Reviewerly/exHarmony huggingface dataset:
Distributed Matrix-Weighted Fusion Consensus Filtering With Two-Stage Filtering for Sensor Networks

This article is concerned with a distributed consensus filtering problem in a sensor network (SN). In the SN, each sensor node plays a local fusion center. By diffusion and fusion of information among sensor nodes, estimates of all nodes in the SN tend to consensus. At each sensor node, a distributed consensus filtering algorithm with a two-stage filtering structure is devised. At each time, the first stage filtering is that each sensor node produces its local filter estimate based on its own measurement and previous fused estimate, and then transmits it to its neighbor nodes; and the second stage filtering is that each sensor node produces a fused estimate by using a matrix-weighted optimal fusion algorithm in the linear unbiased minimum variance (LUMV) criterion to iteratively fuse estimates of sensor itself and neighbor nodes, where cross-covariance matrices (CCMs) between sensor nodes at different fusion times are derived. Its estimation accuracy is gradually improved with the increase of fusion times and approximates a centralized fusion filter (CFF). To avoid calculation of CCMs between sensor nodes, a suboptimal distributed consensus filtering algorithm is also presented by minimizing an upper bound of the fusion filtering error covariance at each fusion in the second stage. It has reduced computational cost at the expense of accuracy loss. The performance of the proposed distributed consensus filters is analyzed. A tracking system is used to verify the effectiveness of the algorithms.
Tailored XZZX codes for biased noise

Quantum error correction (QEC) for generic errors is challenging due to the demanding threshold and resource requirements. Interestingly, when physical noise is biased, we can tailor our QEC schemes to the noise to improve performance. Here we study a family of codes having XZZX-type stabilizer generators, including a set of cyclic codes generalized from the five-qubit code and a set of topological codes that we call generalized toric codes (GTCs). We show that these XZZX codes are highly qubit efficient if tailored to biased noise. To characterize the code performance, we use the notion of effective distance, which generalizes code distance to the case of biased noise and constitutes a proxy for the logical failure rate. We find that the XZZX codes can achieve a favorable resource scaling by this metric under biased noise. We also show that the XZZX codes have remarkably high thresholds that reach what is achievable by random codes, and furthermore they can be efficiently decoded using matching decoders. Finally, by adding only one flag qubit, the XZZX codes can realize fault-tolerant QEC while preserving their large effective distance. In combination, our results show that tailored XZZX codes give a resource-efficient scheme for fault-tolerant QEC against biased noise.
Color image encryption algorithm based on bidirectional spiral transformation and DNA coding

Abstract The security of digital images has become increasingly important as information technology has advanced considerably. To ensure image security and improve the efficiency of image encryption, a novel color image encryption algorithm is proposed in this paper. Firstly, a novel three-dimensional chaotic system with extreme multistability is proposed and employed to generate the chaotic sequences to be used in the permutation and diffusion processes. Secondly, the proposed bidirectional spiral transformation is used to permute the R, G and B components extracted from the color plain image. Thirdly, the permuted pixel sequence is dynamically encoded into a DNA sequence. To further increase the permutation effect, the DNA sequence is permuted again using the index sequence. Finally, the dynamical DNA operations are performed to diffuse the permuted DNA sequence, and the designed look-up table method is used to quickly obtain the DNA operation results. The key space of the proposed algorithm is 10 266 , which is large enough to resist the brute-force attacks. The average entropy value of the cipher image of Peppers is 7.9971, which is much closer to the theoretical entropy value 8. The dynamic DNA operations process only takes up 0.032349s, which shows the advantage of the proposed look-up table method. Therefore, the proposed algorithm can be applied to the application that require high confidentiality and real-time.
Optimizing Rectangle Attacks: A Unified and Generic Framework for Key Recovery

The rectangle attack has shown to be a very powerful form of cryptanalysis against block ciphers. Given a rectangle distinguisher, one expects to mount key recovery attacks as efficiently as possible. In the literature, there have been four algorithms for rectangle key recovery attacks. However, their performance vary from case to case. Besides, numerous are the applications where the attacks lack optimality. In this paper, we investigate the rectangle key recovery in depth and propose a unified and generic key recovery algorithm, which supports any possible attacking parameters. Notably, it not only covers the four previous rectangle key recovery algorithms, but also unveils five types of new attacks which were missed previously. Along with the new key recovery algorithm, we propose a framework for automatically finding the best attacking parameters, with which the time complexity of the rectangle attack will be minimized using the new algorithm. To demonstrate the efficiency of the new key recovery algorithm, we apply it to Serpent, CRAFT, SKINNY and Deoxys-BC-256 based on existing distinguishers and obtain a series of improved rectangle attacks.
Failing Gracefully: Decryption Failures and the Fujisaki-Okamoto Transform

