Reviewerly/exHarmony huggingface dataset:
A Survey: Industrial Anomaly Detection based on Data Mining

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

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

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

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

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.
An Analytical study to develop the traditional craft in the field of creative industries in Egypt

The production of traditional crafts in Egyptian architecture, especially Islamic architecture, has been historically significant, reflecting cultural uniqueness. However, political, economic, and industrial factors have led to a decline in craft production, diminishing their cultural distinctiveness (Abdul Hamid, 2001). Traditional crafts currently face stagnation, lacking modernization in design, functional integration, and technological advancements. Artisans struggle to compete with imported products. This research aims to preserve and revitalize traditional crafts by integrating them with the creative industries. It explores modern Egyptian experiences, traces the craft’s development, and identifies key points for creating creative products. The study evaluates Egyptian models that introduce new products, assessing their suitability and overall development, including impacts on craftsmen, products, and the production process. The application of creative industry principles is also examined. Findings inform proposals and recommendations to preserve traditional crafts in Egypt and explore their local and global development potential.
Encoding prior knowledge in ensemble refinement

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

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%.
Detecting CSV File Dialects by Table Uniformity Measurement and Data Type Inference

The human-readable simplicity with which the CSV format was devised, together with the absence of a standard that strictly defines this format, has allowed the proliferation of several variants in the dialects with which these files are written. The latter has meant that the exchange of information between data management systems, or between countries and regions, requires human intervention during the data mining and cleansing process. This has led to the development of various computational tools that aim to accurately determine the dialects of CSV files, in order to avoid data loss during data loading by a given system. However, current systems have limitations and make assumptions that need to be improved and/or extended. This paper proposes a method for determining CSV file dialects through table uniformity, a statistical approach based on table consistency and records dispersion measurement along with the detection of data type over each field. The new method has a 100% accuracy over a dataset with 147 sampling CSV files from a benchmark framework. Furthermore, the proposed method is accurate enough to determine dialects by reading only ten records, requiring more data to disambiguate those cases where the first records do not contain the necessary information to conclude with a dialect determination.
Is it feasible to design learning media "Tomato Plant Growth" hydroponically based on the discovery learning model?

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.
Application of Computer Science Technology in Computer Education

The arrival of the era of big data, making the computer in all walks of life are widely used in the teaching process is also inseparable from the application of computers, some of the traditional teaching methods have also been replaced by computer technology, computer teaching has become the mainstream of the times, and has become a very effective means of teaching as well as teaching tools. The application and popularization of computers has brought great convenience to people's daily life, and has a great effect on the improvement of teaching quality. In the process of computer popularization, it is widely used in real life. And the computer as an education and learning tools, for computer education itself has great significance. In assisting teachers in the management of teaching and practical work at the same time, but also to provide students with richer learning resources and learning efficiency, so that the teaching and learning process is more concise and easy. The arrival of the era of big data, making the computer in all walks of life are widely used in the teaching process is also inseparable from the application of computers, some of the traditional teaching methods have also been replaced by computer technology, computerized teaching has become the mainstream of the times, and has become a very effective means of teaching as well as the teaching tools of the times. The application and popularization of computers has brought great convenience to people's daily life, and has a great effect on the improvement of teaching quality. Especially in the cultivation of talents in colleges and universities, the application of computers can have a great role in promoting the cultivation of innovation ability of college students. This article briefly analyzes the application of computer science and technology in computer education, starting from computer science and technology.
COOPERATION WITH ROUTE MOVEMENT LEARNING OF AN AGV AND OBSTACLE DETECTION OF A DRONE

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

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

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

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

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.
Privacy preserving machine learning in energy services: a survey

Privacy preserving machine learning (PPML), which focuses on training machine learning models while preserving privacy, has attracted many research interests in the recent past. This paper investigates recent research on privacy preserving machine learning (PPML) techniques and their energy applications. PPML allows training models while protecting sensitive data. We categorize approaches into federated learning, homomorphic encryption, differential privacy, and secure multi-party computation. After explaining each method’s principles, we summarize research applying them to energy tasks like forecasting and anomaly detection. Comparative analysis identifies trade-offs between privacy guarantees, costs, and accuracy. Encryption-based techniques provide strong privacy but impact efficiency. Federated learning is more efficient but has weaker protections. Ongoing challenges include the performance of cryptography, calibrating noise, and developing robust aggregation rules. The survey offers a comprehensive reference on the state-of-the-art in PPML for energy, guiding further advancement of practical systems.
A study on multidimensional evaluation of teaching using loose-leaf textbooks based on improved K-modes algorithm

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.
Entity relation joint extraction model combining pointer network and attention mechanism based on relative position embedding

Extracting entity relations from unstructured text is an important step in constructing a knowledge graph, but most current methods are ineffective in dealing with the complex problem of overlapping entities. In this paper, we propose a novel entity relation joint extraction model. It extracts subjects through pointer annotation, fuses the extracted subjects with the sentence vector, then inputs them into an attention layer (Attention Mechanism Based on Relative Position Embedding, AMBRPE) to enhance feature expression ability. Under predefined relation conditions, the model extracts objects corresponding to the extracted subjects to generate relation triplets. And the model can effectively solve the problem of overlapping entities through hierarchical pointer annotation. In addition, we introduce an Adversarial Training Component (ATC) into the model, which generates adversarial samples for training and acts as a text data augmentation method to improve model generalization ability. On the public datasets NYT and WebNLG, we conducted extensive experiments and the results show that our model outperforms the cascaded binary tagging framework (CasRel) by 2.1 and 0.9 percentage points, respectively. Moreover, the effectiveness of the proposed model is verified through ablation experiments.