Reviewerly/exHarmony huggingface dataset:
Fixed-point oblivious quantum amplitude-amplification algorithm

The quantum amplitude amplification algorithms based on Grover's rotation operator need to perform phase flips for both the initial state and the target state. When the initial state is oblivious, the phase flips will be intractable, and we need to adopt oblivious amplitude amplification algorithm to handle. Without knowing exactly how many target items there are, oblivious amplitude amplification also suffers the "soufflé problem", in which iterating too little "undercooks" the state and too much "overcooks" the state, both resulting in a mostly non-target final state. In this work, we present a fixed-point oblivious quantum amplitude-amplification (FOQA) algorithm by introducing damping based on methods proposed by A. Mizel. Moreover, we construct the quantum circuit to implement our algorithm under the framework of duality quantum computing. Our algorithm can avoid the "soufflé problem", meanwhile keep the square speedup of quantum search, serving as a subroutine to improve the performance of quantum algorithms containing oblivious amplitude amplification procedure.
A Malleable Boundary Network for temporal action detection

Temporal action detection in untrimmed videos is a challenging task aiming to predict the boundary and category of action instances. It can be useful in transportation. In this study, we propose a two-stage framework Malleable Boundary Network (MB-Net) to adaptively regress proposals based on finer scores. In particular, MB-Net consists of a Potential Boundary Generator in the first stage and an Adaptive Proposal Detector in the second stage. First, the Potential Boundary Generator fuses multiple sets of flexible score sequences to obtain tentative proposals through a frame-level feature in an anchor-free way. Then, the Adaptive Proposal Detector employs parallel modules to filter, classify and regress proposals adaptively. Besides, we propose an easy-to-realize feature augmented method Structured Temporal Segment Pooling, which makes full use of the information throughout the whole proposal. Experiments show that MB-Net achieves state-of-the-art performance on popular benchmarks THUMOS-14 and Activity-1.3 with an improvement of 1.9% and 1.2%. • A two-stage MB-Net framework for temporal action detection. • Treating detection process as coarse-grained construction and fine-grained adjustment. • A boundary generator with large receptive fields and a well-designed fusion mechanism. • A brief and parallel instance detector with a designed temporal pooling method.
DISTANCE LEARNING – DEVELOPMENT, TYPES AND TOOLS

What are the key ingredients to learning?What happens when the traditional brick and mortar classroom is no longer an option?Can distance education (DЕ) replace the traditional one?What is distance education compared to online education?The paper answers the posed questions, defining distance education as happening using electronic media, or the Internet, while teachers and students not being at the same place, but working in the comfort of their home.Historically speaking, the roots of distance education can be traced back to the correspondence study as well as to the technological development.When talking about DE, we can differentiate between synchronous and asynchronous, which differ in one component.Synchronous DE is bound by time, implying that students and teachers work simultaneously and interactively.Asynchronous DE is not bound by time, implying that interaction occurs with a time delay.The paper focuses on the advantages and the disadvantages of these two forms of DE.It additionally considers tools such as Microsoft Teams, Zoom and Google Meet as the most suitable for synchronous DE as well as Moodle, Blackboard and Google Classroom as the most convenient for asynchronous DE.
WEBSITE RESOURCES TO BE USED IN TEACHING TURKISH TO FOREIGNERS

Teaching Turkish is a subject that requires meticulousness both at home and abroad.Especially the courses, foundations, institutes and university departments that provide Turkish education abroad are of particular importance.Many resources have been developed and textbooks have been written in terms of teaching Turkish for foreigners over the years.In addition to the resource books used in teaching Turkish, there are websites that can help us and inspire the activities.Especially during the pandemic period, internet-based, visual resources and online tests have gained importance today, when classes are held online.Some websites, which have many content such as some activities, tests, worksheets and visual cards that the instructors can use directly or take as examples, facilitate the work of the instructors in this regard.In this paper, a compilation study was made of websites that
Indoor Contact Awareness on Spatiotemporal Analytics with Smartphone-Based Pedestrian Dead Reckoning

