Reviewerly/exHarmony huggingface dataset:
Special issue on systems and software product line engineering

Context: Higher Order Mutants (HOMs) present some advantages concerning the First-Order Mutants (FOMs). HOMs can better simulate real and subtle faults, reduce the number of generated mutants and test cases, and so on.Objective: In order to characterize the Higher Order Mutation Testing (HOMT) field, this paper presents results of a mapping study, by synthesizing characteristics of the HOMT approaches, HOM generation strategies, evaluation aspects, trends and research opportunities.Method: We followed a research plan to locate, assess, extract and group the outcomes from relevant studies. We found 69 primary studies, which were classified based on dimensions related to aspects of the conducted evaluation, purpose and use of HOMs.Results: Java is the preferred language. Most approaches use Second-Order Mutants (SOMs). We found 50 different techniques used to generate/select HOMs. We observed that from 39 primary studies which apply a strategy, ≈49% use search-based techniques.Conclusions: HOMT has been arising interest in the last years. The results herein presented provide researchers the start-of-the-art on HOMT, allowing them to understand existing approaches, and how the HOMs have been used and evaluated. Furthermore, this paper points out open issues and not addressed topics, which require more investigation, discussing trends and research opportunities in the field.
The Center for Technology and Innovation Support of V. Ya. Shishkov Altai Regional Universal Scientific Library: outcomes and perspectives

The article reviews the activities of the Center for Technology and Innovation Support (CTIS) set up within V. Ya. Shishkov Altay Regional Universal Scientific Library aimed at organizing and holding events that contribute to the patent-information support system formation and progress for the regional innovative development. The author emphases work features and priorities of the library-based CTIS, formulates some proposals for their further development.
The Humanitarian Component of Mathematical Education As a Methodical Basis of Professional Development of Teachers of Mathematics

The article investigates the features of humanization and humanization of mathematical education. These characteristics are considered as a theoretical basis for advanced training of teachers of mathematics in the development of the modern system of school mathematics education. A deep analysis of the term “mathematics” in order to understanding and perception o the role and importance of mathematics teachers for the development of civilization, scientifi c and technological progress, as well as the universal language of any modern science is made. Specifi c examples of integration (as replenishment of knowledge) and interpenetration of the content of mathematics and other educational areas (physics, chemistry, Philology, philosophy, etc.), contributing to the quality of school education are given. The emergence of a new modern metamathematics is noted, which is extremely relevant in the process of achieving metasubject learning outcomes by students. Values, opportunities and necessity of development of the personality of the school student by means of the subject “mathematics” are discussed.
Imperceptible Improvement of Secure Image Steganography based on Wavelet Transform and OTP Encryption using PN Generator

Digital data is widely transacted through internet media that can be accessed publicly, so this requires security of the data. This research proposes a combination of steganography and cryptography methods using wavelet transforms and OTP encryption based on PN generators. The PN generator is used as a tool to generate binary random numbers that are used to improve the quality of the embedding process and the encryption process. Messaging is carried out with four levels of wavelet transformation, the first three levels of the LL subband are selected to be transformed, while the fourth level wavelet transformation is selected for the HH subband to be embedded. This subband selection strategy is carried out with the aim of getting good imperceptibility and robust values. While OTP encryption is proposed because it has strong encryption results with a lightweight computing process. Based on the experimental results, binary random numbers generated by PN generators have been proven to successfully improve the quality of stego images and security. This has been measured by the PSNR and MSE, where there are remembrances on the two measuring instruments.
Abstract machines, optimal reduction, and streams

Abstract In this paper, we propose and explore a new approach to abstract machines and optimal reduction via streams, infinite sequences of elements. We first define a sequential abstract machine capable of performing directed virtual reduction (DVR) and then we extend it to its parallel version, whose equivalence is explained through the properties of DVR itself. The result is a formal definition of the λ -calculus interpreter called Parallel Environment for Lambda Calculus Reduction (PELCR), a software for λ -calculus reduction based on the Geometry of Interaction. In particular, we describe PELCR as a stream-processing abstract machine, which in principle can also be applied to infinite streams.
Preserving the quality of the cover by improving the correlation coefficient’s value

