Reviewerly/exHarmony huggingface dataset:
Fuzzy logic approach in salt and pepper noise

This paper describes a new method based on fuzzy logic to denoising images corrupted by the salt and pepper noise. In this approach, noisy pixels are detected via fuzzy IF-THEN rules and restored with a multi-level fuzzy filter. To reduce the amount of noise corruption, the filtering process begins with a window of size 3 × 3 centered on the noisy pixels. Then, a floating window in the form of fuzzy compositions is used for the remaining unrestored noisy pixels. A fuzzy logic controller is employed to improve the restoration results. By comparing this method with many well-established denoising methods, we have shown that the mentioned technique has high accuracy and reasonably low complexity at very high noise densities while preserving the structure and edge information of the images.
High Dimensional Mode Hunting Using Pettiest Components Analysis

Principal components analysis has been used to reduce the dimensionality of datasets for a long time. In this paper, we will demonstrate that in mode detection the components of smallest variance, the pettiest components, are more important. We prove that for a multivariate normal or Laplace distribution, we obtain boxes of optimal volume by implementing "pettiest component analysis," in the sense that their volume is minimal over all possible boxes with the same number of dimensions and fixed probability. This reduction in volume produces an information gain that is measured using active information. We illustrate our results with a simulation and a search for modal patterns of digitized images of hand-written numbers using the famous MNIST database; in both cases pettiest components work better than their competitors. In fact, we show that modes obtained with pettiest components generate better written digits for MNIST than principal components.
Interpreting Deep-Learned Error-Correcting Codes

Deep learning has been used recently to learn error-correcting encoders and decoders which may improve upon previously known codes in certain regimes. The encoders and decoders are learned "black-boxes", and interpreting their behavior is of interest both for further applications and for incorporating this work into coding theory. Understanding these codes provides a compelling case study for Explainable Artificial Intelligence (XAI): since coding theory is a well-developed and quantitative field, the interpretability problems that arise differ from those traditionally considered. We develop post-hoc interpretability techniques to analyze the deep-learned, autoencoder-based encoders of TurboAE-binary codes, using influence heatmaps, mixed integer linear programming (MILP), Fourier analysis, and property testing. We compare the learned, interpretable encoders combined with BCJR decoders to the original black-box code.
Private Read Update Write (PRUW) with Storage Constrained Databases

We investigate the problem of private read update write (PRUW) in relation to federated submodel learning (FSL) with storage constrained databases. In PRUW, a user privately reads a submodel from a system of N databases containing M submodels, updates it locally, and writes the update back to the databases without revealing the submodel index or the value of the update. The databases considered in this problem are only allowed to store a given amount of information specified by an arbitrary storage constraint. We provide a storage mechanism that determines the contents of each database prior to the application of the PRUW scheme, such that the total communication cost is minimized. We show that the proposed storage scheme achieves a lower total cost compared to what is achieved by using coded storage or divided storage to meet the given storage constraint.
Analysis of Customer Reviews using Deep Neural Network

Customer Review Analysis has become a most important application of Businesses. This will enable the business to analyze the text data and know the sentiment of the customers on their business entities in the market. It requires a thorough computational study of the behavior of discrete entities with respect to customers purchasing affinity and extracting the customer&#x2019;s point of view about the business entity. The business performance is always measured with customers satisfaction. In this era of e-commerce and social networking, the launching of a new product has to undergo with deep study of customers views on existing products and their requirements in the product. Since a huge amount of reviews are being generated from various source, thereby it is becoming exceedingly difficult to make sense of the data. This project considers the problem of analyzing reviews by their overall semantic that is, positive, negative and neutral behavior. In this work a Webapp is developed that classifies the review to any of the 3 cases. The work here is analyzing and classifying the Product Reviews using Deep Learning.
Designing an Accounting Information Management System Using Big Data and Cloud Technology

