Reviewerly/exHarmony huggingface dataset:
Human Evaluation of Interpretability: The Case of AI-Generated Music Knowledge

Interpretability of machine learning models has gained more and more attention among researchers in the artificial intelligence (AI) and human-computer interaction (HCI) communities. Most existing work focuses on decision making, whereas we consider knowledge discovery. In particular, we focus on evaluating AI-discovered knowledge/rules in the arts and humanities. From a specific scenario, we present an experimental procedure to collect and assess human-generated verbal interpretations of AI-generated music theory/rules rendered as sophisticated symbolic/numeric objects. Our goal is to reveal both the possibilities and the challenges in such a process of decoding expressive messages from AI sources. We treat this as a first step towards 1) better design of AI representations that are human interpretable and 2) a general methodology to evaluate interpretability of AI-discovered knowledge representations.
Half-infinite sampling and its FT

In the digital world, signals are discrete and finite. The Fourier representation of discrete and finite signals is FT convolution of the finite sampling function and the continuous signal. Conventionally, finite sampling is treated as a segment of infinite sampling. Though this approach perfectly solves the difference between finite and infinite sampling, it has caused much trouble for signal processing. Mathematically, there is a kind of sampling between finite and infinite sampling, and we name this kind of sampling as half-infinite sampling. Theoretically, finite sampling can also be treated as a segment of half-infinite sampling. Because we can derive the Fourier representation of discrete and finite signals from half-infinite sampling, the FTs of several half-infinite samplings are studied. The results show that the FT of half-infinite sampling is more concise than that of infinite sampling. A numerical experiment verified the theoretical derivations successfully, during which step sampling was proposed. Besides, several interesting equations were built.
STRUCTURING THE CONTENT FOR DEVELOPING EDUCATORS’ DIGITAL COMPETENCIES – BASED ON FRAMEWORKS DIGCOMPEDU AND DIGI.KOMPP

The importance of developing digital competencies of educators is one of the current topics and tasks in education, generally, institutionally, at all levels and particularly for each educator. The necessity to continuously develop the skills has been actively analysed in the literature and proven in the pandemic and global lockdowns. Being aware that the digital revolution and changes, in general, have not brought equal changes to the education system, many educators are unsure about how digital technologies can and should be integrated into education (Caena & Redecker, 2019). There are already frameworks that offer the foundation, structure that provide all stakeholders with the opportunity to improve and develop their digital competencies. As one of the key features of frameworks is built-in flexibility, it opens room for interpretations, creativity, and different forms of use (European Commission, 2013). This paper embraces two frameworks, a national, Austrian digi.KompP and an international, DigCompEdu, for developing digital competencies for educators and shows the concrete application in practice in the form of a catalogue (collection) of the content. By analysing these two frameworks we succeed in structuring the content for developing the digital competencies of our teaching staff at the University of Applied Sciences Upper Austria (UAS). We combined and adapted the categories and competencies (subcategories) of both frameworks to build a corresponding architecture that can meet the requirements of our digital learning catalogue. The items of this catalogue are all products of the Centre for Excellence in Teaching and Learning at UAS. The Centre performs different activities that can be grouped into several product types, such as events, training, materials, tutorials. All items of each product type can be categorised into one of the main categories, and after that, in one of the two or three subcategories of each category and assigned to one of the six progression levels, as suggested in the DigCompEdu. Finally, this paper ties all elements together and offers a model for structuring the content for the digital continuous professional development of educators.
The Study on Graduation Credits, Required Courses and Subjects: Focusing on California and Korean High Schools

The purpose of this study is to compare the curriculum between California, USA and Korea. Research as a basis for curriculum revision needs to clarify its scope and purpose in comparison with a brief introduction of educational trends abroad. Therefore, this study compared high school graduation class hours to compare curriculum in two countries. Materials used for the study are Korean national curriculum documents, California education law documents, and some documents provided by educational districts of the State. In particular, the US curriculum was reconstructed according to the simple graduation requirements proposed by the State of California, USA, to compare the documents of the two countries. The research results are as follows. First, South Korea, due to some differences, required more credits to complete the high school curriculum. Second, although the names of required courses in the US curriculum are similar, the required percentage of courses is slightly different from that in Korea.
FORMATION OF BASIC QUANTITATIVE SKILLS IN PRESCHOOL CHILDREN IN INDIVIDUAL LESSONS WITH NUMICON

The article reveals the problem of the importance of systematic work with the Numikon for the formation of mathematical skills in preschool children with normative development and intellectual disabilities. The personal experience of organizing individual lessons with Numikon is presented.
Application of historical literacy in history learning in the time of COVID-19