There are several different techniques of steganography that have huge impact in various area of technology. In particular, in mobile applications, and communication security, it has been applied widely. Consequently, this study have focused on building a new technique that combines statistical analysis of correlation coefficient with selecting proper cover for information that needs to be embedded inside that cover and send to the second part. The proposed method involves embedding the desired message and calculating the value of correlation that of course effect on the resolution of the cover, and then enhance that resolution within a proposed correlation value. This value should be changed several times until the obtained correlation value keeps the properties of the cover as the same as the original one. The method consist of selecting any image to be a cover and then embeds the desired information inside that cover. Next step involves dividing the selected image to two blocks from 0-255 and calculate its correlation coefficient. Afterwards, choose any value between -1 to 1 and set it in the equation of enhancement in order to determine the new correlation value between the same two blocks. Repeat this process to obtain the value that have the best enhancement cover. The best enhancement value of the correlation is the value that keeps the resolution for the cover with its best quality.
Voice, Gesture and IR Remote Controlled Alphabet Learning Display for Kids Based on 16×16 LED Dot Matrix Display

Learning the alphabet is an important step in teaching kids to read. There are variety of ways and tools to introduce alphabets to kids. This paper represents a device to teach number and alphabet of different languages like Bengali, English and Arabic to kids. The device consists of a 16X16 LED matrix display with MAX7219 display driver IC, ATmega328P microcontroller, IR receiver and BC05 Bluetooth module. The device has multiple control options to make it more interesting to kids. It can be controlled by voice, TV remote and android application. Another interesting option is to show an alphabet or number to the display by drawing gesture in the smartphone. As children love the variety in learning, so we think this device make the learning procedure more appealable to the children. It will also help in lessening their addiction to harmful gadgets for eyesight and keep their mind aside from those.
Jumbled passsteps

In today's fast paced growing technologies, user authentication plays a crucial role in the protection of data and private information from varied system attacks. Graphical-based password techniques have been proposed as a potential alternative to resolve security and usability limitations. However, most of the existing graphical password schemes like PassMatrix are vulnerable to random guess attacks based on hot-spot analyzing whereas some pass-images are relatively easier to guess because they are more memorable, obvious or unique. To overcome this problem, the researchers proposed the Jumbled PassSteps method. By replacing the single discretized image of the PassMatrix method into several independent images and applying a random grid traversal method, attackers cannot easily guess the original pass-image of the user even if they observed the log-in session. Because the acquisition of the one-time grid traversal steps is done with the use of earphones, the direction and number of traversals to be done in a particular session cannot be acquired by attacker even if it is camera recorded. Experimental results show that Jumbled PassSteps effectively overcomes hotspot guessing attacks and achieves better resistance to shoulder-surfing attacks.
Sequential Importance Sampling for Logistic Regression Model

Logistic regression is one of the most popular models to classify in data science, and in general, it is easy to use. However, in order to conduct a goodness-of-fit test, we cannot apply asymptotic methods if we have sparse datasets. In the case, we have to conduct an exact conditional inference via a sampler, such as Markov Chain Monte Carlo (MCMC) or Sequential Importance Sampling (SIS). In this chapter, the authors investigate the rejection rate of the SIS procedure on a multiple logistic regression models with categorical covariates. Using tools from algebra, they show that in general SIS can have a very high rejection rate even though we apply Linear Integer Programming (IP) to compute the support of the marginal distribution for each variable. More specifically, the semigroup generated by the columns of the design matrix for a multiple logistic regression has infinitely many “holes.” They end with application of a hybrid scheme of MCMC and SIS to NUN study data on Alzheimer disease study.
Immersive Audio Coding for Virtual Reality Using a Metadata-assisted Extension of the 3GPP EVS Codec

Virtual Reality (VR) audio scenes may be composed of a very large number of audio elements, including dynamic audio objects, fixed audio channels and scene-based audio elements such as Higher Order Ambisonics (HOA). Potentially, the subjective listening experience may be replicated using a compact spatial format with a set number of dynamic objects and scene-based elements, retaining only the perceptual essence of the audio scene. The compact format would further enable a reduction in the complexity of subsequent compression and rendering. This paper investigates these hypotheses by exploring the use of a compact format that consists of up to four dynamic objects and nine HOA channels, with the Enhanced Voice Services (EVS) codec being applied to a 4-channel down-mix of the compact format.
Photo splicing detection using error level analysis and laplacian-edge detection plugin on GIMP

There are several types of photo manipulation, one of them is splicing manipulation. The problem was nowadays the image manipulation software is used by the person who has not an expert may manipulate the photo. The question was how we can be sure whether the photo is original or not. Therefore, this paper tested the photo splicing detection using two methods: error level analysis and laplacian-edge detection by the plugin of GIMP. The result was to detect photo splicing that has several variables which may enable the method to work better, i.e., the characteristic of the camera and the JPEG compression that characterized by the camera. This experiment and analysis can be used as a reference for photo forensics investigation.
Analysis WhatsApp Forensic and Visualization in Android Smartphone with Support Vector Machine (SVM) Method

