Reviewerly/exHarmony huggingface dataset:
ED2IF2-Net: Learning Disentangled Deformed Implicit Fields and Enhanced Displacement Fields from Single Images Using Pyramid Vision Transformer

There has emerged substantial research in addressing single-view 3D reconstruction and the majority of the state-of-the-art implicit methods employ CNNs as the backbone network. On the other hand, transformers have shown remarkable performance in many vision tasks. However, it is still unknown whether transformers are suitable for single-view implicit 3D reconstruction. In this paper, we propose the first end-to-end single-view 3D reconstruction network based on the Pyramid Vision Transformer (PVT), called ED2IF2-Net, which disentangles the reconstruction of an implicit field into the reconstruction of topological structures and the recovery of surface details to achieve high-fidelity shape reconstruction. ED2IF2-Net uses a Pyramid Vision Transformer encoder to extract multi-scale hierarchical local features and a global vector of the input single image, which are fed into three separate decoders. A coarse shape decoder reconstructs a coarse implicit field based on the global vector, a deformation decoder iteratively refines the coarse implicit field using the pixel-aligned local features to obtain a deformed implicit field through multiple implicit field deformation blocks (IFDBs), and a surface detail decoder predicts an enhanced displacement field using the local features with hybrid attention modules (HAMs). The final output is a fusion of the deformed implicit field and the enhanced displacement field, with four loss terms applied to reconstruct the coarse implicit field, structure details through a novel deformation loss, overall shape after fusion, and surface details via a Laplacian loss. The quantitative results obtained from the ShapeNet dataset validate the exceptional performance of ED2IF2-Net. Notably, ED2IF2-Net-L stands out as the top-performing variant, exhibiting the highest mean IoU, CD, EMD, ECD-3D, and ECD-2D scores, reaching impressive values of 61.1, 7.26, 2.51, 6.08, and 1.84, respectively. The extensive experimental evaluations consistently demonstrate the state-of-the-art capabilities of ED2IF2-Net in terms of reconstructing topological structures and recovering surface details, all while maintaining competitive inference time.
Intelligent Reception of Frequency Hopping Signals Based on CVDP

The frequency hopping communication systems have been widely used in anti-jamming communication due to their anti-interception and anti-interference capabilities. With the increasingly complex electromagnetic environment, the transmitter of the frequency hopping communication system adopts more flexible frequency hopping patterns to cope with interference. Therefore, to address the problem of traditional frequency hopping receiver systems being unable to receive properly due to intelligent frequency hopping sequence decisions made at the transmitter, we design a CVDP (CNN_VIT_Dual Multi-head Probsparse Self-attention) network based on a convolutional neural network (CNN) module and Transformer Encoder module to realize the intelligent reception of frequency hopping signals. The designed CNN module extracts the global features and spatial information of the time-frequency spectrograms and serves as the input of the VIT (Vision Transformer) network, which not only solves the problem that the VIT network is difficult to converge on small data sets but also improves the stability and robustness of the VIT network. In the Transformer Encoder module of the VIT network, a dual multi-head self-attention mechanism is used to extract critical time-frequency features and a Probsparse self-attention idea is introduced to reduce the computational complexity. Simulation results on hopping frequency estimation and received BER(Bit Error Rate) of CVDP networks in different interference and channel environments show that when a hopping sequence is unknown, CVDP-based intelligent hopping receiver systems can achieve nearly the same receiver performance as conventional reception systems in which a hopping sequence is known.
Sentiment analysis in Portuguese tweets: an evaluation of diverse word representation models

During the past years, we have seen a steady increase in the number of social networks worldwide. Among them, Twitter has consolidated its position as one of the most influential social platforms, with Brazilian Portuguese speakers holding the fifth position in the number of users. Due to the informal linguistic style of tweets, the discovery of information in such an environment poses a challenge to Natural Language Processing (NLP) tasks such as sentiment analysis. In this work, we state sentiment analysis as a binary (positive and negative) and multiclass (positive, negative, and neutral) classification task at the Portuguese-written tweet level. Following a feature extraction approach, embeddings are initially gathered for a tweet and then given as input to learning a classifier. This study was designed to evaluate the effectiveness of different word representations, from the original pre-trained language model to continued pre-training strategies, to improve the predictive performance of sentiment classification, using three different classifier algorithms and eight Portuguese tweets datasets. Because of the lack of a language model specific to Brazilian Portuguese tweets, we have expanded our evaluation to consider six different embeddings: fastText, GloVe, Word2Vec, BERT-multilingual (mBERT), BERTweet, and BERTimbau. The experiments showed that embeddings trained from scratch solely using the target Portuguese language, BERTimbau, outperform the static representations, fastText, GloVe, and Word2Vec, and the Transformer-based models BERT multilingual and BERTweet. In addition, we show that extracting the contextualized embedding without any adjustment to the pre-trained language model is the best approach for most datasets.
The impact of an employee’s psychological contract breach on compliance with information security policies: intrinsic and extrinsic motivation

