Reviewerly/exHarmony huggingface dataset:
Hierarchical learning particle swarm optimization using fuzzy logic

Particle swarm optimization (PSO) has been used extensively in numerical and engineering optimization problems in the last decades. However, due to drawbacks such as a single learning sample, PSO has the problems of loss of population diversity and easily falls into local optimum. To enhance optimization capability, a PSO based on fuzzy logic and hierarchical learning mechanism (FHPSO) is proposed. In FHPSO, the parameters are dynamically adjusted through fuzzy logic. The purpose of the fuzzy system is to generate appropriate parameters based on the performance metrics at each iteration, which better balances exploration and exploitation capability. Then particles are classified into different layers in terms of their fitness, and the particles in different layers perform different learning mechanisms. Each layer divides the particles into high-energy particles and low-energy particles. The high-energy particles in each layer are qualified to learn from the particles in the upper layer and the low-energy particles learn from the high-energy particles in their layer. This learning mechanism avoids all individuals to learn the global optimal individual at each iteration which will effectively reduce the speed and possibility of premature convergence and maintain population diversity. The FHPSO was compared with 8 well-known algorithms and 6 state-of-the-art PSO variants in the CEC 2022 and CEC 2021 test suites, respectively. The experimental results show significant performance of FHPSO. Simulation results for 5 complex engineering optimization problems and 3D path planning problem also show that the FHPSO can provides more competitive optimization results.
Influence of Role Playing Learning Model on Learning Outcomes of Speaking Skills in Simple Interviewing for Fifth Grade Students of SDN 2 Tambakrigadung Lamongan

The purpose of this study is to see how the role playing learning model affects students' speaking skills in class 5C SDN 2 Tambakrigadung Lamongan. This is a quantitative study that use the Posttest-Only Control Design approach. The data was collected using a random sampling technique and a learning outcome test in the form of a description (conversational text). The two-sample paired t-test formula was used for hypothesis testing. With 39 students in each class, the sample comprised of two classes: class 5C as the experimental group and class 5A as the control group. The results of the data analysis revealed that the role playing model had an effect on the speaking abilities of 5th grade students at SDN 2 Tambakrigadung Lamongan.The results of the data analysis revealed that the value of tcount was bigger than ttable, or 5.80 &gt; 1.665 at a significance level of 5%, hence H0 was rejected and Ha was given.
The body’s predictive processor <b>The Experience Machine: How Our Minds Predict and Shape Reality</b> <i>Andy Clark</i> Pantheon, 2023. 304 pp.

Brains begin with complete models of the world and work backward to anticipate sensory data, argues a philosopher.
Bayesian approach to micromechanical parameter identification using Integrated Digital Image Correlation

Micromechanical parameters are essential in understanding the behavior of materials with a heterogeneous structure, which helps to predict complex physical processes such as delamination, cracks, and plasticity. However, identifying these parameters is challenging due to micro-macro length scale differences, required high resolution, and ambiguity in boundary conditions, among others. The Integrated Digital Image Correlation (IDIC) method, a state-of-the-art full-field deterministic approach to parameter identification, is widely used but suffers from high sensitivity to boundary data errors and is limited to identification of parameters within well-posed problems. This article employs Bayesian approach to estimate micromechanical shear and bulk moduli of fiber-reinforced composite samples under plane strain assumption, and to improve handling of boundary noise. The main purpose of this article is to quantify the effect of uncertainty in the boundary conditions in the stochastic setting. To this end, the Metropolis–Hastings Algorithm (MHA) is employed to estimate probability distributions of bulk and shear moduli and boundary condition parameters using IDIC, considering a fiber-reinforced composite sample under plane strain assumption. The performance and robustness of the MHA are compared to two versions of deterministic IDIC method, under artificially introduced random and systematic errors in kinematic boundary conditions. Although MHA is shown to be computationally more expensive and in certain cases less accurate than the recently introduced Boundary-Enriched IDIC, it offers significant advantages, in particular being able to optimize a large number of parameters while obtaining statistical characterization as well as insights into individual parameter relationships. The paper furthermore highlights the benefits of the non-normalized approach to parameter identification with MHA (leading, within deterministic IDIC, to an ill-posed formulation), which significantly improves the robustness in handling the boundary noise.
Photonic reservoir computing with a silica microsphere cavity

