Reviewerly/exHarmony huggingface dataset:
Nonlinear Interaction between Two Chirped Broadband Single Photons

Abstract Coherent correlation between two distant broadband single photons can be realized by using the nonlinear optical method, which provides a key technology for spread‐spectrum communication in the quantum network. Here, sum‐frequency generation (SFG) between two broadband single photons is experimentally demonstrated by using chirp technology. The results show that the efficiency of the SFG is 6.51 × 10 −8 , and the visibility of coherent correlation of two distant broadband photons of the telecommunications band is 93.7%. This technique can also be used for other quantum applications, such as quantum entanglement swapping and quantum communication.
Stylistic Imitation Technique in Improving the Writing Skills among Grade 8 Learners

This study aimed to test the effectiveness of stylistic imitation technique in improving the writing skills among the Grade 8 learners.It also pursued identifying the pre and post-assessment scores of the studentrespondents in terms of content, vocabulary, organization, conventions, and purpose.It also sought to determine if there is a significant difference between the pre and post scores of the Grade 8 learners.The experimental method of research is utilized in this study.The respondents were the two sections of Grade 8 learners of Mayao Parada Agricultural Integrated High School with a total of 60 respondents.Respondents were pre-assessed to evaluate their skills in writing.Then, they experienced the stylistic imitation technique, which was six times implemented in the classes.They were post-assessed at the end.Based from the findings, all the writing skills significantly improved after utilizing the stylistic imitation technique.The scores may not show a big difference between their scores, but all of them made a difference in each skill.Most of the learners from group 1 earned higher scores than the group 2. Thus, there is a significant difference between the pre and post-assessed scores of the student-respondents in terms of content, vocabulary, organization, conventions and purpose.
Artificial Intelligence and Inventorship Under the Patent Law Regime: Practical Development from Common Law Jurisdictions

Abstract Artificial intelligence (AI) is gradually becoming not only an emerging technology in the development policies and strategies for the national economy but also a significant challenge posed to various issues of law, including the patent law. Alongside the Industrial Revolution 4.0, the rapid technological and scientific advancement has enabled AI to approach the level of natural intelligence of humans or more. Noswadays, the development of technology has been demonstrated that AI is likely to autonomously generate inventions. In light of the fact that the identification of the inventor impacts the patentability of AI-generated inventions, this poses a new challenge regarding the inventorship of AI, with the most confusing question being whether AI should be recognized as an inventor. This article provides an overview of AI and patentability challenges to clarify the fundamental understanding of AI and material concerns regarding AI-generated inventions. Simultaneously, the authors examine the laws and practices in selected countries to provide the viewpoints of competent authorities on patent protection for AI-generated inventions and the inventorship of AI. Finally, the authors conclude with a discussion of the recognition of AI inventorship, patent protection for AI-generated inventions, an evaluation of the recognizability of Vietnamese patent law, and a call for further research on relevant matters.
Hardware-efficient Softmax Approximation for Self-Attention Networks

Self-attention networks such as Transformer have become state-of-the-art models for natural language processing (NLP) problems. Softmax function, which serves as a normalizer to produce attention scores, turns out to be a severe throughput and latency bottleneck of a Transformer network. Softmax datapath consists of data-dependent sequential nonlinear exponentiation and division operations, which are not amenable to pipelining and parallelism, nor can they be directly linearized for pretrained models without substantial accuracy drop. In this paper, we proposed a hardware efficient Softmax approximation which can be used as a direct plug-in substitution into pretrained transformer network to accelerate NLP tasks without compromising its accuracy. Experiment results on FPGA implementation show that our design outperforms vanilla Softmax designed using Xilinx IPs with 15x less LUTs, 55x less registers and 23x lower latency at similar clock frequency and less than 1% accuracy drop on main language benchmark tasks. We also propose a pruning method to reduce the input entropy of Softmax for NLP problems with high number of inputs. It was validated on CoLA task to achieve a further 25% reduction of latency.
Meningkatkan Keterampilan Literasi pada Siswa SD melalui Pendekatan Pembelajaran yang Inklusif