In known security reductions for the Fujisaki-Okamoto transformation, decryption failures are handled via a reduction solving the rather unnatural task of finding failing plaintexts given the private key, resulting in a Grover search bound. Moreover, they require an implicit rejection mechanism for invalid ciphertexts to achieve a reasonable security bound in the QROM. We present a reduction that has neither of these deficiencies: We introduce two security games related to finding decryption failures, one capturing the computationally hard task of using the public key to find a decryption failure, and one capturing the statistically hard task of searching the random oracle for key-independent failures like, e.g., large randomness. As a result, our security bounds in the QROM are tighter than previous ones with respect to the generic random oracle search attacks: The attacker can only partially compute the search predicate, namely for said key-independent failures. In addition, our entire reduction works for the explicit-reject variant of the transformation and improves significantly over all of its known reductions. Besides being the more natural variant of the transformation, security of the explicit reject mechanism is also relevant for side channel attack resilience of the implicit-rejection variant. Along the way, we prove several technical results characterizing preimage extraction and certain search tasks in the QROM that might be of independent interest.
Stretching Cube Attacks: Improved Methods to Recover Massive Superpolies

Cube attacks exploit the algebraic properties of symmetric ciphers by recovering a special polynomial, the superpoly, and subsequently the secret key. When the algebraic normal forms of the corresponding Boolean functions are not available, the division property based approach allows to recover the exact superpoly in a clever way. However, the computational cost to recover the superpoly becomes prohibitive as the number of rounds of the cipher increases. For example, the nested monomial predictions (NMP) proposed at ASIACRYPT 2021 stuck at round 845 for Trivium. To alleviate the bottleneck of the NMP technique, i.e., the unsolvable model due to the excessive number of monomial trails, we shift our focus to the so-called valuable terms of a specific middle round that contribute to the superpoly. Two new techniques are introduced, namely, Non-zero Bit-based Division Property (NBDP) and Core Monomial Prediction (CMP), both of which result in a simpler MILP model compared to the MILP model of MP. It can be shown that the CMP technique offers a substantial improvement over the monomial prediction technique in terms of computational complexity of recovering valuable terms. Combining the divide-and-conquer strategy with these two new techniques, we catch the valuable terms more effectively and thus avoid wasting computational resources on intermediate terms contributing nothing to the superpoly. As an illustration of the power of our techniques, we apply our framework to Trivium, Grain-128AEAD, Kreyvium and Acorn. As a result, the computational cost of earlier attacks can be significantly reduced and the exact ANFs of the superpolies for 846-, 847- and 848-round Trivium, 192-round Grain-128AEAD, 895-round Kreyvium and 776-round Acorn can be recovered in practical time, even though the superpoly of 848-round Trivium contains over 500 million terms; this corresponds to respectively 3, 1, 1 and 1 rounds more than the previous best results. Moreover, by investigating the internal properties of Möbius transformation, we show how to perform key recovery using superpolies involving full key bits, which leads to the best key recovery attacks on the targeted ciphers.
Algebraic Meet-in-the-Middle Attack on LowMC

By exploiting the feature of partial nonlinear layers, we propose a new technique called algebraic meet-in-the-middle (MITM) attack to analyze the security of LowMC, which can reduce the memory complexity of the simple difference enumeration attack over the state-of-the-art. Moreover, while an efficient algebraic technique to retrieve the full key from a differential trail of LowMC has been proposed at CRYPTO 2021, its time complexity is still exponential in the key size. In this work, we show how to reduce it to constant time when there are a sufficiently large number of active S-boxes in the trail. With the above new techniques, the attacks on LowMC and LowMC-M published at CRYPTO 2021 are further improved, and some LowMC instances could be broken for the first time. Our results seem to indicate that partial nonlinear layers are still not well-understood.
K-CTIAA: Automatic Analysis of Cyber Threat Intelligence Based on a Knowledge Graph

