Reviewerly/exHarmony huggingface dataset:
On the Field-Based Division Property: Applications to MiMC, Feistel MiMC and GMiMC

Recent practical applications using advanced cryptographic protocols such as multi-party computations (MPC) and zero-knowledge proofs (ZKP) have prompted a range of novel symmetric primitives described over large finite fields, characterized as arithmetization-oriented (AO) ciphers. Such designs, aiming to minimize the number of multiplications over fields, have a high risk of being vulnerable to algebraic attacks, especially to the higher-order differential attack. Thus, it is significant to carefully evaluate the growth of their algebraic degree. However, the degree estimation for AO ciphers has been a challenge for cryptanalysts due to the lack of general and accurate methods. In this paper, we extend the division property, a state-of-the-art framework for finding the upper bound of the algebraic degree over binary fields, to the scope of $$\mathbb {F}_{2^n}$$ . It is a generic method to detect the algebraic degree for AO ciphers, even applicable to Feistel ciphers which have no better bounds than the trivial exponential one. In this general division property, our idea is to evaluate whether the polynomial representation of a block cipher contains some specific monomials. With a deep investigation of the arithmetical feature, we introduce the propagation rules of monomials for field-based operations, which can be efficiently modeled using the bit-vector theory of SMT. Then the new searching tool for degree estimation can be constructed due to the relationship between the algebraic degree and the exponents of monomials. We apply our new framework to some important AO ciphers, including Feistel MiMC, GMiMC, and MiMC. For Feistel MiMC, we show that the algebraic degree grows significantly slower than the native exponential bound. For the first time, we present a secret-key higher-order differential distinguisher for up to 124 rounds, much better than the 83-round distinguisher for Feistel MiMC permutation proposed at CRYPTO 2020. We also exhibit a full-round zero-sum distinguisher with a data complexity of $$2^{251}$$ . Our method can be further extended for the general Feistel structure with more branches and exhibit higher-order differential distinguishers against the practical instance of GMiMC for up to 50 rounds. For MiMC in SP-networks, our results correspond to the exact algebraic degree proved by Bouvier et al. We also point out that the number of rounds in MiMC's specification is not sufficient to guarantee the security against the higher-order differential attack for MiMC-like schemes with different exponents. The investigation of different exponents provides some guidance on the cipher design.
YOLO YOSO: Fast and Simple Encryption and Secret Sharing in the YOSO Model

Achieving adaptive (or proactive) security in cryptographic protocols is notoriously difficult due to the adversary's power to dynamically corrupt parties as the execution progresses. Inspired by the work of Benhamouda et al. in TCC 2020, Gentry et al. in CRYPTO 2021 introduced the YOSO (You Only Speak Once) model for constructing adaptively (or proactively) secure protocols in massively distributed settings (e.g. blockchains). In this model, instead of having all parties execute an entire protocol, smaller anonymous committees are randomly chosen to execute each individual round of the protocol. After playing their role, parties encrypt protocol messages towards the next anonymous committee and erase their internal state before publishing their ciphertexts. However, a big challenge remains in realizing YOSO protocols: efficiently encrypting messages towards anonymous parties selected at random without learning their identities, while proving the encrypted messages are valid with respect to the protocol. In particular, the protocols of Benhamouda et al. and of Gentry et al. require showing ciphertexts contain valid shares of secret states. We propose concretely efficient methods for encrypting a protocol's secret state towards a random anonymous committee. We start by proposing a very simple and efficient scheme for encrypting messages towards randomly and anonymously selected parties. We then show constructions of publicly verifiable secret (re-)sharing (PVSS) schemes with concretely efficient proofs of (re-)share validity that can be generically instantiated from encryption schemes with certain linear homomorphic properties. In addition, we introduce a new PVSS with proof of sharing consisting of just two field elements, which as far as we know is the first achieving this, and may be of independent interest. Finally, we show that our PVSS schemes can be efficiently realized from our encryption scheme.
Linear-Map Vector Commitments and Their Practical Applications

Vector commitments (VC) are a cryptographic primitive that allows one to commit to a vector and then "open" some of its positions efficiently. Vector commitments are increasingly recognized as a central tool to scale highly decentralized networks of large size and whose content is dynamic. In this work, we examine the demands on the properties that a vector commitment should satisfy in the light of the emerging plethora of practical applications and propose new constructions that improve the state-of-the-art in several dimensions and offer new tradeoffs. We also propose a unifying framework that captures several constructions and we show how to generically achieve some properties from more basic ones. On the practical side, we focus on building efficient schemes that do not require a new trusted setup (we can reuse existing ceremonies for other pairing-based schemes, such as "powers of tau" run by real-world systems such as Zcash or Filecoin).
A two-branch symmetric domain adaptation neural network based on Ulam stability theory

