Reviewerly/exHarmony huggingface dataset:
Degree-D Reverse Multiplication-Friendly Embeddings: Constructions and Applications

In the recent work of (Cheon & Lee, Eurocrypt'22), the concept of a degree-D packing method was formally introduced, which captures the idea of embedding multiple elements of a smaller ring into a larger ring, so that element-wise multiplication in the former is somewhat "compatible" with the product in the latter. Then, several optimal bounds and results are presented, and furthermore, the concept is generalized from one multiplication to degrees larger than two. These packing methods encompass several constructions seen in the literature in contexts like secure multiparty computation and fully homomorphic encryption. One such construction is the concept of reverse multiplication-friendly embeddings (RMFEs), which are essentially degree-2 packing methods. In this work we generalize the notion of RMFEs to degree-D RMFEs which, in spite of being "more algebraic" than packing methods, turn out to be essentially equivalent. Then, we present a general construction of degree-D RMFEs by generalizing the ideas on algebraic geometry used to construct traditional degree-2 RMFEs which, by the aforementioned equivalence, leads to explicit constructions of packing methods. Furthermore, our theory is given in a unified manner for general Galois rings, which include both rings of the form $$\mathbb {Z}_{p^k}$$ and fields like $$\mathbb {F}_{p^k}$$ , which have been treated separately in prior works. We present multiple concrete sets of parameters for degree-D RMFEs (including $$D=2$$ ), which can be useful for future works. Finally, we discuss interesting applications of our RMFEs, focusing in particular on the case of non-interactively generating high degree correlations for secure multiparty computation protocols. This requires the use of Shamir secret sharing for a large number of parties, which requires large-degree Galois ring extensions. Our RMFE enables the generation of such preprocessing data over small rings, without paying for the multiplicative overhead incurred by using Galois ring extensions of large degree. For our application we also construct along the way, as a side contribution of potential independent interest, a pseudo-random secret-sharing solution for non-interactive generation of packed Shamir-sharings over Galois rings with structured secrets, inspired by the PRSS solutions from (Benhamouda et al., TCC 2021).
Ramp Hyper-invertible Matrices and Their Applications to MPC Protocols

Beerliová-Trubíniová and Hirt introduced hyper-invertible matrix technique to construct the first perfectly secure MPC protocol in the presence of maximal malicious corruptions $$\lfloor \frac{n-1}{3} \rfloor $$ with linear communication complexity per multiplication gate [5]. This matrix allows MPC protocol to generate correct shares of uniformly random secrets in the presence of malicious adversary. Moreover, the amortized communication complexity of generating each sharing is linear. Due to this prominent feature, the hyper-invertible matrix plays an important role in the construction of MPC protocol and zero-knowledge proof protocol where the randomness needs to be jointly generated. However, the downside of this matrix is that the size of its base field is linear in the size of its matrix. This means if we construct an n-party MPC protocol over $$\mathbb {F}_q$$ via hyper-invertible matrix, q is at least 2n. In this paper, we propose the ramp hyper-invertible matrix which can be seen as the generalization of hyper-invertible matrix. Our ramp hyper-invertible matrix can be defined over constant-size field regardless of the size of this matrix. Similar to the arithmetic secret sharing scheme, to apply our ramp hyper-invertible matrix to perfectly secure MPC protocol, the maximum number of corruptions has to be compromised to $$\frac{(1-\epsilon )n}{3}$$ . As a consequence, we present the first perfectly secure MPC protocol in the presence of $$\frac{(1-\epsilon )n}{3}$$ malicious corruptions with constant communication complexity. Besides presenting the variant of hyper-invertible matrix, we overcome several obstacles in the construction of this MPC protocol. Our arithmetic secret sharing scheme over constant-size field is compatible with the player elimination technique, i.e., it supports the dynamic changes of party number and corrupted party number. Moreover, we rewrite the public reconstruction protocol to support the sharings over constant-size field. Putting these together leads to the constant-size field variant of celebrated MPC protocol in [5]. We note that although it was widely acknowledged that there exists an MPC protocol with constant communication complexity by replacing Shamir secret sharing scheme with arithmetic secret sharing scheme, there is no reference seriously describing such protocol in detail. Our work fills the missing detail by providing MPC primitive for any applications relying on MPC protocol of constant communication complexity. As an application of our perfectly secure MPC protocol which implies perfect robustness in the MPC-in-the-Head framework, we present the constant-rate zero-knowledge proof with 3 communication rounds. The previous work achieves constant-rate with 5 communication rounds [32] due to the statistical robustness of their MPC protocol. Another application of our ramp hyper-invertible matrix is the information-theoretic multi-verifier zero-knowledge for circuit satisfiability [44]. We manage to remove the dependence of the size of circuit and security parameter from the share size.
Creative pedagogies in digital STEAM practices: natural, technological and cultural entanglements for powerful learning and activism

