Reviewerly/exHarmony huggingface dataset:
Explanatory machine learning for sequential human teaching

Abstract The topic of comprehensibility of machine-learned theories has recently drawn increasing attention. Inductive logic programming uses logic programming to derive logic theories from small data based on abduction and induction techniques. Learned theories are represented in the form of rules as declarative descriptions of obtained knowledge. In earlier work, the authors provided the first evidence of a measurable increase in human comprehension based on machine-learned logic rules for simple classification tasks. In a later study, it was found that the presentation of machine-learned explanations to humans can produce both beneficial and harmful effects in the context of game learning. We continue our investigation of comprehensibility by examining the effects of the ordering of concept presentations on human comprehension. In this work, we examine the explanatory effects of curriculum order and the presence of machine-learned explanations for sequential problem-solving. We show that (1) there exist tasks A and B such that learning A before learning B results in better comprehension for humans in comparison to learning B before learning A and (2) there exist tasks A and B such that the presence of explanations when learning A contributes to improved human comprehension when subsequently learning B. We propose a framework for the effects of sequential teaching on comprehension based on an existing definition of comprehensibility and provide evidence for support from data collected in human trials. Our empirical study involves curricula that teach novices the merge sort algorithm. Our results show that sequential teaching of concepts with increasing complexity (a) has a beneficial effect on human comprehension and (b) leads to human re-discovery of divide-and-conquer problem-solving strategies, and (c) allows adaptations of human problem-solving strategy with better performance when machine-learned explanations are also presented.
Cloud benchmarking and performance analysis of an HPC application in Amazon EC2

Abstract Cloud computing platforms have been continuously evolving. Features such as the Elastic Fabric Adapter (EFA) in the Amazon Web Services (AWS) platform have brought yet another revolution in the High Performance Computing (HPC) world, further accelerating the convergence of HPC and cloud computing. Other public clouds also support similar features further fueling this change. In this paper, we show how and why the performance of a large-scale computational fluid dynamics (CFD) HPC application on AWS competes very closely with the one on Beskow—a Cray XC40 supercomputer at the PDC Center for High-Performance Computing - in terms of cost-efficiency with strong scaling up to 2304 processes. We perform an extensive set of micro and macro benchmarks in both environments and conduct a comparative analysis. Until as recently as 2020 these benchmarks have notoriously yielded unsatisfactory results for the cloud platforms compared with on-premise infrastructures. Our aim is to access the HPC capabilities of the cloud, and in general to demonstrate how researchers can scale and evaluate the performance of their application in the cloud.
On confidence computation and calibration of deep support vector data description

Deep support vector data description (DeSVDD) is an emerging anomaly detection method based on the deep learning methodology. However, few studies take the confidence of DeSVDD predictions into account so that the present DeSVDD models cannot indicate the reliability degree of the anomaly detection results. In this paper, we enrich the theory of DeSVDD by building the model confidence definition and developing the corresponding calibration strategy. For one thing, by revisiting the methodology of DeSVDD-based anomaly detection, the confidence of detection results is presented to indicate if the prediction of DeSVDD is reliable. For another, we propose a modified power T-scaling strategy to smooth the anomaly scores of DeSVDD model and improve its calibration performance without changing the original detection results. Six open experiment datasets are used to perform the method testing and the experimental results confirm the effectiveness of our proposed calibration strategy.
Fully Kernected Neural Networks

In this paper, we apply kernel methods to deep convolutional neural network (DCNN) to improve its nonlinear ability. DCNNs have achieved significant improvement in many computer vision tasks. For an image classification task, the accuracy comes to saturation when the depth and width of network are enough and appropriate. The saturation accuracy will not rise even by increasing the depth and width. We find that improving nonlinear ability of DCNNs can break through the saturation accuracy. In a DCNN, the former layer is more inclined to extract features and the latter layer is more inclined to classify features. Therefore, we apply kernel methods at the last fully connected layer to implicitly map features to a higher-dimensional space to improve nonlinear ability so that the network achieves better linear separability. Also, we name the network as fully kernected neural networks (fully connected neural networks with kernel methods). Our experiment result shows that fully kernected neural networks achieve higher classification accuracy and faster convergence rate than baseline networks.
On the geometry of the Hermitian Veronese curve and its quasi-Hermitian surfaces