Unsupervised domain adaptation is an important branch of transfer learning, which mainly solves the generalization problem of models from fully labeled datasets to unlabeled datasets. Unsupervised domain adaptation mainly aims to make full use of domain features, suppress inter domain distribution shift, and improve the domain generalization ability of the model. In this paper, we propose a two-branch symmetric domain adaptation neural network. In each independent domain adaptation branch, we use the method of adversarial learning to realize the embedding and alignment of domain features, and use a new generalized autoencoder model based on Ulam stability theory to enhance the feature extraction ability of the network. A fusion network is adapted to fuse the features extracted from each branch of the feature subspace to further improve the domain adaptability and generalization ability of the model. Experimental results show the effectiveness of the proposed method.
USAF: Multimodal Chinese named entity recognition using synthesized acoustic features

Due to the particularity of Chinese word formation, the Chinese Named Entity Recognition (NER) task has attracted extensive attention over recent years. Recently, some researchers have tried to solve this problem by using a multimodal method combining acoustic features and text features. However, the text-speech data pairs required by the above methods are lacking in real-world scenarios, making it difficult to apply widely. To address this, we proposed a multimodal Chinese NER method called USAF, which uses synthesized acoustic features instead of actual human speech. USAF aligns text and acoustic features through unique position embeddings and uses a multi-head attention mechanism to fuse the features of the two modalities, which stably improves the performance of Chinese named entity recognition. To evaluate USAF, we implemented USAF on three Chinese NER datasets. Experimental results show that USAF witnesses a stable improvement compare to text-only methods on each dataset, and outperforms SOTA external-vocabulary-based method on two datasets. Specifically, compared to the SOTA external-vocabulary-based method, the F1 score of USAF is improved by 1.84 and 1.24 on CNERTA and Aishell3-NER, respectively.
Technological pedagogical content ethical knowledge (TPCEK): The development of an assessment instrument for pre-service teachers

Although a variety of studies have explored the framework of Technological Pedagogical Content Knowledge (TPACK), these studies did not incorporate (1) what ethical issues have arisen from the application of digital technologies in teaching, and (2) what kind of knowledge teachers need to face these issues. These questions remain largely unanswered in existing research. This study proposes a newly developed conceptual framework called Technological Pedagogical Content Ethical Knowledge (TPCEK) that introduces ethical knowledge as a new component based on the TPACK framework. A quantitative study was conducted to develop and validate an assessment instrument for Chinese pre-service teachers based on the TPCEK framework. Both exploratory and confirmatory factor analyses were conducted to test the validity and reliability of the measures. Our results indicated that the final version of the TPCEK scale is a reliable and valid one for measuring pre-service teachers’ TPCEK. These insights allow us to understand the importance of ethical knowledge for teaching with digital technologies and the relationships between ethical knowledge and knowledge components of the TPACK framework.
Improving Bayesian radiological profiling of waste drums using Dirichlet priors, Gaussian process priors, and hierarchical modeling

We present three methodological improvements of our recently proposed approach for Bayesian inference of the radionuclide inventory in radioactive waste drums, from radiological measurements. First we resort to the Dirichlet distribution for the prior distribution of the isotopic vector. The Dirichlet distribution possesses the attractive property that the elements of its vector samples sum up to 1. Second, we demonstrate that such Dirichlet priors can be incorporated within an hierarchical modeling of the prior uncertainty in the isotopic vector, when prior information about isotopic composition is available. Our used Bayesian hierarchical modeling framework makes use of this available information but also acknowledges its uncertainty by letting to a controlled extent the information content of the indirect measurement data (i.e., gamma and neutron counts) shape the actual prior distribution of the isotopic vector. Third, we propose to regularize the Bayesian inversion by using Gaussian process (GP) prior modeling when inferring 1D spatially-distributed mass or, equivalently, activity distributions. As of uncertainty in the efficiencies, we keep using the same stylized drum modeling approach as proposed in our previous work to account for the source distribution uncertainty across the vertical direction of the drum. A series of synthetic tests followed by application to a real waste drum show that combining hierarchical modeling of the prior isotopic composition uncertainty together with GP prior modeling of the vertical Pu profile across the drum works well. We also find that our GP prior can handles both cases with and without spatial correlation. Of course, our GP prior modeling framework only makes sense in the context of spatial inference. Furthermore, the computational times involved by our approach are on the order of a few hours, say about 2, to provide uncertainty estimates for all variables of interest in the considered inverse problem. This warrants further investigations to speed up the inference.
Towards generalization on real domain for single image dehazing via meta-learning

