Reviewerly/exHarmony huggingface dataset:
A Secure Network Interface for on-Chip Systems

This paper presents a self-securing decentralized on-chip network interface (NI) architecture to Multicore System-on-Chip (McSoC) platforms. To protect intra-chip communication within McSoC, security framework proposal resides in initiator and target NIs. A comparison between block cipher and lightweight cryptographic algorithms is then given, so we can figure out the most suitable cipher for network-on-chip (NoC) architectures. AES and LED security algorithms was a subject of this comparison. The designs are developed in Xilinx ISE 14.7 tool using VHDL language.
PassGAN for Honeywords: Evaluating the Defender and the Attacker Strategies

The main challenge in a honeywords system is how to generate artificial passwords (honeywords) that are indistinguishable from the genuine password (sugarword). It is straightforward to consider the PassGAN for generating honeywords from the defender’s perspective. In this work, we analyze a game situation between the defender and the attacker assuming the two parties exploit the PassGAN for their own competing advantage, i.e., the defender uses the generator model of PassGAN to generate honeywords, and the attacker uses the discriminator model of PassGAN to detect the sugarword. In this game, we investigate the feasibility of PassGAN as a honeywords generation strategy and the possible strategies that can be used by the defender and the attacker to reach their goal. The best strategy for the attacker is to use a large number of iterations and to use the same dataset as the defender. From the defender’s point of view, the strategy of using many iterations is also beneficial to reduce the attacker’s success rate.
A Study on the Evaluation System through Software Weakness Analysis

Recently, Due to the rapid development of IT technology the software in the social infrastructure is increasing. However, cyber attacks that exploit software vulnerabilities are also increasing. This paper designed and proposed the evaluation system considering in various programming languages and environments for cyber security. In addition, the effectiveness of research was proved by analyzing the results of selecting and evaluating weaknesses.
How Brass Instruments Are Made

This chapter offers an overview of the materials, equipment and techniques used to make brass instruments. It discusses the processes of design and manufacture through which instrument makers exercise control in order to meet the requirements of musicians. Hand craft techniques and operations which can be mechanised are outlined. Designs of valve systems to improve intonation such as those for ‘compensating’ and ‘full double’ instruments are described. The chapter concludes with a note on how instruments are assembled and can be finished by electroplating or lacquering.
A Comparative Study of Different Techniques for Prime Testing in Implementation of RSA

The RSA cryptosystem, invented by Ron Rivest, Adi Shamir and Len Adleman was first publicized in the August 1977 issue of Scientific American. The security level of this algorithm very much depends on two large prime numbers. The large primes have been taken by BigInteger in Java. An algorithm has been proposed to calculate the exact square root of the given number. Three methods have been used to check whether a given number is prime or not. In trial division approach, a number has to be divided from 2 to the half the square root of the number. The number will be not prime if it gives any factor in trial division. A prime number can be represented by 6n±1 but all numbers which are of the form 6n±1 may not be prime. A set of linear equations like 30k+1, 30k+7, 30k+11, 30k+13, 30k+17, 30k+19, 30k+23 and 30k+29 also have been used to produce pseudo primes. In this paper, an effort has been made to implement all three methods in implementation of RSA algorithm with large integers. A comparison has been made based on their time complexity and number of pseudo primes. It has been observed that the set of linear equations, have given better results compared to other methods.
Smart Automated Teller Machine Applications Using Artificial Intelligence

This paper provides various methods for increasing the features and possible applications of an automated teller machine (ATM) using AI and machine learning to keep up with the latest technologies and trends. The methods include targeted advertisement, biometric security, cryptocurrency conversion, remote medical kiosks and much more. The strategic placement of these kiosks allows for a wide range of unique applications, which might not be otherwise possible, and that too with only minor hardware additions to the current machines. In future, these centers will turn into hotspots for services like banking, transportation, documentation, healthcare, survey implementation.
Boosting component-based synthesis with API usage knowledge

Component-based synthesis is one of the hottest research areas in automated software engineering. It aims to generate programs from a collection of components like Java library. However, the program space constituted by all the components in the library is fairly large, which leads to a vast number of candidate programs generated for a long time. The intractability of the program space affects the synthesis efficiency of the program and the size of the program generated. In this paper, we propose Itas, a framework of iterative program synthesis via API usage knowledge from the Internet, which can significantly improve the efficiency of program synthesis. Itas aims to constrain the program space by combining two main ideas. First, narrow down the program space from the outside via the guidance of API usage knowledge. Second, expand the program space from the inside via iterative strategy based on knowledge. For evaluation, we collect a set of programming tasks and compare our approach with a program synthesis tool on synthesizing these tasks. The experiment results show that Itas can significantly improve the efficiency of program synthesis, which can reduce the synthesis time by 97.1% than the original synthesizer.
A new Approach of Documents Indexing Using subject modelling and Summarization