This research aims to explore the effectiveness of inclusive learning approaches in improving students' literacy skills in Elementary Schools (SD). Literacy is a key component in education that plays a crucial role in shaping the foundation of lifelong learning for students. However, various factors such as different ability levels, diverse learning styles, and variations in social and cultural backgrounds often hinder students from achieving optimal literacy levels. In this study, an inclusive learning approach was adopted to create a learning environment that accommodates the needs and individual differences of each student. The research method used was Classroom Action Research (PTK) involving several classes in an elementary school. Data were collected through pre-and post-literacy tests, classroom observations, and interviews with teachers and students. The results of the study show that the inclusive learning approach has a significant positive impact on improving students' literacy skills. When students are empowered to learn in an inclusive environment, they tend to show better improvements in reading, writing, and text comprehension. Additionally, students also demonstrate increased active participation and motivation in learning, as they feel valued and supported throughout the learning process. The findings of this research underscore the importance of creating inclusive and supportive learning environments for students in SD. Teachers and educational institutions should strive to identify individual student needs, provide appropriate support, and employ diverse teaching methods to facilitate improved literacy skills. Thus, inclusive learning approaches can be an effective means to enhance the quality of education and create a brighter future for SD students in facing the challenges of a knowledge-based society.
Perancangan Sistem Informasi Absensi Berbasis Android Menggunakan Geolocator

Introduction: The development of information and communication technology greatly influences the current civilization which makes it possible to simplify work in an organization. One of them is CV Raudha Design which has utilized information technology in various aspects of operations, but does not include employee attendance which is still manual using an attendance card. Purpose: to create an android-based attendance application system to get data in realtime and accompanied by coordinates using a geolocator when employees are absent. Method: The development of this application uses the waterfall method. Testing the system using black box testing. System coding uses the dart language with the flutter framework. Results: The results of this study are the design of an android-based attendance information system to make it easier for employees to take attendance online. Conclusion: It can be concluded that the attendance application uses an Android-based geolocator to facilitate the attendance process because there is no need to fill in and out of attendance data on the attendance paper.
Memories About Bogdan Mielnik

This is a collection of my personal memories, somewhat extended toward Bogdan’s extra-scientific activities. It is a modest contribution to Bogdan’s portrait as a human being. An elaborate record (in Spanish) of Bogdan’s scientific activities can be found here: https://www.fis.cinvestav.mx/~david/papers/semblanzabm-df.pdf .
Analysis of socio-economic systems using quantumlike mathematical models based on status functions. Part II

It is proposed to use quantum-like models with the use of status functions for mathematical modeling and subsequent analysis of complex socio-economic systems. The limitations of the methods of classical probability theory and mathematical statistics, as well as the theory of fuzzy sets, algorithms Mamdani, Suzuki and others for solving similar problems are described. A description of the main assumptions that are used in the mathematical modeling of socio-economic objects based on status functions is given. Examples are considered that describe the features of transition paths through intermediate states. An operator for the transition of a socio-economic system to various states, similar to the Hamiltonian, is presented. A spectrum of possible virtual trajectories is introduced to describe transitions to different states. A mathematical model based on status functions is proposed to describe the transition of the system to a measurable state. In the proposed Hamiltonian, the first term represents a subsystem of indica-tors, the second is an analog of the energy of indicators in the information environment. At the same time, terms are distinguished that are analogues of the energies of the system of two controlled indica-tors: interaction, kinetic and potential. The description of the results of mathematical modeling and the analysis of the interaction of two hypothetical indicators of the socio-economic system are given. The indicators are taken from the statistics of innovation indicators of one of the regions of the Russian Federation.
PENINGKATAN LITERASI DIGITAL DAN PENGELOLAAN MEDIA SOSIAL SEBAGAI PENCEGAHAN PENYEBARAN HOAX PADA SISWA/I DI SMA IMMANUEL KOTA BATAM

The high exposure to information and the rapid circulation of hoax information in Indonesia, especially during the Covid 19 pandemic, indicate the importance of digital literacy education for all parties. Literacy education for students needs to be carried out considering that students are the highest internet users in Indonesia. Literacy education activities are highly recommended to be carried out in formal institutions/organizations or the school environment. SMA Immanuel is one of the educational institutions in the city of Batam that requires assistance regarding digital literacy at SMA Immanuel. This training was carried out to provide (1). Knowledge and understanding of digital literacy and digital security (2). Understanding of hoax news and skills in avoiding the spread of hoax news, and (3). Skills in wise social media management. This coaching method are using lecture, discussion, and practice methods with a duration of 1 hour per session for 4 alternate meetings and is scheduled to involve 7 service members. This coaching is conducted in online meetings using the Zoom application. This coaching will be given to Immanuel school students in the city of Batam, totaling around 10-15 people which will be carried out in the January-June 2022 range. The results of the activity show that there is a significant increase in skills both in digital literacy skills, handling hoaxes, and also social media management.
Effect of Avatar Anthropomorphism on Bodily Awareness and Time Estimation in Virtual Reality

