Reviewerly/exHarmony huggingface dataset:
A Novel Approach to Reuse Custom Enterprise Software Extensions for Cloud Migration

Enterprise Software has been the backbone for running businesses since last many decades now. It's a common practice for the consumers to enhance these software as per their specific business needs. In general software development terminology, such enhancements are called custom extensions of the software. Depending upon the specific business needs, the magnitude of custom extension may vary from customer to customer. With the advent of cloud computing and digital transformation, organizations are paving their path towards cloud in all sectors. This is also applicable for organizations developing and consuming enterprise software. Although cloud computing brings a lot of value to the software eco-system, however it also presents a challenge towards disruption in businesses where the existing custom extensions cannot be reused while moving to cloud. In this paper, we present a novel approach to automatically enable custom extensions from an old On-premise installed Enterprise software to new version of the software hosted on a Cloud Platform.
IPFC: An Attentive Face Completion Network with Identity Preserving

Face completion is a challenging task since the facial textures and structures are hard to be captured and generated. Meanwhile, the blank area is usually completed by an average face, which may blur the individual information during the face completion processing. To solve these problems, we propose an Identity-Preserving Face Completion Network (IPFC). Firstly, we design an attentive generative module for achieving the pixel-level spatial relations and use the relations to capture the details of the face images. Then, we employ two discriminators to distinguish face authenticity and individual differences. During the optimization stage, we develop three schemes to minimize the generation error: (1) Full image and eye reconstruction losses are used to provide global and partial supervision, respectively. (2) The face authenticity loss is utilized as a basic restriction to keep the generated data distribution. (3) The individual discriminate loss is designed to capture the identity distribution from the training data without any other extra data. We apply our method on a popular benchmark CelebA-HQ, and it achieves state-of-the-art performances in four metrics.
An Analytical Study of Selfish Mining Attacks on Chainweb Blockchain

Chainweb and some other parallel blockchain systems have recently been proposed, with the objectives of improving the throughput and enhancing the tamper-proof capability. While many security related studies have been conducted for traditional single-chain based blockchain systems, the security aspect of parallel chain systems is yet to be well studied and understood. Our paper presents a systematic study on selfish mining attacks in Chainweb based on mathematical modeling. Specifically, selfish mining is conducted by concentrating the computation power on a subset of parallel chains and operating a proper withholding strategy. We demonstrate how to establish a Markov chain based analytical model with innovative techniques to handle the very large state space. Our Markov chain model is also capable of handling different number of parallel chains. The mathematical analysis brings an insightful, in fact counterintuitive, finding that the attackers need less computation power to harvest additional rewards through withholding when Chainweb contains a larger number of chains; while the common understanding is that the more chains are used, the more tamper-proof the system is. The accuracy of the Markov chain analysis is demonstrated via comparison to the simulation results.
A Research On The Compatibility Between MR/VR Devices And Exergaming

In recent years, Exergaming which allows people to exercise easily indoors has been gaining attention. A lot of Exergaming is often played using VR devices or dedicated devices, however, since Exergaming is played indoors, there is a risk of colliding with objects in the room or people around the player. We hypothesized that Exergaming using MR devices would solve the problems of VR devices. Therefore, we conducted a comparison of Exergaming using MR and VR devices. Subjects were asked to play Exergaming with the same level using an MR device (HoloLens2) and a VR device (Oculus Quest 2), and then compared in three categories: “Breathability”, “Op-erability”, and “Safety”. Additionally, the difference in gaming sickness and heart rate between the two devices were investigated. An MR device was superior in terms of “Breathability” and “Safety”, and a VR device was superior in terms of “Operability”. For the difference in gaming sickness, VR Exergaming resulted in no problem in all items, however, MR gave serious symptoms in “Oculomotor disturbance”, “Disorientation” and “Total”. The difference in heart rate was not significant. The results of this experiment show that although MR devices are superior in terms of “Safety” and “Breathability”, there is a tendency that MR devices can easily make users feel bad, and MR devices are not always more comfortable to play than VR devices.
Oblivious Piecewise-Linear Decision Trees

Gradient boosting ensembles of decision trees are a very popular machine learning model with several popular implementations. Some of those implementations utilize oblivious decision trees - decision trees of specific structures that allow for improved regularization and prediction speed. Over the last years, several research works have been published related to piecewise-linear decision trees and their utilization in gradient boosting ensembles. In this paper, oblivious piecewise-linear decision trees were introduced, and it was shown that it is possible to train such trees in gradient boosting ensembles efficiently. Furthermore, it was shown that such oblivious piecewise-linear decision trees have significantly faster prediction time than regular decision trees and piecewise-linear decision trees of similar depths. Ensembles of oblivious piecewise-linear decision trees achieve competitive quality on open datasets.
Analysis of pattern matching algorithms considering their practical application based on experiments

