Reviewerly/exHarmony huggingface dataset:
A note on the $ c $-differential spectrum of an AP$ c $N function

<p style='text-indent:20px;'>Motivated by a recent work of Zhang and Yan on the <inline-formula><tex-math id="M3">\begin{document}$ c $\end{document}</tex-math></inline-formula>-differential spectrum of some power functions over finite fields, we further study an AP<inline-formula><tex-math id="M4">\begin{document}$ c $\end{document}</tex-math></inline-formula>N function and express its <inline-formula><tex-math id="M5">\begin{document}$ c $\end{document}</tex-math></inline-formula>-differential spectrum in terms of <inline-formula><tex-math id="M6">\begin{document}$ (i, j, k)_2 $\end{document}</tex-math></inline-formula>, i.e., the cardinality of the intersection <inline-formula><tex-math id="M7">\begin{document}$ (\mathcal{C}^{(2)}_i+1)\cap\mathcal{C}^{(2)}_j\cap(\mathcal{C}^{(2)}_k-1) $\end{document}</tex-math></inline-formula> for <inline-formula><tex-math id="M8">\begin{document}$ i, j, k\in\{0, 1\} $\end{document}</tex-math></inline-formula>, where <inline-formula><tex-math id="M9">\begin{document}$ \mathcal{C}^{(2)}_0, \mathcal{C}^{(2)}_1 $\end{document}</tex-math></inline-formula> are the cyclotomic classes of order two over the finite field <inline-formula><tex-math id="M10">\begin{document}$ \mathbb{F}_{p^n} $\end{document}</tex-math></inline-formula>, <inline-formula><tex-math id="M11">\begin{document}$ p $\end{document}</tex-math></inline-formula> is an odd prime and <inline-formula><tex-math id="M12">\begin{document}$ n $\end{document}</tex-math></inline-formula> is a positive integer. By virtue of the cyclotomic numbers of orders two and four, we determine the values of <inline-formula><tex-math id="M13">\begin{document}$ (i, j, k)_2 $\end{document}</tex-math></inline-formula> for <inline-formula><tex-math id="M14">\begin{document}$ i, j, k\in\{0, 1\} $\end{document}</tex-math></inline-formula>, which may be of independent interest. As an application, we give another proof of the <inline-formula><tex-math id="M15">\begin{document}$ c $\end{document}</tex-math></inline-formula>-differential spectrum of an AP<inline-formula><tex-math id="M16">\begin{document}$ c $\end{document}</tex-math></inline-formula>N function over finite fields with characteristic <inline-formula><tex-math id="M17">\begin{document}$ 5 $\end{document}</tex-math></inline-formula>. Further, we refine the result of Zhang and Yan in the sense that we completely characterize the conditions when the <inline-formula><tex-math id="M18">\begin{document}$ c $\end{document}</tex-math></inline-formula>-differential equation of the AP<inline-formula><tex-math id="M19">\begin{document}$ c $\end{document}</tex-math></inline-formula>N function has one solution and two solutions, respectively.
A Practical Study on Building a Distributed Artificial Intelligence Experimental Teaching Platform Based on Traditional Laboratories

In the process of integration of AI teaching into various disciplines, theoretical teaching and practical teaching are given equal importance, while the experimental teaching of AI has specific and complex requirements for the experimental environment.This paper introduces the practice and reflection on the method of microservice cluster transformation into AI experimental teaching platform based on the traditional laboratory, which makes full use of the existing laboratory hardware resources, fully introduces K8s, Docker and other open source technology solutions, creates a full process experimental environment for AI teaching that can fully improve the efficiency of teaching and research without affecting the daily teaching activities, optimizes the operation mechanism and service mode, realize the on-demand customization of teaching resources, improve teaching governance, and provide useful ideas and methods for the construction of experimental teaching environment of artificial intelligence.
Learning Similarity for Discovering Inspirations of Western Arts in Japanese Culture

Several paintings by Japanese artists in the beginning of 20th century were largely inspired by works of western artists. Finding correspondences between the Japanese and the western artworks can reveal how the western arts were introduced in Japan. Until now, to discover such correspondences, art historians usually annotated them manually. This is a tedious process which generally requires a lot of effort and time. In computer vision literature, there are several techniques that can find similarities in images. To find such similarities some techniques are based on objects appearing in the images, while some techniques compare fine-grain details. However, inspirations in art illustrations are sometimes from global outlines of the images. Another difficulty is that annotations of correspondences are rare in historical data. This makes a lot of techniques which are based on supervised learning not applicable. In this paper, we propose a novel technique to find correspondences between two related artworks, which compares the global outlines information. It is based on Siamese neural networks (SNNs) and self-supervised learning method. In addition, we create a dataset of illustrations from two different types of artworks: one from Japanese artists, Seiki Kuroda, and one from western artists, Raphaël Collin. Correspondence annotations are also given. We evaluate the algorithm using recall@k as metrics, and also qualitatively show that the proposed method provides profiles of image correspondences different from the state of the art.
Adaptivity in Research Practice with People Living with Dementia: A Designer's Reflection

