Reviewerly/exHarmony huggingface dataset:
A STUDY ON THE COMPLEX PUBLIC FACILITY DEVELOPMENT USING PURE-CM SYSTEM BY LOCAL GOVERNMENT (PART 1): CASE STUDY OF CMR’S ROLE IN THE PROCUREMENT PROCESS OF THE DESIGN AND CONSTRUCTION BY SMALL MUNICIPALITY, YABU CITY

We clarified the role of CMR and other stakeholders in the complex public facility development of small municipality by analyzing the minutes and documents used in the development. In (Part 1) CMR and the expert committee as two wheels could implement to prevent from the unsuccessful bid and to adopt the ECI method through the procurement process. Furthermore, small municipality that do not have sufficient internal and technical experts could carry out large-scale construction project supported by not only CMR but also the expert committee.
Blogging as an Instrument for Co-Creation and Collaborative Learning in University Education

A number of university educators decided to implement an experience “pilot”: the co-creation of a blog with students in which they disseminated some topics of the subject “Spanish political system”, taken as part of a joint honors degree in law and political science from the Faculty of Political Sciences and Sociology of the Complutense University of Madrid. Four professors and 42 students participated in this experience. In the first phase, the students, under the supervision of the professors, collaboratively developed each of the assigned topics. To do so, the professors explained the dynamics, provided them with a series of resources, trained them in collaborative work techniques, supervised the whole process, and resolved all their doubts. In a second phase, they jointly evaluated one subject to see the possible deviations that had occurred with respect to the initial model, in order to be able to correct them in the rest. In addition, the professors conducted a student survey to measure teamwork and the impact of the blog, the results of which were shared with the participants and at specialized conferences. Thanks, this pilot program has shown that blogging is an important technological tool for the transformation and improvement of teaching and learning processes, and that it encourages the active role of students and promotes their collaborative learning, generating knowledge with and for students.
Air quality prediction model based on genetic algorithm and weighted extreme learning machine

Air quality forecasting is closely related to people's daily life. At present, the WRF-CMAQ simulation system is commonly used to forecast air quality. However, due to the uncertainty of the simulated weather field and the emission inventory, the results of the WRF-CMAQ forecast model are not ideal. In order to solve the above problems, this paper proposes an air quality prediction model based on Genetic Algorithm (GA) to optimize the parameters of Weighted Extreme Learning Machine (WELM). The experimental results show that the new GA-WELM model has good generalization ability and effectively improves the forecast accuracy
Developing a Chatbot–Clinician Model for Hypertension Management

SummaryAutomated chatbots offer the promise of reducing reliance on personnel while maintaining or increasing effective and comfortable customer engagement. But accuracy standards are higher and quality assurance more difficult in health care settings, where patients are the customers. The University of Pennsylvania Health System implemented a prototype chatbot to reduce message burden on clinicians in a text-based remote hypertension management program. The chatbot accurately triaged 99% of patient messages (1,379 of 1,393), with most messages, nearly 75% (1,073), not requiring escalation to the clinician. The authors share both their approach for building while doing using innovation methodology and the lessons learned in developing a chatbot for clinical care.
More than Meets One Core: An Energy-Aware Cost Optimization in Dynamic Multi-Core Processor Server Consolidation for Cloud Data Center

The massive number of users has brought severe challenges in managing cloud data centers (CDCs) composed of multi-core processor that host cloud service providers. Guaranteeing the quality of service (QoS) of multiple users as well as reducing the operating costs of CDCs are major problems that need to be solved. To solve these problems, this paper establishes a cost model based on multi-core hosts in CDCs, which comprehensively consider the hosts’ energy costs, virtual machine (VM) migration costs, and service level agreement violation (SLAV) penalty costs. To optimize the goal, we design the following solution. We employ a DAE-based filter to preprocess the VM historical workload and use an SRU-based method to predict the computing resource usage of the VMs in future periods. Based on the predicted results, we trigger VM migrations before the hosts move into the overloaded state to reduce the occurrence of SLAV. A multi-core-aware heuristic algorithm is proposed to solve the placement problem. Simulations driven by the VM real workload dataset validate the effectiveness of our proposed method. Compared with the existing baseline methods, our proposed method reduces the total operating cost by 20.9~34.4%.
Determining the Best Answers for Balinese Language Problems using Latent Semantic Analysis

