Reviewerly/exHarmony huggingface dataset:
The Effects of the Service Quality and Innovativeness of Unmanned Robot Cafes on Perceived Usefulness and the Intention to Reuse: Test for the Moderating Effects of the Frequency of Coffee Drinking

The purpose of this study was to determine the effects of the service quality and innovativeness of unmanned robot cafes on perceived usefulness and the intention to reuse and the moderating effects of the frequency of coffee drinking. This study obtained the following results: first, the system quality and innovativeness significantly affected perceived usefulness while the product quality had no significant effect on perceived usefulness. Second, the system quality significantly affected the intention to reuse while neither the product quality nor innovativeness had any significant effect on the intention to reuse. Third, perceived usefulness significantly affected the intention to reuse. Fourth, the frequency of coffee drinking had moderating effects on the effects of the product quality, the system quality, and innovativeness on perceived usefulness and the intention to reuse, with the exception of the product quality and the intention to reuse. This study aimed to contribute to an improvement in perceived usefulness through the service quality of unmanned robot cafes and an increase in the intention to reuse unmanned robot cafes.
CLIP2GAN: Towards Bridging Text with the Latent Space of GANs

In this work, we are dedicated to text-guided image generation and propose a novel framework, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e</i> ., CLIP2GAN, by leveraging CLIP model and StyleGAN. The key idea of our CLIP2GAN is to bridge the output feature embedding space of CLIP and the input latent space of StyleGAN, which is realized by introducing a mapping network. In the training stage, we encode an image with CLIP and map the output feature to a latent code, which is further used to reconstruct the image. In this way, the mapping network is optimized in a self-supervised learning way. In the inference stage, since CLIP can embed both image and text into a shared feature embedding space, we replace CLIP image encoder in the training architecture with CLIP text encoder, while keeping the following mapping network as well as StyleGAN model. As a result, we can flexibly input a text description to generate an image. Moreover, by simply adding mapped text features of an attribute to a mapped CLIP image feature, we can effectively edit the attribute to the image. Extensive experiments demonstrate the superior performance of our proposed CLIP2GAN compared to previous methods.
Reasoning with Concepts: A Unifying Framework

Abstract Over the past few decades, cognitive science has identified several forms of reasoning that make essential use of conceptual knowledge. Despite significant theoretical and empirical progress, there is still no unified framework for understanding how concepts are used in reasoning. This paper argues that the theory of conceptual spaces is capable of filling this gap. Our strategy is to demonstrate how various inference mechanisms which clearly rely on conceptual information—including similarity, typicality, and diagnosticity-based reasoning—can be modeled using principles derived from conceptual spaces. Our first topic analyzes the role of expectations in inductive reasoning and their relation to the structure of our concepts. We examine the relationship between using generic expressions in natural language and common-sense reasoning as a second topic. We propose that the strength of a generic can be described by distances between properties and prototypes in conceptual spaces. Our third topic is category-based induction. We demonstrate that the theory of conceptual spaces can serve as a comprehensive model for this type of reasoning. The final topic is analogy. We review some proposals in this area, present a taxonomy of analogical relations, and show how to model them in terms of distances in conceptual spaces. We also briefly discuss the implications of the model for reasoning with concepts in artificial systems.
Hybrid Optimization Algorithm for Data Leakage Reduction in Cloud Computing

Abstract In a Multi cloud system, we can store a large amount of privet data and sensitive information without any proper optimization of the user information. We will be focused on reducing data leakages to every cloud storage provider and provide a better technique for distributed user privet information over different CSPs in a leakage aware manner. The process of an unplanned distribution of data chunks will result in disposing of the essential information while using multiple clouds. To reduce data leakage, the hybrid optimization algorithm is proposed in this research work. The proposed model is implemented in cloud sim and results is analysed in terms of certain parameters.
Detection of temporal fluctuation in superconducting qubits for quantum error mitigation

We have investigated instability of a superconducting quantum computer by continuously monitoring the qubit output. We found that qubits exhibit a step-like change in the error rates. This change is repeatedly observed, and each step persists for several minutes. By analyzing the correlation between the increased errors and anomalous variance of the output, we demonstrate quantum error mitigation based on post-selection. Numerical analysis on the proposed method was also conducted.
MEVade: An MEV-Resistant Blockchain Design

Ethereum is a popular blockchain that facilitates the creation of decentralized applications (dApps) and enables digital transactions to be executed without the need for a central authority. However, as in traditional markets, information asymmetry and market inefficiencies are used to the detriment of ordinary users via trading strategies that exploit “Miner Extractable Value” (MEV). We propose two extensions of Ethereum, one for proof of work (PoW), and one for proof of stake (PoS), that eliminate most forms of MEV by randomizing the execution order of transactions and hiding the content of transactions until their inclusion in a block. We simulate attack scenarios for both settings and provide detailed security properties and proofs.
Educational achievements and export earnings: a comparison between leader and follower countries