Despite the rapid rise in social engineering attacks, not all employees are as compliant with information security policies (ISPs) to the extent that organisations expect them to be. ISP non-compliance is caused by a variety of psychological motivation. This study investigates the effect of psychological contract breach (PCB) of employees on ISP compliance intention by dividing them into intrinsic and extrinsic motivation using the theory of planned behaviour and the general deterrence theory. Data analysis from UK employees (n = 206) showed that the higher the PCB, the lower the ISP compliance intentions. The study also found that PCBs significantly reduced intrinsic motivation (attitude and perceived fairness) for ISP compliance intentions, whereas PCBs did not moderate the relationship between extrinsic motivation (sanction severity and sanctions certainty) and ISP compliance intentions. As a result, this study successfully addresses the risks of PCBs in the field of Information System (IS) security and proposes effective solutions for employees with high PCBs.
A dynamic adaptive iterative clustered federated learning scheme

Clustered federated learning (CFL), as an important research branch of personalized federated learning (FL), can better cope with the highly statistically heterogeneous federated learning environment and provide higher quality services to clients. However, existing CFL schemes have difficulties in adapting to real-time data distribution changes due to disadvantages such as relatively fixed cluster structure. This poses a great challenge to the practical deployment of CFL schemes. To address the common problems of existing CFL schemes, we propose a more flexible dynamic adaptive cluster federated learning scheme (AICFL). AICFL uses the mutual sensitivity between models and data as intuition to perform cluster identity estimation, cluster addition, cluster model updating, and cluster deletion in the early iterations of FL to find the optimal client cluster partitioning. Firstly, this process does not require a priori estimation of the number of clusters and does not require the online participation of all clients. Secondly, during cluster partitioning, AICFL is able to adjust the cluster structure in real time based on the overall data distribution. Moreover, AICFL has the same ability to adapt to changes in the system environment in the middle and late stages of FL. The experimental results show that our scheme gives the most reasonable cluster partitioning results in all cases which indicates that AICFL is able to cope with the above-mentioned distribution changes well, and has better adaptability and better flexibility than other schemes.
A high-capacity and robust steganography algorithm for quantum images

The improved flexible representation of a quantum image (IFRQI) employs an effective method to encode grayscale image information in a normalized quantum state vector, which allows for accurate retrieval of image information through projective measurements. In this paper, we propose a high-capacity and robust steganography algorithm based on the IFRQI model. We embed the grayscale information of a 2n×2n sized secret image in a 2n+1×2n+1 sized cover image using controlled rotations. The secret image is divided into four image planes, each with a bit depth of 2. For each image plane, an array of angle values encoding the 2-bit color information is prepared. The encoded information is then embedded in the IFRQI state of the cover image using controlled rotations determined by the key K, which is only known to the operator. The secret image extraction algorithm is the inverse process of the embedding algorithm, which requires the inverse key K′. Analyses of the embedding capacity, time complexity, and visual effects reveal that the proposed steganography algorithm is comparable with some state-of-the-art algorithms.
Learning more discriminative clues with gradual attention for fine-grained visual categorization

Fine-grained visual categorization, which aims to identify the different subcategories of images within the same category, is a very challenging task due to the large intra-class differences and subtle inter-class variances. The existing methods mostly focus on the salient local regions and ignore other features which probably help to recognize the images more precisely. To address this issue, in this paper, we propose a novel end-to-end network composed of the self-calibrated convolution, gradual attention module and feature inverse module for fine-grained visual categorization. To extract the salient features, the self-calibrated convolution is exploited which can avoid the influence of irrelevant information and locate salient regions more accurately. In aiming to extract the discriminative features, we propose the gradual attention module which consists of alternate channel-spatial attention and hierarchical feature grouping. The gradual attention module can extract the subtle discriminative features gradually even when the semantic information of shallow stages is not rich. Moreover, we design the feature inverse module which forces the next stage of network to search for other different useful features by feature inverse. The gradual attention module combined with the feature inverse module is capable of finding more detailed regions and of benefit to improving classification performance. Finally, the stage features and fused features are jointly used for classification. The proposed method is evaluated on three classical fine-grained image datasets and compared with a number of state-of-the-art methods. Our method achieves 89.5%,95.2% and 93.9% accuracies on CUB-200–2011, Stanford Cars and FGVC-Aircraft datasets respectively. The experimental results demonstrate the effectiveness and superiority of the proposed method.
Contrastive learning with semantic consistency constraint