Abstract This paper delves deeply into the creative pedagogies which support cutting edge digital STEAM practice across primary and secondary school settings. It contextualises the research within current STEAM agendas including transdisciplinarity, and STEAM and technology and goes on to offer insight from the novel context of ocean learning to develop and extend a theorisation of creative pedagogies as entwining both creative teaching and teaching for creativity as embodied, democratic, dialogic and material processes. Intra-action between theory, praxis, nature, culture, the digital and humans enables an emergent perspective about changing the dynamics of power to develop ocean or environmental learning and related activism. Derived from research into an ocean education project, which aimed to develop students’ ocean literacy through the combined educative principles of creative pedagogies and digital technologies (Augmented and Virtual Realities), the research draws on data from six projects across primary and secondary school settings in Denmark, Spain and England. It used a ‘diffractive’ analytic technique, inspired by new materialist theory, to explore the messy mixtures of natural, cultural and technological environments that were being learned through. This involved the development of four material-dialogic assemblages each including diffractive switches. Each is presented first through a ‘piece’ which demonstrates each assemblage’s connection to the core question, followed by ‘ripples’, which briefly articulate the new learning and questions arising from that assemblage. The four assemblages cover the irresistibility of making kin, the relationships between lively bodies and virtual environments, the importance of spacetimematter in environmental edu-activism and trajectories between transience, stability and dialogic space. The paper leaves the reader/engager with a selection of prompts to highlight the research’s contribution to current STEAM agendas related to changing power dynamics, and to provoke reader/engagers’ own practices. These can include new pedagogies and activisms, as well as theoretical developments to the combined educative principles of creative pedagogies and digital technologies within STEAM education.
Predicting copper-polymetallic deposits in Kalatag using the weight of evidence model and novel data sources

Abstract The Kalatag Ore Cluster Area, located in the Eastern Tianshan metallogenic belt of Xinjiang, stands out as a notable copper polymetallic mineralization zone, recognized for its diverse ore types and untapped potential. Despite the foundational nature of traditional exploration methods, they have not fully exploited this potential. Addressing this, our study leverages modern geospatial technologies, especially ArcGIS, combined with multi-source geoscience data to refine ore formation predictions in Kalatag. We identified key ore-controlling factors: the ore-bearing strata of Daliugou and Dananhu Groups, buffer zones around faults and intrusions, and geophysical anomalies. From these, a conceptual model was developed using the weight of evidence model. This model pinpointed four ‘A’ class and three ‘B’ class targets for mineral exploration, highlighting the central role of faults in ore control. Significantly, all known ore deposits were encompassed within these targets. Our approach not only paves the way for improved ore prediction in Kalatag but also offers a blueprint for other mineral-rich areas. Merging traditional geology with advanced technology, we elevate mineral exploration’s precision, emphasizing the synergy of an integrated method, especially in geologically complex areas. The effectiveness of our model provides insights for future exploration, particularly in mining areas’ deeper zones.
Research On The Legal Protection Of The Copyright Of Network Film And Television Works In China

With the rapid development of information technology in people's lives, the performance more and more obvious in the information environment of film and television works in people's lives mentioned the higher and higher probability. Film and television works have become a very important part of people's lives. In the network environment, film and television works are no longer traditional and fixed forms but have become more flexible, and the way to obtain work is more convenient, which can meet the needs of users to watch movies at all times and is no longer limited by time, region and communication media. However, the two-sided nature of the development of things has also led to frequent copyright disputes of film and television works under the network environment. At the same time, the copyright protection legislation for network film and television works in our country is too scattered and imperfect, which makes the judicial practice unable to rely on different judgments in the same case, which is not conducive to the promotion and development of the copyright industry of network film and television work in our country. Against this background, this article analyzes the copyright protection of film and television works under the network environment. It puts forward corresponding opinions or suggestions to the relevant legal system of our country, according to the advanced experience of foreign countries, to solve the copyright disputes of film and television works under the network environment to provide a healthy, harmonious and stable development space for this field.
<b>Preliminary geological work</b>, based on remote sensing analysis, using artificially enhanced satellite data