Cyber threat intelligence (CTI) sharing has gradually become an important means of dealing with security threats. Considering the growth of cyber threat intelligence, the quick analysis of threats has become a hot topic at present. Researchers have proposed some machine learning and deep learning models to automatically analyze these immense amounts of cyber threat intelligence. However, due to a large amount of network security terminology in CTI, these models based on open-domain corpus perform poorly in the CTI automatic analysis task. To address this problem, we propose an automatic CTI analysis method named K-CTIAA, which can extract threat actions from unstructured CTI by pre-trained models and knowledge graphs. First, the related knowledge in knowledge graphs will be supplemented to the corresponding position in CTI through knowledge query and knowledge insertion, which help the pre-trained model understand the semantics of network security terms and extract threat actions. Second, K-CTIAA reduces the adverse effects of knowledge insertion, usually called the knowledge noise problem, by introducing a visibility matrix and modifying the calculation formula of the self-attention. Third, K-CTIAA maps corresponding countermeasures by using digital artifacts, which can provide some feasible suggestions to prevent attacks. In the test data set, the F1 score of K-CTIAA reaches 0.941. The experimental results show that K-CTIAA can improve the performance of automatic threat intelligence analysis and it has certain significance for dealing with security threats.
Transformer transfer learning emotion detection model: synchronizing socially agreed and self-reported emotions in big data

Tactics to determine the emotions of authors of texts such as Twitter messages often rely on multiple annotators who label relatively small data sets of text passages. An alternative method gathers large text databases that contain the authors' self-reported emotions, to which artificial intelligence, machine learning, and natural language processing tools can be applied. Both approaches have strength and weaknesses. Emotions evaluated by a few human annotators are susceptible to idiosyncratic biases that reflect the characteristics of the annotators. But models based on large, self-reported emotion data sets may overlook subtle, social emotions that human annotators can recognize. In seeking to establish a means to train emotion detection models so that they can achieve good performance in different contexts, the current study proposes a novel transformer transfer learning approach that parallels human development stages: (1) detect emotions reported by the texts' authors and (2) synchronize the model with social emotions identified in annotator-rated emotion data sets. The analysis, based on a large, novel, self-reported emotion data set (n = 3,654,544) and applied to 10 previously published data sets, shows that the transfer learning emotion model achieves relatively strong performance.
Feature-rich multiplex lexical networks reveal mental strategies of early language learning

Knowledge in the human mind exhibits a dualistic vector/network nature. Modelling words as vectors is key to natural language processing, whereas networks of word associations can map the nature of semantic memory. We reconcile these paradigms-fragmented across linguistics, psychology and computer science-by introducing FEature-Rich MUltiplex LEXical (FERMULEX) networks. This novel framework merges structural similarities in networks and vector features of words, which can be combined or explored independently. Similarities model heterogenous word associations across semantic/syntactic/phonological aspects of knowledge. Words are enriched with multi-dimensional feature embeddings including frequency, age of acquisition, length and polysemy. These aspects enable unprecedented explorations of cognitive knowledge. Through CHILDES data, we use FERMULEX networks to model normative language acquisition by 1000 toddlers between 18 and 30 months. Similarities and embeddings capture word homophily via conformity, which measures assortative mixing via distance and features. Conformity unearths a language kernel of frequent/polysemous/short nouns and verbs key for basic sentence production, supporting recent evidence of children's syntactic constructs emerging at 30 months. This kernel is invisible to network core-detection and feature-only clustering: It emerges from the dual vector/network nature of words. Our quantitative analysis reveals two key strategies in early word learning. Modelling word acquisition as random walks on FERMULEX topology, we highlight non-uniform filling of communicative developmental inventories (CDIs). Biased random walkers lead to accurate (75%), precise (55%) and partially well-recalled (34%) predictions of early word learning in CDIs, providing quantitative support to previous empirical findings and developmental theories.
Adaptive Aggregation For Federated Learning

In this paper, we present a new scalable and adaptive architecture for FL aggregation. First, we demonstrate how traditional tree overlay based aggregation techniques (from P2P, publish-subscribe and stream processing research) can help FL aggregation scale, but are ineffective from a resource utilization and cost standpoint. Next, we present the design and implementation of AdaFed, which uses serverless/cloud functions to adaptively scale aggregation in a resource efficient and fault tolerant manner. We describe how AdaFed enables FL aggregation to be dynamically deployed only when necessary, elastically scaled to handle participant joins/leaves and is fault tolerant with minimal effort required on the (aggregation) programmer side. We also demonstrate that our prototype based on Ray [1] scales to thousands of participants, and is able to achieve a > 90% reduction in resource requirements and cost, with minimal impact on aggregation latency.
Impact of social metrics in decentralized finance

In our study, we have evaluated the impact of tweets, social indicators, uncertainty, and attention indices on the selected variables calculated from a pool of 51 decentralised finance entities. In so doing, we have identified some evidence that returns are impacted by tweets, but not by social indicators that appear to be more relevant for volatility. We have further confirmed that the S&P500 Index negatively influences cryptocurrency returns, which means that these two asset classes are substitutes. Uncertainty and attention indices are relevant in determining returns and the alternative measurement of volatility. However, they remain insignificant for illiquidity and our initial volatility choice.
Lessons Learned from the Educational Experience during COVID-19 from the Perspective of Latin American University Students