Due to the prevalence of COVID-19, providing safe environments and avoiding exposure to the virus play a pivotable role in our daily lives. As a well-established measurement, contact tracing is widely applied to suppress its spread. Most of the digital contact tracing systems merely detect direct face-to-face contact based on estimated proximity and do not quantify the exposed virus concentration. Indirect environmental exposure due to virus survival time in the air and constant airborne transmission is rarely considered quantitatively. In this work, to provide accurate awareness of the virus quanta concentration in different origins at various times, we propose iSTCA, a self-containing contact awareness approach with spatiotemporal information considered explicitly. Smartphone-based PDR is employed to precisely achieve the location and trajectories for distance estimation and time induction without extra infrastructure involved, in which the accumulative error is calibrated by recognized landmarks in space. A custom deep learning model composed of CNN and LSTM for both the local correlation and long-term dependency extraction is utilized to identify landmarks. By the integration of spatial distance and time difference, the virus quanta concentration of the entire indoor environment is quantitatively calculated at any time with all contributed virus particles. We conduct an experiment based on practical scenario to evaluate the performance of the proposed system, showing that the average positioning error is reduced to less than 0.8 m with high confidence and demonstrating the validity of our system for the virus quanta concentration quantification involving virus movement in a complex indoor environment.
Evaluating Measurement Invariance of Contextual Questionnaire Indices in NAEP

Measurement invariance is the property that an instrument measures the same construct across groups, and is defined mathematically within latent variable frameworks such as Item Response Theory and factor analysis.The property of measurement invariance is critical to justify interpreting group differences on the observed scores in terms of construct differences, and not due to differences in how the construct is measured across groups.The method of measurement invariance is reviewed, and core issues, such as the choice and interpretation of fit statistics, are discussed.Results from the 2017 National Assessment of Educational Progress (NAEP) contextual questionnaire indices analyses are described across major groups of race/ethnicity, gender, and mode of administration.No evidence of measurement non-invariance was found.
Kinship Face Synthesis Evaluation Website with Gamified Mechanism

Kinship face synthesis has been drawing significant attention recently. Nevertheless, it is challenging to evaluate the performance of the generated images due to lacking ground truths and objective metrics. In this paper, we propose an interactive online website for kinship synthesis model evaluation. The website not only showcases the result of the model but also collects feedback data from the users for analysis and model improvement. The website allows users to 1) build their kinship dataset and generate kinship images with our models, and 2) play games to distinguish between a real child face image and synthesized child images. Through the incentives triggered by the gamified rating mechanism, we expect the collected feedback data will be more promising.
Application of flow feature association mining algorithm in network security situation assessment

In order to strengthen the power network structure of our country, reduce the frequency of electrical equipment and lines, so that the power network can operate normally and smoothly. Based on the above reasons, the flow feature association mining algorithm is introduced into the research of power network security situation assessment to evaluate and predict the safe running state of power network. Firstly, this paper explains the basic algorithm of stream feature mining, including time correlation, IP (Ingress Protection) correlation, type correlation and super warning correlation. Secondly, it is necessary to determine the weight of network security evaluation index and the calculation method of evaluation, and carry out the relevant case analysis. Finally, the network security situation assessment model based on the flow feature association mining algorithm was compared with the BP(Back Propagation) neural network and RBF(Radial Basis Function) neural network evaluation model, and the comparison results showed that Flow feature association mining algorithm can improve the feasibility and accuracy of power network situation assessment. In conclusion, the study of this paper can provide a new research approach for the construction of network security situation assessment.
Fine-grained image recognition based on integrated transfer learning

