Reviewerly/exHarmony huggingface dataset:
Does bitcoin still enhance an investment portfolio in a post Covid-19 world?

We investigate the ability of bitcoin to enhance investment portfolios of traditional assets. We find evidence of a structural break in the correlation and volatility processes around the onset of the Covid-19 pandemic. The correlations between bitcoin and traditional assets have increased, thereby reducing bitcoin’s diversification benefits. As a result prior studies using data from pre-2020 may have inadvertently overestimated the value of bitcoin in improving Sharpe ratios and certainty equivalent returns in the post-pandemic era.
MuLAN: Multi-level attention-enhanced matching network for few-shot knowledge graph completion

Recent years have witnessed increasing interest in the few-shot knowledge graph completion due to its potential to augment the coverage of few-shot relations in knowledge graphs. Existing methods often use the one-hop neighbors of the entity to enhance its embedding and match the query instance and support set at the instance level. However, such methods cannot handle inter-neighbor interaction, local entity matching and the varying significance of feature dimensions. To bridge this gap, we propose the Multi-Level Attention-enhanced matching Network (MuLAN) for few-shot knowledge graph completion. In MuLAN, a multi-head self-attention neighbor encoder is designed to capture the inter-neighbor interaction and learn the entity embeddings. Then, entity-level attention and instance-level attention are responsible for matching the query instance and support set from the local and global perspectives, respectively, while feature-level attention is utilized to calculate the weights of the feature dimensions. Furthermore, we design a consistency constraint to ensure the support instance embeddings are close to each other. Extensive experiments based on two well-known datasets (i.e., NELL-One and Wiki-One) demonstrate significant advantages of MuLAN over 11 state-of-the-art competitors. Compared to the best-performing baseline, MuLAN achieves 14.5% higher MRR and 13.3% higher Hits@K on average.
Hardness of (M)LWE with Semi-Uniform Seeds

Let (D,S,χ,m)-LWEn,q be the LWE problem in matrix form (A,y=As+emodq), where A,s,e are randomly chosen respectively from the seed distribution D over Zqm×n, secret distribution S over Zqn and noise distribution χm over Zm (or Rm), i.e., A←D,s←S,e←χm. For various secret-noise distributions (S,χ), the (D,S,χ,m)-LWEn,q problem is shown to be as hard as some standard worst-case lattice problems, but most of the known results require D to be the uniform distribution over Zqm×n. In this paper, we show that under the standard LWE assumption, the problem (D,S,χ,m)-LWEn,q can still be hard for some distribution D that is not (even computationally indistinguishable from) the uniform distribution over Zqm×n. Specifically, we show that if D is a semi-uniform distribution over Zqm×n (namely, D can be publicly derived from and has a “small difference” to the uniform distribution over Zqm×n), then for appropriate choices of (S,χ), the problem (D,S,χ,m)-LWEn,q is hard under the standard LWE assumption. Moreover, we also show that the semi-uniform MLWE problem is hard under the standard MLWE assumption. As a direct application, our results pave the way to prove the security of public-key encryptions with rounded public keys under the standard (M)LWE assumption.
A robust script independent handwriting system for gender identification

Gender identification at the word level in a multi-script environment is challenging due to variations posed by free-style handwriting of individuals and geographical differences in writing styles. This paper presents a new approach, Multi-Orientation-Scale Gabor Response Fusion (MOSGF), for gender identification at the word level using handwritten text. Our method has two steps: (i) word segmentation from unconstrained lines and (ii) gender identification at the word level. In the first step, the method explores the number of zero crossing points and gradient information for word segmentation from handwritten text lines. In the second step, employs Gabor responses at different orientations and scales to detect fine details in female and male handwriting. For each Gabor response, the proposed model estimates the correlation between average templates of all Gabor responses and the individual Gabor response to extract global consistency in writing. To strengthen correlation features, the proposed method uses the Mahalanobis distance measure, which extracts local similarity. Further, the proposed approach fuses correlation coefficient and distance-based features in a novel way. The fused features are then fed to a Neural Network (NN) for gender identification. Experiments on our dataset, which comprises Roman (English), Chinese, Farsi (Persian), Arabic, and Indian scripts, and a benchmark dataset, namely, IAM which includes English text, KHATT which includes Arabic, and QUWI which includes both English and Arabic, show that the proposed system outperforms the existing methods in terms of word segmentation and gender identification.
Secure and Decentralized Federated Learning Framework with Non-IID Data based on Blockchain