The article presents the results of a study of deterministic algorithms for solving the pattern matching problem: the simplest sequential search algorithm, the Rabin-Karp algorithm, the Knuth-Morris-Pratt algorithm, and the Boyer-Moore algorithm. After analysing the asymptotic complexity indices of the algorithms, a probabilistic search algorithm was constructed and implemented in software. Computational experiments were conducted to simulate the practical application of the considered algorithms. The results of the experiments reflect the methods of application of each algorithm.
Sketch2Makizushi: Interactive Guidance System for Decorative Sushi Rolls with User Sketches

Decorative sushi rolls are produced to look like flowers or cartoon characters at their cross-sections. They are prepared by placing vinegared rice and other ingredients on a laver, which is then rolled. To create unique decorative sushi rolls, it is difficult to come up with a recipe containing descriptions regarding the number and size of the laver, the amount of vinegared rice, and the arrangement of ingredients. To address this issue, we propose using Sketch2Makizushi, an interactive guidance system that allows users to make their desired decorative sushi rolls using a sketch as an input. The proposed system can automatically calculate the recipe for the decorative sushi rolls according to the input sketch and present the cooking process. The proposed system enables the free design of decorative sushi rolls compared with conventional production approaches. We conducted an evaluation study to assess the proposed system to confirm its effectiveness and obtain information on user experience.
Different Techniques of Facial Image Generation from Textual Input : A Survey

The task of Text-to-Face synthesis is quite intricate and there hasn't been much research done on it, until recently. This is mainly due to the complex nature of human face and it's features, which vary widely over different situations. That being said, the field of Text-to-Image synthesis has gathered considerable interest quite late. In earlier research, generation is mainly done using reconstruction of visuals which correlate to the given words. Due to the rise of generative models in the field of deep learning, there has been a departure from these traditional computer vision based retrieval methods. One of the most significant factors for this change is the introduction of GANs. The idea of learning and reproducing the image as a whole, helped in producing better images. The introduction of attention based mechanisms, helped in synthesizing more detailed images which manages to show several facial features like eye brows, hair color, nose shape etc. In this survey, we discuss and summarize some of the methods used for the purpose of image & face generation and their development over the years.
Design methodology for a multi-satellite global continuous observation system of the Earth

The article considers approaches to the selection of design parameters of a multi-satellite space system for global continuous monitoring of the Earth. The objective of this research is to develop a method for determining the optimal parameters of a multi-satellite space system for global continuous monitoring of the Earth. Firstly, existing space-based observing systems of the Russian Federation and foreign multi-satellite space-based observing systems were analysed, secondly, the requirements for multi-satellite systems of global continuous monitoring of the Earth's surface were considered, thirdly, the main restrictions imposed by the technical capabilities of the ground segment and on-board equipment of the observing spacecraft were identified, and finally, a design problem for the synthesis of a multi-satellite space system for global continuous monitoring of the Earth's surface is formulated, taking into account the restrictions of the geographical location of ground receiving stations as well as the inter-satellite communications. As a result, a method of designing a multi-satellite space system for global continuous monitoring of the Earth was proposed.
SARS-CoV-2- hábitos alimentares e comportamentais da população de Parauapebas (PA)

This study aims to evaluate the eating and behavioral habits of the population of Parauapebas (PA) during social isolation as a result of the COVID-19 pandemic. The methodology used was a quali-quantitative cross-sectional study. An online form was applied, consisting of open and closed questions, in which 472 valid responses were obtained from participants of both sexes, 26.27% (n=124) male and 73.72% (n=348) female, aged between 18 and 60 years. The forms contained questions about age, gender, race, education, height, body weight before and after isolation, neighborhood where they live, emotional state, eating and behavioral habits. The survey results indicated that the pandemic contributed to changes in habits and behaviors associated with emotional factors such as stress and anxiety. Most frequently reported by the research subjects that culminated in dietary changes in the participants' lives. Based on the results of this study, it is suggested that the isolation resulting from the COVID-19 pandemic may have a direct relationship with changes in eating habits with emotional factors, enhancing this entire context.
A Tutorial for Adopting the µMPS3/Pandos Project in the Operating Systems Course