With the rapid development of science and technology pushing people into the information age, the appearance of information technology is a major change for the accounting industry, which will redefine the traditional accounting profession. The new management techniques and concepts also gradually change the traditional accounting working mode by promoting the development of accounting professionals from traditional accounting to accounting information. In recent years, cybercrime and attacking information system vulnerability have occurred frequently. This study utilizes the model and technology in the design of an accounting information management system by simply explaining the idea of cloud technology and examining its logical structure of cloud technology. After that, it designs the cloud platform architecture of the accounting information management system (AIMS) by building the SaaS model. It defines the distributed storage mode of the cloud platform and tests the cluster performance of the system after completing the system design and construction to judge the application effect of the system. Finally, the system operation time, local rows of data, and load balancing are tested experimentally. These results demonstrate that the response relationships of distinct tasks are modified higher than TeraSort, TeraSort higher than Inquiry, and response time is proportional to the amount of Reduce slots when input tasks are the same. The analysis shows that the large data cloud platform used in this paper has high operational efficiency, acceleration rate, and task execution rate.
Practical Recommendations for Replay-Based Continual Learning Methods

Continual Learning requires the model to learn from a stream of dynamic, non-stationary data without forgetting previous knowledge. Several approaches have been developed in the literature to tackle the Continual Learning challenge. Among them, Replay approaches have empirically proved to be the most effective ones. Replay operates by saving some samples in memory which are then used to rehearse knowledge during training in subsequent tasks. However, an extensive comparison and deeper understanding of different replay implementation subtleties is still missing in the literature. The aim of this work is to compare and analyze existing replay-based strategies and provide practical recommendations on developing efficient, effective and generally applicable replay-based strategies. In particular, we investigate the role of the memory size value, different weighting policies and discuss about the impact of data augmentation, which allows reaching better performance with lower memory sizes.
HybridGNN: Learning Hybrid Representation for Recommendation in Multiplex Heterogeneous Networks

Recently, graph neural networks have shown the superiority of modeling the complex topological structures in heterogeneous network-based recommender systems. Due to the diverse interactions among nodes and abundant semantics emerging from diverse types of nodes and edges, there is a bursting research interest in learning expressive node repre-sentations in multiplex heterogeneous networks. One of the most important tasks in recommender systems is to predict the potential connection between two nodes under a specific edge type (i.e., relationship). Although existing studies utilize explicit metapaths to aggregate neighbors, practically they only consider intra-relationship metapaths and thus fail to leverage the potential uplift by inter-relationship information. Moreover, it is not always straightforward to exploit inter-relationship metapaths comprehensively under diverse relationships, espe-cially with the increasing number of node and edge types. In addition, contributions of different relationships between two nodes are difficult to measure. To address the challenges, we propose HybridGNN, an end-to-end GNN model with hybrid aggregation flows and hierarchical attentions to fully utilize the heterogeneity in the multiplex scenarios. Specifically, HybridGNN applies a randomized inter-relationship exploration module to exploit the multiplexity property among different relationships. Then, our model leverages hybrid aggregation flows under intra-relationship metapaths and randomized exploration to learn the rich semantics. To explore the importance of different aggregation flow and take advantage of the multiplexity property, we bring forward a novel hierarchical attention module which leverages both metapath-Ievel attention and relationship-level attention. Extensive experimental results on five real-world datasets suggest that HybridGNN achieves the best performance compared to several state-of-the-art baselines (p < 0.01, t-test) with statistical significance.
Distributed Matrix-Vector Multiplication with Sparsity and Privacy Guarantees

We consider the problem of designing a coding scheme that allows both sparsity and privacy for distributed matrix-vector multiplication. Perfect information-theoretic privacy requires encoding the input sparse matrices into matrices distributed uniformly at random from the considered alphabet; thus destroying the sparsity. Computing matrix-vector multiplication for sparse matrices is known to be fast. Distributing the computation over the non-sparse encoded matrices maintains privacy, but introduces artificial computing delays. In this work, we relax the privacy constraint and show that a certain level of sparsity can be maintained in the encoded matrices. We consider the chief/worker setting while assuming the presence of two clusters of workers: one is completely untrusted in which all workers collude to eavesdrop on the input matrix and in which perfect privacy must be satisfied; the other is partly trusted, only up to z workers may collude and to which revealing small amount of information about the input matrix is allowed. We design a scheme that trades sparsity for privacy while achieving the desired constraints. We use cyclic task assignments of the encoded matrices to tolerate partial and full stragglers.
Close spatial distance and direct gaze bring better communication outcomes and more intertwined neural networks