Engaging people with dementia in the design of technology is increasingly gaining more attention—and rightfully so. Yet to be sensitive and responsive to the variety of participants with dementia in different contexts and phases of a design process can be challenging. In this case-study, we applied retrospective reporting to elicit information on this topic from an experienced designer. The findings show how a designer practices adaptivity in duration, explanations, and approach to access experiences during in-situ evaluation of a technical prototype. Insights from this case show the need for reflecting on the potential impact of adaptivity and discussing in the field how adaptivity should be practiced to add value to the work of designers in dementia care contexts.
Unveiling 100 years of digitalization as a scholarly object

Digitalization is frequently mentioned in government policies and academic discourse where it is often being associated with expectations of societal rebirth and large-scale changes. However, little attention has been given to evolutionary aspects of the phenomenon of digitalization. Thus, in this paper, we aim to contribute by focusing on the concept of digitalization in a structured manner and answering the following question: How has the concept of digitalization travelled throughout academic discourse? To focus on digitalization as a scholarly object, we utilized bibliometric analyses of research articles ranging across 10 decades. We produced bibliographic maps of keywords and co-citation networks of sources and performed visual cluster analysis of these maps using techniques from VOSviewer and ScientoPy bibliometric software. This operation was conducted to facilitate discussion on the evolution of digitalization from a semi-genealogical perspective and to open up a discussion on continuity, density, convergence, and digitalization as a signifier. By combining bibliometric data with genealogical analysis, we identified how digitalization has traveled from specific contexts (medical use and information conversion) to a more general use after the turn of the millennium. Moreover, a pattern of convergence during the last decade surfaced, where digitalization has become associated with ideas of a digitally transformed society. Through these findings, this paper contributes to current literature on digitalization with a novel analysis of the term’s evolution.
Dilemmas, Challenges and Opportunities under Double Reduction Policy Faced by Teachers in Urban Primary School of China

The double reduction policy has had a great impact on the education sector since its promulgation.This paper analyze and discuss the double reduction policy, it can be found that the double reduction policy still has a lot of room for optimization.This article analyze the policy background of the double reduction policy and the development of teachers' load reduction, and discusses the challenges and opportunities faced by teachers in the context of the "double reduction" policy, with a view to safeguarding teachers' rights and interests, alleviating teachers' work pressure, and promoting teachers' professional development, which is also the proper way to promote the "double reduction" policy.This is also a necessary step in the implementation of the "double reduction" policy.
MultiAligNet: Cross-lingual Knowledge Bridges Between Words and Senses

Numerous NLP applications rely on the accessibility to multilingual, diversified, context-sensitive, and broadly shared lexical semantic information. Standard lexical resources tend to first encode monolithic language-bounded senses which are eventually translated and linked across repositories and languages. In this paper, we propose a novel approach for the representation of lexical-semantic knowledge in - and shared from the origin by - multiple languages, based on the idea of k-Multilingual Concept ( $$MC^k$$ ). $$MC^k$$ s consist of multilingual alignments of semantically equivalent words in k different languages, that are generated through a defined linguistic context and linked via empirically determined semantic relations without the use of any sense disambiguation process. The $$MC^k$$ model allows to uncover novel layers of lexical knowledge in the form of multifaceted conceptual links between naturally disambiguated sets of words. We first present the conceptualization of the $$MC^k$$ s, along with the word alignment methodology that generates them. Secondly, we describe a large-scale automatic acquisition of $$MC^k$$ s in English, Italian and German based on the exploitation of corpora. Finally, we introduce MultiAlignNet, an original lexical resource built using the data gathered from the extraction task. Results from both qualitative and quantitative assessments on the generated knowledge demonstrate both the quality and the novelty of the proposed model.
Measurement-outlier robust Kalman filter for discrete-time dynamic systems

This paper proposes a recursive filter for discrete-time linear dynamic systems subject to output outliers or heavy-tailed noises. First, we introduce a weight matrix in the conventional MAP estimation. It is shown that this matrix plays an influential role in the innovation whitening and asymptotic variance of the modified MAP estimation and, consequently, can be used in outlier detection. Then, we propose two different constrained optimization problems to obtain this weight. These constraints, stemming from environmental noise characteristics, help to obtain the weight matrix more precisely, which increases the filtering performance significantly. In the first approach, we introduce a convex optimization problem to minimize the estimation upper bound of the error covariance matrix. The second approach converts the modified MAP estimation to a min–min optimization problem with a concave cost function. Consequently, to reduce the effect of outliers in estimation, a semidefinite program (SDP) is proposed for outlier detection. At last, simulation results show the effectiveness and verify the performance of the proposed filter for dynamic systems in the presence of measurement outliers.
An Investigation in Tone Characteristics of 3D Printed Ukulele Sound Chambers