In Balinese, descriptions or essays are formed in an interrogative format using question words such as “akuda”, “apa”, “dija”, “kenken”, “kuda”, dan “nyen”. The assessment process on description questions tends to be more difficult and complex than multiple choice questions, this is because the description questions are described in sentence form. The solution to facilitate the assessment process on description questions can be done using automated essay scoring. Based on the results of previous studies, the Latent Semantic Analysis (LSA) method provides a better level of accuracy, because the LSA method uses the Singular Value Decomposition (SVD) method to obtain a new pattern of relationships between terms and reference terms. The data used in this study are five questions and their answer keys and there are five candidate answers for each question in Balinese. Based on the tests that have been carried out, the method used obtained an overall average accuracy of 70.26%, this shows that the LSA method can be used well in the assessment process or automatic essay assessment.
RUSSIAN POSTGRADUATE STUDIES IN THE FACE OF MODERN CHALLENGES

The article touches upon the problem of the state of the domestic graduate school as a stage of education, which is highly susceptible to various managerial changes and other factors. The relevance of the study is due, firstly, to the task of effective reproduction of scientific personnel, which the state is facing, and secondly, the active transformational processes that have been taking place within the Institute of Postgraduate Studies in recent years. The purpose of the study is to identify the key challenges and problems that graduate school faces today. The COVID-19 pandemic has had a significant impact on higher education in general and the delivery of graduate programs in particular. However, this challenge for the system of training highly qualified personnel is only a temporary negative factor, in contrast to the systemic problems and difficulties that have accompanied the development of postgraduate studies for a long time since the collapse of the USSR. It is concluded that digitalization, on the one hand, has become a challenge for the education system as a whole, on the other hand, this process has contributed to the adaptation of educational and scientific organizations to the difficult epidemiological conditions in the country. The analysis of the essence and consequences of the current stage of domestic postgraduate education reform, associated with the changes in the legal framework that took place in 2020-2022, as well as the preparation of recommendations to mitigate the difficulties of the transition period of modernization of the third stage of higher education can be a promising direction for further study of this problematic.
Hybrid Particle Swarm Optimization for a Feature Selection Problem with Stability Analysis

In this paper a hybrid Particle Swarm Optimization (HPSO) method is developed and applied to a Hybrid Feature Selection problem. The objective of applying the HPSO method to the Hybrid Feature Selection problem is to increase the classification accuracy and decrease the number of features. In the proposed HPSO method we have introduced the concepts of mutation and crossover taken from Genetic algorithm and then introduced into Particle Swarm Optimization (PSO) method. It helps in order to increase the exploration opportunity. Using Von Neumann stability criterion and the concepts of Fourier series, the mathematical explanation of stability of the proposed HPSO method has been explained with proof. Convergence of the proposed HPSO algorithm is explained using the Markov chain concept. Then results are compared graphically and statistically with other meta-heuristic algorithms. Friedman test and Mann-Whitney U test are used to check the statistical significance of the proposed algorithm against other meta-heuristic algorithms.
Avatar-based virtual reality and the associated gender stereotypes in a university environment

Abstract Avatar-based virtual reality (VR) is becoming more prevalent in industry and educational settings. There is, however, limited research on the extent to which gender stereotypes are present in this environment. The university laboratory study presented in this paper was conducted in a VR environment with participants who were randomly assigned to male or female avatars and instructed to negotiate the role of a manager or member of staff. The results reveal differences in satisfaction regarding their roles and gender. Participants who embodied a female avatar were less happy when they were subordinates interacting with a male avatar, compared to participants embodying a male avatar in the staff role (interacting with a female avatar). Male avatars with staff roles were also more content with their avatar than male avatars with manager roles and also reported being more comfortable in the VR experience. Relevant for diversity management when integrating VR in education and business, the results are discussed in regard to self-similarity and social identity dynamics and provide insight into understanding the extent to which gender stereotypes may be present in avatar-based VR.
CBNet: A Composite Backbone Network Architecture for Object Detection

Modern top-performing object detectors depend heavily on backbone networks, whose advances bring consistent performance gains through exploring more effective network structures. In this paper, we propose a novel and flexible backbone framework, namely CBNetV2, to construct high-performance detectors using existing open-sourced pre-trained backbones under the pre-training fine-tuning paradigm. In particular, CBNetV2 architecture groups multiple identical backbones, which are connected through composite connections. Specifically, it integrates the high- and low-level features of multiple backbone networks and gradually expands the receptive field to more efficiently perform object detection. We also propose a better training strategy with assistant supervision for CBNet-based detectors. Without additional pre-training of the composite backbone, CBNetV2 can be adapted to various backbones (CNN-based vs. Transformer-based) and head designs of most mainstream detectors (one-stage vs. two-stage, anchor-based vs. anchor-free-based). Experiments provide strong evidence that, compared with simply increasing the depth and width of the network, CBNetV2 introduces a more efficient, effective, and resource-friendly way to build high-performance backbone networks. Particularly, our Dual-Swin-L achieves 59.4% box AP and 51.6% mask AP on COCO test-dev under the single-model and single-scale testing protocol, which is significantly better than the state-of-the-art result (57.7% box AP and 50.2% mask AP) achieved by Swin-L, while the training schedule is reduced by 6$\times$. With multi-scale testing, we push the current best single model result to a new record of 60.1% box AP and 52.3% mask AP without using extra training data. Code is available at https://github.com/VDIGPKU/CBNetV2.
HyperDomainNet: Universal Domain Adaptation for Generative Adversarial Networks