Abstract This study aims to identify areas that present similar spectral characteristics to collected rock samples through the use of satellite imagery and spectral analysis. The results indicate that pixels marked in the satellite images exhibit similarities to the spectral characteristics of the samples. Misclassified objects or areas may be included in the results due to mixed pixels and spatial resolution limitations. The similarities identified could result from the region’s mineral composition of building materials, bare land, or dry vegetation. The averaged spectral curve patterns of the samples show similarity overall, but they are not identical, as reflected by the tenth quantile of the similarity coefficient. This research provides a valuable support tool for preliminary geological assessment, and information relating to vast and challenging-to-access parts of prospective areas for further investigation.
Secondary School Students’ Perspective on The Effectiveness of Using E-Learning in Muar District

The 2019 Covid-19 disease disrupts Malaysia's current educational system. Unexpected closures of educational institutions introduced students to a new type of education called e-learning. The study aimed to determine the effectiveness of e-learning among secondary school students. The objective of this study is to determine students' levels of understanding of e-learning, evaluate its effectiveness, and identify its advantages and disadvantages. This study makes use of information collected from 200 upper secondary school students in Muar via a google form online. The google form has five components that meet the study objective, and the data was analyzed using SPSS. The results of the study found that the level of students' understanding of e-learning is at a normal level but in their perspective, this e-learning is effective and provides them many benefits, especially in terms of visual learning and more fun illustrations. Other than that, e-learning also has disadvantages, especially in the financial context of providing data plans and gadgets. This study achieves the expected objectives and it is hoped that the government and the ministry of education improve this e-learning learning system from time to time.
Statistical similarity matching and filtering for clinical image retrieval by machine learning approach

Abstract Clinical image retrieval plays a pivotal role in modern healthcare for diagnostics and research, but prior research has grappled with the challenge of achieving high accuracy due to limited filtering techniques. The proposed method includes statistical distance measurements for similarity comparison and a machine learning technique for image filtering. Throughout this framework, the search area for similarity matching is reduced by first filtering away irrelevant images using the probabilistic outcomes of the Support Vector Machine (SVM) classification as class predictions of search and database images. Resizing is done as part of the preprocessing. Then, using Principal Component Analysis (PCA), the preprocessed data’s textural features, visual characteristics, and low-level features are extracted. The study also suggested an adaptive similarity matching method centered on a linear integration of feature-level similarities on the individual-level level. The precision and ranking order details of the most appropriate images retrieved and predicted by SVMs are considered when calculating the feature weights. The system continually alters weights for every distinctive search to generate beneficial outcomes. The supervised and unsupervised learning strategies are studied to link low-level global image features in the generated PCA-based Eigen Space using their high-level semantic and visual classifications to reduce the semantic gap and enhance retrieval effectiveness. The ground-truth database used in experiments has 1594 unique medical images with 3 different databases. Our method significantly improves the precision and recall rates in image retrieval tasks by combining sophisticated feature extraction, data-driven algorithms, and deep learning models. Research obtained an impressive accuracy of 0.99, demonstrating the effectiveness of our approach. This novel methodology addresses the limitations of prior research and provides a robust and reliable solution for clinicians and researchers in the medical field seeking to access and analyze relevant clinical images.
Bare‐Bones particle Swarm optimization‐based quantization for fast and energy efficient convolutional neural networks

Abstract Neural network quantization is a critical method for reducing memory usage and computational complexity in deep learning models, making them more suitable for deployment on resource‐constrained devices. In this article, we propose a method called BBPSO‐Quantizer, which utilizes an enhanced Bare‐Bones Particle Swarm Optimization algorithm, to address the challenging problem of mixed precision quantization of convolutional neural networks (CNNs). Our proposed algorithm leverages a new population initialization, a robust screening process, and a local search strategy to improve the search performance and guide the population towards a feasible region. Additionally, Deb's constraint handling method is incorporated to ensure that the optimized solutions satisfy the functional constraints. The effectiveness of our BBPSO‐Quantizer is evaluated on various state‐of‐the‐art CNN architectures, including VGG, DenseNet, ResNet, and MobileNetV2, using CIFAR‐10, CIFAR‐100, and Tiny ImageNet datasets. Comparative results demonstrate that our method delivers an excellent tradeoff between accuracy and computational efficiency.
The role of professional culture in the formation of future pedagogues of vocational education