Contrastive representation learning (CL) can be viewed as an anchor-based learning paradigm that learns representations by maximizing the similarity between an anchor and positive samples while reducing the similarity with negative samples. A randomly adopted data augmentation strategy generates positive and negative samples, resulting in semantic inconsistency in the learning process. The randomness may introduce additional disturbances to the original sample, thereby reversing the sample identity. Also, the negative sample demarcation strategy makes the negative samples containing semantically similar samples to the anchors, called false negative samples. Therefore, CL's maximization and reduction process cause distractors to be incorporated into the learned feature representation. In this paper, we propose a novel Semantic Consistency Regularization (SCR) method to alleviate this problem. Specifically, we introduce a new regularization item, pairwise subspace distance, to constrain the consistency of distributions across different views. Furthermore, we propose a divide-and-conquer strategy to ensure that the proposed SCR is well-suited for large mini-batch cases. Empirically, results across multiple benchmark mini and large datasets demonstrate that SCR outperforms state-of-the-art methods. Codes are available at https://github.com/PaulGHJ/SCR.git.
Constructions of broadcast encryption with personalized messages from bilinear map

Broadcast encryption (BE) is a one to many encryption protocol which sends a message to desired set of users securely and efficiently. Broadcast encryption with personalized messages (BEPMsg) is a cryptographic primitive that sends a common broadcast message together with user-specific personalized messages to a group of users. This work contains the transformation of broadcast encryption from the bilinear map to broadcast encryption with personalized messages. The transformation of BE from bilinear map to BEPMsg helps to design BEPMsg with different flavors. We have also proposed two BEPMsg constructions and discussed their security. Our first construction is an adaptive secure BEPMsg scheme. It also supports inclusive–exclusive property in encryption algorithm as the broadcaster can select the set of subscribed or revoked users to decrease the encryption cost depending on the size of the set of subscribed and revoked users. More positively, it has a constant header size. Our second construction provides outsider anonymity in BEPMsg by performing the decryption without using the set of subscribed or revoked users.
CLSpell: Contrastive learning with phonological and visual knowledge for chinese spelling check

The task of Chinese Spelling Check (CSC) is to identify and correct spelling errors in text, which are mainly caused by phonologically and visually similar characters. Although pre-trained language models are helpful for this task, they lack phonological and visual information. Previous works have primarily focused on identifying errors based on local contextual data, while neglecting the importance of sentence-level information. To address the above issues, Contrastive Learning Spell (CLSpell) is proposed, which combines phonetic and glyphic information through contrastive learning and simultaneously acquires local and global information through multi-task joint learning. During pretraining, token representations are learned using a combination of phonological, visual, and semantic information. Moreover, we propose to include an auxiliary task of correct sentence discrimination in the multi-task joint training process to capture sentence-level information. Experiments on widely used benchmarks demonstrate that the proposed method surpasses all competing methods.
Explainable cyber threat behavior identification based on self-adversarial topic generation

Cyber Threat Intelligence (CTI) provides ample evidence and information regarding the detection of cyber attack activities. Existing methods employ CTI reports to extract Tactics, Techniques and Procedures (TTPs) for attack detection. Nevertheless, these methods are challenged in providing necessary and sufficient evidentiary support for recognition decisions, making it difficult for human operators to comprehend and accept the decision-making process. This paper proposes a topic prototype-based explainable TTPs classification approach, which provides accurate boundaries for key evidences to justify the results of TTPs classification. The proposed method introduces a self-adversarial framework for obtaining necessary and sufficient evidence for TTPs classification. The framework consists of an evidence generator and a TTPs classifier discriminator. The evidence generator utilizes a topic prototype-based keyword importance filtering method to extract evidence from CTI text while removing noise, resulting in an evidence set and a perturbation set. Subsequently, the impact of the evidence set and the perturbation set on TTPs classification is assessed using our siamese discriminator. The discriminator is specifically trained to ensure that only the elements belonging to the evidence set are accurately classified as TTPs information. The experiments primarily test the necessity and sufficiency of TTPs and evidence. In the sufficiency evaluations, classical deep learning methods are used for TTPs classification to verify the accuracy of the results, where the proposed method improves the Micro F1 scores by 0.16% to 6.63% and Macro F1s by 0.26% to 6.85%. To prove necessity, various case-based explainable methods are used to measure the completeness of CTI evidence. The results shows that the proposed method is able to obtain more stable prediction, more reasonable evidence sets, and more significant boundaries.
Spiritus Asper versus Lambda: On the Nature of Functional Abstraction