In this paper we discuss about the pattern of data WhatsApp application in the form of text, image, and sound. This application is widely used as a media to deliver and exchange information, so this is also open opportunity to commit crimes through this media. In this research, we focus on forensic and create visual about patterns data from WhatsApp in form of text, image, audio and video. In this study, 288 instances have been examined using 9 features. The feature that characterizes any personal data in the selected data has not been used. From the results, we get by a reading database which stored on the client smartphone is obtained results of the conversation time, client contact number, key and message types between client applications use WhatsApp. Receiver Operating Characteristic (ROC) curves was also presented for each of the classes.
Regularizing Neural Networks with Gradient Monitoring

Neural networks are the most evolving artificial intelligence method in recent times and have been used for the most complex cognitive tasks. The success of these models has re-scripted many of the benchmark tests in a wide array of fields such as image recognition, natural language processing and speech recognition. The state of the art models leverage on a large amount of labelled training data and a complex model with a huge number of parameters to achieve good results. In this paper, we present a regularization methodology for reducing the size of these complex models while still maintaining generalizability of shallow and deep neural networks. The regularization is based on the monitoring of partial gradients of the loss function with respect to weight parameters. Another way to look at it is the percentage learning evident in a mini-batch training epoch and thereafter removing the weight connections where a certain percentage of learning is not evident. Subsequently, the method is evaluated on several benchmark classification tasks with a drastically smaller size and better performance to models trained with other similar regularization technique of DropConnect.
FARES: Fast and Accurate Recognition of Exact Scenes on Mobile Devices

Mobile devices represented by smartphones have been continuously evolving to support various applications. In this paper, we study a new mobile application, named exact scene recognition, where a user can identify a particular place by comparing two images taken there. This application framework enables users to annotate a scene supporting more descriptive image-based interactions such as mobile augmented-reality applications. We enhance the regular approaches with the assistance of the angle-of-view (AOV) information obtained from the smartphone. Our experimental results show a significant improvement on accuracy compared to the existing solutions.
MLE: A General Multi-Layer Ensemble Framework for Group Recommendation

As the number of users and locations has increased dramatically in location-based social networks, it becomes a big challenge to recommend point-of-interests (POIs) meeting users’ preference. In traditional recommendation tasks, personalized recommendations performs well, however, these methods also have many disadvantages such as the long-tailed problem and the strong assumption. Further, in general scenarios, a group of users (e.g., colleagues, friends, and family members) often visit a specific location to enjoy time together (e.g., meal and shopping). Thus, it is more meaningful to recommend locations to the group than to individuals. However, the existing group recommendation approaches also have some limitations that hardly capture the preferences of a group of users effectively. To make full use of the users’ preferences and improve the effectiveness of group recommendation, in this paper, we propose a multi-layer ensemble framework which has a two-step fusion process. For the first step, we employ several personalized recommendation methods to generate the recommendations for individuals, and the recommendation list is obtained using the proposed fusion approach based on the supervised learning. For the second step, we utilize several ranking aggregation algorithms to fuse the recommendations list of individuals in the group and propose an unsupervised learning based ranking algorithm (URank) to further fuse the results of ranking aggregations to obtain the final group recommendation list. The experiments are conducted on a real-world dataset, and the results demonstrate the effectiveness of our proposed general framework.
Lattice Boltzmann Model based 3D Image Segmentation

Image segmentation plays a very important role in three-dimensional volume data processing. In three-dimensional segmentation, the curve evolution method based on geometric active contour model and level set method is one of the most widely concerned methods. Among these methods, the CV model is a hot research topic. Based on the Munfor-Shah model, CV model is proposed by Chan and Vese. Because of the huge amount of data in three-dimensional images, the computation of solving CV model is very large, which consumes a lot of time. In this study, a 3D image segmentation algorithm based on Lattice Boltzmann model is proposed. The experiment shows our method is effective. The algorithm is simple and efficient. More importantly, the LB model has natural parallelism. Especially, our method can be implemented on massively parallel image processing platform, for example, FPGAs, DSPs and GPUs.
Linguistic Structure Analysis of Thai and White Hmong Languages for Machine Translation