Federated learning enables the collaborative training of machine learning models across multiple organizations, eliminating the need for sharing sensitive data. Nevertheless, in practice, the data distributions among these organizations are often non-independent and identically distributed (non-IID), which poses significant challenges for traditional federated learning. To tackle this challenge, we present a hierarchical federated learning framework based on blockchain technology, which is designed to enhance the training of non-IID data., protect data privacy and security, and improve federated learning performance. The framework builds a global shared pool by constructing a blockchain system to reduce the non-IID degree of local data and improve model accuracy. In addition, we use smart contracts to distribute and collect models and design a main blockchain to store local models for federated aggregation, achieving decentralized federated learning. We train the MLP model on the MNIST dataset and the CNN model on the Fashion-MNIST and CIFAR-10 datasets to verify its feasibility and effectiveness. The experimental results show that the proposed strategy significantly improves the accuracy of decentralized federated learning on three tasks with non-IID data.
Video generalized semantic segmentation via Non-Salient Feature Reasoning and Consistency

Video semantic segmentation is beneficial for dynamic scene processing in real-world environments, and achieves superior performance on independent and identically distributed data. However, it suffers from performance degradation in environments with various domain styles, which is known as the distribution shift problem. Although some previous studies on image generalized semantic segmentation considered the distribution shift problem, temporal-frame information could not be used to obtain more accurate prediction. Thus, in this study, we explore a new task, known as the video generalized semantic segmentation (VGSS) task, which establishes a connection between continuous frames and domain generalization. We propose a novel method named Non-Salient Feature Reasoning and Consistency (NSFRC) for this task. Specifically, we first define the class-wise non-salient feature, which describes the features of the class-wise non-salient region that carry more generalized information. We then propose a class-wise non-salient feature reasoning strategy to select and enhance generalized channels adaptively. This strategy adopts a new form to use domain-invariant features by treating the domain-invariant features as prior information to assist domain-invariant model learning. Finally, we propose a non-salient centroid alignment loss to alleviate the temporally inconsistent and negative transfer problems in the VGSS task. We also extend our video-based framework to the image generalized semantic segmentation (IGSS) task. Experiments demonstrate that our NSFRC framework yields significant improvements in both the VGSS and IGSS tasks. To explain the idea of this research in a clear and attractive way, we provide the visual abstract shown in Fig. 1.
Recent advancements and challenges of NLP-based sentiment analysis: A state-of-the-art review

Sentiment analysis is a method within natural language processing that evaluates and identifies the emotional tone or mood conveyed in textual data. Scrutinizing words and phrases categorizes them into positive, negative, or neutral sentiments. The significance of sentiment analysis lies in its capacity to derive valuable insights from extensive textual data, empowering businesses to grasp customer sentiments, make informed choices, and enhance their offerings. For the further advancement of sentiment analysis, gaining a deep understanding of its algorithms, applications, current performance, and challenges is imperative. Therefore, in this extensive survey, we began exploring the vast array of application domains for sentiment analysis, scrutinizing them within the context of existing research. We then delved into prevalent pre-processing techniques, datasets, and evaluation metrics to enhance comprehension. We also explored Machine Learning, Deep Learning, Large Language Models and Pre-trained models in sentiment analysis, providing insights into their advantages and drawbacks. Subsequently, we precisely reviewed the experimental results and limitations of recent state-of-the-art articles. Finally, we discussed the diverse challenges encountered in sentiment analysis and proposed future research directions to mitigate these concerns. This extensive review provides a complete understanding of sentiment analysis, covering its models, application domains, results analysis, challenges, and research directions.
Couplings and matchings combinatorial notes on Strassen’s theorem

