Reviewerly/exHarmony huggingface dataset:
A visually secure image encryption scheme based on parallel compressive sensing

Authors:
raw_author_name: Peijun Luo
raw_affiliation_string: Army Engineering Univ. (China)
raw_author_name: Jiayuan Tan
raw_affiliation_string: Army Engineering Univ. (China)
raw_author_name: Lin Jiang
raw_affiliation_string: Army Engineering Univ. (China)

Abstract: Visually secure image encryption has become a hot topic in the information security field. However, the embedding capacity of the existing visually secure image encryption schemes is too limited. To solve this problem, a visually secure image encryption scheme using parallel compressive sensing (PCS) is proposed in this paper. Firstly, the secret image is preprocessed by using global random scrambling. Secondly, the scrambled secret image is compressed by PCS. Thirdly, the compressed bitstream is encrypted through a XOR encryption operation. Finally, the encrypted bit stream is embedded into the low bit-plane of the carrier image. The experiment results demonstrate that the proposed scheme significantly increases the embedding capacity in comparison with the current visually secure image encryption schemes.
An Effectiveness Evaluation on Critical Thinking Exercises in Chinese College English Textbooks

Authors:
raw_author_name: Hui Lyu
raw_affiliation_string: Beijing Normal University

Abstract: Cultivating students’ critical thinking has long been the consensus reached by the international education circle and a common goal of international higher education reform. However, the training of critical thinking of college students in China started late, and no mature talent training system has been formed in higher education. As a compulsory public basic course for college students in China, College English is one of the most involved courses in universities. As an important material and tool for teaching and learning, the design quality of college English textbook exercises will directly affect the quality of teaching and talent training. Therefore, this study analyzes the effectiveness of textbook exercise design, with a view to contributing to college English textbooks and teaching reform, so as to help students develop critical thinking. In this study, exercises in New Horizons College English (Reading and Writing Course 1), one of the most widely used textbooks, were selected for discourse analysis, and the Delphi Panel’s Dualistic Model was used as a comparison for internal evaluation. Moreover, taking a Chinese university as an example, questionnaire survey are used to conduct external evaluation of textbook exercise design, found that: (a) Exercises in Chinese college English textbooks correspond to the cognitive skills mentioned in the Dualistic Model, which is helpful to the training of students' critical thinking; (b) There are also some problems, such as the imbalance of skills training and the homogenization of questioning methods; (c) Students of College English generally have a high recognition of the importance of critical thinking and textbook design, but believe that textbook design, course teaching and assessment need to be reformed.
A Survey: Industrial Anomaly Detection based on Data Mining

Authors:
raw_author_name: Jinrui Li

Abstract: Industrial defect detection plays a crucial role in modern manufacturing. Identifying and addressing inferior products contributes to enhancing product quality, strengthening product competitiveness, and increasing customer satisfaction. Existing surveys of industrial defect detection are relatively scarce and struggle to reflect the latest development trends. Therefore, this article provides a more detailed and in-depth survey of industrial defect detection technologies. The article first reviews the development history of industrial defect detection methods. It then covers three aspects: the concept of general anomalies, concepts related to image anomaly detection, and industrial defects, providing an overview of industrial defect detection in these areas. It also summarizes the current state of development, as well as the advantages and disadvantages of each aspect. Additionally, the article identifies the limitations of industrial detection methods in practical industrial applications. Finally, it looks forward to the future development trends and potential research directions in this field, aiming to inspire future research.
Post-Quantum Cryptography Migration: Challenges in Education

Authors:
raw_author_name: Tomáš Fabšič
raw_affiliation_string: Faculty of Electrical Engineering and Information Technology, Slovak University of Technology in Bratislava, Ilkovičova 3, 841 04 Bratislava, Slovakia
raw_author_name: Otokar Grošek
raw_affiliation_string: Faculty of Electrical Engineering and Information Technology, Slovak University of Technology in Bratislava, Ilkovičova 3, 841 04 Bratislava, Slovakia
raw_author_name: Viliam Hromada
raw_affiliation_string: Faculty of Electrical Engineering and Information Technology, Slovak University of Technology in Bratislava, Ilkovičova 3, 841 04 Bratislava, Slovakia
raw_author_name: Pavol Zajac
raw_affiliation_string: Faculty of Electrical Engineering and Information Technology, Slovak University of Technology in Bratislava, Ilkovičova 3, 841 04 Bratislava, Slovakia

Abstract: PQC migration represents multiple challenges in education. We will need to educate our future cryptography researchers and engineers on new topics, introduce new challenges, and explain new cryptographic primitives. In the paper, we present our view on education challenges associated with the transition to post-quantum cryptography.
Post-Quantum Cryptography Efforts in Türkiye

Authors:
raw_author_name: Kamil Otal
raw_affiliation_string: National Research Institute of Electronics and Cryptology, Gebze, Kocaeli, Türkiye