In order to improve the accuracy of fine-grained image recognition, a fine-grained image recognition method based on integrated transfer learning is proposed. This method uses two models of VGG-16 and inception-v3, combined with transfer learning to construct a K-selection transfer learning algorithm. The network model parameters pre-trained on the large-scale natural image dataset ImageNet are transferred to the transfer framework model of the fine-grained image dataset, and the network model is fine-tuned. The algorithm proposed in this paper is tested on CUB-200-2011 dataset, Oxford 102 Flower dataset, Stanford Cars dataset, FGVC-Aircraft dataset and Stanford Dogs dataset. The experimental results show that, compared with existing algorithms, the proposed algorithm has improved fine-grained image recognition accuracy under different models, and has optimized the adaptive effect of migration.
Graph Analytics Workflows Enactment on Just in Time Data Centres Position Paper

This paper discusses our vision about multirole-capable decision-making systems across a broad range of Data Science (DS) workflows working on graphs through disaggregated data centres. Our vision is that an alternative is possible, to work on a disaggregated solution for the provision of computational services under the notion of a disaggregated data centre. We define this alternative as a virtual entity that dynamically provides resources crosscutting the layers of edge, fog and data centre according to the workloads submitted by the workflows and their Service Level Objectives.
Data Centred Intelligent Geosciences: Research Agenda and Opportunities Position Paper

This paper describes and discusses our vision to develop and reason about best practices and novel ways of curating data-centric geosciences knowledge (data, experiments, models, methods, conclusions, and interpretations). This knowledge is produced from applying statistical modelling, Machine Learning, and modern data analytics methods on geo-data collections. The problems address open methodological questions in model building, models’ assessment, prediction, and forecasting workflows.
THE DESIGN OF INFORMATION SYSTEMS FILING OUTPATIENT MEDICAL RECORDS

Abstract: The system information section filing in UPT Puskesmas Rahayu to submit a medical record file to any of the poly, assembling medical records, store medical records, retention medical records are already inactive. This research aims to design and create a system of information filing outpatient medical records in UPT Puskesmas Rahayu in terms of borrowing and return the medical record file as well as facilitate the retention of medical records with the use of qualitative methods. The Data used in the study in UPT Puskesmas Rahayu, namely interviews, observation, and FGD (Focus Group Discussion). This study shows the input data of the patients is in accordance with UPT Puskesmas Rahayu. Input patient data include No RM, the Name of the KK, the Patient's Name, Gender, Address, and Villages. This research resulted in the information system of medical record filing outpatient procedures that can facilitate the officer in the management of the filing of medical records. Keywords: borrowing and return; medical record filing; puskesmas Abstrak: Sistem informasi bagian filing di UPT Puskesmas Rahayu untuk mengirimkan berkas rekam medis ke setiap poli, assembling rekam medis, menyimpan rekam medis, meretensi rekam medis yang sudah in aktif. Penelitian ini bertujuan untuk merancang dan membuat sistem informasi filing rekam medis rawat jalan di UPT Puskesmas Rahayu dalam hal peminjaman dan pengembalian berkas rekam medis serta mempermudah dalam retensi rekam medis dengan menggunakan metode kualitatif. Data yang digunakan dalam penelitian di UPT Puskesmas Rahayu yaitu wawancara, observasi dan FGD (Focus Group Discussion). Penelitian ini menunjukkan input data pasien sudah sesuai dengan yang ada di UPT Puskesmas Rahayu. Input data pasien meliputi No RM, Nama KK, Nama Pasien, Jenis Kelamin, Alamat, dan Kelurahan. Penelitian ini menghasilkan sistem informasi rekam medis filing rawat jalan yang dapat mempermudah petugas dalam pengelolaan filing rekam medis. Kata kunci: peminjaman dan pengembalian; puskesmas; rekam medis filing
ANALYSIS OF SVM AND NAIVE BAYES ALGORITHM IN CLASSIFICATION OF NAD LOANS IN SAVE AND LOAN COOPERATIVES