Some mathematical theorems represent ideas that are discovered again and again in different forms. One such theorem is Hall’s marriage theorem. This theorem is equivalent to several other theorems in combinatorics and optimization theory, in the sense that these results can easily be derived from each other. Remarkably, this equivalence extends to Strassen’s theorem, a celebrated result on couplings of probability measures. In this paper the equivalence between Strassen’s theorem and Hall’s theorem is investigated from a combinatorial perspective. A novel combinatorial lemma will be introduced that can be used to deduce both Hall’s theorem and a finite version of Strassen’s theorem, providing a simple proof of their equivalence.
Development of an intelligent system for early forecasting and modelling of flood situation on the example of the Republic of Bashkortostan using a proprietary machine and deep learning library

Reliable early flood forecasting models and systems are essential for minimizing and preventing the impacts of floods worldwide. However, the predictive accuracy of such models and systems is often unacceptable for long-term forecasting, creating uncertainty in flood management decision-making. To address this problem, we propose an intelligent «Flood 2.0» system based on a proprietary machine learning and deep learning library and a flood zone visualisation module. The development of a new version of the system is preceded by a previous study «A system based on an artificial neural network of the second generation for decision support in especially significant situations». The machine learning and deep learning library developed within «Flood 2.0» consists of two parts: preprocessing and prediction. Initially, the study considers the method of data preprocessing. The essence of this method is the development and application of an impulse neural network for generation of information filtering rules, which makes it possible to generate a high-quality initial set of retrospective data in an automated mode, cutting off unnecessary digital noise. Next, a predictive part (method) for water level prediction based on a dataset generated by a data preprocessing method is discussed. This water level prediction method consists of: a) a machine learning model represented as a polynomial regression; b) a deep learning model implemented as a modified recurrent neural network (RNN), a classical neural network with long-term short-term memory (LSTM) and a pulse neural network (PNN); c) a flood zone visualisation module, which is implemented on the basis of the Leaflet library and allows modelling flood zones without the use of digital elevation models, i.e. only on the basis of predicted water levels. To verify the effectiveness of the developed «Flood 2.0» system in general and the machine learning and deep learning library in particular, many 10-day-ahead prediction experiments have been conducted. The empirical results show that the developed recurrent neural network model predicts more accurately under the proposed intelligent system “Flood 2.0″. Moreover, the difference compared to other models ranges from 49.22% to 78.98%, which proves the effectiveness of the development of the Flood 2.0 intelligent system in general and mathematical/technical modifications in particular. Thus, an early and accurate forecast, as well as visualised flood zones will give special services the necessary time to carry out flood control measures to prepare for the protection of technical facilities of enterprises and evacuation of the population. Additional research has also shown that the Flood 2.0 system can be used to predict water levels and model flood zones in other regions.
Existence of the Map <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si1.svg"><mml:mi>d</mml:mi><mml:mi>e</mml:mi><mml:msup><mml:mrow><mml:mi>t</mml:mi></mml:mrow><mml:mrow><mml:msup><mml:mrow><mml:mi>S</mml:mi></mml:mrow><mml:mrow><mml:mn>3</mml:mn></mml:mrow></mml:msup></mml:mrow></mml:msup></mml:math>

In this paper we show the existence of a nontrivial linear map detS3:Vd⊗(3d3)→k with the property that detS3(⊗1≤i<j<k≤3d(vi,j,k))=0 if there exists 1≤x<y<z<t≤3d such that vx,y,z=vx,y,t=vx,z,t=vy,z,t. This gives a partial answer to a conjecture form [10]. As an application, we use the map detS3 to study those d-partitions of the complete hypergraph K3d3 that have zero Betti numbers. We also discuss algebraic and combinatorial properties of a map detSr:Vd⊗(rdr)→k which generalizes the determinant map, the map detS2 from [9], and detS3.
User-defined weight based multi objective task scheduling in cloud using whale optimisation algorithm