The time elapsed during a virtual reality (VR) experience is estimated to be short. Time estimation, a feeling related to timescales longer than a few seconds, is thought to be related to interoception. The shortening of time estimation may be caused by the VR experience distorting bodily awareness based on interoception; however, the details of this change have not been clarified. The characteristics of avatars are likely to affect bodily awareness. Therefore, based on the hypothesis that bodily awareness during a VR experience is altered by manipulating the avatar's degree of anthropomorphism, this study investigated the effect of avatar anthropomorphism on bodily awareness and time estimation. In an experiment, participants performed a heartbeat discrimination task and a one-minute time estimation task, in the real world (Real condition), in the virtual world using a human avatar (VR-Human condition), and in the virtual world using a robot avatar (VR-Robot condition). The results showed that the perceived sense of agency was significantly lower in the VR-Human condition than in the VR-Robot condition, and interoceptive accuracy was significantly higher in the VR-Human condition than in the VR-Robot condition. However, there were no differences in time estimation between conditions. These results indicate that it is possible to conduct experiments to manipulate bodily awareness by manipulating the avatar characteristics. These findings can provide insights into understanding the relationship between bodily awareness and time estimation.
Towards a Blockchain-based Software Engineering Education

Blockchain technologies for rewards in education are gaining attraction as a promising approach to motivate student learning and promote academic achievement. By providing tangible rewards for educational attainment and engagement, such as digital tokens, educators can motivate learners to take a more active role in their learning and increase their sense of ownership and responsibility for their academic outcomes. In this context, this work proposes the Software Engineering Skill (SES) token as a way of rewarding students in order to improve their experiences in Software Engineering Education (SEE). We performed a proof of concept and conclude that SES token can be deployed in a platform to support SEE.
The acceptance of mobile learning for the subject of geography among pre university students (Form 6)

21st century technology and skills in education are two of the key issues in research over the years. Mobile learning has shown great potential of 21st century technology in education today. Technological developments such as mobile learning have changed the way students communicate, learn, think, and share information among them. This study was conducted to look at the acceptance of mobile learning among form 6 students in geography subjects. The quantitative method was used in this study involving 137 form 6 student from Pre University center located in the district of Kota Kinabalu, Sabah. All students taking geography subjects were taken as samples. The results from statistical analysis show that the acceptance level of students on mobile learning is in the high level.
Deep Learning-Based Self-Admitted Technical Debt Detection Empirical Research

&lt;p&gt;Self-Admitted Technical Debt (SATD) is a workaround for current gains and subsequent software quality in software comments. Some studies have been conducted using NLP-based techniques or CNN-based classifiers. However, there exists a class imbalance problem in different software projects since the software code comments with SATD features are significantly less than those without Non-SATD. Therefore, to design a classification model with the ability of dealing with this class imbalance problem is necessary for SATD detection. We propose an improved loss function based on information entropy. Our proposed function is studied in a variety of application scenarios. Empirical research on 10 JAVA software projects is conducted to show the competitiveness of our new approach. We find our proposed approach can perform significantly better than state-of-the-art baselines. &lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;
Safe evolution of smart contracts

We present a framework that supports the safe deployment and upgrade of smart contracts based on the design-by-contract paradigm. The starting point is an interface specification with invariants and pre- and postconditions for each function. The first deployed smart contract must conform to this specification. Specification evolution might involve both changing the data representation as well as extending the interface with new functions, provided the evolved specification is a refinement of the original one. Implementation evolution must conform to the corresponding specification. We report on the applicability of the framework in the verification of smart contracts that implement some Ethereum standards.
Blockchain Usage in Smart Cities: A Systematic Review

Blockchain has been widely used to store decentralized and secure cryptocurrency transactions (for instance, Bitcoin solution). On the other hand, smart city applications are concerned with how data and services can be stored and shared securely. In this regard, this paper investigates the use of blockchain in the context of smart city environments. We conducted a systematic review to understand how blockchain could be used in smart city scenarios. This review resulted in a set of important requirements to meet the needs of the blockchain in the context of smart cities.
Half&amp;Half: Demystifying Intel’s Directional Branch Predictors for Fast, Secure Partitioned Execution

This paper presents Half&Half, a novel software defense against branch-based side-channel attacks. Half&Half isolates the effects of different protection domains on the conditional branch predictors (CBPs) in modern Intel processors. This work presents the first exhaustive analysis of modern conditional branch prediction structures, and reveals for the first time an unknown opportunity to physically partition all CBP structures and completely prevent leakage between two domains using the shared predictor. Half&Half is a software-only solution to branch predictor isolation that requires no changes to the hardware or ISA, and only requires minor modifications to be supported in existing compilers. We implement Half&Half in the LLVM and WebAssembly compilers and show that it incurs an order of magnitude lower overhead compared to the current state-of-the-art branch-based side-channel defenses.
Development of Malignant Call Automatic Blocking System for Call Center Counselor

