with the R prompt, followed by an example of doing a calculation at the R prompt. Afterward, it describes the RStudio main menus and the four RStudio subwindows. One nice thing about RStudio is that when you type, it provides autocompletion for object names. But RStudio can be slow to load and run.
Vowel-Oriented String Search Algorithm with Vowel-Oriented Binary Tree

As the size of text documents based on cloud storage increases, the time and cost of string search and keyword search increase. However, when searching for words or sentences in documents, most string search algorithms do not take the lexical structure used in the real world, or the constitutional characteristics of the character, into account. In particular, the previous string search algorithms have not considered well-formatted official document (articles, news, novels, academic papers, patents, etc.) characteristics of a limited number of characters and composition. In this paper, we propose a vowel-oriented binary tree that considers the probability of the occurrence of a character in real world documents and its compositional characteristics in well-formatted documents and well-formatted words. Based on the vowel-oriented binary tree, we propose a vowel-centered string search algorithm that searches for a specific word in a document. Based on several dictionaries (Free Dictionary Project Dictionary, Scrabble Helper), the frequency and pattern of occurrence of vowels and consonants were analyzed. A strategy and an algorithm for constructing a vowel-oriented binary tree that can express the frequency and probability patterns of the occurrence of vowels are proposed. The vowel-oriented binary tree is reconstructed according to the characteristics of the occurrence of vowels, and the consonants existing between vowels are distinguished and expressed. In addition, based on the vowel-oriented binary tree, we propose an enhanced vowel-oriented string search algorithm that quickly searches for words that can occur in real world documents.
Privacy Preserving Facial Image Processing Method Using Variational Autoencoder

Over the recent years, a massive number of face images are widely used in each field of Internet, which makes the privacy leakage problem due to the abuse of facial images become more serious. The existing simple face de-identification methods like occlusion or blurring seriously damage the useful information and structure of image, which leads to a poor visual effect and is of little significant in practical application. In this work, we propose a novel privacy preserving facial image processing method. The proposed method removes the identity information of face images by four different measures first, and then fuse these de-identified images through a multi-input generative model based on Variational Autoencoder (VAE) to reconstruct the useful information of image. The proposed method aims to generate images that is close to the real face but does not contain the privacy information in the original human face image, which avoids the disclosure of privacy. We demonstrate the information reconstruction capabilities of our method with contrast experiments on the CelebA datasets. Experimental results emphasize the superiority of our method in terms of maintaining useful information and visual effects over traditional face de-identification methods.
Interference of Three Frequency Distinguished Photons

When connecting different physical platforms, it often encounters the interference of non-identical photons. In this chapter, I will mainly introduce how to use the time-resolved measurement method to achieve the interference of non-identical photons with completely distinguishable frequencies, and obtain the information of the interference network through the interference results. Our work will help lay the foundation for the establishment of quantum networks among different physical systems. It also provides a method for using non-identical photon interference to carry out Bose sampling experiments and illustrate quantum advantages.
Accelerated Computation of Eigenvectors

In Chapter 5, I discussed several adaptive algorithms for computing principal and minor eigenvectors of the online correlation matrix Ak∈ℜnXn from a sequence of vectors {xk∈ℜn}. I derived these algorithms by applying the gradient descent on an objective function. However, it is well known [Baldi and Hornik 95, Chatterjee et al. Mar 98, Haykin 94] that principal component analysis (PCA) algorithms based on gradient descents are slow to converge. Furthermore, both analytical and experimental studies show that convergence of these algorithms depends on appropriate selection of the gain sequence {ηk}. Moreover, it is proven [Chatterjee et al. Nov 97; Chatterjee et al. Mar 98; Chauvin 89] that if the gain sequence exceeds an upper bound, then the algorithms may diverge or converge to a false solution.
Guidelines for the Use of Active Learning in Engineering Lecture Courses

We introduced active learning; for example, poster tour, and jigsaw method, to the existing classroom lectures in the educational programs of KOSEN. Based on student questionnaires and grade information, the implementation flow and selection guidelines for the five class formats are presented.
Study and Analysis of Zoning in Meitei Mayek Recognition

Handwritten character recognition is an exciting and challenging problem in computer vision due to its vast variation in writing style. The accuracy of a recognition system largely depends on what features have been used and in what manner they are extracted. So, selection of zoning for feature extraction is another challenge in mapping an unknown object (character) to give a correct label. In this paper, feature extraction, such as background directional distribution (BDD), uniform local binary pattern (ULBP), zone density (ZD) and profile features considering various decomposition of an image in the form of zones have been studied and analyzed.
Classification Through Data Mining Algorithm

Classification is a data mining technique in the machine learning domain. Various algorithms such as K-nearest neighbor, support vector machines, random forest, logistic regression, and decision trees are used to solve the classification problem. Out of them, logistic regression and decision trees are perhaps the most used classification techniques. The study seeks to compare the performance of these two techniques in classifying observations from two different data sets. This study aims to identify cases where one would prefer a particular algorithm over the other and explore the advantages and disadvantages associated with these two algorithms. After a data discovery phase, which included checking for missing values and removing redundant predictors, logistic regression and decision tree models were built for both datasets. The models were compared based on their ROC curves, and their predictive ability was obtained from their confusion matrices.