Reviewerly/exHarmony huggingface dataset:
Accelerating HE Operations from Key Decomposition Technique

Lattice-based homomorphic encryption (HE) schemes are based on the noisy encryption technique, where plaintexts are masked with some random noise for security. Recent advanced HE schemes rely on a decomposition technique to manage the growth of noise, which involves a conversion of a ciphertext entry into a short vector followed by multiplication with an evaluation key. Prior to this work, the decomposition procedure turns out to be the most time-consuming part, as it requires discrete Fourier transforms (DFTs) over the base ring for efficient polynomial arithmetic. In this paper, an expensive decomposition operation over a large modulus is replaced with relatively cheap operations over a ring of integers with a small bound. Notably, the cost of DFTs is reduced from quadratic to linear with the level of a ciphertext without any extra noise growth. We demonstrate the implication of our approach by applying it to the key-switching procedure. Our experiments show that the new key-switching method achieves a speedup of 1.2–2.3 or 2.1–3.3 times over the previous method, when the dimension of a base ring is $$2^{15}$$ or $$2^{16}$$ , respectively.
Effect of Group Based Synchronization on User Anonymity in Mix Networks

In so-called closed environments, the MIX network can theoretically provide perfect security, i.e. if perfect protection is envisaged, all senders and receivers should be perfectly synchronized and participate equally in each communication round of the MIX technique. In the context of open environments (e.g., the Internet), there is no synchronization between the participants and here the technique is vulnerable to known analyses such as (statistical) disclosure attacks. In short, the Mix technology is highly dependent on its application context in which it involves the participants. In this work, we study the effect of context in terms of synchronization rate, present two different synchronization approaches and evaluate their protection against disclosure attacks.
Identifying Visualization Opportunities to Help Architects Manage the Complexity of Building Codes

We report a study investigating the viability of using interactive visualizations to aid architectural design with building codes. While visualizations have been used to support general architectural design exploration, existing computational solutions treat building codes as separate from, rather than part of, the design process, creating challenges for architects. Through a series of participatory design studies with professional architects, we found that interactive visualizations have promising potential to aid design exploration and sensemaking in early stages of architectural design by providing feedback about potential allowances and consequences of design decisions. However, implementing a visualization system necessitates addressing the complexity and ambiguity inherent in building codes. To tackle these challenges, we propose various user-driven knowledge management mechanisms for integrating, negotiating, interpreting, and documenting building code rules.
LDPORR: A localized location privacy protection method based on optimized random response

The broad use of mobile intelligent terminals with locating functions encourages the rapid development of location-based services (LBS), which are widely used in a variety of industries such as social networking, transportation, finance, and entertainment. While enjoying the convenience brought by LBS, the threat of location privacy leakage should not be overlooked. In view of the complex encoding mechanism and low availability of the current location privacy protection methods, a local differential privacy location protection method based on optimized random response is proposed in this paper. A spatial decomposition and Hilbert encoding mechanism are designed based on the Hilbert curve, which can reduce the two-dimensional location data into one-dimensional Hilbert encoding results. A local differential privacy location perturbation method based on optimized random response is proposed to improve the availability of perturbed locations and the accuracy of data aggregation. Experiments on actual location datasets prove that the proposed method can provide better location data availability and operational efficiency based on realizing local differential privacy protection of users’ location.
Video Conferencing Subsystem of the Secure Serverless Internet (TheOoL.Net)

The article discusses prerequisites for the development of the private serverless internet with a built-in super secure video and audio communications subsystem featuring the possibility of holding conferences with three or more participants. A brief description of the principles underlying the private serverless internet architecture is given, which, in addition to its overlay data transfer protocol, includes serverless hosting, cloud computing, serverless search engine, as well as an integrated payment system. A simplified graphical model of the architecture and operation of the serverless super secure video and audio communications subsystem is presented. The basic modes of its operation and the methods for ensuring the security and privacy of conversations and correspondence are reviewed.
Transmission and Color-guided Network for Underwater Image Enhancement