The Machine Translation (MT) is an automatic human language translation from one language to another language by computer namely “Interlingual MT”. The most importance key of translation process is to understand the structure of the language. The research objectives are study the appropriate language structure, present the translation pattern, and develop a dictionary for Thai and White Hmong translations. The scope of the research is limited to the language structure which defines the word not exceed than 4 syllables. Linguistic structure analysis used the translation pattern and Human Aided Machine Translation (HAMT) process. The experimental results indicate that the translation process can be used in two ways are translation from primary language to target language and direct translation. Our dictionary has been developed with classification 6,677 words,3,229 sentences were employed for system testing. The proposed prototype translation system is achieved a superior efficiency outcome of translation than Google and Microsoft by F-Measure are 0.496, 0.316, and 0.244 respectively. For future work, the researcher intend to present of the appropriate machine translation algorithm and committed to developing a translation application between Thai and Hmong languages.
Engineering Authentication Process with Cloud Computing

Cloud computing is a way to increase the capacity or add capabilities dynamically without any upfront investments. Despite the growth achieved from the cloud computing, security is still questionable which impacts the cloud model adoption. Aside of having network and application securities being adopted, there must be a security that authenticate the user when accessing the cloud services that is bound to the rules between the cloud computing provider and the client side. The existing system provides authentication based on keys Encryption algorithms either symmetric key-based or asymmetric are key-based. Both encryption approaches have a major problem related to encryption key management i.e. how to securely generate, store, access and exchange secrete keys. In this paper, an optimized infrastructure for secure authentication and authorization in Cloud Environment using SSO (Single Sign-On) is proposed. SSO is a process of authenticating once and gain access of multiple resources that aims at reducing number of login and password in heterogeneous environment and to gain balance in Security, Efficiency and Usability. Also an authentication model for cloud computing based on the Kerberos protocol to provide single sign-on and to prevent against DDOS attacks is also presented in this paper.
Detecting and Facing Information Demand for New Target Groups such as Start-up Founders – A Case Study at the WHU Library

Abstract Future oriented libraries can make use of the current start-up trend. An orientation towards new and unorthodox target groups can lead to an enhanced extension of demand and can emphasize the status of libraries. The library of the WHU – Otto Beisheim School of Management is considering to involve a new target group, start-up founders amongst their alumni. To that end, a survey was carried out and evaluated in cooperation with the Institute of Information Science at the TH Köln – University of Applied Sciences in form of a bachelor thesis, which this article is based upon. Here, a structured pre-analysis tries to determine the demand of this specific target group (founders) and develops a concept to serve the demand of this target group specifically. The example of the case study illustrates a method for target groups specific information demand and also checks the consequences for libraries and their services who venture out of their regular clientele.
Measuring usability scale and factors that influence the implementation of internship information system in engineering faculty of Jenderal Soedirman University

Kerja Praktik or Internship is a mandatory course which is part of the final study courses in Jenderal Soedirman University. The successful final study courses will make students finished their undergraduate program in time. Thus, it is important to build an established system that support students and all the stakeholders and to make sure that they have a good process during the course. The Internship Information System or SIKAP in Engineering Faculty of Jenderal Soedirman University is built to support this objective. By using an online and integrated system, including the data integration for the internship monitoring process, every step on the internship course could be managed easier. In detail, this system automates the process and creates collaboration among students more easily. For the Final Study Commission, Internship Supervisor and The Academic Department this system manages the internship process from start to finish such are: issuing the permit letter, proposal submission, setting supervisor, making work order, managing the seminar paperwork, submitting grade until report distribution. This research aims to measure usability scale from SIKAP and to find what factors that influence the difficulty of its user from February 2018 when it was first implemented. The data is surveyed and collected using a modified System Usability Scale/SUS form Brooke. The result form 121 respondent shows factors that appears to be the cause of high level user complaints but overall usability score of SIKAP is 64,5, considered between OK and Good usability level.
Faster Scalar Multiplication on the x-Line: Three-Dimensional GLV Method with Three-Dimensional Differential Addition Chains

On the quadratic twist of a GLV curve, we explore faster scalar multiplication on its x-coordinate system utilizing three-dimensional GLV method. We construct and implement two kinds of three-dimensional differential addition chains, one of which is uniform and the other is non-uniform but runs faster. Implementations show that at about 254-bit security level, the triple scalar multiplication using our second differential addition chains runs about $$26\%$$ faster than the straightforward computing using Montgomery ladder, and about $$6\%$$ faster that the double scalar multiplication using DJB chains.