Musical instruments, and in turn their respective sounds, have changed and developed over time with advents and advances in various manufacturing methods and technologies. In order to effectively interpret the causes of different experimental sound nuances, it becomes important to investigate them metrically in light of precedent sounds. As 3D printing technology emerges into the arena of musical instrument development, so does the need for a thorough understanding of present tools and empirical acoustic groundwork for future endeavors. For the scope of this research, three different filament types (ABS, PLA, and HIPS) were used to print soprano ukulele style resonating chambers using a TAZ 6 fused filament fabrication machine. Normalized sound samples were compared to Makanu soprano ukulele baseline using three tone metrics, drawing from MATLAB Fast Fourier transform and spectrogram functionality. The selected metrics of fullness, sustain and strength demonstrated definitive, early progress toward better understanding sound characteristics of 3D printed string instruments.
Semantic Software Traceability Using Property Listing Task: Pilot Study

In recent years, the quest for finding practical software traceability approaches has intensified. Several existing approaches have not utilized every level of information in creating trace link pairs, so their accuracy is low. Since software artifacts are term-heavy, semantics becomes an essential feature. This study proposes a comprehensive concept-based Property Listing Task (PLT) encompassing semantic, structural, and physical features for traceability. PLT is a novel concept in software traceability in which it extracts properties from software artifacts. PLTs from source and target software artifacts are used as a guide in finding the trace link. Based on our case study experimentation on Coffee Maker Project, the result indicates that the proposed Property Listing Task can generate an accurate trace link.
Distributed Computer Solution Design of Tourism English Translation Strategy based on Cloud Real-Time Collaborative System

Aiming at the subjectivity of user evaluation behavior and the sparsiness of data results in a problem that the score prediction result produces errors, a cloud model-based BP neural network score prediction algorithm is proposed. Tourism Foreign Xuan Material Translation Should take readers as a tourist or potential tourist identity; discusses the reader's expected vision, and compares the reader's directional expectations, distributed control system structure, and designed new distribution The overall structure of the computer interlock system. Including the design of various components and system networks in the system, implementing control functions, operation management concentration, structural redundancy, distributed intelligence, dispersion communication, distributed load.
Application of Deep Learning Technology in the Recommendation System of Constitutional and Constitutional Cases

Due to the huge system in the field of constitution, the content of cases is complicated, and the number of documents related to constitutional cases is huge. It is incredibly tough to find the information that suits your demands in such a large volume of constitutional documents. The artificial neural network-based deep learning method has made significant progress. Deep learning has been used in the disciplines of computer image and video processing, as well as natural language processing, and has outperformed typical machine learning methods. To that purpose, the content-based recommendation algorithm in this work is improved using deep learning approaches. This paper primarily covers the following topics: (1) the current research status of deep learning technology in recommender systems is reviewed, and the theoretical knowledge of deep neural networks is introduced. (2) A text classification model OCCNN based on character-level convolutional neural network is proposed to solve the problem of document classification of massive cases. A semantic similarity calculation model TF-W2V based on word frequency and word vector is also proposed to solve the problem of semantic similarity calculation of cases. (3) The two methods are tested, and the experimental results show that the accuracy is much higher than that of the conventional method. The model can recommend cases that meet the needs of users according to the requirements put forward by users in the process of handling cases.
Multispectral mosaic design using a sphere packing filter array

We propose an optimal distribution of spectral filters in a multispectral filter array based on packing congruent spheres in a 3D-euclidean space, promoting uniformity in the sensing of the 3D-datacube while leading to improved reconstructions.
Localized Vision-Language Matching for Open-vocabulary Object Detection

In this work, we propose an open-vocabulary object detection method that, based on image-caption pairs, learns to detect novel object classes along with a given set of known classes. It is a two-stage training approach that first uses a location-guided image-caption matching technique to learn class labels for both novel and known classes in a weakly-supervised manner and second specializes the model for the object detection task using known class annotations. We show that a simple language model fits better than a large contextualized language model for detecting novel objects. Moreover, we introduce a consistency-regularization technique to better exploit image-caption pair information. Our method compares favorably to existing open-vocabulary detection approaches while being data-efficient. Source code is available at https://github.com/lmb-freiburg/locov .
Improving Robustness and Calibration in Ensembles with Diversity Regularization

