Reviewerly/exHarmony huggingface dataset:
Jacobian Versus Infrastructure in Split Hyperelliptic Curves

Split (also known as real) hyperelliptic curves admit two main algebraic structures: the Jacobian and the infrastructure. In this paper, we describe exactly how the infrastructure and the Jacobian are related. We show that computations in the infrastructure using a new modified notion of distance and computations in a particular subgroup of the Jacobian heuristically have exactly the same cost for curves defined over sufficiently large finite fields. We also present a novel set of explicit formulas for genus three split hyperelliptic curves that improves on the current state-of-the-art.
Text editor application to automate the format of writing a final project based on java programs

Abstract Final project is a prerequisite that must be taken by students to get a Bachelor or Master Degree. Final project contains the results of research made in a specific format as reporting the research process to the institution. Each institution has a standard format for the final project or thesis writing rules which are usually written in the instructions for writing a thesis. Institutions usually provide writing templates to follow, but because the templates only make a number of important parts and are still manual, the format of student writing often changes and is not uniform with other students or some are deviant and not in accordance with the standard written format. This study designed a text editor application to help students or researchers so that it was not too problematic in the writing format. This application is based on a graphical user interface, making it easier to operate. The rules in the program are made to automate the writing entered to produce written output in accordance with the format specified by the institution. With this system, student or researcher will be easier in writing the script according to the standard of writing and get a final project or thesis report in a uniform format.
Some Families of FSP Functions and Their Properties

We report properties of fixed-structure parametrized (FSP) functions that give insights into the effectiveness of the “Extended Ritz Method” (ERIM) as a methodology for the approximate solution of infinite-dimensional optimization problems. First, we present the structure of some widespread FSP functions, including linear combinations of fixed-basis functions, one-hidden-layer (OHL) and multiple-hidden-layer (MHL) networks, and kernel smoothing models. Second, focusing on the case of OHL neural networks based on ridge and radial constructions, we report their density properties under different metrics. Third, we present rates of function approximation via ridge OHL neural networks, by reporting a fundamental theorem by Maurey, Jones, and Barron, together with its extensions, based on a norm tailored to approximation by computational units from a given set of functions. We also discuss approximation properties valid for MHL networks. Fourth, we compare the classical Ritz method and the ERIM from the point of view of the curse of dimensionality, proving advantages of the latter for a specific class of problems, where the functional to be optimized is quadratic. Finally, we provide rates of approximate optimization by the ERIM, based on the concepts of modulus of continuity and modulus of convexity of the functional to be optimized.
Performance Considerations of a Logging System Simultaneously with a Customer Virtual Machine in Infrastructure as a Service Cloud

An Infrastructure as a Service or IaaS cloud can offer a virtual machine or VM to the cloud customers to rent with pay per use basis. Although this cloud is widely used in many areas of applications, its security issues are obstacles for its adoptions. A logging system can mitigate the risks associated with these issues. This system can monitor and log malicious processes in IaaS customer’s VMs. However, the performance of this system is important to be measured. Moreover, the performance of this VM is also needed to be measured. Thus, this paper focuses on simultaneously measuring and evaluating the performance of both the logging system and the VM. To the best of our knowledge, there is no this kind of measuring methods in the literature. The main result of this paper is that we found the optimal point when the logging system and the VM can have suitable performance levels when both of them are working. This optimal point can be used by the IaaS providers to offer marketing packages for mitigating the risks associated with the issues of a customer VM with the consideration of his/her VM performance simultaneously. To mitigate risks associated with the security issues of an IaaS, this paper can be a guild-line to enable logging systems to be truly worked in the IaaS real-word production.
Characterising Machine Learning Programs: A European Compilation

The first part of this paper is an introduction to the Machine Learning (ML) methodologies stemming from the Artificial Intelligence (AI) approach. The second part describes a number of possible characteristic features of ML programs. We present a questionnaire as it was submitted to European researchers, then summarize their answers in tables, and conclude with a quick analysis of the answers. The questions we have been asking were chosen to mark the Artificial Intelligence (AI) approach to ML, as illustrated for instance in [Michalski et al. 1983, 1986, Kodratoff 1988]. It follows that some of the systems that perform learning essentially by adjusting coefficients may find it difficult to fit into our schemes. On the contrary, all AI oriented programs should fit in, unless we have made mistakes with our description. Artificial as it looks, this effort of characterization addresses two deep concerns. The first one is a clarification of the terms used, and sometimes abused, in the ML community. The second is to promote communication in the sub-field. In most cases, learning systems do learn, but it is neither clear what nor how they learn. Setting bench marks for ML is a necessity the community should be more aware of. Some of the systems known as knowledge acquisition systems perform some learning as well. The last section is devoted to this kind of program.
A Conceptual System of Blockchain-Based Electronic Bill of Lading