In recent years, with the continuous development of the marine industry, underwater image enhancement has attracted plenty of attention. Unfortunately, the propagation of light in water will be absorbed by water bodies and scattered by suspended particles, resulting in color deviation and low contrast. To solve these two problems, we propose an Adaptive Transmission and Dynamic Color guided network (named ATDCnet) for underwater image enhancement. In particular, to exploit the knowledge of physics, we design an Adaptive Transmission-directed Module (ATM) to better guide the network. To deal with the color deviation problem, we design a Dynamic Color-guided Module (DCM) to post-process the enhanced image color. Further, we design an Encoder-Decoder-based (EDC) structure with attention and a multistage feature fusion mechanism to perform color restoration and contrast enhancement simultaneously. Extensive experiments demonstrate the state-of-the-art performance of the ATDCnet on multiple benchmark datasets.
ORTPiece: An ORT-Based Turkish Image Captioning Network Based on Transformers and WordPiece

Recent transformers-based systems are advancing image captioning applications. However, those works have been mainly applied to English-based image captioning problems. In this paper, we introduce a transformers-based Turkish-based image captioning algorithm. Our proposed algorithm uses appearance and geometry features from the input image and combines them along with the WordPiece embeddings to generate the Turkish-based caption. Our experimental results show improvement when compared to the other existing techniques including the original ORT and the show-and-tell algorithms.
A Spatial-Specific Neural Network-Based OFDM Channel Estimation Under Time-Varying Channels

Deep learning (DL) has exhibited notable effectiveness in channel estimation for orthogonal frequency division multiplexing (OFDM) systems. However, most existing neural networks have not explicitly accounted for the intercarrier interference (ICI) and fail to achieve desirable performance under high-mobility air-ground communication environments. To address this problem, a modified Involution-based Channel Estimation Network (InvoEsNet) is proposed in this letter, which is designed to explicitly cope with ICI. The InvoEsNet consists of a modified involution-based preprocessing subnetwork (InvoPreNet) to mitigate ICI, followed by a residual channel state information subnetwork (ResCSINet) to further refine the channel estimates. Compared with existing convolutional networks, the proposed modified involution-based network is more suitable for dealing with ICI, especially under fast time-varying channels. Moreover, different weights are assigned to the training loss with respect to different signal-to-noise ratios to improve the neural network’s generalization. Experimental results show that our proposed InvoEsNet outperforms state-of-the-art benchmarks.
A Saliency-Aware Method for Arbitrary Style Transfer

Arbitrary style transfer has drawn widespread attention from academic communities for its extensive application in reality. However, existing methods fail to make a proper trade-off between flexibility and capability. All of these methods directly synthesize style textures on the whole content image indiscriminately, leading to local distortions when there are obvious visual distinctions in different regions. In this paper, we propose a novel Saliency-Aware Instance Normalization (SAIN) module considering the visual distinctions between salient area and non-salient area for arbitrary style transfer. To extract region-specific stylized features, SAIN separately performs the local feature alignment in each region. Besides, for the full integration of global and local features, we design a global branch in our model, where content features are adjusted with the globally calculated statistics of style features. Comprehensive evaluations demonstrate our method’s superiority over six state-of-the-art arbitrary style transfer methods. Source code is available at https://github.com/yitongli123/SAIN.
Document Change Detection With Hierarchical Patch Comparison

Contract documents can be modified just before signing, after the consensus, with the intention of defrauding the other party, which can have serious consequences for the deal. To prevent the issue, we propose a method to detect document changes between a scanned final document and its original electronic file using image-based comparison. Our method first finds the most appropriate augmentation for various document changes, such as rotations, contrast, ratio, or brightness changes which can occur while scanning documents. Then, we employ a hierarchical search strategy from large patches to small patches in a sliding window manner, which can reduce the computational complexity to compare all the details of the documents using the deep learning model. We built a new dataset of original-scanned document pair for the validation of our method. In the experiments, we show that our method outperforms the previous approaches using segmentation and character recognition models, even when the document suffers from both non-lingual and lingual changes.
MixUp Brain-Cortical Augmentations in Self-supervised Learning

Learning biological markers for a specific brain pathology is often impaired by the size of the dataset. With the advent of large open datasets in the general population, new learning strategies have emerged. In particular, deep representation learning consists of training a model via pretext tasks that can be used to solve downstream clinical problems of interest. More recently, self-supervised learning provides a rich framework for learning representations by contrasting transformed samples. These methods rely on carefully designed data manipulation to create semantically similar but syntactically different samples. In parallel, domain-specific architectures such as spherical convolutional neural networks can learn from cortical brain measures in order to reveal original biomarkers. Unfortunately, only a few surface-based augmentations exist, and none of them have been applied in a self-supervised learning setting. We perform experiments on two open source datasets: Big Healthy Brain and Healthy Brain Network. We propose new augmentations for the cortical brain: baseline augmentations adapted from classical ones for training convolutional neural networks, typically on natural images, and new augmentations called MixUp. The results suggest that surface-based self-supervised learning performs comparably to supervised baselines, but generalizes better to different tasks and datasets. In addition, the learned representations are improved by the proposed MixUp augmentations. The code is available on GitHub ( https://github.com/neurospin-projects/2022_cambroise_surfaugment ).
Data‐driven performance metrics for neural network learning