Domain adaptation framework of GANs has achieved great progress in recent years as a main successful approach of training contemporary GANs in the case of very limited training data. In this work, we significantly improve this framework by proposing an extremely compact parameter space for fine-tuning the generator. We introduce a novel domain-modulation technique that allows to optimize only 6 thousand-dimensional vector instead of 30 million weights of StyleGAN2 to adapt to a target domain. We apply this parameterization to the state-of-art domain adaptation methods and show that it has almost the same expressiveness as the full parameter space. Additionally, we propose a new regularization loss that considerably enhances the diversity of the fine-tuned generator. Inspired by the reduction in the size of the optimizing parameter space we consider the problem of multi-domain adaptation of GANs, i.e. setting when the same model can adapt to several domains depending on the input query. We propose the HyperDomainNet that is a hypernetwork that predicts our parameterization given the target domain. We empirically confirm that it can successfully learn a number of domains at once and may even generalize to unseen domains. Source code can be found at https://github.com/MACderRu/HyperDomainNet
Motivation of Student Teachers’ Professional Career

The negative consequences of an incorrectly chosen profession affect both the person himself/herself and his/her social environment. The right choice of profession helps to reduce staff turnover and increase labor productivity. This truth is directly related to the teaching profession. The problem of motivating a professional teaching career, as well as the problem of motivating human behavior and developed. There are practically no special studies that would trace the relationship between the motives for choosing a pedagogical career and the motivation for pedagogical activity among students at the initial stage of teacher education. The purpose of the study: to identify the career orientations preferences of first-year student teachers from various types of universities implementing teacher education programs: the capital’s pedagogical university, federal university, classical university, regional multidisciplinary university in the national region, regional pedagogical university and regional university of humanities and technology. E. Shein’s method of studying professional career motivation (adapted by V.E. Vinokurova and V.A. Chiker) was chosen as the research method. First year students of six Russian universities enrolled in the Pedagogical Education program participated in the study. The study presents the student teachers’ career orientations on the following scales: professional competence, management, autonomy, job stability, residence stability, service, challenge, lifestyle integration, entrepreneurship. Dispositions, value orientations and social attitudes of students that influence career orientations are also presented.
Otsu based Differential Evolution Method for Image Segmentation

This paper proposes an OTSU based differential evolution method for satellite image segmentation and compares it with four other methods such as Modified Artificial Bee Colony Optimizer (MABC), Artificial Bee Colony (ABC), Genetic Algorithm (GA), and Particle Swarm Optimization (PSO) using the objective function proposed by Otsu for optimal multilevel thresholding. The experiments conducted and their results illustrate that our proposed DE and OTSU algorithm segmentation can effectively and precisely segment the input image, close to results obtained by the other methods. In the proposed DE and OTSU algorithm, instead of passing the fitness function variables, the entire image is passed as an input to the DE algorithm after obtaining the threshold values for the input number of levels in the OTSU algorithm. The image segmentation results are obtained after learning about the image instead of learning about the fitness variables. In comparison to other segmentation methods examined, the proposed DE and OTSU algorithm yields promising results with minimized computational time compared to some algorithms.
Making a MIRACL: Multilingual Information Retrieval Across a Continuum of Languages

MIRACL (Multilingual Information Retrieval Across a Continuum of Languages) is a multilingual dataset we have built for the WSDM 2023 Cup challenge that focuses on ad hoc retrieval across 18 different languages, which collectively encompass over three billion native speakers around the world. These languages have diverse typologies, originate from many different language families, and are associated with varying amounts of available resources -- including what researchers typically characterize as high-resource as well as low-resource languages. Our dataset is designed to support the creation and evaluation of models for monolingual retrieval, where the queries and the corpora are in the same language. In total, we have gathered over 700k high-quality relevance judgments for around 77k queries over Wikipedia in these 18 languages, where all assessments have been performed by native speakers hired by our team. Our goal is to spur research that will improve retrieval across a continuum of languages, thus enhancing information access capabilities for diverse populations around the world, particularly those that have been traditionally underserved. This overview paper describes the dataset and baselines that we share with the community. The MIRACL website is live at http://miracl.ai/.
A NOVEL MALWARE DETECTION APPROACH USING PERFORMANCE IMPORTANCE WEIGHTED RANDOM FOREST (PERI-WRF) LEARNING MODEL