Learning-based image dehazing methods are essential to assist autonomous systems in enhancing reliability. Due to the domain gap between synthetic and real domains, the internal information learned from synthesized images is usually sub-optimal for real domains. Thus, the dehazing models trained on synthetic images often suffer from severe performance drops on real-world samples. Driven by the ability to explore internal information from a few unseen-domain samples, meta-learning is commonly adopted to address this issue via test-time training, which is hyperparameter-sensitive and time-consuming. In contrast, we present a domain generalization framework based on meta-learning to dig out representative and discriminative internal properties of real hazy domains without test-time training. To obtain representative domain-specific information, we attach two entities termed adaptation network and distance-aware aggregator to our dehazing network. The adaptation network assists in distilling domain-relevant information from a few hazy samples and caching it into a collection of features. The distance-aware aggregator strives to summarize the generated features and filter out misleading information for more representative internal properties. To enhance the discrimination of distilled internal information, we present a novel loss function called domain-relevant contrastive regularization. It encourages the similarity of the internal features generated from the same domain and the distinction of the features from diverse domains. The generated representative and discriminative features are regarded as some external variables of our dehazing network to regress a particular function for a given domain. The extensive experiments on real hazy datasets validate that our proposed method has superior generalization ability than the state-of-the-art competitors.
Non trust detection of decentralized federated learning based on historical gradient

As a paradigm of distributed machine learning, federated learning is widely used in various real scenarios due to its excellent privacy protection performance on preventing local data from being disclosed. However, the traditional federated learning has the defect that a third-party server aggregates the models of various users since it's difficult to guarantee the reliability of the third party, and multicentre phenomena frequently appeared in various applications, such as social networks, banking and finance, medical health, etc. Users can't be reassured in decentralization setting due to the mixture of malicious and untrustworthy ones among them. Although untrustworthy users are benign, they may be classified as the saboteurs because of poor efficiency performance in decentralized federated learning which is caused by missing or ambiguity of data. In this paper, we propose Decentralized Federated Learning Historical Gradient (DFedHG) approach to distinguish normal users, untrustworthy users and malicious users in the decentralized federated learning setting. Simultaneously, by means of DFedHG, malicious users are sub-divided into targetless attacks and targeted attacks, which is verified by adopting two types of data sets for confirmation. The experimental results show that the proposed approach achieves better performance compared with the conventional decentralized federated learning without untrustworthy users, and further present excellent differentiation of malicious users.
Feature selection: Key to enhance node classification with graph neural networks

Abstract Graphs help to define the relationships between entities in the data. These relationships, represented by edges, often provide additional context information which can be utilised to discover patterns in the data. Graph Neural Networks (GNNs) employ the inductive bias of the graph structure to learn and predict on various tasks. The primary operation of graph neural networks is the feature aggregation step performed over neighbours of the node based on the structure of the graph. In addition to its own features, for each hop, the node gets additional combined features from its neighbours. These aggregated features help define the similarity or dissimilarity of the nodes with respect to the labels and are useful for tasks like node classification. However, in real‐world data, features of neighbours at different hops may not correlate with the node's features. Thus, any indiscriminate feature aggregation by GNN might cause the addition of noisy features leading to degradation in model's performance. In this work, we show that selective aggregation of node features from various hops leads to better performance than default aggregation on the node classification task. Furthermore, we propose a Dual‐Net GNN architecture with a classifier model and a selector model. The classifier model trains over a subset of input node features to predict node labels while the selector model learns to provide optimal input subset to the classifier for the best performance. These two models are trained jointly to learn the best subset of features that give higher accuracy in node label predictions. With extensive experiments, we show that our proposed model outperforms both feature selection methods and state‐of‐the‐art GNN models with remarkable improvements up to 27.8%.
Sinhala Hate Speech Detection in Social Media Using Machine Learning and Deep Learning

Communication and presentation of beliefs became easier than in previous decades due to the rapid rise of information technology and computer science. Because social media is accessible worldwide via the internet, anyone can simply target someone or a group who adheres to a different culture or belief. While everyone has the freedom to express their own opinions, it should not be destructive, and everyone has the right to be free of hate speech. Because there are no automatic mechanisms for detecting hate speech on social media, anyone can be readily targeted. Because social media service providers do not have extensive linguistic expertise of some languages, such as Sinhala, it may take a few days for them to delete hate-related comments from the material after they become aware of them. As a result, detecting hate speech in the Sinhala language is an urgent and crucial task. Machine learning and deep learning based algorithms were employed in this study to automatically recognize Sinhala hate speeches broadcast on social media. Bag of words, Tf-idf, Word2Vec, and FastText feature extraction methods were used to extract features from the comments. Logistic Regression, Multinomial Naïve Bayes, Support Vector Machine, XGBoost, Random Forest machine learning models and CNN, RNN, LSTM deep learning models were trained using two pre-collected datasets with different sizes. The best six models were then chosen and test set performances were shown. According to this study, FastText with RNN has the greatest AUC ROC 0.71 with 70% accuracy for the test set.
Why Use Evolving Neuro-Fuzzy and Spiking Neural Networks for incremental and explainable learning of time series? A case study on predictive modelling of trade imports and outlier detection