We experimentally demonstrate a photonic reservoir computing (RC) system using a passive silica microsphere cavity. The microsphere cavity exhibits a consistent nonlinear response to the non-return-to-zero signal and the multiple-level signal due to strong interference between numerous whispering gallery modes in the “over-coupling” state. Benefiting from the fact that the long photon lifetime inside the microsphere cavity provides a memory of past inputs, this photonic reservoir does not require a delayed feedback loop. We evaluate the generalization property of the RC system and obtain a correlation coefficient of 0.923. In addition, we obtain a NMSE of 0.06 for the Santa-Fe chaotic time series prediction task and a SER of 0.02 at a SNR of 12 dB for the nonlinear channel equalization task. Moreover, a microsphere cavity with a higher quality factor can provide a larger memory capacity. The application of the silica microsphere cavity as a small-volume passive device in a reservoir furnishes a new avenue for achieving a low-consumption and integrated RC system.
“Who are you?”: Identifying Young Users from a Single Search Query

As an initial step towards enabling the adaptation of (popular, and widely used) web search environments so that they can better serve children and ease their path towards information discovery, we introduce Recognizing Young Searchers (RYSe). RYSe leverages lexical, syntactical, spelling/punctuation, and vocabulary features that align with the Concrete Operational stage of development (originally identified by Jean Piaget) in an attempt to identify users that are in this stage. The concrete operational stage is commonly associated with children ages 7-11. Findings emerging from our initial empirical exploration using single queries formulated by children and sample queries from adults showcase the feasibility of relying on different cognitive traits inferred from the short text of a single query to distinguish those that are formulated by younger searchers.
Evaluation of pre-training large language models on leadership-class supercomputers

Large language models (LLMs) have arisen rapidly to the center stage of artificial intelligence as the foundation models applicable to many downstream learning tasks. However, how to effectively build, train, and serve such models for many high-stake and first-principle-based scientific use cases are both of great interests and of great challenges. Moreover, pre-training LLMs with billions or even trillions of parameters can be prohibitively expensive not just for academic institutions, but also for well-funded industrial and government labs. Furthermore, the energy cost and the environmental impact of developing LLMs must be kept in mind. In this work, we conduct a first-of-its-kind performance analysis to understand the time and energy cost of pre-training LLMs on the Department of Energy (DOE)’s leadership-class supercomputers. Employing state-of-the-art distributed training techniques, we evaluate the computational performance of various parallelization approaches at scale for a range of model sizes, and establish a projection model for the cost of full training. Our findings provide baseline results, best practices, and heuristics for pre-training such large models that should be valuable to HPC community at large. We also offer insights and optimization strategies for using the first exascale computing system, Frontier, to train models of the size of GPT-3 and beyond.
High-Purity Entanglement of Hot Propagating Modes Using Nonreciprocity

Distributed quantum information processing and communication protocols demand the ability to generate entanglement among propagating modes. However, thermal fluctuations can severely limit the fidelity and purity of propagating entangled states, especially for low-frequency modes relevant for radio-frequency (RF) signals. Here we propose nonreciprocity as a resource to render continuous-variable entanglement of propagating modes robust against thermal fluctuations. By utilising a cold-engineered reservoir we break the symmetry of reciprocity in a standard two-mode squeezing interaction between a low- and a high-frequency mode, and show that the rerouting of thermal fluctuations allows the generation of flying entangled states with high purity. Our approach requires only pairwise Gaussian interactions and is thus ideal for parametric circuit QED implementations.
THE USE OF INTERNET OF THINGS TECHNOLOGY IN THE PEDAGOGICAL PROCESS

Today's trend is the use of information technology in all areas of our lives. Emotions are a basic human characteristic, although they are difficult to define, recognise and classify. Proper assessment and recognition of human emotions can lead to a better understanding of user behaviour. The aforementioned technologies can also be used as a suitable teaching aid, which forms the core of the research and is able to guarantee an increase in the success rate of the teaching process itself in terms of students' understanding of the learning materials. The aim of the research was the use of sensor networks as an element of information and communication technologies in the educational process. Using the possibility of measuring physiological functions through smart wristbands in order to identify changes in students' emotional states. The overall proposed system was able to identify changes in students' emotional state, specifically levels of arousal. Based on the results from the proposed system, teachers should be able to adjust their teaching style in specific situations to suit the students and provide a basis for better teaching and learning. Keywords: emotional states, internet of things, physiological functions, teaching process, smart wristbands, sensory networks
Artificial Intelligence and Service Quality of Telecommunication Firms in Nigeria