Abstract: The field of post-quantum cryptography has seen significant global progress, with a notable contribution from the Post-Quantum Cryptography Standardization Process managed by the National Institute of Standards and Technology (NIST) in the United States. At the same time, the advancement in programmable quantum computers has exceeded earlier predictions. Consequently, numerous nations, including the United States, United Kingdom, Germany, France, Türkiye, China, and (South) Korea, have made significant strides, particularly within the last decade, towards preparing for the quantum computing era. This article seeks to present an overview of relevant institutions and their corresponding endeavors within Türkiye. Specifically, we provide a concise summary of public announcements, NATO events, conferences, and projects primarily from the past five years. The intention is to offer a succinct and enlightening reference for relevant individuals and institutions.
MultiMatch: Multi-task Learning for Semi-supervised Domain Generalization

Authors:
raw_author_name: Lei Qi
raw_affiliation_string: School of Computer Science and Engineering (Southeast University), and Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications (Southeast University), Ministry of Education, Nanjing, China
raw_author_name: Hongpeng Yang
raw_affiliation_string: School of Cyber Science and Engineering, Southeast University, Nanjing, China
raw_author_name: Yinghuan Shi
raw_affiliation_string: The State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China
raw_author_name: Xin Geng
raw_affiliation_string: School of Computer Science and Engineering (Southeast University), and Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications (Southeast University), Ministry of Education, Nanjing, China

Abstract: Domain generalization (DG) aims at learning a model on source domains to well generalize on the unseen target domain. Although it has achieved great success, most of the existing methods require the label information for all training samples in source domains, which is time-consuming and expensive in the real-world application. In this paper, we resort to solving the semi-supervised domain generalization (SSDG) task, where there are a few label information in each source domain. To address the task, we first analyze the theory of multi-domain learning, which highlights that 1) mitigating the impact of domain gap and 2) exploiting all samples to train the model can effectively reduce the generalization error in each source domain so as to improve the quality of pseudo-labels. According to the analysis, we propose MultiMatch, i.e. , extending FixMatch to the multi-task learning framework, producing the high-quality pseudo-label for SSDG. To be specific, we consider each training domain as a single task ( i.e. , local task) and combine all training domains together ( i.e. , global task) to train an extra task for the unseen test domain. In the multi-task framework, we utilize the independent batch normalization and classifier for each task, which can effectively alleviate the interference from different domains during pseudo-labeling. Also, most of the parameters in the framework are shared, which can be trained by all training samples sufficiently. Moreover, to further boost the pseudo-label accuracy and the model’s generalization, we fuse the predictions from the global task and local task during training and testing, respectively. A series of experiments validate the effectiveness of the proposed method, and it outperforms the existing semi-supervised methods and the SSDG method on several benchmark DG datasets.
Evolving Paradigms in Complex IVC Filter Retrieval

Authors:
raw_author_name: Michael Lewis
raw_affiliation_string: University of Nottingham

Abstract: The landscape of interventional radiology has witnessed a remarkable evolution in the domain of inferior vena cava (IVC) filterretrievals, heralding a new era of procedural innovation and technical prowess.The impetus for this transformation has been the development and integration of diverse technical tools and the refinement of skill sets among practitioners, facilitating a paradigmatic shift in how complex retrievals are approached and executed.Historically, the procedural orthodoxy for IVC filter retrieval has been predominantly anchored in the utilization of standard techniques such as the loop and snare or the snare and sheath methods. These conventional methodologies have been explicitly delineated within the Instructions for Use (IFU) by filter manufacturers as the recommended approaches for retrieval, thus shaping the procedural norms within the field. However, the clinical landscape is markedly dynamic, characterized by a continuous influx of complex cases where traditional retrieval methods may not suffice due to various complicating factors such as filter tilt, migration, embedding within the venous wall, or significant endothelialization.Such complexities necessitate a reevaluation of conventional strategies and the exploration of alternative, more versatile techniques capable of addressing the unique challenges presented by these difficult-to-retrieve filters.
Encoding prior knowledge in ensemble refinement

Authors:
raw_author_name: Jürgen Köfinger
raw_affiliation_string: Max Planck Institute of Biophysics
raw_author_name: Gerhard Hummer
raw_affiliation_string: Goethe University Frankfurt; Max Planck Institute of Biophysics