Blockchain-based electronic bill of lading will reshape the global logistics industry, replacing old-style paper bill of lading documents. This chapter establishes blockchain-based electronic bill of lading conceptual model, with which stakeholders may be able to state and transfer cargo ownership rights without the hassle of the handling paper, and proposes an implementation of the framework for researchers. This chapter provides a way for stakeholders to exchange these documents digitally, securely, and with no possibility of fraud in a neutral environment – extremely quickly and much more affordably than currently possible. The ecosystem of blockchain-based electronic bill of lading services will transform the global logistics industry. The study findings offer logistics managers and software programmers the ability to develop their documentation strategies and own blockchain-based electronic bill of lading platforms.
The Hydraulic Modelling of Capacity of Water Pool in Universitas Jambi

Abstract This research was carried out with the primary and secondary data collection process. The primary data prepared are data on the cross-section area into the pond, the pool area data, the water level data, the length data of the inlet into the pond, and current pool and the drainage conditions. While the secondary data used in this study is the average daily rainfall data from 2001 to 2010 in the district of Muaro Jambi. Furthermore, by using these data, a direct survey was conducted to see the conditions of land use in every part of the area at Jambi University. Land use data will be used as a CN (curve number) determination in hydrological analysis. Based on the results of hydrological analysis, it is known that the value of UNJA Mendalo water storage capacity is around 85060 m 3 . By using the results in the hydrological analysis, further analysis of the hydraulics of the Jambi Mendalo University’s channels and ponds can be determined.
Quantization approach to steganography perceptual color spaces

In this study, we present a new steganography method depend on quantizing the perceptual color spaces bands. Four perceptual color spaces are used to test the new method which is HSL, HSV, Lab and Luv, where different algorithms to calculate the last two-color spaces are used. The results reveal the validity of this method as a steganoic method and analysis for the effects of quantization and stegano process on the quality of the cover image and the quality of the perceptual color spaces bands are presented.
Modified method of a path planning on a local maps for transport and aerospace robots

In this paper we consider the modifi cation of the method of planning the A-star path for transport and aerospace robots.It is necessary to take into account obstacles in the local neighborhood of aerospace robot, but not suffi cient for the rational path planning.The essence of the modifi cation is to use additional information about the features of original cell array.This additional information is represented by binary fl ags.They represent obstacle-free rows and matrix columns.The modifi cation allows you to build paths that include straight sections of free cells.The main feature of this method is the use of bitwise logical operations on cell values.
Topic Aware Context Modelling for Dialogue Response Generation

Response generation is an important direction in conversation systems. Currently a lot of approaches have been proposed and achieved significant improvement. However, an important limitation has been widely realized as most models tend to generate general answers. To cope with this limitation, besides the needs of more sophisticated generation models, how to use extra information is also an important direction. In this research, inspired by the importance of topics in conversation, we proposed a topic aware context modelling framework by utilizing similar question answer pairs in the repository. Furthermore, we use adversarial learning to improve the quality of generated response. The experimental study has shown the propose framework’s potential.
Developing a competency-based of language skills and literary abilities in the primary school

Abstract The competency-based (CB) 1994 (Supplement 1999) curriculum is a milestone of an efforts to improve the Indonesian education quality. Regarding this matter, this study wanst to reveal the CB development of language skills and literary abilities in the primary school (PS) and the influencing factors. This ethnographic study was carried out in the fifth grade of PS in Surakarta from 2004 to 2015. The phenomenon of literacy learning (LL) in the classroom as a source of evidence was explored through cases, interviewees, archives. The evidence is collected through an observation, question-answer, content analysis. The evidence legality is checked through adding participation, careful observation, triangulation, checking key informant techniques. The data are analyzed using domain analysis, taxonomy, and theme. In conclusion, literary subject is a part of Indonesian language (IL) lessons. Its achievements are integrated through listening, speaking, reading, and writing as well as understanding and using linguistic elements using various forms of literary works (LW). The verball and writing students’ skills of IL can be fostered and developed nicely. However, LL has not optimally introduced students to the various values of LW through literary appreciations, expressions, and creations. These result are influenced by the teachers, students, learning techniques, learning materials, evaluation systems, facilities, environments, and socio-cultural factors.
Exploring Neural Network to Predict Car Tyre Inflation Time and Power Requirement of a Tyre Pressure Control Unit.