Globally, artificial intelligence (AI) technology spans various industries, but relatively little attention is given to the use of AI technologies by telecommunication industries. This study evaluated the effect of AI on the service quality of telecommunications companies in Nigeria, specifically the effect of data mining, machine learning, and chatbots on the service quality of these firms. The research employed a survey research design, and its population was heterogeneous. A sample size of 400 participants was chosen using Taro Yamane's formula, and the Cronbach alpha test yielded an average of 75%, confirming the reliability of the instrument. To analyze the data collected, descriptive and ordinary least squares regression methods were used. The study revealed that data mining and chatbots exhibited a significant positive effect while machine learning showed a negative relationship to the service quality of the telecommunications industry. Based on these findings, it is concluded that artificial intelligence affects service quality in Nigeria, with strong reference to data mining and chatbot, which enhance the quality of service to customers in Nigeria. It is therefore recommended that telecommunication firms in Nigeria should embrace the philosophy of AI to improve their quality of service.
Africa’s businesswomen – underfunded or underperforming?

Abstract While the recent success of Africa’s ‘Lionesses’ – successful female entrepreneurs – is internationally celebrated, less is known about how liquidity can fuel the success of the ‘Lionesses’ and other businesswomen. Using information from a panel of over 800 male- and female-owned businesses in Ghana (ISSER-IGC survey), we capture a measure of underfunding, in addition to data on supplier credit, equity and other finance sources. Our regressions reveal a female-to-male productivity gap of between − 11 and − 19 per cent, values similar to estimates for other African countries. However, when financial constraints are taken into account, the gender performance gap disappears. Accordingly, female business owners who indicate that funding is not a problem are associated with higher productivity than males, all things equal. In a finding new to the literature, our regressions reveal the importance of supplier credit for Africa’s businesswomen.
Blockchain-Based Federated Learning System: A Survey on Design Choices

The vanilla federated learning is made for a trusted environment, while in contrast, its actual use cases require collaborations in an untrusted setting. For this reason, using blockchain as a trusted platform to run federated learning algorithms has gained traction lately and has become a significant research interest. This paper performs a literature survey on state-of-the-art blockchain-based federated learning systems and analyzes several design patterns researchers often take to solve existing issues through blockchain. We find about 31 design item variations throughout the whole system. Each design is further analyzed to find pros and cons, considering fundamental metrics such as robustness, efficiency, privacy, and fairness. The result shows a linear relationship between fairness and robustness in which, if we focus on improving fairness, it will indirectly become more robust. Furthermore, improving all those metrics altogether is not viable because of the efficiency trade-off. Finally, we classify the surveyed papers to spot which designs are popular among researchers and determine which areas require immediate improvements. Our investigation shows that future blockchain-based federated learning systems require more effort regarding model compression, asynchronous aggregation, system efficiency evaluation, and the application for cross-device settings.
A Knowledge Graph-Based Many-Objective Model for Explainable Social Recommendation

As an application area of social computing, social recommendation aims to exploit the richness of social relationships to improve recommendation accuracy. Current research mainly considers social information that can be directly observed, with little attention to indirect social relationships between users or the explainability of social recommendation results. To address this challenge, this article proposes a knowledge graph-based many-objective model for explainable social recommendation (KGMESR) by considering the explainability, accuracy, novelty, and content quality of social recommendation results. The model takes advantage of social behavior information to calculate user similarity and quantifies the explainability of social recommendation results using entity vectors and embedding vectors. To ensure model efficiency, a many-objective recommendation algorithm based on the partition deletion strategy is designed. It employs the association of individuals with the nearest reference vector to render the diversity of the population and then obtains the final solution by deleting poorly converged individuals in each partition. Experimental results show that many-objective optimization recommendation algorithm based on partition deletion strategy (MaOEA-PDS) allows for preferable recommendation results for users in real datasets. The explainability of the proposed model is demonstrated by two case studies.
A CRITICAL APPRAISAL OF MIXED REALITY PROTOTYPING TO SUPPORT STUDIO DESIGN EDUCATION

Abstract Mixed Reality (MR) technologies are widely available and applied in a variety of design and engineering applications. MR prototypes capture the respective benefits of physical and digital prototypes by merging these domains saving the time and resources required to create them. This advantage is compelling in the context of design education where tight time and resource constraints exist. However, it is known that new digital prototyping tools can cause problems for students applying appropriate prototyping tools during practice-based studio design projects. Our paper contributes a systematic appraisal of MR prototyping's proposed dimensions value against constraints and issues in design studio education. This highlights MR Visualisation and Knowledge Management dimensions as most readily realised in education. Recommendations are then reflected on via an illustrative case study into the implementation of MR prototyping via these dimensions. Reflections corroborate the value proposition, but also highlight a need for further research exploring activities to scaffold MR prototyping to further support reflective design thinking.
360° Digital Travel to Improve Emotional State and Well-Being During the COVID-19 Pandemic: The Role of Presence and Sense of Place