Malware detection has gained huge attention in recent times.This is mainly because of the increase in new malware variants which pose a significant threat to information security.The conventional malware detection systems are not capable of detecting new generation malwares due to the constant changes in the network behavior.An efficient malware detection approach must be able to handle the dynamic changes in the malware behavior with a very minimum processing time to identify malicious attacks at the initial stage.This paper presents a novel Performance Importance Weighted Random Forest (PERI-WRF) for detecting different types of malwares in network systems.The proposed PERI-WRF incorporates a novel data reduction technique which is capable of reducing the size of training data to maximize the classification accuracy.A clustering algorithm consisting of GWO and K-means++ algorithm is implemented to group the malicious data samples collected from input.To validate the effectiveness of the detection framework, the system was tested using various evaluation metrics.Results show that the proposed malware detection model with novel data reduction techniques achieves superior classification accuracy, and the proposed approach is appropriate for detecting real-time malwares with superior accuracy and low MAE score.
A Device Free Lunch Break program: An experiment to promote a balanced used of electronic devices in Middle Secondary International schools.

Abstract Since the introduction of a Bring Your Own iPad scheme in the Middle Years section of a large International School, concerns have been raised from school community members regarding students’ overuse of their devices, especially during lunch breaks. Hence, the device free lunch break (DFLB) programme was introduced with the aim of reinforcing existing guidance for students about achieving and maintaining a balanced approach to their use of devices. The DFLB programme was planned based on market research strategies and included all the school community members including students, parents/guardians and staff. The findings from statistical analysis of the data showed significant similarities between staff and parents/guardians’ perceptions of student device use in terms of the student overuse of electronic devices. However, analysis showed that the opinions of parent/guardians and staff contrasted those expressed by students. The data also showed evidence that the introduction of the DFLB programme resulted in increased student recognition of the importance of making face-to-face social contacts with their classmates during their lunch break.
Multi‑strategy Equilibrium Optimizer: An improved meta-heuristic tested on numerical optimization and engineering problems

The Equilibrium Optimizer (EO) is a recently proposed intelligent optimization algorithm based on mass balance equation. It has a novel principle to deal with global optimization. However, when solving complex numerical optimization problems and engineering problems, the algorithm will get stuck into local optima and degrade accuracy. To address the issue, an improved Equilibrium Optimizer (IEO) based on multi-strategy optimization is proposed. First, Tent mapping is used to generate the initial location of the particle population, which evenly distributes the particle population and lays the foundation for diversified global search process. Moreover, nonlinear time parameter is used to update the position equation, which dynamically balances the exploration and exploitation phases of improved algorithm. Finally, Lens Opposition‑based Learning (LOBL) is introduced, which avoids local optimization by improving the population diversity of the algorithm. Simulation experiments are carried out on 23 classical functions, IEEE CEC2017 problems and IEEE CEC2019 problems, and the stability of the algorithm is further analyzed by Friedman statistical test and box plots. Experimental results show that the algorithm has good solution accuracy and robustness. Additionally, six engineering design problems are solved, and the results show that improved algorithm has high optimization efficiency achieves cost minimization.
A Prototype For Quantum Database In Hybrid Quantum: A Complete Ecosystem

Abstract This study proposes a prototypeAnd a possibility to converge both this quantum database and classical database. This study mostly identifies the gap between this classical database and quantum database and it proposes a hypothesis and prototype which can be implemented in future products. It is a way that can be used in future industrial product development on hybrid quantum computers. The existing concept used to consider oracle as a black box this study opens up the possibility for the quantum industry to develop the QASAM module so that we can develop a fully quantum database instead of using a classical database as BlackBox.As the Toffoli gate is basically an effective NAND gate it is possible to run any algorithm theoretically in quantum computers. So we will propose a logical design for memory management for the quantum database, security enhancement model, Quantum Recovery Manager &amp; automatic storage management model and more for the quantum database which will ensure the quantum advantages.In this study, we will also explain the Quantum Vector Database as well as the possibility of improvement in duality quantum computing.It opens up a new scope, possibilities and research area in a new approach for quantum database and duality quantum computing.