The purpose of this study was to determine how the application of historical literacy during the Covid-19 pandemic is forced to Online Learning. This research uses a qualitative method in the form of a case study in a private school in East Jakarta, with history teachers and students as key informants. The result of this study is that history teachers have difficulty with allocating time. The difficulty of the teacher increases if history learning is associated with historical literacy which requires students not only to read and understand one source but various historical sources including primary sources. History teachers feel that their students' literacy levels are still lacking even though they do not dare to generalize all of them because they have not implemented any tests. The teacher thinks that both the level of historical literacy and the students' lack of historical literacy can be measured through the practice questions given in tests. Teachers are also hesitant to say the historical literacy level of their students because online distance learning uses zoom meetings. In conclusion, learning history experienced many obstacles during the Covid-19 pandemic. There are more obstacles if history learning wants to fulfill the concept of historical literacy.
IDENTIFIKASI PUTUSAN KARIR MAHASISWA UNIVERSITAS TEKNOLOGI YOGYAKARTA MENGGUNAKAN APLIKASI ANDROID MIHG (MULTIPLE INTELLIGENCE BY HOWARD GARDNER)

The future challenges of higher education students for determining their future career in the era of industrial revolution 4.0 and society 5.0, getting more complex. Student may facing career uncertainty, lack of access to information and career development programs, and economic and technological challenges. Previous research shown many student need for career guidance to be addressed for choosing their course study. Student at the University level are grouped into groups of fields according to their choice of interests. Research findings in the field reveal that not a few students have a career decision level that is still not optimal, whereas many as 71% of students still need help regarding career decisions. The existence of this individual intelligence identification application can help students in making career decisions. This is shown by the results of the media use test in the field that 89% of UTY students experienced an increase in career decisions after using Howard Gardner's Multiple intelligence identification application. Thus, the application of multiple intelligence in helping to make career decisions is said to be suitable as a medium to help students make career decisions for UTY students.
Learning with Noisy Labels by Efficient Transition Matrix Estimation to Combat Label Miscorrection

Recent studies on learning with noisy labels have shown remarkable performance by exploiting a small clean dataset. In particular, model agnostic meta-learning-based label correction methods further improve performance by correcting noisy labels on the fly. However, there is no safeguard on the label miscorrection, resulting in unavoidable performance degradation. Moreover, every training step requires at least three back-propagations, significantly slowing down the training speed. To mitigate these issues, we propose a robust and efficient method that learns a label transition matrix on the fly. Employing the transition matrix makes the classifier skeptical about all the corrected samples, which alleviates the miscorrection issue. We also introduce a two-head architecture to efficiently estimate the label transition matrix every iteration within a single back-propagation, so that the estimated matrix closely follows the shifting noise distribution induced by label correction. Extensive experiments demonstrate that our approach shows the best performance in training efficiency while having comparable or better accuracy than existing methods.
Teaching Practice and Exploration of Engineering Mathematics Based on Informatization

How to make mathematics a fun course closely related to life is urgent problems for college teachers.Informationalized teaching provides a new turn for Engineering Mathematics, which can significantly improve the teaching efficiency, make some difficult mathematical theorem definitions vivid, create an interesting classroom and increase the participation of students.This paper carries out the reform and exploration of Engineering Mathematics course from teaching strategy, informationalized instructional design, resource development, mathematics experiment and innovation evaluation, which can be effectively applied to the teaching practice and improve the teaching effect.
The body-ownership is unconsciously distorted in the brain: An event-related potential study of rubber hand illusion

Many studies have reported that bottom-up multisensory integration of visual, tactile, and proprioceptive information can distort our sense of body-ownership, producing rubber hand illusion (RHI). There is less evidence about when and how the body-ownership is distorted in the brain during RHI. To examine whether this illusion effect occurs preattentively at an early stage of processing, we monitored the visual mismatch negativity (vMMN) component (the index of automatic deviant detection) and N2 (the index for conflict monitoring). Participants first performed an RHI elicitation task in a synchronous or asynchronous setting and then finished a passive visual oddball task in which the deviant stimuli were unrelated to the explicit task. A significant interaction between Deviancy (deviant hand vs. standard hand) and Group (synchronous vs. asynchronous) was found. The asynchronous group showed clear mismatch effects in both vMMN and N2, while the synchronous group had such effect only in N2. The results indicate that after the elicitation of RHI bottom-up integration could be retrieved at the early stage of sensory processing before top-down processing, providing evidence for the priority of the bottom-up processes after the generation of RHI and revealing the mechanism of how the body-ownership is unconsciously distorted in the brain.
Utilization of Electronic Resources and Services among Annamalai University Library Science Students: A Study