The complete classification of the orbits on subspaces under the action of the projective stabilizer of (classical) algebraic varieties is a challenging task, and few classifications are complete. We focus on a particular action of PGL(2,q2) (and PSL(2,q2)) arising from the Hermitian Veronese curve in PG(3,q2), a maximal rational curve embedded on a smooth Hermitian surface with some fascinating properties. The study of its orbits leads to a new construction of quasi-Hermitian surfaces: sets of points with the same combinatorial and geometric properties as a non-degenerate Hermitian surface.
Vision Transformer-Based Video Hashing Retrieval for Tracing the Source of Fake Videos

With the increasing negative impact of fake videos on individuals and society, it is crucial to detect different types of forgeries. Existing forgery detection methods often output a probability value, which lacks interpretability and reliability. In this paper, we propose a source-tracing-based solution to find the original real video of a fake video, which can provide more reliable results in practical situations. However, directly applying retrieval methods to traceability tasks is infeasible since traceability tasks require finding the unique source video from a large number of real videos, while retrieval methods are typically used to find similar videos. In addition, training an effective hashing center to distinguish similar real videos is challenging. To address the above issues, we introduce a novel loss function, hash triplet loss, to capture fine-grained features with subtle differences. Extensive experiments show that our method outperforms state-of-the-art methods on multiple datasets of object removal (video inpainting), object addition (video splicing), and object swapping (face swapping), demonstrating excellent robustness and cross-dataset performance. The effectiveness of the hash triplet loss for nondifferentiable optimization problems is validated through experiments in similar video scenes.
Innovative approaches for training precision optics technicians

The dramatic shortage of optics technicians is having an impact on the ability of America to compete in the international marketplace. This has caused educational institutions to rethink how they educate the future workforce. Innovative approaches such as, quick-start, microcredentials, remote learning and partnerships are changing the educational landscape.
The usage of immersive technologies in learning environments

Currently, immersive technologies are enjoying great popularity. This trend is reflected in technological advances and the emergence of new products for the mass market, such as augmented reality glasses. The range of applications for immersive technologies is growing with more efficient and affordable technologies and student adoption. Especially in education, the use will improve existing learning methods. Immersive application use visual, audio and haptic sensors to fully engage the user in a virtual environment. This impression is reinforced with the help of realistic visualizations and the opportunity for interaction. In particular, Augmented reality is characterized by a high degree of integration between reality and the inserted virtual objects. An augmented interactive simulation for the determination of the specific charge of an electron will be used as an example to demonstrate how such immersion can be created for users. A virtual Helmholtz coil is used to measure and calculate the e/m constant. The voltage at the cathode for generating the electron beam, but also the voltage of the homogeneous magnetic field for deflecting the electron beam, can be variably controlled by haptic user input. Based on these voltages, an immersive virtual electron beam is calculated and visualized. In this paper, the authors present the conceptual steps of this immersive application and address the challenges associated with designing and developing an augmented and interactive simulation.
Thirty-five years of optics education at Rose-Hulman: from optical sciences to optical engineering, 3 cycles of ABET accreditation

Rose-Hulman Institute of Technology has 35 years of graduating optical scientists and engineers. The change from the Applied Optics degree program to the Optical Engineering degree program occurred in 2003, which paved the way for ABET accreditation. The past and present program reinforces the idea that we are educating our students in optics applications to deal with real-world problems and practice the profession of optical engineers. The RHIT program has been at the forefront of developing project-based learning since its inception in 1983. Since the name changed, the optical engineering program (OE) has completed three cycles of ABET accreditation. We are continuously improving our OE curriculum to meet the current and future needs of the industry and cutting-edge research. To lead the ABET review, Rose-Hulman initiated the dialogue with SPIE to be the lead society for optical engineering in 2004. This paper will discuss several steps taken to develop a world-class OE program with state-of-the-art laboratory facilities for undergraduate optical engineering education.
Leveraging dual-process theories of reasoning to understand and support student reasoning

Introductory physics courses aim to improve students’ problem-solving and reasoning skills. To aid in attaining this goal, researchers in physics education have studied students’ qualitative inferential reasoning to develop and refine theoretical frameworks for how students reason through qualitative physics problems. Recently, researchers have begun to apply dual-process theories of reasoning (DPToR), from cognitive science and psychology, to support mechanistic predictions of student reasoning in physics. This talk will explore the DPToR framework and discuss the ways in which it is being used to expand student reasoning skills in the physics classroom.
Toy box optics: bringing optical technologies home and to schools