An operating system is a software artifact. Like all software artifact-based courses (e.g. compilers, neural networks), the gold standard accompanying lab exercise is to build an instance of the artifact. µMPS3/Pandos is a new semester-long project, independent of text choice, for use in an operating systems course. The project guides students in writing, from scratch, a complete, though simple, operating system in C to run on an emulated MIPS-based system. This workshop will introduce participants to, µMPS3, the system emulator, the Pandos project --a detailed set of specifications/assignments that result in a completed operating system capable of concurrently executing up to eight student written C programs, and the wealth of supporting educational materials designed to aid both the instructor and their students. Though a Linux laptop (virtualization is fine) will allow for a hands-on experience with µMPS3/Pandos; one is not required for the workshop. Participants will learn how to use µMPS3, and will walk through each of the three core assignments and sample solutions. The workshop will culminate with the execution of participant written C program(s) running under a completed instance of Pandos. This workshop is designed to empower anyone who teaches an operating systems course to be successful in deploying µMPS3/Pandos in their operating system course.
Getting Started with Source Code Analysis for Programming Education Research

Interesting ideas for computing education research often involve analysis of source code written by beginners. The process is often difficult to carry out: data must be collected from learners and then the code must be analysed. This can be daunting and time-intensive. In this workshop we will show how to get started with syntactic source analysis using a pre-provided data set and an easier analysis technique. The dataset is Blackbox Mini; the Blackbox project has been collecting data from Java novices for the last eight years and a subset of the data, named Blackbox Mini, has been extracted into a SrcML data format. SrcML is an XML-based format for storing program code so that the syntax tree can be navigated using existing powerful XML libraries. This makes source code analysis easier and more accessible to a wider audience. Attendees at the workshop will learn how to use SrcML to work with the Blackbox Mini data set, including basic statistics gathering, and basic syntactic source code analysis. As a result, attendees will be able to design and execute their own source-based research studies. Attendees should bring their own device with an SSH client installed.
Application of Small World Network in the Dissemination of Unsafe Behaviors with Masks

In order to study the spread process of the unsafe behavior of employees wearing masks after returning to work, an SIR (virus propagation) model based on the small world network model was established to construct the propagation model of the unsafe behavior of employees wearing masks. The results show that: the SIR model based on small world network can simulate the complex propagation process involving employee behavior, and can make visual prediction on the propagation process of related violation behaviors related to wearing masks after returning to work. When the enterprise does not carry out any intervention, with the increase of enterprise scale, the time for employees who do not wear masks as required to reach 50&#x0025; of the enterprise scale is longer and longer; in large-scale enterprises, the interpersonal transmission speed of employees who do not wear masks as required is faster, and the number of people growing in a single day is more likely to lead to large-scale infection. However, the effect of pre intervention is better than that of post intervention. The effective rate of intervention before and after the event has an important impact on the transmission process of not wearing masks as required. When the effective rate of enterprise control pre intervention reaches 30&#x0025; or post intervention effectiveness reaches more than 50&#x0025;, large-scale spread can be prevented. When the effective rate of enterprise control pre intervention reaches 70&#x0025; and after intervention reaches more than 80&#x0025;, it can effectively prevent the spread of non wearing masks among employees. The results showed that both pre intervention and post intervention had better effect on slowing down and preventing the spread of non wearing masks.
The 2nd World Logic Prizes Contest

I discuss the origin and development of logic prizes around theworld. In a first section I describe how I started this project by creatingthe Newton da Costa Logic Prize in Brazil in 2014. In a second section Iexplain how this idea was extended into the world through the manifestoA Logic Prize in Every Country! and how was organized the Logic PrizesContest at the 6th UNILOG (World Congress and School on UniversalLogic) in Vichy in June 2018 with the participation of 9 logic prizes win-ners from 9 countries. In a third section I discuss how this project willdevelop in the future with the creation of more logic prizes, an Ency-clopædia of Logic, the book series Logic PhDs, as well as the creation of aWorld Logic Day, January 14, day of birth of Alfred Tarski and of deathof Kurt G¨odel.
Chaotic multi-population ABC algorithm based on memory and levy flight for solving dynamic job shop scheduling problems

In the real world, most of the problems are dynamic optimization ones. In other words, optima may change over time. Algorithms that can solve these kinds of problems can adapt well, by using the ability to track the optima in case of evolution. In this paper, a novel chaotic multi-population artificial bee colony optimization with levy flight algorithm (CMABCLA) is proposed to minimize makespan in dynamic job shop scheduling problem. The dynamic events considered in this paper are machine breakdowns, random job arrivals and processing time changes. Dynamic job shop scheduling is a known NP-hard combinatorial optimization problem. The chaotic system used in this algorithm has more precise prediction of the future than the random system, and it increases the convergence rate of the algorithm. Also, after a change, the information obtained from the previous state makes quick adaptation possible. Moreover, the utilization of levy flight in the scout bees phase has led to the improvement of exploration. Moving Peaks Benchmark has been chosen to examine the effectiveness of the proposed method. The results of conducted experiments show the superiority of the proposed method to state-of-the-art algorithms in terms of offline error and CPU time.
Reform and Exploration of Higher Vocational English Teaching Mode under the Background of "Internet +"