Purpose This study examines whether education in developing countries directly impacts their foreign income from the top export sector. Design/methodology/approach As most developing countries follow developed nations to shape their development, this study assumes developing countries as education-follower and developed countries as education-leader countries. Considering selected countries from the South Asian Regional Cooperation (SAARC) and African countries as follower countries and Group of Seven (G7) as leader countries, this study employs Dumitrescu-Hurlin Granger non-causality tests. Findings This study finds that education-follower countries' achievements do not directly impact foreign earnings from their leading export sectors. However, findings also confirm that leader countries have a bidirectional causal relationship between tertiary education and earnings from high technology exports. Originality/value To the best of the authors' knowledge, this is the first study urging research-intensive education with comparative advantages in international trade. Using educational attainment on export earnings from the leading sector, findings support dependency theory in education is still existed.
PELATIHAN PENYUSUNAN BUKU AJAR UNTUK GURU DI SD NEGERI NOGOSARI 06 KECAMATAN RAMBIPUJI

Teachers are crucial in increasing student interest in learning through books or teaching materials that are attractive, contextual, and interactive. This service activity is carried out considering the needs of the elementary teachers at SD Nogosari 06 Rambipuji district. The problem that arises is the lack of understanding and ability of elementary teachers to establish interactive textbooks. This service activity was conducted for three months, with the substantial covering the introduction of the categories of textbooks and the preparation of textbook drafts. The method used in this activity is using lectures and discussions. The findings indicated that teachers are becoming more aware of the importance of preparing textbooks. The implementation of this activity also received a positive response from the SDN Nogosari 06 Rambipuji District teachers.
MAP-Elites with Descriptor-Conditioned Gradients and Archive Distillation into a Single Policy

Quality-Diversity algorithms, such as MAP-Elites, are a branch of Evolutionary Computation generating collections of diverse and high-performing solutions, that have been successfully applied to a variety of domains and particularly in evolutionary robotics. However, MAP-Elites performs a divergent search based on random mutations originating from Genetic Algorithms, and thus, is limited to evolving populations of low-dimensional solutions. PGA-MAP-Elites overcomes this limitation by integrating a gradient-based variation operator inspired by Deep Reinforcement Learning which enables the evolution of large neural networks. Although high-performingin many environments, PGA-MAP-Elites fails on several tasks where the convergent search of the gradient-based operator does not direct mutations towards archive-improving solutions. In this work, we present two contributions: (1) we enhance the Policy Gradient variation operator with a descriptor-conditioned critic that improves the archive across the entire descriptor space, (2) we exploit the actor-critic training to learn a descriptor-conditioned policy at no additional cost, distilling the knowledge of the archive into one single versatile policy that can execute the entire range of behaviors contained in the archive. Our algorithm, DCG-MAP-Elites improves the QD score over PGA-MAP-Elites by 82% on average, on a set of challenging locomotion tasks.
Lexical Diversity of Russian Poets

The paper discusses the lexical diversity of the works by 183 Russian poets included in the Poetic subcorpus of the Russian National Corpus. The study employs seven measures of lexical diversity (five versions of Standardised Type-Token Ration, Simpson’s diversity index, and Zipf–Mandelbrot’s a), which are linked to different aspects of interpretation, namely preference to / avoidance of lexical repetition, topical homo-/heterogeneity, and overall complexity of language and style, including vocabulary richness. A corpus-driven approach makes it possible to analyze how lexical diversity is related to two sociolinguistic variable present in the markup of the Poetic subcorpus, namely birth year and gender. It is demonstrated that topical heterogeneity and complexity of language and style have been on the rise since the eighteenth century. Male poets exhibit larger values of diversity linked to topic heterogeneity, but this only manifests itself in the use of nouns and verbs and does not apply to adjectives.
Construction of Optimal Frequency Hopping Sequence Set with Low-Hit-Zone

In quasi-synchronous frequency-hopping multiple access (QS-FHMA) systems, low-hit-zone (LHZ) frequency-hopping sequence (FHS) sets have been well-applied to reduce mutual interference (MI). In this paper, we propose three constructions of LHZ FHS sets with new parameters via interleaving techniques. The obtained sequences can be verified that they are optimal with respect to the Peng-Fan-Lee bound.
Analysis of Gender Disparity in Economic Growth of Third World Economy