Abstract: Non-performing loan (NPL) is a risk that credit unions must face and to avoid that, prospective debtors need to be surveyed. With previous loan data, support vector machine and naïve bayes can be used as classification methods to give a decision about NPL. We use a data set with 61 data and process the data with orange 3.30 application to see the difference between SVM using linear (SVM-L), polynomial (SVM-P), RBF (SVM-R) and sigmoid (SVM-S) kernel with naïve bayes. We use a cross validation technique with various folds to measure the classification results and a convusion matrix to measure the data training classification results. Naïve bayes scores the highest in terms of accuracy and SVM-R scores the highest in terms of F1, precision and recall. SVM-P scores the lowest in terms of accuracy, F1, precision and recall. Naïve bayes scores the highest in terms of proportion of predicted for true negative class and proportion of actual for true positive class. SVM-S scores the highest in terms of proportion of predicted for true positive class and proportion of actual for true negative class. SVM-P scores the lowest in both proportion of predicted and proportion of actual. Keywords: classification; naïve bayes; non-performing loan; support vector machine Abstrak: Kredit macet merupakan resiko yang sering dialami koperasi simpan pinjam, sehingga perlu dilakukan survei terhadap calon debitur agar kredit menjadi sehat. Dengan menggunakan data pemberian kredit sebelumnya, support vector machine dan naïve bayes digunakan sebagai metode klasifikasi untuk memberikan keputusan macet atau tidaknya kredit anggota koperasi Mutiara Sejahtera. Data set yang berjumlah 61 data diolah menggunakan aplikasi Orange 3.30 dan dilihat perbandingan antara metode SVM dengan kernel linear, polynomial, RBF dan sigomoid dengan metode naïve bayes. Cross validation dengan jumlah fold bervariasi digunakan sebagai nilai ukur klasifikasi dan convusion matrix digunakan sebagai nilai ukur klasifikasi data training. Hasil yang diperoleh adalah naïve bayes memiliki nilai accuracy tertinggi dan SVM kernel RBF memiliki nilai F1, precision dan recall tertinggi. SVM kernel polynomial memiliki nilai terendah untuk accuracy, F1, precision dan recall. Naïve bayes memiliki nilai tertinggi untuk proportion of predicted (PoP) kelas true negative dan proportion of actual (PoA) kelas true positive. SVM kernel sigmoid memiliki nilai tertinggi untuk PoP kelas true positive dan PoA kelas true negative. SVM kernel polynomial memiliki nilai terendah baik untuk PoP maupun PoA true negative dan kelas true positive. Kata kunci: klasifikasi; kredit macet; naive bayes; SVM
A Utopian Need: Artificial Intelligence Systems to Evaluate Open-Ended Questions

This study explores comparative effects of both open-ended and multiple-choice exams regarding metacognitive and affective dimensions.Secondly, it discusses the reason of the needs of intelligence systems to evaluate open-ended questions.Dimensions (cognitive strategy, self-checking, worry, effort) were explained via perceptions from experiences of students, teachers, and academicians.Data gathered through phenomenological approach was organized under twenty-three categories based on four main themes.The results indicated Open Ended exams allow students using their cognitive strategy and self-checking skills more often than Multiple Choice.Also, Multiple Choice and the dimension 'worry' have positive correlation respectively.Thus, it can be concluded that changing or preferring one type to another has no spontaneous heal in improving student success.
Reducing Subjectivity in Value Tables: An Empirically Based Approach

Seven states (Alaska, Delaware, Iowa, Michigan, Minnesota, Utah, and Virginia) have used, currently use, or are considering using categorical gain models are part of their state accountability systems in K-12 education.Categorical gain models typically involve a subjective process of assigning values to a value table to use in conjunction with a transition matrix to make accountability decisions (e.g., in compliance) for schools, districts, or the state.Two examples of creating value tables are provided.These subjective decisions are not desirable in a high-stakes accountability system, so a program was developed that uses minimal user input to empirically derive a value table.The program is described and an example dataset from the Early Child Longitudinal Study -Kindergarten cohort (ECLS-K) is used for purposes of illustration.Applications and uses for the program are discussed.
The Effect of Computer-Supported Collaborative Learning on Students' Performance: A Meta-Analysis