Abstract Document indexing is a field of research in Natural Language Processing (NLP) that has been rapidly evolving for 70 years. It is an operation that focuses on the synthetic representation of a document according to a model in order to facilitate their subsequent use. This work is concerned with document indexing. Two points are addressed. This work is concerned with document indexing, we are trying to accelerate the indexing process of large document datasets, two points are addressed. The first one concerns the development of a document indexing system using the system's operating process based on three phases namely pre-processing, weighting, and subject modelling. The second point concerns the proposal for a new system that integrates a new developed automatic summary subsystem, the goal of this point is to minimize indexing time.
AKURASI METODE JOHNSON- TOHSACH DAN METODE DAREDALAM PENAFSIRAN BERAT BADAN JANIN PADA IBU INPARTU

Obstetric trauma that importantto be noted in delivering process arethe distocia shoulder and infantmacrosomia . This trauma wascaused by inadequate of birthweight measurement and can leadto abnormalities on birth processingand neonatal complications both forthe baby and the mother and mayhave an impact on morbidity andmortality of infants or mothers. Theinterpretation of the baby's weightin inpartu segment then still aninteresting and important topicbecause of the differences methodsin birth weight measurement . Thisstudy aims to determine theaccuracy of the method of Johnson - Tohsach and interpretation methodsDare in birth weight infants . This comparative descriptivestudy was conducted at deliveryroom Panti Rini and PantiNugroho Hospital , with crosssectional approach with totalpopulation were 100 patients. Usingnon-probability sampling withconsecutive sampling the samplewere 60 inpartu mothers. Statisticunit to analyze this study werepaired t-test and Product Momentcorrelation.The results of this study showedthat there was no significantdifference between the weightmeasurement by method of Johnson- Tohsach birth weight infants withp value = 0.249 . Similarly, therewas no significant differencebetween the methods DareMethod’s by weight infants with pvalue = 0.154 . It means that bothmethods can be used forinterpretation birth weight of thebaby . The result of ProductMoment Correlation test wasindicated that the Dare method’smore accurate in weight babyinterpretation with p value = 0.0000.The study then reccomendatethat health workers , especiallymidwives are advised to use theDare method in interpreting birthweight infants when helping inpartumother in delivering her baby.
Robust Fault-Tolerant Control using Double Feedback

In this paper, we consider a condition of the control system with low-sensitivity and robust stability and fault-tolerant using double feedback control. Several researchers study the robust stabilization problem. Yu et al. propose a double feedback control system. However, Yu et al. do not consider a problem of fault-tolerant control using double feedback control. In this paper, we consider a condition of the control system with low-sensitivity and robust stability and fault-tolerant using double feedback control.
A SysML-based product configuration method

With the increasing demand for product features and performance, the difficulty of the development for multi-disciplinary product systems is increasing as well. Document-based system engineering is difficult to meet the current requirements for development, and model-based system engineering is becoming the best choice. Although the models have become an important artifact of the current complex product development, and do help to improve the efficiency, and the information transmission efficiency is not high due to the complex process. Thus, a system model configuration method based on RFBS is proposed in this paper. Firstly, the system model graph is constructed based on SysML meta-model ontology. Then, a Requirement-Function-Behavior-Structure (RFBS) based system model configuration method is proposed. Finally, a case of The Automatic Climate Control Integrated System is given to show the effectiveness of the method.
Non-reference image quality evaluation method based on HVS characteristics

In order to effectively measure the image quality of different distortion types, a non-reference image quality evaluation algorithm based on natural scene statistics (NSS) model is proposed. This algorithm uses the statistical characteristics of the normalized brightness coefficients of natural images to tend to obey the characteristics of the generalized Gaussian probability distribution. Firstly, the regions of interest and non-interest regions of natural images are extracted, and then the image features are extracted in the corresponding regions, and finally realized image quality evaluation method based on visual regions of interest. Based on the LIVE2 image database, a large number of experiments are performed and compared with other classic algorithms. Experimental results show that the improved algorithm in this paper has high subjective consistency in the evaluation results of degraded images with multiple types of distortion coexisting, and can accurately reflect the visual perception quality of the human eye.
Separation of Two-Dimensional Mixed Circular Fringe Patterns Based on Spectral Projection Property in Fractional Fourier Transform Domain