Cloud computing has revolutionized the IT landscape, providing scalable, on-demand computing resource. For efficiency in cloud environments, it is essential for modern organizations, where objectives often include cost reduction, resource consumption, operational efficiency and load balancing etc, to implement multi objective solutions. Single-objective systems can fail in handling dynamic and diverse workloads. This study introduces the Multi-Objective Whale Optimization-Based Scheduler (WOA-Scheduler) for efficient task scheduling in cloud computing environments. Leveraging the Whale Optimization Algorithm (WOA), the scheduler optimizes multiple objectives simultaneously, including cost, time, and load balancing. A key feature of the WOA-Scheduler is its flexibility in accommodating user-defined weights for different objectives, allowing organizations to prioritize optimization goals based on their specific requirements. Comparative analysis across various cloud environments demonstrates the superiority of the WOA-Scheduler over traditional single-objective approaches. By achieving a better balance between cost, time, and resource utilization, the scheduler enhances overall performance. Moreover, its multi-objective optimization capabilities enable dynamic adjustment of task assignments in response to changing workload conditions, ensuring efficient resource utilization and workload distribution. Overall, the WOA-Scheduler offers a customizable and adaptable solution for addressing the complexities of modern cloud services, ultimately improving performance and efficiency.
Between money and speculative asset: The role of financial literacy on the perception towards Bitcoin in Italy

With Bitcoin at the forefront, cryptocurrencies are gaining traction as an alternative asset investment, particularly among young investors. Although most of the empirical evidence has shown that it could not be defined as a currency, some Bitcoin users argue the opposite. This paper analyzes the factors influencing the perception of Bitcoin, i.e., whether it is a currency or an asset, with a focus on financial literacy among a subject pool of university students in Italy. The results show that, after controlling for several individual characteristics such as behavioral biases, personal attitudes, psychological traits, and socio-demographic information, this cryptocurrency is considered more than just an asset, and thus it could replace currency, among subjects with lower financial literacy, higher knowledge of Bitcoin, and those who do not trust the banking system. In contrast, Bitcoin is considered a speculative asset among those individuals with higher financial literacy. In line with the recent evidence that cryptocurrencies are mostly owned by young investors, results indicate the importance of increasing the level of financial education among them.
L2-BiTCN-CNN: Spatio-temporal features fusion-based multi-classification model for various internet applications identification

Identifying various types of Internet applications is a key issue for network management. Traffic classification is one of the important technologies for identifying Internet applications traffic. However, some secure technologies such as protocol encryption, information hiding, and traffic obfuscation pose new challenges to traffic identification and classification, especially in feature extraction. In addition, multi-classification has always been a difficult problem in traffic classification research. This paper proposes a spatio-temporal features fusion-based multi-classification model, named as L2-BiTCN-CNN. Our approach combines bidirectional temporal convolutional network (TCN) and convolutional neural network (CNN) to create an enhanced model. The L2-BiTCN model utilizes a bidirectional approach to learn both forward and backward sequence features of traffic, with a focus on the hierarchical structure of network traffic known as “byte-packet-session flow”. To fully extract timing characteristics between adjacent bytes and packets, this model incorporates attention mechanisms for both “byte hierarchy” and “packet hierarchy”. Filters of various sizes are utilizes in the CNN stage to capture packet features of different ranges. The spatial features of the packets are then obtained through two convolution and average pooling processes. The results of the experiments provide evidence of the effectiveness and superiority of our approach. Our model achieved an impressive overall accuracy of 99.839% and 97.186% in two public available datasets, namely USTC-TFC2016 and ISCX VPN-nonVPN2016, respectively. It outperforms the state-of-the-art models in terms of classification performance, which suggests that the model has the potential to be a valuable tool in various applications that require high accuracy.
A randomized algorithm for clustering discrete sequences