The purpose of this study was to perform a meta-analysis to investigate the effect of CSCL on student learning performance.In addition, the study was interested in exploring the moderator variables which influence student achievements.An analysis of moderator variables was conducted to examines whether associated moderators could promote or hinder collaborative learning impacting student achievements.The following three research questions guided this meta-analysis:1. What is the overall effect of CSCL research on student learning performance?2. To what extent do the effects of CSCL on student learning performance?3. What are major moderator variables influencing the effects of CSCL on student learning performance? PerspectivesComputer-Supported Collaborative Learning (CSCL) has become pervasive in education over the past two decades.In CSCL, two or more students, each sharing and creating knowledge through a series of collaborative activities, and working together to solve problems or build knowledge with supported by computer technology (Prinsen, Volman, & Terwel, 2007).Research on the effectiveness of CSCL has shown divergent results.Many studies on CSCL have reported that CSCL is effective in enhancing learning improvement, since it helps to test their ideas against others' opinions and reveals incorrect assumptions, misunderstandings, and conflicting solutions (Walker, 2005;Doise, Mugny, & Pe´rez, 1998).However, some studies have pointed out that students for different reasons do not participate very intensely in CSCL, and not all collaboration is effective (Hara, Bonk, & Angeli, 2000; Hoadley & Linn, 2000; Lipponen, Rahikainen, Lallimo, & Hakkarainen, 2003; Muukkonen, Lakkala, & Hakkarainen, 2005).Previous studies have shown that it is necessary to use meta-analysis to estimate the impact of CSCL on student achievement, which helps to gain a deeper understanding of the effectiveness of collaborative learning. Method Data sources and search strategyJournal articles published between 2008 onwards were searched electronically, and via referencelist checking to retrieve the relevant literature.For electronic searches, the main databases were Springer, IEEExplore, Science Direct, Web of Science, ERIC, and Google Scholar.The following two sets of keywords were used: (1) keywords related to CSCL, including computer-supported collaborative learning, CSCL, and group learning; and (2) keywords related to learning performance, including performances, outcomes, and achievements.The two sets of keywords were combined when searching the electronic databases.To ensure that no relevant literature was overlooked, another round of searches was conducted using the reference lists found in the literature yielded by the electronic search.
An Expectancy Value Cost Analysis of the Factors Impacting the Drive to Complete Grad School

This multiphasic mixed-methods study distils the complex and dynamic motivational forces of completing a university graduate degree into an Expectancy-Value-Cost model.EVT identifies that the motivation to complete tasks like completing a graduate degree involves balancing the expectancies and the perceived values of the task against the perceived costs.Participants were pragmatic and tended to report their confidence being prefaced on past successes like success in classes, publications, and being accepted at conferences.Participants reported that the main perceived costs that they identified were pressure, and loss of valued alternatives.This study shows specific areas where program administrators could focus their energies for two goals building expectancies and value, while seeking and mitigating costs to promote graduate student completion. ObjectivesThis multiphasic mixed-methods study (Creswell, 2013) distils the complex and dynamic motivational forces of completing a university graduate degree into an Expectancy-Value-Cost model (Barron & Hulleman, 2015;Flake et al., 2015).This model isolates the supports and barriers that drive or inhibit graduate students in their completion of this program.1. What factors contribute to expectancies to complete a graduate degree? 2. What factors contribute to the subjective task value to complete a graduate degree?3. What factors contribute to the holistic perceived costs of completing a graduate degree?
Research on real-time positioning and map construction technology of intelligent car based on ROS

Real-time localization and map construction (SLAM) is a key technology to realize autonomous navigation of smart cars, which mainly solves the problems of mobile robots in mapping, positioning and path planning. This paper introduces and analyzes SLAM. By comparing three different mapping algorithms, gmapping, hector, and cartographer, and through analysis and comparison experiments, the gmapping-based algorithm is finally used for mapping. On the basis of AMCL positioning, global and local path planning is carried out through A <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">*</sup> algorithm and DWA algorithm to realize autonomous navigation and obstacle avoidance functions. And the experimental verification was carried out under the autolabor smart car. The experiment proved that using this algorithm, autolabor can perform accurate pose estimation, map construction and autonomous navigation in an unfamiliar environment.