In this paper, a method for automatically separating the mixed circular fringe patterns based on the fractional Fourier transform (FrFT) analysis is proposed. Considering the mixed two-dimensional (2-D) Gaussian-based circular fringe patterns, detected by using an image sensor, we propose a method that can efficiently determine the number and parameters of each separated fringe patterns by using the FrFT due to the observed higher sparsity in the frequency domain than that in the spatial domain. First, we review the theory of FrFT and the properties of the 2-D circular fringe patterns. By searching the spectral intensities of the various fractional orders in the FrFT projected along both the frequency axes, the proposed method can automatically determine the total fringe number, the central position, binary phase, and the maximum fringe width of each 2-D circular fringe pattern. In the experimental results, both the computer-simulated and optically mixed fringe patterns are used to verify the proposed method. In addition, the additive Gaussian noise effects on the proposed method are investigated. The proposed method can still successfully separate the mixed fringe pattern when the signal-to-noise ratio is higher than 7 dB.
Electronic Educational Resource in the Discipline «Information Technologies in Ecology»

The article discusses the use of electronic educational resources in the modern educational process. The article gives the principles of the development of electronic educational resources and their qualification. The article analyzes such problems of modern education as the lack of participation of a certain part educational institutions in innovative development and the information space of society, as well as the insufficient use of modern educational technologies. An example of the creation and use of an electronic educational re-source in the study of the discipline “Information technology in ecology” is examined.
An Asymmetric Two-Sided Penalty Term for CT-GAN

Generative Adversarial Networks (GAN) is undoubtedly one of the most outstanding deep generation models in the tasks such as image-to-image translation and image generation. In recent years, many improved algorithms have been proposed for GAN, and Wasserstein GAN with a consistency term (CT-GAN) has been noticed because of excellent stability. However, we find that its penalty term is not only too restrictive, but also ignores the boundary region of model distribution. This makes it challenging to enforce 1-Lipschitz constraint required by CT-GAN. In this paper, we propose a more efficient and less restrictive penalty term, named asymmetric two-sided penalty, to enforce the 1-Lipischtz constraint. As a application, we introduce Wasserstein GAN with Asymmetric Two-sided Penalty (WGAN-AP). In addition, we use Spectral Normalization enforcing generator to enhance the stability of our model. Through experiments on image generation task, our WGAN-AP shows stability and superiority compared to the state-of-the-art methods in terms of Inception Score and Fréchet Inception Distance. In particular, The Inception Score of our WGAN-AP achieves 9.14 and 8.41 separately on CIFAR-10 dataset in supervised and unsupervised tasks, which exceeds that of state-of-the-art models.
Improving Convolutional Network using K-max mechanism for Sentiment Analysis Tasks

Sentiment analysis is a process in which natural language processing uses a computer to automatically comprehend specified topics from written or spoken language. In this paper, by comparing the experiments of recursive neural network and convolutional neural network on the English film review data set, and then adding attention mechanism, K-max mechanism, and highway network mechanism to the two models respectively, comparing the results of the models, we found The original convolutional neural network lags far behind the recursive neural network in the sentiment analysis task for English movie reviews, but it adds an optimized K-max mechanism and attention to the convolutional neural network and the recursive neural network respectively. After the mechanism, through analysis, the results show that among several optimization mechanisms, the K-max mechanism has a great optimization effect on the performance of the convolutional neural network in the field of English movie critic sentiment analysis tasks.
Multi-generational Stories of Urban Renewal: Preliminary Interviews for Map-Based Storytelling

Urban renewal was a project of the American government that aimed to reconstruct poorly-managed neighborhoods. Because community-level data that shows underlying mechanisms of urban renewal has not been curated systematically, due to the complexity and volume of relevant archival collections, we aim to digitally curate property acquisition documents from the urban renewal projects that affected the Southside neighborhood of Asheville, North Carolina, in the form of a map-based, interactive web application. This paper presents part of the interview analysis to understand how Asheville citizens of different generations remember their neighborhood before and after the gentrifications that they have experienced. The result of this analysis provides design implications for the archival system we are developing by revealing generation-driven value structures of potential users.
Achieving Sustainable Financial Transactions under Regimes without a Central Bank—An Intertemporal Comparison