Calibration and uncertainty estimation are crucial topics in high-risk environments. We introduce a new diversity regularizer for classification tasks that uses out-of-distribution samples and increases the overall accuracy, calibration and out-of-distribution detection capabilities of ensembles. Following the recent interest in the diversity of ensembles, we systematically evaluate the viability of explicitly regularizing ensemble diversity to improve calibration on in-distribution data as well as under dataset shift. We demonstrate that diversity regularization is highly beneficial in architectures, where weights are partially shared between the individual members and even allows to use fewer ensemble members to reach the same level of robustness. Experiments on CIFAR-10, CIFAR-100, and SVHN show that regularizing diversity can have a significant impact on calibration and robustness, as well as out-of-distribution detection.
DEKS: A Secure Cloud-Based Searchable Service Can Make Attackers Pay

Many practical secure systems have been designed to prevent real-world attacks via maximizing the attacking cost so as to reduce attack intentions. Inspired by this philosophy, we propose a new concept named delay encryption with keyword search (DEKS) to resist the notorious keyword guessing attack (KGA), in the context of secure cloud-based searchable services. Avoiding the use of complex (and unreasonable) assumptions, as compared to existing works, DEKS optionally leverages a catalyst that enables one (e.g., a valid data user) to easily execute encryption; without the catalyst, any unauthenticated system insiders and outsiders take severe time consumption on encryption. By this, DEKS can overwhelm a KGA attacker in the encryption stage before it obtains any advantage. We leverage the repeated squaring function, which is the core building block of our design, to construct the first DEKS instance. The experimental results show that DEKS is practical in thwarting KGA for both small and large-scale datasets. For example, in the Wikipedia, a KGA attacker averagely takes 7.23 years to break DEKS when the delay parameter $$T=2^{24}$$ . The parameter T can be flexibly adjusted based on practical needs, and theoretically, its upper bound is infinite.
Semantic Preservation for a Type Directed Translation Scheme of Featherweight Go

Featherweight Go (FG) is a minimal core calculus that includes essential Go features such as overloaded methods and interface types. The most straightforward semantic description of the dynamic behavior of FG programs is to resolve method calls based on run-time type information. A more efficient approach is to apply a type-directed translation scheme where interface-values are replaced by dictionaries that contain concrete method definitions. Thus, method calls can be resolved by a simple lookup of the method definition in the dictionary. Establishing that the target program obtained via the type-directed translation scheme preserves the semantics of the original FG program is an important task. To establish this property we employ logical relations that are indexed by types to relate source and target programs. We provide rigorous proofs and give a detailed discussion of the many subtle corners that we have encountered including the need for a step index due to recursive interfaces and method definitions.
Continuous Prompt Enhanced Biomedical Entity Normalization

Biomedical entity normalization (BEN) aims to link the entity mentions in a biomedical text to referent entities in a knowledge base. Recently, the paradigm of large-scale language model pre-training and fine-tuning have achieved superior performance in BEN task. However, pre-trained language models like SAPBERT [21] typically contain hundreds of millions of parameters, and fine-tuning all parameters is computationally expensive. The latest research such as prompt technology is proposed to reduce the amount of parameters during the model training. Therefore, we propose a framework Prompt-BEN using continuous Prompt to enhance BEN, which just needs to fine-tune few parameters of prompt. Our method employs embeddings with the continuous prefix prompt to capture the semantic similarity between mention and terms. We also design a contrastive loss with synonym marginalized strategy for the BEN task. Finally, experimental results on three benchmark datasets demonstrated that our method achieves competitive or even greater linking accuracy than the state-of-the-art fine-tuning-based models while having about 600 times fewer tuned parameters.
An Interactive Artificial Intelligence System for Inventive Problem-Solving

There is a vast space of potentiality for inspiration in the design and engineering of technical systems that are poorly valorized; the cyberspace that stores and daily adds high volumes of global collective intelligence. This space could be more productively tackled with the assistance of Artificial Intelligence algorithms led by Natural Language Processing (NLP) models. We investigate the application of Structured Activation Vertex Entropy (SAVE) method in combination with Question Answering Machine (QAM) algorithms to explore information that is stored in big datasets, accessible within unstructured dataspaces. The SAVE method is transformed with the assistance of TRIZ into a set of searching meta-terms or meta-concepts. Taking off from a clear description of the problem, target results, and the current (eco)system, meta-terms, and concepts are incorporated into a spiral searching-answering process called ‘D-SIT-SIT-C’, driven by a Retrieval Augmented Generation (RAG) model to create an “intelligent” Natural Language Processing pipeline, with inserting the human in the loop at each iteration. We have found that the proposed pipeline based on a RAG model brings new valences to the creative thinking process and unleashes new dimensions of investigations that lead to higher quality solutions than those formulated with limited resources.