Optical technologies can be found in many aspects of our daily lives. We have developed three experiments using items that can be found in a toy box or around the house to demonstrate and explain optical concepts. Videos of the experiments, their principle, and applications are available on our YouTube channel. The first experiment uses moldable putty to make a shapeable lens and light guide to observe refraction and total internal reflection respectively. The putty’s characteristics allows for hand molding into different shapes to observe how light propagation is changed within the putty in real time. The second experiment is to learn about color absorption and reflection. The color pattern of clothing would change depending on the color of light that is used to illuminate it. The experiment illustrates how optical communication can use different colors to support multiple users. The third experiment uses a bubble to illustrate light interference, the principle behind eyeglasses coating. Different colors are seen at different locations on the bubble due to light interference. The above experiments can be carried out at home or at school through our outreach program. During our school outreach, we relate the above hands-on experiments with two demonstrations. The first demonstration is a laser-transmitted audio system that explains how electrical signals can be transmitted using optical fibers. While the second allows for the observation of how laser light is guided within an optical fiber. The toy-based experiments are a fun approach to introduce complex concepts to students.
Dynamic curricular concepts for research orientated programs in optics and photonics: the importance of consistent evaluation

Redesigning a curriculum for teaching media technology is a major challenge. Up-to-date teaching and learning concepts are necessary that meet the constant technological progress and prepare students specifically for their professional life. Teaching and studying should be characterized by a student-oriented teaching and learning culture. In order to achieve this goal, consistent evaluation is essential. The aim of the evaluation concept presented here is to generate structured information regarding the quality of content-related, didactic and organizational aspects of teaching. The exchange of opinions between students and lecturers should be encouraged in order to continuously improve the teaching and learning processes.
Develop the innovative and pioneering concepts for photonics and optics outreach program

Optical and laser engineering are not only prevalent in science fiction movies but find numerous technological applications ranging from additive manufacturing over machining of micro/nano-scale features to biomedical imaging or space telescopes applications. In a related context, science simplification lectures with diverse visualization techniques and OSA optics suitcase can be utilized to bring attention and inspire careers in future technologies. The suggested work focuses on developing the outreach program to train the trainees (as photonic ambassadors) specifically in the field of photonics and optical applications which is the expanded programme for multidisciplinary outreach activities for school students over 10 years (or pre-university students in the future) [1]. The extended activities can support a wide range of students in Ireland, Europe, and the international prospects as well, which increases the possibility of promoting photonics technology careers in the future. The paper will explain the guidelines and topics of the proposed practical workshop (INNOVATIVE WAYS TO PHOTONICS FUTURE "TRANSFER THE KNOWLEDGE") which can help trainees and give them some techniques for engaging targeted students and creating an interactive environment. The detailed characterization of the workshop structure and related pragmatic sessions will be illustrated in this work as well as the systematic steps of the developed program outline.
Regulatory Changes in German and Austrian Power Systems Explored with Explainable Artificial Intelligence

A stable supply of electrical energy is essential for the functioning of our society. Therefore, energy and balancing markets of power grids are strictly regulated. With changes in technology, the economy and society, these regulations are also constantly adapted. However, whether these regulatory changes lead to the intended results is not easy to assess. Could eXplainable Artificial Intelligence (XAI) models distinguish regulatory settings and support the understanding of the effects of these changes? In this article, we explore two examples of regulatory changes: The splitting of the German-Austrian bidding zone and changes in the pricing schemes of the German balancing energy market. We find that boosted tree models and feedforward neural networks before and after a regulatory change differ in their respective parametrizations. Using Shapley additive explanations, we reveal model differences, e.g., in terms of feature importance, and identify key features of these distinct models. With this study, we demonstrate how XAI can be applied to investigate system changes in power systems.
A study of the skills required by the Canadian photonics industry

This study provides a comprehensive and up-to-date portrait of the skills desired by the Canadian photonics industry. To accomplish this, we investigate Canadian job postings on popular employment websites in the fields of optics and photonics to characterize clusters of skills in high demand. We supplement this investigation with an analysis of responses to a questionnaire distributed to over 300 companies with Canadian operations. We present the resulting information in a manner to support evidence-based policy decisions, such as recommendations for improvements to educational programs to better meet the training needs conveyed by the Canadian photonics industry.
On Reporting Robust and Trustworthy Conclusions from Model Comparison Studies Involving Neural Networks and Randomness