Abstract This study used Artificial Neural Network (ANN) for the prediction of power required to inflate different tyre sizes and inflation times. ANN is a widely accepted machine learning method that uses past data to predict future trend. An existing database obtained experimentally from a tyre pressure control test rig was optimized using genetic algorithm(GA) which is an optimization tool that can find better subsets of input variables for importing into ANN. The ANN results were compared with the results obtained experimentally. The results show that the model can be implemented in modern day tyre pressure control designs and be used to predict inflation times and power required to inflate different tyre sizes.
From Functional Optimization to Nonlinear Programming by the Extended Ritz Method

This chapter describes the approximate solution of infinite-dimensional optimization problems by the “Extended Ritz Method” (ERIM). The ERIM consists in substituting the admissible functions with fixed-structure parametrized (FSP) functions containing vectors of “free” parameters. The larger the dimensions, the more accurate the approximations of the optimal solutions of the original functional optimization problems. This requires solving easier nonlinear programming problems. In the area of function approximation, we review the definition of approximating sequences of sets, which enjoy the property of density in the sets of functions one wants to approximate. Then, we provide the definition of polynomially complex approximating sequences of sets, which are able to approximate functions provided with suitable regularity properties by using, for a desired arbitrary accuracy, a number of “free” parameters increasing at most polynomially when the number of function arguments grows. In the less studied area of approximate solution of infinite-dimensional optimization problems, the optimizing sequences and the polynomially complex optimizing sequences of FSP functions are defined. Results are presented that allow to conclude that, if appropriate hypotheses occur, polynomially complex approximating sequences of sets give rise to polynomially complex optimizing sequences of FSP functions, possibly mitigating the curse of dimensionality.
A Construction of Bent Functions on a Finite Group

In this paper, we discuss when a class function on a finite group is a bent function. We have found a necessary condition for a class function on a finite abelian group to be bent. Also, we have found a necessary and sufficient condition for a class function on a finite cyclic group to be bent.
A UML Profile for Prediction of Significant Software Requirements

The preliminary phase of the software development life cycle is Requirements engineering that is nearest to the user's world. This phase contains tasks that are knowledge concentrated. Therefore, the practice of Bayesian Belief Network (BBN) for modelling this knowledge would be worthful assistance. Accordingly, predicting significant requirements is essential. When poorly identified, numerous problems happen, such as budget overrun, software failures and schedule overrun. However, this phase is usually not performed and skipped by assuming as an inconsequential phase. Significant requirements identification become more vital and challenging when the complexity of the software increases. Therefore, managers and developers recurrently lose confidence in software artifacts. Wavering in the developer's confidence may in return distress the decisions of which requirement is to implement first. In this paper Bayesian belief network (BBN) approach for predicting significant requirements is proposed to solve the problem by UML Profile mechanism. This probabilistic model supports in accomplishing the uncertainties and inaccuracy generally exists in the requirements engineering process. It makes the prediction more precise, intuitionistic and reasonable. The Profile is imported into a UML tool, which helps in prompt validation of meta-model concepts in practice. The approach is practicable in a realistic context and addresses uncertainties.
Multi-Layer, Fully Reversible Watermarking Scheme Using Odd-Even Modification of Fourier Magnitude

In this paper, we proposed a novel reversible watermarking scheme based on Discrete Fourier Transform (DFT) and on Even-Odd Modulation technique. The proposed scheme supports the following features: blind extraction of the watermark, multi-layer mode of the watermark embedding, high quality of the watermarked image, complete recovering of the original image. We proposed to embed the watermark into DFT magnitudes by changing the parity of the magnitude digits. During experiment we used the watermarks of sizes 512×512 and 32×32. The watermark of size 512×512 has been embedded four times into the original gray scale image of size 512×512 with the following indicators: the values of Peak Signal Noise Ratio (PSNR) of the watermarked images were about 65 dB, the Normalized Coefficient of Correlation (NCC) between the embedded and extracted watermarks was equal to one and after performing the recovering procedure, the values of PSNR between the original and recovered images tend to infinity, which indicates 100% recovering of the original images. The watermark of size 32×32 has been embedded into the same image five times with the following indicators: PSNR of the watermarked image was equal to about 91 dB, NCC=1 and PSNR of recovered images tends to infinity. Experimental results prove that the presented scheme achieves high capacity, visual quality, complete reversibility, and better robustness in comparison with the schemes proposed in the literature.
The Use of Multimedia for the Development of Reading Literacy of Primary Schoolchildren