Non-verbal cues tone our communication. Previous studies found that non-verbal factors, such as spatial distance and gaze direction, significantly impact interpersonal communication. However, little is known about the behind multi-brain neural correlates and whether it could affect high-level creative group communication. Here, we provided a new, scalable, and neuro-based approach to explore the effects of non-verbal factors on different communication tasks, and revealed the underlying multi-brain neural correlates using fNIRS-based hyperscanning technique. Across two experiments, we found that closer spatial distance and more direct gaze angle could promote collaborative behaviors, improve both creative and non-creative communication outcomes, and enhance inter-brain neural synchronization. Moreover, compared to the non-creative communication task, participants' inter-brain network was more intertwined when performing the creative communication task. These findings suggest that close spatial distance and direct gaze serve as positive social cues, bringing interacting brains into alignment and optimizing inter-brain information transfer, thus improving communication outcomes.
A confirmatory factorial analysis of the Chatbot Usability Scale: a multilanguage validation

Abstract The Bot Usability Scale (BUS) is a standardised tool to assess and compare the satisfaction of users after interacting with chatbots to support the development of usable conversational systems. The English version of the 15-item BUS scale (BUS-15) was the result of an exploratory factorial analysis; a confirmatory factorial analysis tests the replicability of the initial model and further explores the properties of the scale aiming to optimise this tool seeking for the stability of the original model, the potential reduction of items, and testing multiple language versions of the scale. BUS-15 and the usability metrics for user experience (UMUX-LITE), used here for convergent validity purposes, were translated from English to Spanish, German, and Dutch. A total of 1292 questionnaires were completed in multiple languages; these were collected from 209 participants interacting with an overall pool of 26 chatbots. BUS-15 was acceptably reliable; however, a shorter and more reliable solution with 11 items (BUS-11) emerged from the data. The satisfaction ratings obtained with the translated version of BUS-11 were not significantly different from the original version in English, suggesting that the BUS-11 could be used in multiple languages. The results also suggested that the age of participants seems to affect the evaluation when using the scale, with older participants significantly rating the chatbots as less satisfactory, when compared to younger participants. In line with the expectations, based on reliability, BUS-11 positively correlates with UMUX-LITE scale. The new version of the scale (BUS-11) aims to facilitate the evaluation with chatbots, and its diffusion could help practitioners to compare the performances and benchmark chatbots during the product assessment stage. This tool could be a way to harmonise and enable comparability in the field of human and conversational agent interaction.
ONP-Miner: One-off Negative Sequential Pattern Mining

Negative sequential pattern mining (SPM) is an important SPM research topic. Unlike positive SPM, negative SPM can discover events that should have occurred but have not occurred, and it can be used for financial risk management and fraud detection. However, existing methods generally ignore the repetitions of the pattern and do not consider gap constraints, which can lead to mining results containing a large number of patterns that users are not interested in. To solve this problem, this article discovers frequent one-off negative sequential patterns (ONPs). This problem has the following two characteristics. First, the support is calculated under the one-off condition, which means that any character in the sequence can only be used once at most. Second, the gap constraint can be given by the user. To efficiently mine patterns, this article proposes the ONP-Miner algorithm, which employs depth-first and backtracking strategies to calculate the support. Therefore, ONP-Miner can effectively avoid creating redundant nodes and parent-child relationships. Moreover, to effectively reduce the number of candidate patterns, ONP-Miner uses pattern join and pruning strategies to generate and further prune the candidate patterns, respectively. Experimental results show that ONP-Miner not only improves the mining efficiency but also has better mining performance than the state-of-the-art algorithms. More importantly, ONP mining can find more interesting patterns in traffic volume data to predict future traffic.
Hybridization of Deep Convolutional Neural Network for Underwater Object Detection and Tracking Model