Summary Effectiveness of data‐driven neural learning in terms of both local mimima trapping and convergence rate is addressed. Such issues are investigated in a case study involving the training of one‐hidden‐layer feedforward neural networks with the extended Kalman filter, which reduces the search for the optimal network parameters to a state estimation problem, as compared to descent‐based methods. In this respect, the performances of the training are assessed by using the Cramér‐Rao bound, along with a novel metric based on an empirical criterion to evaluate robustness with respect to local minima trapping. Numerical results are provided to illustrate the performances of the training based on the extended Kalman filter in comparison with gradient‐based learning.
Dually Enhanced Delayed Feedback Modeling for Streaming Conversion Rate Prediction

In online industrial advertising systems, conversion actions (e.g., purchases or downloads) often occur significantly delayed, even up to several days or weeks after the user clicks. This phenomenon leads to the crucial challenge calleddelayed feedback problem in streaming CVR prediction, that is, the online systems cannot receive the true label of conversions immediately for continuous training. To mitigate the delayed feedback problem, recent state-of-the-art methods often apply sample duplicate mechanisms to introduce early certain conversion information. Nevertheless, these works have overlooked a crucial issue of rapid shifts in data distribution and considered both the newly observed data and duplicated early data together, resulting in biases in both distributions. In this work, we propose a Dually enhanced Delayed Feedback Model (DDFM), which tackles the above issues by treating the newly observed data and duplicated early data separately. DDFM consists of dual unbiased CVR estimators that share the same form but utilize different latent variables as weights: one for the newly observed data and the other for the duplicated early data. To avoid high variance, we adopt an addition-only formula for these latent variables, eliminating multiplication or division operations. Furthermore, we design a shared-bottom network that efficiently and jointly estimates the latent variables in DDFM. Theoretical analysis demonstrates the unbiasedness and convergence properties of DDFM. Extensive experiments on both public and industrial large-scale real-world datasets exhibit that our proposed DDFM consistently outperforms existing state-of-the-art methods.
Exploring unsupervised textual representations generated by neural language models in the context of automatic tweet stream summarization

Users are often overwhelmed by the amount of information generated on online social networks and media (OSNEM), in particular Twitter, during particular events. Summarizing the information streams would help them be informed in a reasonable time. In parallel, recent state of the art in summarization has a special focus on deep neural models and pre-trained language models. In this context, we aim at (i) evaluating different pre-trained language model (PLM) to represent microblogs (i.e., tweets), and (ii) to identify the most suitable ones in a summarization context, as well as (iii) to see how neural models can be used knowing the issue of input size limitation of such models. For this purpose, we divided the problem into 3 questions and made experiments on 3 different datasets. Using a simple greedy algorithm, we first compared several pre-trained models for single tweet representation. We then evaluated the quality of the average representation of the stream and sought to use it as a starting point for a neural approach. First results show the interest of using USE and Sentence-BERT representations for tweet stream summarization, as well as the great potential of using the average representation of the stream.
Simulation of the BB84 Quantum Key Exchange Protocol

Currently, quantum cryptography is a topical and well-researched topic in the face of the emerging quantum computing platform. The BB84 quantum key exchange protocol was first proposed in 1984, and then there have been improvements to this protocol, such as the E91 and B92 protocols. The idea of this study is based on the BB84 quantum key exchange method, simulating the key exchange process in quantum to obtain a shared key, and design programming of basic quantum gates such as Hadamard, X used in the BB84 protocol. From that, determine the corresponding alphabet table for the quantum states used. As a result, calculating the key exchange between the two parties with a public key size of about 1000 bits, the computation time takes 2,3913s. When the public key size goes up to 4000 bits, it takes about 15,7111s. This shows that the execution speed of quantum key exchange according to the BB84 protocol has significantly improved and ensured safety in the quantum computer system.
PENGEMBANGAN E-MODUL BERBASIS PMRI PADA MATERI BANGUN RUANG SISI DATAR KELAS VIII SMPIT AN-NIDA’ LUBUKLINGGAU