On March 5,2015, Premier Li Keqiang proposed the "Internet +" plan at the Third Session of the 12th National People&#x2019;s Congress held in the morning. Since that day, the "Internet +" has been widely circulated. On July 6, 2015, China officially issued the Guiding Opinions on Actively Promoting the "Internet +" Plan. The release of this document made steady progress in China&#x2019;s "Internet +" plan. China is actively developing towards the era of "Internet +", following the pace of The Times, and the original traditional higher vocational English teaching mode is also reformed under this background. In the era of "Internet +", a variety of emerging technologies are emerging, such as cloud flash payment, big data and cloud computing, and the rise of many technologies provides extremely favorable conditions for information processing in the education industry. In terms of higher vocational English teaching mode is concerned, the new environment of "Internet +" provides many new development opportunities for the reform of higher vocational English education, and lays a foundation for the exploration of the teaching mode of higher vocational English education.
ON SOME APPROACHES TO KNOWLEDGE VISUALIZATION BASED ON SYSTEM METHODOLOGY

Some aspects of knowledge management in the context of managing complex systems are considered. The purpose of the study is to determine the most convenient forms of knowledge visualization at the early (initial) stages of the study of complex realworld situations that practically cannot be formalized. A single process of constructing conceptual (soft) models is proposed, integrating several types of models: rich pictures, mind maps, conceptual maps, mess maps. This approach to knowledge visualization can be applied in various fields both for solving applied problems and for conducting scientific research.
Towards Components-of-Interest Feedback Control and State Estimation of Robotic Manipulator

Closed-loop methods are commonly used in manipulator motion planning field to remove errors in joint encoders. Compared to placing markers on both manipulator and obstacles, the method that tracks the robot arm using point cloud data does not rely on calibration of the relative pose between the marker and the end-effector. In this paper, instead of tracking the entire arm, we focus on the components-of-interest (COI), which is the robot end-effector and the obstacle in the collision avoidance task. Since by tracking the endeffector it is not possible to directly get the states of other joints, two methods of solving these problems are proposed, namely visual servoing with nominal Jacobian method and inverse kinematic update method. Experiments demonstrate that tracking the COI can obtain a more accurate end-effector pose compared to tracking the entire arm and the proposed methods can successfully achieve the expect goals.
BENTUK PARTISIPASI MASYARAKAT DALAM PROGRAM RUMAH BAHASA KOTA SURABAYA PADA MASA PANDEMI COVID-19

Surabaya Language House Program is established by the Mayor of Surabaya Tri Rismaharini in 2014 aimed at improving the quality of human resources by learning foreign languages for free. The Covid-19 pandemic that hit the world in early 2020 made the Language House Program temporarily suspended and began to be held again using online media. This study aims to determine the form of community participation in the Language House Program during Covid-19. This study uses the theory of Keith Davis in Sastropoetro (1988). The method uses qualitative case study approach. Data collection techniques uses documentation, online interviews, online observations. The results indicate that the form of mind participation is in online classes so that Language House Program does not completely stop. The form of energy participation carried out is by tutors in teaching language through online media. The form of mind and energy participation is done by making syllabus which will be taught in each class through discussions. The form of skill participation is to develop the language skills by taking language certification and teaching practices.
Natural Language Generation and Creative Writing A Systematic Review

Among studies on natural language generation (NLG), computational creativity, and human-computer interaction; there is a vision of witnessing these tools collaborating with humans in generating and authoring creative content. This study aims to systematically review published studies discussing creative writing and story generation during the period of 2016-2021. This work seeks to identify the primary research methods used in NLG and creative writing studies, to locate how these studies are distributed geographically, and finally, to classify the subfields or common keywords primarily used in NLG involving creative writing. The findings suggest that experiment studies and problem-solving were the most common research methods in NLG and creative writing. Major identified themes in the reviewed articles include story generation, language models, and co-creativity, along with some gaps in foreign language translation and humour generation studies. The majority of the studies suggest that NLG tasks had a positive impact on creative writing. Common tasks related to NLG and creative writing are typically using keywords such as story generation, co-creativity, co-writing, user interface and writing tools. In future work, we aim to explore more GPT-3 capabilities in creative writing, in addition to creative writing applications in foreign language translation tasks.