Objective: The purpose of this study is to build a system base that automatically blocks malicious calls such as customer"s verbal abuse by utilizing voice recognition technology before occurrence.Background: Call center counselors frequently have labor processes based on the characteristics of the service industry and emotional labor. Counselors working in call centers are experiencing serious problems of verbal abuse and sexual harassment. Work exhaustion and job stress due to emotional labor negatively affect the mental and physical health of counselors.Method: In this study, a call center operation system investigation, voice and text data collection, voice guidance phrase derivation, data-based automatic blocking system development procedure establishment, and automatic blocking system prototype development were carried out.Results: Voice and text-based malicious data were collected based on media content, swear word dictionary and comment search base, and call center recording data provided by three public institutions. Malicious calls were classified into 1) abusive language, 2) short language, 3) sexual harassment, and 4) insulting sentences and words, and data were collected. The procedure for developing a system that can automatically block malicious calls based on the collected data consisted of four major steps. The main code for creating software for operating the automatic blocking system to protect counselors from malicious calls in the call center is STT (Speech To Text) function, microphone voice recording, STT result and verbal comparison function in advance, blocking function when detecting abusive language, call Codes for 5 functions, including call quality score receiving function at the end, were developed. Conclusion: Although this system does not consider emotional consumption and exhaustion that occur during counseling, it is a system that can extract words related to profanity and sexual harassment that have high mental stress.Application: It is judged that the application of this system can help change the culture beyond simply preventing prevention by using a systematic methodology.
Taylor Polynomial Enhancer using Genetic Programming for Symbolic Regression

Unlike most research of symbolic regression with genetic programming (GP) concerning black-box optimization, this paper focuses on the scenario where the underlying function is available, but due to limited computational resources or product imperfection, the function needs to be approximated with simplicity to fit measured data. Taylor polynomial (TP) is commonly used in such scenario; however, its performance drops drastically away from the expansion point. On the other hand, solely using GP does not utilize the knowledge of the underlying function, even though possibly inaccurate. This paper proposes using GP as a TP enhancer, namely TPE-GP, to combine the advantages from TP and GP. Specifically, TPE-GP utilizes infinite-order operators to compensate the power of TP with finite order. Empirically, on functions that are expressible by TP, TP outperformed both gplearn and TPE-GP as expected, while TPE-GP outperformed gplearn due to the use of TP. On functions that are not expressible by TP but expressible by the function set (FS), TPE-GP was competitive with gplearn while both outperformed TP. Finally, on functions that are not expressible by both TP and FS, TPE-GP outperformed both TP and gplearn, indicating the hybrid did achieve the synergy effect from TP and GP.
Large-Scale Optimization and Learning

tutorial Open Access Share on Large-Scale Optimization and Learning Authors: Mohammad Nabi Omidvar School of Computing, University of Leeds, Leeds, UK School of Computing, University of Leeds, Leeds, UK https://orcid.org/0000-0003-1944-4624View Profile , Yuan Su La Trobe University, Melbourne, Australia La Trobe University, Melbourne, Australia https://orcid.org/0000-0003-2911-0070View Profile , Xiaodong Li RMIT University, Melbourne, Australia RMIT University, Melbourne, Australia https://orcid.org/0000-0003-0346-1526View Profile Authors Info & Claims GECCO '23 Companion: Proceedings of the Companion Conference on Genetic and Evolutionary ComputationJuly 2023Pages 1477–1502https://doi.org/10.1145/3583133.3595037Published:24 July 2023Publication History 0citation74DownloadsMetricsTotal Citations0Total Downloads74Last 12 Months74Last 6 weeks35 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF
Relational Bayesian Optimization for Permutation

Relational Bayesian optimization for permutation (RBOP) is a new permutation estimation of distribution algorithm proposed in this paper. RBOP uses binary relations to represent the common property in permutations. Inspired by the Bayesian optimization algorithm, RBOP first builds a Bayesian network using binary relations. Then, RBOP samples genes using the most certain edge in the Bayesian network. In the scenario of black-box optimization, RBOP aims to solve various permutation problems with a limited number of function evaluations. Experiments show that in terms of average relative percentage deviation, RBOP outperforms edge histogram-based sampling algorithm on quadratic assignment problems, permutation flow shop problems and linear ordering problems. Additionally, RBOP also outperforms both node histogram-based sampling algorithm and kernels of Mallows model using Cayley distance on traveling salesman problems, permutation flow shop problems, linear ordering problems and 6 out of 10 instances of quadratic assignment problems.