The spiritus asper as used by Frege in a letter to Russell from 1904 bears resemblance to Church's lambda. It is natural to ask how they relate to each other. An alternative approach to functional abstraction developed by Per Martin-Löf some thirty years ago allows us to describe the relationship precisely. Frege's spiritus asper provides a way of restructuring a unary function name in Frege's sense such that the argument place indicator occurs all the way to the right. Martin-Löf's alternative approach shows that this is only half of what lambda does. The other half is the deletion of the argument place indicator, resulting in what Frege would have called an isolated function name.
Nature features OA.Report's work putting OA policy into practice!

After a couple of years of working to support institutions implementing their OA policies, we're so proud to see OA.Report, our development partner, and one of our collaborators featured in Nature.
Machine Learning Detects Anomalies in OPS-SAT Telemetry

Detecting anomalies in satellite telemetry data is pivotal in ensuring its safe operations. Although there exist various data-driven techniques for the task of determining abnormal parts of the signal, they are virtually never validated over real telemetries. Analyzing such data is challenging due to its intrinsic characteristics, as telemetry may be noisy and affected by incorrect acquisition, resulting in missing parts of the signal. In this paper, we tackle this issue and propose a machine learning approach for detecting anomalies in single-channel satellite telemetry. To validate its capabilities in a practical scenario, we build a dataset capturing the nominal and anomalous telemetry data captured on board OPS-SAT—a nanosatellite launched and operated by the European Space Agency. Our extensive experimental study showed that the proposed algorithm offers high-quality anomaly detection in real-life satellite telemetry, reaching 98.4% accuracy over the unseen test set.
An Application of Evolutionary Algorithms and Machine Learning in Four-Part Harmonization

The task of four-voice harmonization of a given melody is one of the most fundamental, but at the same time the most complex problems in functional harmony. This problem can be formulated as a discrete optimization problem with constraints using a set of rules coming from the theory of music. Unfortunately, a straightforward solution of such a problem, i.e., a mere fulfillment of the rules, ensures only the formal correctness of the obtained chord sequences, which does not necessarily imply overall musical quality as perceived by humans. Trying to catch some non-formalized factors of this quality we have decided to utilize artificial intelligence methods with some ‘creative‘ potential that can provide solutions at acceptable level of formal correctness. In this paper we perform the harmonization using a genetic algorithm, an algorithm based on a Bayesian network, as well as a hybrid of these. In a series of experiments we compare the performance of the three algorithms with each other and with a rule-based system that provides chord sequences at a high level of formal correctness. Besides the formal evaluation all obtained solutions were rated by musical experts. The results show that the studied algorithms can generate solutions musically more interesting than those produced by the rule-based system, even if the former are less formally correct than the latter.
Siamese Autoencoder-Based Approach for Missing Data Imputation

Missing data is an issue that can negatively impact any task performed with the available data and it is often found in real-world domains such as healthcare. One of the most common strategies to address this issue is to perform imputation, where the missing values are replaced by estimates. Several approaches based on statistics and machine learning techniques have been proposed for this purpose, including deep learning architectures such as generative adversarial networks and autoencoders. In this work, we propose a novel siamese neural network suitable for missing data imputation, which we call Siamese Autoencoder-based Approach for Imputation (SAEI). Besides having a deep autoencoder architecture, SAEI also has a custom loss function and triplet mining strategy that are tailored for the missing data issue. The proposed SAEI approach is compared to seven state-of-the-art imputation methods in an experimental setup that comprises 14 heterogeneous datasets of the healthcare domain injected with Missing Not At Random values at a rate between 10% and 60%. The results show that SAEI significantly outperforms all the remaining imputation methods for all experimented settings, achieving an average improvement of 35%.
Inference of Over-Constrained NFA of Size $$k+1$$ to Efficiently and Systematically Derive NFA of Size k for Grammar Learning

Grammatical inference involves learning a formal grammar as a finite state machine or set of rewrite rules. This paper focuses on inferring Nondeterministic Finite Automata (NFA) from a given sample of words: the NFA must accept some words, and reject others. Our approach is unique in that it addresses the question of whether or not a finite automaton of size k exists for a given sample by using an over-constrained model of size $$k+1$$ . Additionally, our method allows for the identification of the automaton of size k when it exists. While the concept may seem straightforward, the effectiveness of this approach is demonstrated through the results of our experiments.