The analysis of scientific works proved that, in general, the concept of “culture”, as a terminological unit, acquires various “specialist” or so-called “professional” colours in various fields of scientific research. It was revealed that the concept of “professional culture” is understudied in the system of training future teachers of professional education. The aim of the study was to determine the essence of the professional culture of future pedagogues of vocational training based on the specifics of the professional activity of a specialist in the field of “Vocational Education (Design)”. The author’s understanding of the essence of the professional culture of future pedagogues of professional training in the field of design is formulated, based on the general understanding of the concept of “professional culture” in the following interpretation: it is a specific subsystem of general culture, a systemic and holistic unity of cultural and social experience regarding the ways and means of interaction between generations, socio-cultural and value traditions professional activity and personal achievements of mankind. The professional culture of future pedagogues of professional training in the field of design is interpreted as follows: it is a specific type of professional and pedagogical culture, a universal (basic) characteristic of pedagogical reality, which reflects (decodes) specific markers of pedagogical activity and functions in the plane of both pedagogical (educational, didactic, developmental) and purely professional (design, construction, project, technological) self-realization of the teacher as a specialist and subject of professional activity.
Secure and Efficient Crop Tracking in Agriculture using Block chain

Crop tracking and traceability are crucial aspects of the modern agriculture supply chain, ensuring the safety and authenticity of products for consumers while improving efficiency and reducing waste. This paper presents a secure and efficient solution for crop tracking in agriculture by leveraging blockchain technology. The proposed system employs distributed ledger technology to record and verify the journey of crops from the field to the consumer, enhancing transparency and accountability. We explore the integration of smart contracts to automate key supply chain processes, such as quality assessment and payment settlement. Our solution not only strengthens the security of crop data but also streamlines the supply chain, reducing administrative overhead. We demonstrate the feasibility of our approach through a practical implementation, highlighting the benefits of blockchain in agriculture supply chain management.
On Gaussian Sampling, Smoothing Parameter and Application to Signatures

We present a general framework for polynomial-time lattice Gaussian sampling. It revolves around a systematic study of the discrete Gaussian measure and its samplers under extensions of lattices; we first show that given lattices $$\varLambda '\subset \varLambda $$ we can sample efficiently in $$\varLambda $$ if we know how to do so in $$\varLambda '$$ and the quotient $$\varLambda /\varLambda '$$ , regardless of the primitivity of $$\varLambda '$$ . As a direct application, we tackle the problem of domain extension and restriction for sampling and propose a sampler tailored for lattice filtrations, which can be seen as a broad generalization of the celebrated Klein’s sampler. Then, we demonstrate how to sample using a change of bases, or even switching the ambient space, even when the target lattice is not represented as full-rank in the ambient space. We show how to correct the induced distortion with the “convolution-like” technique of Peikert (Crypto 2010) (which we encompass as a byproduct). Since our framework aims at modularity and leverage the combinations of smaller samplers to build new ones, we also propose ad-hoc samplers for the so-called root lattices $$\textsf{A}_n, \textsf{D}_n, \textsf{E}_n$$ as base cases, extending the state-of-the-art for root lattice sampling, which was limited to $$\textbf{Z}^n$$ . We also show how our framework blends with the so-called king construction and provides a sampler for the remarkable Leech and Barnes-Wall lattices. As a by-product, we obtain novel, quasi-linear samplers for prime and smooth conductor (as $$2^\ell 3^k$$ ) cyclotomic rings, achieving essentially optimal Gaussian width. In a practice-oriented application, we showcase the impact of our work on hash-and-sign signatures over ntru lattices. In the best case, we can gain around 200 bytes (which corresponds to an improvement greater than 20%) on the signature size. We also improve the new gadget-based constructions (Yu, Jia, Wang, Crypto 2023) and gain up to 110 bytes for the resulting signatures. Lastly, we sprinkle our exposition with several new estimates for the smoothing parameter of lattices, stemming from our algorithmic constructions and by novel methods based on series reversion.
Robust Decentralized Multi-client Functional Encryption: Motivation, Definition, and Inner-Product Constructions

Decentralized Multi-Client Functional Encryption (DMCFE) is a multi-user extension of Functional Encryption (FE) without relying on a trusted third party. However, a fundamental requirement for DMCFE is that the decryptor must collect the partial functional keys and the ciphertexts from all clients. If one client does not generate the partial functional key or the ciphertext, the decryptor cannot obtain any useful information. We found that this strong requirement limits the application of DMCFE in scenarios such as statistical analysis and machine learning. In this paper, we first introduce a new primitive named Robust Decentralized Multi-Client Functional Encryption (RDMCFE), a notion generalized from DMCFE that aims to tolerate the problem of negative clients leading to nothing for the decryptor, where negative clients represent participants that are unable or unwilling to compute the partial functional key or the ciphertext. Conversely, a client is said to be a positive one if it is able and willing to compute both the partial functional key and the ciphertext. In RDMCFE scheme, the positive client set S is known by each positive client such that the generated partial functional keys help to eliminate the influence of negative clients, and the decryptor can learn the function value corresponding to the sensitive data of all positive clients when the cardinality of the set S is not less than a given threshold. We present such constructions for functionalities corresponding to the evaluation of inner products.