This article focuses on the gender gap in economic growth across the third world. It has been noted in the past that women’s labour is never viewed as being on par with men’s labour. Promoting a stronger role for women helps the economy, improves child survival and family health, and decreases reproduction, reducing population growth rates, according to global experience. In other words, egalitarianism is vital for long-term prosperity. In every country, women make up half of the population. We need to consider how women contribute to a nation’s economic development. According to the United Nations (1993), they only receive a modest percentage of chances and advantages. Women’s empowerment will directly impact economic diversification, increased productivity, and income equality, all of which will lead to other beneficial development outcomes. In this paper, emphasis has been put up on the comparative understanding of developed and developing nations. Additionally, a broad comparison is made for both nations. Some instances are also mentioned where women have contributed significantly to global economic progress.
[Source Analysis and Pollution Assessment of Soil Heavy Metals in Typical Geological High Background Area in Southeastern Chongqing].

In order to understand the pollution characteristics of soil heavy metals in typical geological high background areas in southeastern Chongqing, analyze its pollution sources, and put forward risk management and control suggestions, 211 topsoil samples (0-20 cm) were collected in Xinglong Town and Muye Township in the north of Youyang County, Chongqing City, and the contents of eight heavy metals (As, Cd, Cr, Cu, Hg, Ni, Pb, and Zn) and the soil pH value in the surface soil were analyzed. Additionally, mathematical statistics, pollution load index (P), potential ecological risk index (RI), correlation analysis, and the positive matrix factorization (PMF) model were used to study the content and spatial distribution characteristics of heavy metals in the topsoil, and pollution assessment and source analysis of heavy metals were carried out. The results showed that the average concentrations of heavy metals in the surface soil all exceeded the soil background value in Chongqing, showing different levels of enrichment. Among them, the proportion of Cd exceeding the screening value in the "soil environmental quality risk control standard for soil contamination of agricultural land" (GB 15618-2018) was as high as 83.41%, the proportion of exceeding the control value was 7.58%, and the potential risk was relatively high. The evaluation results of the pollution load index method showed that the study area was slightly polluted as a whole, and the evaluation results of the potential ecological risk index showed that the overall ecological risk of the study area was relatively high. Among them, the contribution rates of heavy metals Hg, Cd, and As were 50.27%, 28.63%, and 11.70%, respectively. The PMF results showed that there were three main sources of heavy metals in the soil in the study area:natural parent material sources, agricultural sources, and mixed sources of industrial activities and traffic emissions, accounting for 40.49%, 29.12%, and 30.39%, respectively. The quantitative source analysis results of soil heavy metals in the study area have important reference significance for soil heavy metal pollution control in geologically high background areas.
Automatic Noise Detection and Reduction in Images

Data classification in presence of noise will cause a lot of worse results than expected for pure patterns. In the proposed work we tend to investigate the drawback within the case of deep convolutional neural networks so as to propose solutions which will mitigate influence of noise. The main contributions presented in this proposed work include using convolution neural network as an image classifier for detecting noise in the images and using different opencv2 inbuilt methods to mitigate noise in the images. Though a number of techniques are introduced for this purpose, using neural networks we can achieve a greater accuracy.
AI-Based Currency Exchange and Identification Model

American tourism is a multi-million dollar market. In 2022 alone, 80.7 million Americans traveled outside the United States of America. Since most of the destinations where American tourists travel, such as Mexico, Europe, and Canada, do not use U.S. currency, American tourists are subject to unnecessary stress due to this economic difference. There is a need for an identification system to classify different types of currency to aid in the American tourist’s travel experience. The proposed AI method collaborates with an online currency exchange rate database to update the rate of exchange for each currency to the US dollar in real-time. We applied our proposed method on currencies from Canada and Mexico; however, it could be extended to other currencies as well. The proposed model is trained on captured images of currencies, and the accurateness for the Mexican and Canadian currency models are 79% and 81%, respectively.
Regressor-Rater: A Resource-Efficient One-Shot Learner For Estimating Prediction Intervals