In this paper, by performing an intertemporal comparison, we investigate two monetary policy regimes where a central bank is absent, and we further refer on the mechanisms they developed so as to ensure the reliability of transactions between the parties involved. In particular, we mainly focus on the economic–monetary institutions of Athens during the Classical period (508–322 BCE) and we argue that (in principle) there are inter-temporal similarities between the Athenian and the current digital currencies regimes regarding the auditing principles with which the reliability of financial transactions is ensured. We found that in both cases, what is crucial for the success of the system is to achieve trust on the currency. By focusing on Classical Athens, we analyze the nature of the mechanisms and the auditing techniques used to ensure reliable commercial transactions. We also briefly analyze the modern cryptocurrency techniques. We found that the success of both financial regimes was based on achieving: low transactional costs, speed in commercial transactions, and what we characterize as security regarding the commercial transactions.
Improved weighted local linear embedding algorithm based on Laplacian eigenmaps

To solve the shortcomings of local linear embedding (LLE), such as sensitive to noise and poor generalization ability for new samples, an improved weighted local linear embedding algorithm based on Laplacian eigenmaps (IWLLE-LE) is proposed in this paper. In the proposed algorithm, Laplacian eigenmaps are used to reconstruct the objective function of dimensionality reduction. The weights of it are introduced by combining the geodesic distance with Euclidean distance, which can effectively represent the manifold structure of nonlinear data. Compared the existing LLE algorithm, the proposed one better maintains the original manifold structure of the data. The merit of the proposal is enhanced by the theoretical analysis and numerical experiments, where the classification recognition rate is 2%–8% higher than LLE.
Proficient Data Retrieval Technique in Cloud Using TF-IDF Model

Storage of secure records and recovering is one of the most bursting investigations heading in cloud computing. In spite of the way that various available encryption plans have been proposed, very few of them support profitable recuperation over the records which are encoded reliant on their characteristics. Currently, progressive quality based encryption model is intended for a record collection. A lot of records can be encoded together on the off chance that they share an integrated access structure. For improving the search viability DFS algorithm is used for the ARF tree. It can be further improved using cloud computing. Our system can be implemented for the purpose of document collection by slightly modifying the ARF tree.
An Efficient Technique for Finding Longest Common Subsequence of DNA Sequences

Molecular biologists rely very heavily on computer science algorithms as research tools. The process of finding the longest common subsequence of two DNA sequences has a wide range of applications in modern bioinformatics. Genetics databases can hold enormous amounts of raw data, for example the human genome consists of approximately three billion DNA base pairs. The processing of this gigantic volume of data necessitates the use of extremely efficient string algorithms. This paper introduces a space and time effective technique for retrieving the longest common subsequence of DNA sequences.
Predicting software design patterns from text using neural embedding

Software design patterns are solutions to common software problems that are proven to work adequately in particular scenarios. Deciding which design pattern to use for a given software problem often requires practical knowledge acquired with experience in a similar domain and can be highly subjective and error-prone. Further, for novice programmers, an automated approach would be of tremendous help as it would provide practical knowledge required for deciding which design pattern to use for a particular software problem. The majority of research in software design pattern prediction involves using software structure and features in determining which design pattern to implement. However, there are circumstances where software engineers would prefer to know which design pattern to be used by looking at the design problem during or before the implementation phase. Existing design pattern prediction tools cannot be utilized in this scenario due to the absence of code and class structures. To address this issue, this paper proposes a new approach that analyzes the context of the software problem from text and predicts a suitable design pattern for the given problem context using feature learning, neural embedding, and classification. We evaluate our approach on a case study from Stack Overflow with more than 66,000 questions that discuss problems and consequences related to 23 design patterns. Results show that our approach can predict design patterns from the text with 82% overall accuracy.
Augmented Reality in Language and STEM Education

Rapid advancement in technology has altered language and STEM education, especially during the coronavirus pandemic. Augmented reality (AR) has emerged as one of the latest technologies offering a new way to bridge virtual and real worlds, which can enhance both teaching and learning experience. Due to the widespread proliferation of digital technology, the implementation of AR on internet-based devices and mobile devices has become a growing trend in educational settings related to language and content-based instruction. There has been an increasing number of studies exploring the design, evaluation, and applications of AR for pedagogical purposes. This chapter aims to 1) delineate AR technology and its theoretical foundations, 2) review AR-pertinent literature regarding language and STEM education, 3) discuss the potentials as well as challenges of this innovative technology, and 4) propose practical applications for ESL/EFL teachers, educators, and practitioners alike.