This paper describes a Utilization of E-Resources and Services among the Annamalai University Library Science Students: A Study. The Department of Library and Information Science studying the 87 respondents are studying regular and distance education in the survey. The study analyses the Awareness of Electronic resources, Awareness of e-resources and services, Usage of Preference Level and E-resource, Frequency of use, Time spent, the purpose, Access the location, using the File Formats, Use of storage, Satisfaction Level of Electronic resources and E-resources based Library Services. Conclude the study has 80 per cent an Awareness of Electronic resource, the majority of the respondents are “Male” (72.64%) and Satisfaction Level of Electronic resources are highly satisfied with 67.82 per cent.
Fighting Fire with Fire: Contrastive Debiasing without Bias-free Data via Generative Bias-transformation

Deep neural networks (DNNs), despite their impressive ability to generalize over-capacity networks, often rely heavily on malignant bias as shortcuts instead of task-related information for discriminative tasks. To address this problem, recent studies utilize auxiliary information related to the bias, which is rarely obtainable in practice, or sift through a handful of bias-free samples for debiasing. However, the success of these methods is not always guaranteed due to the unfulfilled presumptions. In this paper, we propose a novel method, Contrastive Debiasing via Generative Bias-transformation (CDvG), which works without explicit bias labels or bias-free samples. Motivated by our observation that not only discriminative models but also image translation models tend to focus on the malignant bias, CDvG employs an image translation model to transform one bias mode into another while preserving the task-relevant information. Additionally, the bias-transformed views are set against each other through contrastive learning to learn bias-invariant representations. Our method demonstrates superior performance compared to prior approaches, especially when bias-free samples are scarce or absent. Furthermore, CDvG can be integrated with the methods that focus on bias-free samples in a plug-and-play manner for additional enhancements, as demonstrated by diverse experimental results.
METHODOLOGICAL TRAINING OF FUTURE TEACHERS OF PHYSICS TO WORK WITH EXPERIMENTAL TASKS FOR FIGURATIVE-SENSORY IDENTIFICATION OF PHYSICAL QUANTITIES

METHODOLOGICAL TRAINING OF FUTURE TEACHERS OF PHYSICS TO WORK WITH EXPERIMENTAL TASKS FOR FIGURATIVE-SENSORY IDENTIFICATION OF PHYSICAL QUANTITIES
Unsupervised Landmark Detection-Based Spatiotemporal Motion Estimation for 4-D Dynamic Medical Images

Motion estimation is a fundamental step in dynamic medical image processing for the assessment of target organ anatomy and function. However, existing image-based motion estimation methods, which optimize the motion field by evaluating the local image similarity, are prone to produce implausible estimation, especially in the presence of large motion. In addition, the correct anatomical topology is difficult to be preserved as the image global context is not well incorporated into motion estimation. In this study, we provide a novel motion estimation framework of dense-sparse-dense (DSD), which comprises two stages. In the first stage, we process the raw dense image to extract sparse landmarks to represent the target organ's anatomical topology, and discard the redundant information that is unnecessary for motion estimation. For this purpose, we introduce an unsupervised 3-D landmark detection network to extract spatially sparse but representative landmarks for the target organ's motion estimation. In the second stage, we derive the sparse motion displacement from the extracted sparse landmarks of two images of different time points. Then, we present a motion reconstruction network to construct the motion field by projecting the sparse landmarks' displacement back into the dense image domain. Furthermore, we employ the estimated motion field from our two-stage DSD framework as initialization and boost the motion estimation quality in light-weight yet effective iterative optimization. We evaluate our method on two dynamic medical imaging tasks to model cardiac motion and lung respiratory motion, respectively. Our method has produced superior motion estimation accuracy compared to the existing comparative methods. Besides, the extensive experimental results demonstrate that our solution can extract well-representative anatomical landmarks without any requirement of manual annotation. Our code is publicly available online: https://github.com/yyguo-sjtu/DSD-3D-Unsupervised-Landmark-Detection-Based-Motion-Estimation.
Magic-Pipe: self-optimizing video analytics pipelines