The article discusses the problem of the need to use the enthusiasm of schoolchildren with gadgets to activate their need for reading, the formation of reading literacy among them. As a means of solving this problem, the integration of interdisciplinary programs for the development of universal educational actions “Reading. Work with text” and “Formation of ICT readiness of schoolchildren” is proposed. The universality of the proposed model and technologies is emphasized, which allows in the process of extracurricular activities to increase the level of universal reading educational actions of schoolchildren at the initial and main stages of general education.
Mitigating Adaptive PRNU Denoising in Camera Model Identification: An Anti–Counter Forensic Approach

Adaptive PRNU Denoising (APD) is one of the strongest forms of state-of-the-art counter-forensic attacks, that has largely succeeded to prevent Photo Response Non-Uniformity (PRNU) based source identification of digital images. A major way to thwart such attack would be finding a successful mapping of a counter-forensically modified image, back to its source. In other words, such attack will be rendered obsolete if accurate source identification can be made possible with the counter-forensically modified images. For the first time, we perform a feature based identification of image sources, working directly with counter-forensically modified images, without trying to recover their authentic versions. We quantize the effects of APD on forensic source identification techniques, and explore how image texture helps in source identification of APD counter-forensic images. We propose Local Binary Pattern (LBP) extraction on APD image texture layer, the key component of our work, enabling successful counter-forensic image source attribution. Our experimental results prove that the proposed methodology is highly effective to mitigate the threats posed by APD, one of the strongest counter-forensic attacks on camera model identification today.
Brain tumor segmentation by level sets methods and brain extraction using a simple standard deviation method

The revolution of digital imaging has led to an explosion in the information produced. The segmentation framework is composed of three stages. First, we use (BSDM) to extract the region of interest brain; this is a pre-segmentation of brain tissues. In the second stage, we detect abnormal regions using level sets methods. This method incorporates some noise removal functions; the algorithm has been tried on a number of patients with MRI data of brain tumor images.
A Phenomenological Understanding of Music Upbringing, Education and Training

The article presents a phenomenological understanding of musical upbringing, education and training. This approach to the consideration of musical upbringing, education and training is unique because it is the first study of these phenomena from the standpoint of phenomenology. Analysis of these phenomena is carried out taking into account their connection with upbringing, education and training in general. The article notes that the problems of musical upbringing, education and training are a reflection of the problems that exist in the entire system of upbringing, education and training. It is indicated that the use of phenomenological approach can help to cope with these problems. The author uses the basic principles of phenomenological reflection, established by the German philosopher J.H. Lambert. Lambert believed that phenomenological comprehension of the truth of the phenomenon is supposed to proceed through three sequential stages: perception (sensory contact), cognition and usage of the will as a source for manifestation. According to the distinction of the three sequential stages proposed by Lambert, in the article upbringing, education and training are treated as three sequential stages of personal comprehension of the phenomenon. This comprehension of the world is actually the ultimate goal of pedagogical influence, and musical upbringing, education and training. The author concludes that music is the perfect manifestation of the world, which means that the introduction of a person to music (through three stages: musical upbringing, education, training) is actually the introduction of a person to the world, in other words, the solution of the pedagogical problem.
The Convergence Batch Dataset Algorithm Based on Deep Learning Model

The following topics are dealt with: learning (artificial intelligence); feature extraction; medical image processing; image classification; field programmable gate arrays; convolutional neural nets; image segmentation; biomedical ultrasonics; integrated circuit design; low-power electronics.
Tool Support for Confidentiality-by-Construction

In many software applications, it is necessary to preserve confidentiality of information. Therefore, security mechanisms are needed to enforce that secret information does not leak to unauthorized users. However, most language-based techniques that enable information flow control work post-hoc, deciding whether a specific program violates a confidentiality policy. In contrast, we proposed in previous work a refinement-based approach to derive programs that preserve confidentiality-by-construction. This approach follows the principles of Dijkstra's correctness-by-construction. In this extended abstract, we present the implementation and tool support of that refinement-based approach allowing to specify the information flow policies first and to create programs in a simple while language which comply to these policies by construction. In particular, we present the idea of confidentiality-by-construction using an example and discuss the IDE C-CorC supporting this development approach.