Abstract: The proper balancing of information from experiment and theory is a long-standing problem in the analysis of noisy and incomplete data. Viewed as a Pareto optimization problem, improved agreement with the experimental data comes at the expense of growing inconsistencies with the theoretical reference model. Here, we propose how to set the exchange rate a priori to properly balance this trade-off. We focus on gentle ensemble refinement, where the difference between the potential energy surfaces of the reference and refined models is small on a thermal scale. By relating the variance of this energy difference to the Kullback-Leibler divergence between the respective Boltzmann distributions, one can encode prior knowledge about energy uncertainties, i.e., force-field errors, in the exchange rate. The energy uncertainty is defined in the space of observables and depends on their type and number, and on the thermodynamic state. We highlight the relation of gentle refinement to free energy perturbation theory. A balanced encoding of prior knowledge increases the quality and transparency of ensemble refinement. Our findings extend to non-Boltzmann distributions, where the uncertainty in energy becomes an uncertainty in information.
Reqomp: Space-constrained Uncomputation for Quantum Circuits

Authors:
raw_author_name: Anouk Paradis
raw_affiliation_string: ETH Zurich, Switzerland
raw_author_name: Benjamin Bichsel
raw_affiliation_string: ETH Zurich, Switzerland
raw_author_name: Martin Vechev
raw_affiliation_string: ETH Zurich, Switzerland

Abstract: Quantum circuits must run on quantum computers with tight limits on qubit and gate counts. To generate circuits respecting both limits, a promising opportunity is exploiting <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>u</mml:mi><mml:mi>n</mml:mi><mml:mi>c</mml:mi><mml:mi>o</mml:mi><mml:mi>m</mml:mi><mml:mi>p</mml:mi><mml:mi>u</mml:mi><mml:mi>t</mml:mi><mml:mi>a</mml:mi><mml:mi>t</mml:mi><mml:mi>i</mml:mi><mml:mi>o</mml:mi><mml:mi>n</mml:mi></mml:math> to trade qubits for gates. We present Reqomp, a method to automatically synthesize correct and efficient uncomputation of ancillae while respecting hardware constraints. For a given circuit, Reqomp can offer a wide range of trade-offs between tightly constraining qubit count or gate count. Our evaluation demonstrates that Reqomp can significantly reduce the number of required ancilla qubits by up to 96%. On 80% of our benchmarks, the ancilla qubits required can be reduced by at least 25% while never incurring a gate count increase beyond 28%.
Is it feasible to design learning media "Tomato Plant Growth" hydroponically based on the discovery learning model?

Authors:
raw_author_name: Md. Anisur Rahman

Abstract: Information technology has significantly transformed various facets of life, including education, specifically the learning process in higher education. Integrating information technology in education has empowered students to create and utilize diverse digital learning resources. Mobile devices play a vital role in improving student learning outcomes in hydroponic plant growing lessons when used as an additional tool with demonstration and discovery learning approaches, often known as demons-disco learning. This course utilizes hydroponic techniques and mobile devices to improve students' understanding and skill in cultivating vegetables. Mobile applications offer innovative experiences in information and communication technology and opportunities for students to engage in self-directed learning. The chosen learning methodology involves imparting extensive knowledge, a thorough understanding of many components and variations, and an assessment through inquiries about the cultivation of hydroponic plants. Thus, a teacher must develop instructional material that helps students overcome difficulties associated with cultivating hydroponic plants. This study employed a research and development (R&amp;D) approach to fabricate a product, encompassing three of the four research phases: definition (identifying possibilities and issues, gathering data), product design, and development (validating the design, improving the design, and testing the product). Nevertheless, this research is limited to the third phase, notably the validation phase. The findings suggest that the THD (Tomato-Hydroponic-Demonsdisco) learning media is suitable for use. This article will thoroughly analyze THD media and its utilization in lectures.
COOPERATION WITH ROUTE MOVEMENT LEARNING OF AN AGV AND OBSTACLE DETECTION OF A DRONE

Authors:
raw_author_name: Yusuke NAKAMURA
raw_affiliation_string: Graduate School of Engineering, Chiba Univ.
raw_author_name: Gakuhito HIRASAWA
raw_affiliation_string: Graduate School of Engineering, Chiba Univ.

Abstract: Labor shortages and long working hours have long been problems at construction sites. In order to solve this problem and improve the productivity of the sites, automation is progressing on various occasions according to the process, and AGV is used especially when transporting materials. There are various driving methods for AGV, but in this research, we let AGV drive automatically after learning using data obtained by detecting obstacles with a drone so that it can travel safety with any loads.
<i>p</i>-Values as QWERTY: Curating Evidence in the Computational Era

Authors:
raw_author_name: Fred Feinberg

Abstract: McShane et al.'s (2024) wide-ranging critique of null hypothesis significance testing provides a number of specific suggestions for improved practice in empirical research. This commentary amplifies several of these from the perspective of computational statistics—particularly nonparametrics, resampling/bootstrapping, and Bayesian methods—applied to common research problems. Throughout, the author emphasizes estimation (as opposed to testing) and uncertainty quantification through a comprehensive process of “curating” a variety of graphical and tabular evidence. Specifically, researchers should be encouraged to estimate the quantities that matter, with as few assumptions as possible, in multiple ways, then try to visualize it all, documenting their pathway from data to results for others to follow.
A Minimal Computing Approach to Southern African Language Resources