This study aims to develop PMRI-based e-modules on class VIII SMPIT An-Nida' Lubuklinggau flat sided geometric material that is valid, practical and has potential effects. The development model used is ADDIE. Collection techniques were carried out by interviews, filling out questionnaires, and learning achievement tests. Based on this study, the results obtained were (1) the quality of learning media seen from the aspect of language validity was 0.8 including the valid category, the media validity aspect was 0.73 included in the valid category, and the material validity aspect was 0.82 including the very valid category; (2) The quality of the media seen from the practical aspect based on the teacher's response of 87.5% is included in the very practical category, the student response (one to one) is 83.3% included in the very practical category, and the student response (small group) is 90% included in the very practical category; (3) Learning media has a potential effect on student learning outcomes, as many as 20 students (76.92%) are included in the complete category and 6 (23.07%) have not completed the KKM set by the school at 70. The potential effect of this learning media is categorized as good.
Enhancing Blockchain Adoption through Tailored Software Engineering: An Industrial-grounded Study in Education Credentialing

Recent years have witnessed a marked increase in both academic proposals and industrial adoptions of blockchain technology. However, a majority of the projects remain at the stage of prototype proposals and their real-world deployment has not met the anticipated level. This gap can be attributed to three major barriers - technical difficulties, human factors, and social context. Most of the existing research leans towards addressing the technical challenges, leaving the human and social aspects inadequately explored. Moreover, a lack of practical insights in the existing blockchain software engineering frameworks further exacerbates the adoption problem. To address these gaps, we introduce a Blockchain-oriented Software Engineering Approach for Higher Adoption Possibility (BOSE-HAP) . This approach emphasizes collaboration, reflective thinking, and iterative development, aiming to bolster implementation consistency and stimulate industry adoption. We have applied this approach in the design, development, and launch of a blockchain credentialing product, CValid.org , in the context of a university-level summer school. The product achieves industry-accepted System Usability Score and has seen successful real-world deployment. In addition, this study embed usability considerations throughout the process, involved a total of 112 stakeholders across different development stages, with 25 of them participating in our in-depth interviews and usability testing. Drawing from our firsthand experience and industrial-grounded findings, we deliver eight reflections and propose five best practice suggestions relevant to blockchain adoption. We believe these insights will provide invaluable guidance for both academic researchers and industry practitioners involved in the field of blockchain technology.
AKSIN “ AKSESORIS RESIN "

Abstract (90) words This entrepreneurship is one of the student creativity programs, in this study we created an entrepreneurial resin accessories business. This resin accessory or abbreviated as axin is a business product made from liquid resin and its supports. This method of entrepreneurship uses resin manufacturing and marketing methods. The result of this entrepreneurial activity is that we have succeeded in making resin with makeshift tools and materials, but still needs a lot of development. more for the future. Aksin business is a business that makes and sells accessories made from liquid resin and several supporting motifs to increase the attractiveness of the product.
em-Test for Univariate Finite Gaussian Mixture Models

While the success of the EM-test in the previous two chapters was confined to finite mixture models with subpopulation distributions belonging to a one-parameter distribution family, the underlying principle of the EM-test is generally applicable. This principle involves comparing the degree of improvement achieved after several EM-iterations starting from a fitted null model. However, this approach does not always yield a clear limiting distribution for the resulting test statistic, which is essential for practical testing procedures. A remarkable revelation occurs when the EM-test is applied to finite Gaussian mixture models. The meticulously designed EM-test for determining the order of the finite Gaussian mixture model leads to elegant and unexpected chi-square limiting distributions. These distributions align with the striking results seen in the likelihood ratio test for regular models. Chapter 15 offers a detailed account of this remarkable achievement.
Digital compliance or professional competence? Representations of teachers and digital futures in the Norwegian Qualification Framework

This paper presents a document analysis of the Norwegian qualification framework Professional Digital Competence Framework for Teachers (PDC) to explore the underlying assumptions within the policy. Employing a post-structural policy analysis, the paper analyses the representation of the ‘problem’ of PDC by asking what the document does in relation to teachers’ professionalism. To do this, the analysis explores the underlying assumptions of the policy document and its construction of the problem. The paper argues that the policy document is constructing a particular image of a digital future and presenting the teacher as the ‘problem’ in realising this imagined future. The paper discusses the discursive effects of this policy and explores the implications for the teaching profession.