The continuation of the COVID-19 pandemic has caused a decline in people's subjective well-being and emotional states. Digital travel based on 360° videos provides an alternate way for people to improve their mental health at home during this specific period. Yet, how to construct effective digital travel content that improves emotions remains an issue. This investigation assessed the impact of people's perceived presence and sense of place (SOP) on emotional improvement during a 360° digital travel experience. A total of 156 undergraduate students volunteered to participate, and anxiety, emotion levels, and life satisfaction were measured before and after the digital travel experience; presence and SOP ratings were also collected after the experience. A Latent Change Score model was then developed, and the results indicated that the greater presence and SOP individuals experienced during their digital travel, the better their digital travel experience and emotional improvement. Furthermore, the current data highlight that SOP has a greater impact on emotional improvement than presence. This result provides a novel understanding that how SOP is generated may be more critical to digital travel than presence. This new understanding should help improve relevant applications in the field of digital travel, such as the possibility of providing meaningful narrative context in a virtual environment to induce SOP more effectively, and improve the digital travel experience. Overall, the findings of this study expand our understanding of the digital travel experience and lay the groundwork for future research on SOP and digital travel.
Maintaining Religious Harmony through Predicting the Level of Lawlessness using Linear Regression

Crimes are basically committed due to low human faith and ultimately disrupt harmony, including inter-religious harmony. Therefore, the purpose of this study is to predict and determine the number of law violations that occurred in Yogyakarta Province in 2022 as a case study using a linear regression algorithm based on data ranging from 2012 to some of the data on law violations in 2022. The method used is the forecasting method in which the stages of data collection, data preprocessing, data modeling, and prediction processes are carried out. The accuracy of the linear regression model is 90%. However, there is a difference in accuracy between the training data and the test data. The training data gets an accuracy of 99% while the accuracy achieved by the test data is at 94% which is 5% lower than the training data. The prediction results using the linear regression model shows the number of the data on law violation that will occur in 2022 with correlation between crimes.
Improving Data Transfer Efficiency for AIs in the DareFightingICE using gRPC

This paper presents a new communication interface for the DareFightingICE platform, a Java-based fighting game focused on implementing AI for controlling a non-player character. The interface uses an open-source remote procedure call, gRPC to improve the efficiency of data transfer between the game and the AI, reducing the time spent on receiving information from the game server. This is important because the main challenge of implementing AI in a fighting game is the need for the AI to select an action to perform within a short response time. The DareFightingICE platform has been integrated with Py4J, allowing developers to create AIs using Python. However, Py4J is less efficient at handling large amounts of data, resulting in excessive latency. In contrast, gRPC is wellsuited for transmitting large amounts of data. To evaluate the effectiveness of the new communication interface, we conducted an experiment comparing the latency of gRPC and Py4J, using a rule-based AI that sends a kick command regardless of the information received from the game server. The experiment results showed not only a 65% reduction in latency but also improved stability and eliminated missed frames compared to the current interface.
Quantitative security analysis of three-level unitary operations in quantum secret sharing without entanglement

Quantum secret sharing (QSS) protocols without entanglement have showed high security by virtue of the characteristics of quantum mechanics. However, it is still a challenge to compare the security of such protocols depending on quantitative security analysis. Based on our previous security analysis work on protocols using single qubits and two-level unitary operations, QSS protocols with single qutrits and three-level unitary operations are considered in this paper. Under the Bell-state attack we propose, the quantitative security analyses according to different three-level unitary operations are provided respectively in the one-step and two-step situations. Finally, important conclusions are drawn for designing and implementing such QSS protocols. The method and results may also contribute to analyze the security of other high-level quantum cryptography schemes based on unitary operations.
Application Progress of Smart Phone Applications in Continuous Care of Stroke Patients

With the rapid development of information technology, mobile healthcare has become a hot topic of widespread concern among scholars at home and abroad.Applying smartphone applications to the continuous care of stroke patients can improve their self-management level, significantly improve their quality of life, and also promote the effective utilization of nursing resources.This article provides a review of the demand for smartphone applications in the continuous care of stroke patients, as well as the current application status at home and abroad.By drawing on foreign research results, it analyzes the main problems currently faced in China and makes prospects for the future development of the program, with the aim of providing reference and reference for Chinese medical staff in future research and application.