Microservices-based video analytics pipelines routinely use multiple deep convolutional neural networks. We observe that the best allocation of resources to deep learning engines (or microservices) in a pipeline, and the best configuration of parameters for each engine vary over time, often at a timescale of minutes or even seconds based on the dynamic content in the video. We leverage these observations to develop Magic-Pipe, a self-optimizing video analytic pipeline that leverages AI techniques to periodically self-optimize. First, we propose a new, adaptive resource allocation technique to dynamically balance the resource usage of different microservices, based on dynamic video content. Then, we propose an adaptive microservice parameter tuning technique to balance the accuracy and performance of a microservice, also based on video content. Finally, we propose two different approaches to reduce unnecessary computations due to unavoidable mismatch of independently designed, re-usable deep-learning engines: a deep learning approach to improve the feature extractor performance by filtering inputs for which no features can be extracted, and a low-overhead graph-theoretic approach to minimize redundant computations across frames. Our evaluation of Magic-Pipe shows that pipelines augmented with self-optimizing capability exhibit application response times that are an order of magnitude better than the original pipelines, while using the same hardware resources, and achieving similar high accuracy.
An Effective Approach on Data Visualization Using Cluster Graphs

Load balancing, which distributes changing workloads and computing resources in a cloud context, is the main difficulty in cloud computing. The load balancing algorithm seeks to balance the total system load by moving workload from overloaded to underloaded nodes in order to ensure the system's overall performance. This work offers a Hybrid Dynamic Degree Balance (HDDB) algorithm for allocating virtual machines (VMs) to the best-suited host based on CPU availability and host membership value. The proposed scheduling hybridizes two algorithms namely Dynamic Degree Balance CPU Based (D2B_CPU based) and Dynamic Degree Balanced Membership based (D2B_Membership). The suggested algorithm HDDB was tested using the CloudSim simulation tool. To verify the performance of proposed algorithms, performance metrics used are turnaround time of cloudlets, execution cost, throughput time, degree of imbalance, CPU utilization, bandwidth utilization and memory utilization. The results show a considerable improvement in performance when compared to other current algorithms such as Dynamic Degree Balance CPU Based (D2B CPU), Dynamic Degree Balanced Membership Based (D2B Membership), Round Robin (RR), First Come First Serve (FCFS), and Shortest Job First (SJF).
CoDiM: Learning with Noisy Labels via Contrastive Semi-Supervised Learning

Labels are costly and sometimes unreliable. Noisy label learning, semi-supervised learning, and contrastive learning are three different strategies for designing learning processes requiring less annotation cost. Semi-supervised learning and contrastive learning have been recently demonstrated to improve learning strategies that address datasets with noisy labels. Still, the inner connections between these fields as well as the potential to combine their strengths together have only started to emerge. In this paper, we explore further ways and advantages to fuse them. Specifically, we propose CSSL, a unified Contrastive Semi-Supervised Learning algorithm, and CoDiM (Contrastive DivideMix), a novel algorithm for learning with noisy labels. CSSL leverages the power of classical semi-supervised learning and contrastive learning technologies and is further adapted to CoDiM, which learns robustly from multiple types and levels of label noise. We show that CoDiM brings consistent improvements and achieves state-of-the-art results on multiple benchmarks.
Analisis Jenis Pertanyaan Guru dalam Proses Pembelajaran Bahasa

This study discusses the types of teacher questions used in the teaching and learning process in class VIII SMP St. Antonius Padua Leworahang. The purpose of this study was to identify the types of questions that teachers generate in classroom interactions. This research was conducted on the eighth grade students of SMP St. Antonius Padua, East Flores. Research data collection was done through observation, audio recording and interviews. The teacher's questions were analyzed using Miles and Huberman's (1995) qualitative data analysis model. The results showed that in the process of learning English in class VIII, the teacher was more dominant in using procedural and convergent types of questions to interact with students. These questions are used to determine the level of student understanding of the material that has been taught can help students to recall the material. In addition, the use of these questions can help students participate actively by responding to these questions. The teacher does not use the type of divergent questions that can increase the mindset of students who are more critical in conveying ideas or opinions about something
Construction of College English Ecological Classroom Under the Background of Big Data

The construction of college English ecological classroom is related to the stimulation of students' interest in learning, the establishment of harmonious relationship between students and teachers, the exertion of students' initiative, and the entertainment included in teaching, which is the mainstream teaching concept at present.From an ecological perspective, it analyzes the relationship between students and teachers and discusses the impact of classroom environment on students' learning, expecting to create a harmonious and comfortable learning environment and realize the friendly interaction between teaching and learning, which is also the focus of education reform in the era of big data.Based on the background of big data, this work actively discussed the construction of college English ecological classroom, and put forward effective construction ideas and measures based on discussing its teaching advantages and present situation, which will promote the effectiveness of college English teaching.