The performance of neural networks differ when the only difference is the seed initializing the pseudo-random number generator that generates random numbers for their training. In this paper we are concerned with how random initialization affect the conclusions that we draw from experiments with neural networks. We run a high number of repeated experiments using state of the art models for time-series prediction and image classification to investigate this statistical phenomenon. Our investigations show that erroneous conclusions can easily be drawn from such experiments. Based on these observations we propose several measures that will improve the robustness and trustworthiness of conclusions inferred from model comparison studies with small absolute effect sizes.
Iron triangle framework for the health recommender system: perspectives from intelligent care

School of Nursing, Fudan University (Shanghai, China) Sponsorships or competing interests that may be relevant to content are disclosed at the end of this article. Published online ■ ■ *Corresponding author. Address: CHINA. E-mail address: [email protected] (C. Yuan). This is an open access article distributed under the terms of the Creative Commons Attribution-Non Commercial-No Derivatives License 4.0 (CCBY-NC-ND), where it is permissible to download and share the work provided it is properly cited. The work cannot be changed in any way or used commercially without permission from the journal. http://creativecommons.org/licenses/by-nc-nd/4.0/
Fingerprinting and Building Large Reproducible Datasets

Obtaining a relevant dataset is central to conducting empirical studies in software engineering. However, in the context of mining software repositories, the lack of appropriate tooling for large scale mining tasks hinders the creation of new datasets. Moreover, limitations related to data sources that change over time (e.g., code bases) and the lack of documentation of extraction processes make it difficult to reproduce datasets over time. This threatens the quality and reproducibility of empirical studies.
An efficient CNN-RNN recognition network for complex interference signal

Facing an increasingly complex electromagnetic environment, modern communication systems must adopt certain antiinterference technology when deploying system equipment and network to ensure the normal operation of wireless communication. Currently, interference recognition is the foundation and key link of anti-interference technology. Among them, the recognition accuracy and the dependence of the algorithm model on training data are challenges that need to be solved urgently. In this paper, a CNN-RNN joint network architecture combining residual network and LSTM network is proposed to recognize the interfering signals. The joint network architecture adopts the parallel combination of residual and LSTM network, where the time-frequency image data of signals is input to the residual network branches while the real part, imaginary part, and spectral amplitude data of signals are input to the LSTM network branches. After simulation verification, the interference recognition result of the joint network is significantly improved compared with the single network. Firstly, compared with the single LSTM network, even though the single LSTM network has reached a very high recognition accuracy, the recognition accuracy of the joint network is still about 1%∼2% higher. What’s more, compared with the single network, the interference noise ratio (INR) generalization ability of the joint network is obviously improved. After training the network with different INR distributions, the recognition accuracy can be maintained. Therefore, it’s not sensitive to the INR distribution of the training data, which can adapt to different distribution conditions of training data and reduces the dependence of the algorithm on training data.
Sustainable education in the age of the second quantum revolution: fifteen years of the University of Rochester National Science Foundation supported efforts

Quantum optics/quantum information and nano-optics educational laboratory facility (QNOL) at the University of Rochester (UR) is located within three rooms of the Institute of Optics with a total area of 587 ft2 . Four teaching labs were prepared on the generation and characterization of entangled and single (antibunched) photons demonstrating the laws of quantum mechanics: (1) entanglement and Bell’s inequalities, (2) single-photon interference (Young’s double slit experiment and Mach–Zehnder interferometer), (3) single-photon source I: confocal fluorescence microscopy of single nanoemitters, and (4) single-photon source II: a Hanbury Brown and Twiss setup, fluorescence antibunching. We also describe a coherent undergraduate educational program in nanoscience/nanoengineering at the UR based on the QNOL and Integrated Nanosystems Center resources. From 2006 to May 2023, a total of ~900 students have utilized the quantum/nano labs for lab report submission (including 144 Monroe Community College students) and more than 300 students have used them for lab demonstrations. These two projects have three main outcomes: (1) developing a curriculum and offering the Certificate in Nanoscience and Nanoengineering; (2) creating an exemplary model of collaboration in quantum/nanotechnology between a university with state-of-the-art, expensive experimental facilities and a nearby two-year community college; and (3) developing universally accessible “hands-on” experiments (minilabs) on quantum/nanophotonics, learning materials, and pedagogical methods. The inexpensive mini-labs described herein can be adopted in small colleges. All developed materials and students’ reports are available at http://www.optics.rochester.edu/workgroups/lukishova/QuantumOpticsLab/. Two papers in a special issue of Optical Engineering describe these two programs with more details: https://doi.org/10.1117/1.OE.61.8.081811 and https://doi.org/10.1117/1.OE.61.8.081810.