Cluster analysis is one of the most important research issues in data mining and machine learning. To date, numerous clustering algorithms have been proposed to tackle the fixed-length vector data. In many real applications, we need to detect clusters from a set of discrete sequences in which each sequence is an ordered list of items. Due to the sequential and discrete nature, the discrete sequence clustering problem is more challenging and most of existing vector data clustering algorithms cannot be directly employed. In this paper, we present a stochastic algorithm for clustering discrete sequences. Our method first quickly generates a set of random partitions over the sequential data set and then merges these random clustering results via weighted graph construction and partition. We perform extensive empirical comparisons on real data sets to show that our method is comparable to those state-of-the-art clustering algorithms with respect to both accuracy and efficiency.
Blockchain for secure and decentralized artificial intelligence in cybersecurity: A comprehensive review

As the usage of artificial intelligence (AI) grows within the field of cybersecurity, so too does the demand for secure and decentralized AI systems to protect against potential cyber threats. Blockchain technology (BT) has emerged as the ideal approach for increasing both the security and privacy of AI systems since it provides decentralized and immutable data storage. This systematic literature review focuses on the integration of BT with decentralized AI within cybersecurity. It provides a comprehensive taxonomy of BT and decentralized AI for cybersecurity serving as the starting point for the study. This paper begins with an overview of BT and its possible uses in cybersecurity and also analyzes its challenges and opportunities. Decentralized AI is also covered in the study, along with its potential advantages and difficulties in decentralized AI cybersecurity. Building on that foundation, this study provides convincing findings highlighting the beneficial relationships between BT and decentralized AI for cybersecurity, contributing to a nuanced comprehension of their integration. Moreover, it offers real-world uses of blockchain-enabled decentralized AI to discuss its usefulness in tackling cybersecurity concerns. Finally, this paper discusses future research directions for blockchain-enabled decentralized AI in cybersecurity, including potential applications and implications of this technology in the field, emphasizing the potential of blockchain-enabled decentralized AI in cybersecurity to enhance security, privacy, and trust in AI systems.
An Inductive Reasoning Model based on Interpretable Logical Rules over temporal knowledge graph

Extrapolating future events based on historical information in temporal knowledge graphs (TKGs) holds significant research value and practical applications. In this field, the methods currently utilized can be classified as either embedding-based or logical rule-based. Embedding-based methods depend on learned entity and relation embeddings for prediction, but they suffer from the lack of interpretability due to the opaque reasoning process. On the other hand, logical rule-based methods face scalability challenges as they heavily rely on predefined logical rules. To overcome these limitations, we propose a hybrid model that combines embedding-based and logical rule-based methods to capture deep causal logic. Our model, called the Inductive Reasoning Model based on Interpretable Logical Rule (ILR-IR), aims to provide interpretable insights while effectively predicting future events in TKGs. ILR-IR delves into historical information, extracting valuable insights from logical rules embedded within relations and interaction preferences between entities. By considering both logical rules and interaction preferences, ILR-IR offers a comprehensive perspective for predicting future events. In addition, we propose the incorporation of a one-class augmented matching loss during optimization, which serves to enhance performance of the model during training. We evaluate ILR-IR on multiple datasets, including ICEWS14, ICEWS0515, and ICEWS18. Experimental results demonstrate that ILR-IR outperforms state-of-the-art baselines, showcasing its superior performance in TKG extrapolation reasoning. Moreover, ILR-IR demonstrates remarkable generalization capabilities, even when applied to related datasets that share a common relation vocabulary. This suggests that our proposed model exhibits robust zero-shot reasoning abilities. For interested parties, we have made our code publicly available at https://github.com/mxadorable/ILR-IR.