Prediction Intervals (PIs) are helpful to increase trustworthiness and user confidence in neural network regressions because they specify a range that contains the true estimate. Several methods for estimating PIs have been developed in the past, some of which rely on ensemble models whereas others rely on single neural networks. Although these methods have been successfully applied, there is still lots of room to improve the estimation accuracy of PIs. In this paper, we present a novel network architecture for estimating PIs with two interconnected networks. A Regressor network approximates the actual function; a Rater network estimates in tandem the PI boundaries around the ongoing regression by learning to estimate the deviation between the true output value and the computed regression. While the Regressor is trained with a training data set, there is no a-priori data set to train the Rater. To address this challenge, this paper presents a novel one-shot learning approach to train the Rater in parallel with the Regressor. Our evaluation on seven popular benchmarks shows that our proposed Regressor-Rater architecture constructs PIs that achieve high coverage probabilities of over 90% while being up to 55% narrower than the PIs generated by traditional approaches. Due to its small size and convenient design, our proposed architecture is particularly suitable to boost the trustworthiness of AIIoT applications.
성인계속교육의 관점에서 바라본 전공과 교사의 자격적 배치 방안 연구 : 한국장애인고용공단의 사례를 중심으로

The purpose of this study is to explore the ways in which majoring courses teachers can achieve a qualified placement for the KEAD from the perspective of adult and continuing education. The contents of the study focused on the need for teachers in majoring courses to promote interagency cooperation with KEAD to improve professional performance competencies related to vocational training for PDD, and suggested measures to be qualitatively assigned to KEAD. For the research method, FGI was used for leading teachers who are working in the majoring courses and have experience of project exchange with KEAD. Focusing on the point of view that teachers in majoring courses can develop professionalism in vocational training for PDD from the viewpoint of adult and continuing education when cooperative foundation with KEAD is involved, cooperative support type qualification placement, joint operation type qualification placement, independent link type qualification placement, and dispatch type qualification placement were composed. As a result of the study, it was possible to analyze the direction of the vocational training for PDD by cooperating with KEAD in various types based on the qualifications currently held. In addition, it was also suggested that teachers in majoring courses should acquire the same convergence qualification as lifelong education professionals centered on vocational training so that they can continue to develop professional performance competencies related to vocational training for PDD through institutional and long-term cooperation with KEAD.
Memory-based Augmentation Network for Video Captioning

Video captioning focuses on generating natural language descriptions according to the video content. Existing works mainly explore this multimodal learning with the paired source video and corresponding sentence, which have achieved competitive performances. Nonetheless, learning from video-description pair cannot capture implicit external knowledge, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e.</i> multiple visual context information and linguistic clues existing in the video-language dataset, which may limit the cognitive capability of the model to generate diverse descriptions. To this end, we propose a Memory-based Augmentation Network (MAN), in which a memory structure is designed to augment the current encoder-decoder framework by incorporating implicit external knowledge with a neural memory. Specifically, we first propose a visual memory for the encoder to store multiple visual contexts across videos in the dataset, which is utilized to obtain memory-augmented contextual features for the source video. In addition, a textual memory is introduced for the decoder to capture the external language clues across sentences in the dataset. It is adapted to capture memory-augmented language features in each time step. The proposed approach is able to capture comprehensive contextual understanding compared to the basic encoder-decoder framework, which is more compatible with the human cognitive process. Extensive experiments on three video captioning datasets including MSVD, MSR-VTT, and VATEX demonstrate the effectiveness of the proposed method. The source code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/zchoi/MAN</uri> .
Correctness-by-Construction Meets Refinement Types

Type systems provide specifications and enable reasoning about the programs. Among other benefits, type systems provide guarantees of the absence of certain (ill-typed) program behaviors. Refinement types further enrich the expressiveness of type systems by allowing a predicate alongside the type. The crucial divide by zero and null pointer errors are safely eliminated with refinement types. Interestingly, Correctness-by-Construction (CbC) also provides a way to set specifications for the programs. The specifications are provided for the smaller modules. Larger programs are built by composing the functionally correct smaller modules. Therefore, CbC naturally results in verified and correct programs following the program specifications. In this short paper, we highlight that CbC meets refinement types in a sense that both approaches provide specifications for program correctness and larger programs are built by composing the functionally correct smaller modules.
The Effective Commands Used By The Teacher During English Teaching in Managing Students with Disruptive Behavior

Commands are a crucial tool for classroom management used by the teacher to direct the students to start and end the activities. This qualitative descriptive research was aimed at identify types of students’ disruptive behavior that found at SD Lab Undiksha Singaraja especially in fifth grade, identify types of effective commands are given by an English teacher and analyze how the effective commands are given to students with disruptive behavior. This study was a case study research. The data were collected through observation, questionnaire, and interview. The result of this study showed that students ‘disruptive behavior at SD Lab Undiksha Singaraja classified as The Immature category such as talking with their friend, paying no attention to the teacher and lesson, walking around the class, and making a noise. To overcome students’ disruptive behavior, the teacher was used several types of commands. There were five types of Effective command such as Interrogation command, Don’t command, Indirect command, Regular command and Other command. Those commands were effective commands because there was compliance and response from the students.