Authors:
raw_author_name: William Turkel

Abstract: This new collaboration between a historian of Southern Africa (RC) and a specialist in computational methods (WJT), is designed to draw on our respective backgrounds and provide opportunities to enlist students and other collaborators in research and teaching. Our goal is to create tools that can be used to help explain unfamiliar languaging in historical contexts. We follow the tenets of minimal computing (Risam &amp; Gil 2022) and take the perspective of language as a complex adaptive system (Kretzschmar 2015). We also situate our work within the postcolonial digital humanities generally (Risam 2018) and the specific critique of knowledge production and racism that Fields &amp; Fields (2012, pp. 5-6) identified as ‘racecraft’, which “highlights the ability of pre- or non-scientific modes of thought to hijack the minds of the scientifically literate”. As practitioners of academic language research and computing, we must be attentive to the history of colonizers trying to not only kill ‘native languages’ but their speakers and cultures (Ngũgĩ wa Thiong'o 2009). To date, we have partially implemented one prototype for automating interlinear morphemic glossing of chiShona and English as shown in Figure 1 (Charumbira et al 2023). Here our intent is speculative design: to imagine a more inclusive space of computational tools and practices that jettisons some of the assumptions that have shaped the digital cultural record in the Global North.
Landmark extraction for hippocampal surfaces

Authors:
raw_author_name: Gang Wang
raw_affiliation_string: Ludong Univ. (China)

Abstract: The cognitive decline caused by Alzheimer's disease (AD) is closely related to changes in the hippocampal structure captured by magnetic resonance imaging (MRI). To accurately analyze the morphological changes in the hippocampus caused by AD, it is necessary to extract the landmarks on the hippocampal surfaces which can ensure accurate registration between the different hippocampal surfaces, to eliminate morphological measurement errors caused by factors such as image acquisition, age, and gender. In this paper, we propose a method that can automatically extract the intrinsic landmarks of the hippocampal surfaces. More specifically, we use the eigen-graph of the hippocampal surfaces to extract the intrinsic and unified landmarks of all the hippocampal surfaces and calibrate along the longitudinal direction of the hippocampus surfaces along with the landmark correction for a few irregular hippocampal surfaces. Finally, we apply the extracted landmarks to the landmark-based registration algorithm, and the results show that landmark-based registration using our extracted landmarks can improve the accuracy of AD classification.
Image watermarking algorithm based on localized FAPHFMs magnitude

Authors:
raw_author_name: Siyu Yang
raw_affiliation_string: Dalian Maritime Univ. (China)
raw_author_name: Ansheng Deng
raw_affiliation_string: Dalian Maritime Univ. (China)
raw_author_name: Hui Cui
raw_affiliation_string: Dalian Maritime Univ. (China)

Abstract: Image watermarking technique involves three important attributes of robustness, imperceptibility and watermarking capacity, which constrain each other and need to be weighed in designing watermarking schemes. Improving watermarking imperceptibility, watermarking capacity and robustness has been a challenge for image watermarking algorithms. In this paper, the algorithm utilizes the magnitudes of fast and accurate polar harmonic Fourier moments (FAPHFMs) as the watermark carrier, which improves the robustness of the algorithm. Using the multiplicative embedding method, the watermark embedding strength can be flexibly adjusted according to the size of the original coefficients to achieve better robustness and imperceptibility. Experimental verification confirms that the algorithm outperforms others in terms of robustness and imperceptibility.
A study on multidimensional evaluation of teaching using loose-leaf textbooks based on improved K-modes algorithm

Authors:
raw_author_name: Zijiao Tang
raw_affiliation_string: Liuzhou City Vocational College (China)

Abstract: Loose-leaf textbook is a new type of textbook in the new era of education environment, which has better interactivity and flexibility of use than traditional textbooks. In order to improve effect analysis of teaching with loose-leaf textbooks, a multi-dimensional evaluation method of teaching with loose-leaf textbooks based on the improved K-modes algorithm is designed. In the process, the multi-dimensional evaluation system of teaching using loose-leaf textbooks is constructed by combining objective and subjective directions, and the corresponding index value calculation method is designed, and then the data processing of multi-dimensional evaluation based on the improved K-modes algorithm is optimised by introducing the error sum of squares and the co-presence rate to optimise the clustering process, so as to obtain a complete multi-dimensional evaluation method. Results show that the research method maintains the accuracy rate above81.2% when the number of sample points increases to 1200 when performing data classification accuracy analysis; when generating the multidimensional evaluation results, the research method generates realistic evaluation results for 8indicators. It shows that the research method can effectively carry out the multidimensional evaluation of teaching using loose-leaf textbooks, and can provide certain technical references to the education industry.