The COVID-19 pandemic impacted the educational context. University students were exposed to an educational transition from a face-to-face context to emergency remote teaching (ERT). This change affected the educational experience of students and teachers in general, and impacted their educational performance, as well as their emotional and mental health, among other aspects. However, learning from the successes during the ERT and reflecting on good and bad practices will allow us to configure effective learning scenarios that respond to the new normal. The objective of this paper is to describe and present the lessons learned during ERT from the experience of university students in Latin America who have already returned to face-to-face instruction. The study used a qualitative inductive approach and a phenomenographic design. The sample consisted of 640 undergraduate students (63% women) of higher education who experienced online education during the year 2021 and a face-to-face modality during the first semester of 2022, belonging to universities in Chile, Venezuela, and Ecuador. The results suggest that new learning scenarios should consider specific pedagogical practices, including active, collaborative, meaningful, and problem-based strategies, together with a diversity of feedback practices. It is concluded that the ERT brought good practices that should guide university educational policies.
An extract-then-abstract based method to generate disaster-news headlines using a DNN extractor followed by a transformer abstractor

Generating news headlines has been one of the predominant problems in Natural Language Processing research. Modern transformer models, if fine-tuned, can present a good headline with attention to all the parts of a disaster-news article. A disaster-news headline generally focuses on the event, its effect, and the major impacts, which a transformer model lacks when generating the headline. The extract-then-abstract based method proposed in this article improves the performance of a state-of-the-art transformer abstractor to generate a good-quality disaster-news headline. In this work, a Deep Neural Network (DNN) based sentence extractor and a transformer-based abstractive summarizer work sequentially to generate a headline. The sentence extraction task is formulated as a binary classification problem where the DNN model is trained to generate two binary labels: one corresponding to the sentence similarity with ground truth headlines and the other corresponding to the presence of disaster and its impact related information in the sentence. The transformer model generates the headline from the sentences extracted by the DNN. ROUGE scores of the headlines generated using the proposed method are found to be better than the scores of the headlines generated directly from the original documents. The highest ROUGE 1, 2, and 3 score improvements are observed in the case of the Text-To-Text Transfer Transformer (T5) model by 17.85%, 38.13%, and 21.01%, respectively. Such improvements suggest that the proposed method can have a high utility for finding effective headlines from disaster related news articles.
Sustainability in Higher Education: Digital Transformation of the Fourth Industrial Revolution and Its Impact on Open Knowledge

Education is important for sustainable development and drives innovation within national innovation systems. As developed economies move to matured stages of the fourth industrial revolution, the quality of education needs to keep up with the required technological changes brought about by the digital transformation created by these technologies. However, studies on the impact of the digital transformation of the higher education system and its effect on sustainability are few. This study bridged this gap by providing empirical evidence on the subject matter through a synergy of extant studies. The methodology used herein involves a combination of a systematic literature review and a bibliometric analysis. The results of this study show that the digital transformation of the higher education sector is leading to the development of sustainable curriculums, digitalization of the higher education, enhancement of innovation, and an improvement in the performance of students. It also shows that the future of the digital transformation of the higher education sector will include—the advancement of the concept of ‘Education 4.0’, an increase in gamification within the sector, a rise in the use of datafication in decision making, and the integration of artificial intelligence/augmented intelligence to reform higher education.
CSGVD: A deep learning approach combining sequence and graph embedding for source code vulnerability detection

In order to secure software, it is critical to detect potential vulnerabilities. The performance of traditional static vulnerability detection methods is limited by predefined rules, which rely heavily on the expertise of developers. Existing deep learning-based vulnerability detection models usually use only a single sequence or graph embedding approach to extract vulnerability features. Sequence embedding-based models ignore the structured information inherent in the code, and graph embedding-based models lack effective node and graph embedding methods. As a result, we propose a novel deep learning-based approach, CSGVD (Combining Sequence and Graph embedding for Vulnerability Detection), which considers function-level vulnerability detection as a graph binary classification task. Firstly, we propose a PE-BL module, which inherits and enhances the knowledge from the pre-trained language model. It extracts the code’s local semantic features as node embedding in the control flow graph by using sequence embedding. Secondly, CSGVD uses graph neural networks to extract the structured information of the graph. Finally, we propose a mean biaffine attention pooling, M-BFA, to better aggregate node information as a graph’s feature representation. The experimental results show that CSGVD outperforms the existing state-of-the-art models and obtains 64.46% accuracy on the real-world benchmark dataset from CodeXGLUE for vulnerability detection.