What algorithms to choose for an incremental, predictive, and more importantly, explainable learning and modelling of time series data, and specifically – economic and financial time series? Will these algorithms reveal and explain abnormality in time series over time? This problem is part of the challenges in the area of explainable AI and life-long learning systems. Three widely used evolving connectionist systems (ECOS) that offer a solution to the above problems, are compared in the paper. The first two models, EFuNN and DENFIS, are neuro-fuzzy models that deal with vector-based data, while the spiking neural network model NeuCube deals with temporal and spatio-temporal data. The case study data used to demonstrate the qualities of these techniques is time series data related to Bulgarian petroleum oil imports from various trading partners. Graphical visualization enhances the deep analysis, pattern recognition, and knowledge extraction from the models. Conclusions are drawn in the sense that each of these techniques reveals different aspects of the data and the problem in hand and a single model solution of a complex problem is never going to be complete. Future work outlines integration of time series and on-line news to achieve a better predictive accuracy and a better understanding of the data.
DASH+: Download Multiple Video Segments with Stream Multiplexing of QUIC

The emerging cloud computing and big data technologies provide enlightenment to intelligent processing for large-scale video data. MPEG-DASH (DASH) is becoming the unified adaptive bitrate (ABR) streaming open-source standard in video streaming. However, since DASH runs over TCP, it can lead to Head-of-Line (HOL) blocking problem that seriously degrades network performance. Moreover, we find that the advantage of multiplex transmission for Quick UDP Internet Connections protocol (QUIC) can tackle HOL completely. But the traditional DASH algorithm can not support multiplex transmission because it can only transmit one segment every time. Therefore, in this paper, we propose DASH+, a modified DASH algorithm to support the multiplex transmission of QUIC. DASH+ allows clients to request multiple video segments on separate streams using the same QUIC connection. When the playback buffer occupancy is sufficient, the segments from the server are adaptively transmitted through multiple streams according to the corresponding deadline. We conduct the simulation under ns3 and compare DASH+ with the traditional DASH algorithm under different link delays. The results show that under the throughput-based ABR algorithm, DASH+ over QUIC performs higher throughput and better video quality than the others. And the higher the link delay, the more pronounced the advantage.
AccNet24: A deep learning framework for classifying 24-hour activity behaviours from wrist-worn accelerometer data under free-living environments

Although machine learning techniques have been repeatedly used for activity prediction from wearable devices, accurate classification of 24-hour activity behaviour categories from accelerometry data remains a challenge. We developed and validated a deep learning-based framework for classifying 24-hour activity behaviours from wrist-worn accelerometers.Using an openly available dataset with free-living wrist-based raw accelerometry data from 151 participants (aged 18-91 years), we developed a deep learning framework named AccNet24 to classify 24-hour activity behaviours. First, the acceleration signal (x, y, and z-axes) was segmented into 30-second nonoverlapping windows, and signal-to-image conversion was performed for each segment. Deep features were automatically extracted from the signal images using transfer learning and transformed into a lower-dimensional feature space. These transformed features were then employed to classify the activity behaviours as sleep, sedentary behaviour, and light-intensity (LPA) and moderate-to-vigorous physical activity (MVPA) using a bidirectional long short-term memory (BiLSTM) recurrent neural network. AccNet24 was trained and validated with data from 101 and 25 randomly selected participants and tested with the remaining unseen 25 participants. We also extracted 112 hand-crafted time and frequency domain features from 30-second windows and used them as inputs to five commonly used machine learning classifiers, including random forest, support vector machines, artificial neural networks, decision tree, and naïve Bayes to classify the 24-hour activity behaviour categories.Using the same training, validation, and test data and window size, the classification accuracy of AccNet24 outperformed the accuracy of the other five machine learning classification algorithms by 16%-30% on unseen data.AccNet24, relying on signal-to-image conversion, deep feature extraction, and BiLSTM achieved consistently high accuracy (>95 %) in classifying the 24-hour activity behaviour categories as sleep, sedentary, LPA, and MVPA. The next generation accelerometry analytics may rely on deep learning techniques for activity prediction.