In this present work, underwater object detection and tracking was studied using the efficient Hybridization of Deep Convolutional Neural Network for Underwater Object Detection and Tracking (HDCNN-UODT) model for three bench mark data sets namely UOT32, brackish, and URPC 2020 datasets. The HDCNN-UODT technique primarily employs data augmentation process for increasing the size of the training dataset to improve the detection and average precision. Besides, a hybridization of two Deep Learning (DL) models namely RetinaNet and EfficientNet models was applied as feature extractors. In addition, the bounding box prediction process takes place via support vector regression (SVR) followed by kernel extreme learning machine (KELM) model. The novelty of the present work was demonstrated by the design of SVR based bounding box regression and fusion based feature extractions. The average precision examination (APE), average success rate(ASR) & average frame per second(AFPS) analysis of the HDCNN-UODT model using UOT32 dataset showed better APE (51.27%), ASR (43.19) & AFPS (310.25) over other reported techniques. Additionally, the detection accuracy of the HDCNN-UODT model for brackish and URPC datasets showed improved accuracies for different objects over YOLO v4, T-YOLO v4, and AFFM-YOLO v4 techniques. Moreover, using brackish dataset, HDCNN-UODT model showed highest accuracy of 94.85% for ‘Crab’ object and for URPC dataset, a highest accuracy of 88.34% for ‘Scallop’ object was obtained. Hence based on our outcome, HDCNN-UODT technique might be better suited for the object detection and tracking application.
Can wavelets produce a clearer picture of weak-form market efficiency in Bitcoin?

This study proposes a new approach for testing for random walk behavior in daily Bitcoin returns (19/07/2010–03/03/2022) by contextualizing the Dickey-Fuller test in time-frequency space using continuous complex wavelet transforms. By splitting our full sample into smaller sub-sample periods segregated by Bitcoin halving dates, we find that Bitcoin returns are most predictable or least market efficient (i) at higher frequency or short-run cycles of between 2 and 16 days, (ii) between November-February months, (iii) during ‘bubble’ periods, (iv) across the consecutive halving dates, (v) during the ‘Black Swan event’ caused by financial market turmoil arising from the COVID-19 pandemic, and (vi) subsequent to the announcements of new COVID-19 variants. Altogether, our findings have important policy implications for different stakeholders in Bitcoin markets.
Self-training method based on GCN for semi-supervised short text classification

Semi-supervised short text classification is a challenging problem due to the sparsity and limited labeled data. Due to the lack of labeled data, many models focus on the generation of text samples, which is cumbersome and has poor scalability. To overcome this deficiency, in this paper, we propose a Self-Training Text method based on Graph Convolutional Networks (ST-Text-GCN). Differently from the previous literature, our self-training method is convenient. The labeled information is propagated to target samples along the structure of the manifold, instead of introducing the extra knowledge. Specifically, instead of adding text training samples, our method adds keywords to training set. The model will calculate the confidence of each word. Confidence indicates the degree of ambiguity of a word. Some words with high confidence are automatically marked as pseudo-labeled data. Meanwhile, word confidence is added to the calculation of the edge weights of the graph to reduce the classification error caused by word ambiguity. Our method makes full use of the keywords in short texts when labeled data is scarce. Extensive experimental results have demonstrated that our proposed method outperforms state-of-the-art models on multiple benchmark datasets.
G-VCFL: Grouped Verifiable Chained Privacy-Preserving Federated Learning

Federated learning, as a typical distributed learning paradigm, shows great potential in Industrial Internet of Things, Smart Home, Smart City, etc. It enables collaborative learning without data leaving local users. Despite the huge benefits, it still faces the risk of privacy breaches and a single point of failure for aggregation server. Adversaries can use intermediate models to infer user privacy, or even return incorrect global model by manipulating the aggregation server. To address these issues, several federated learning solutions focusing on privacy-preserving and security have been proposed. However, theses solutions still faces challenges in resource-limited scenarios. In this paper, we propose G-VCFL, a grouped verifiable chained privacy-preserving federated learning scheme. Specifically, we first use the grouped chain learning mechanism to guarantee the privacy of users, and then propose a verifiable secure aggregation protocol to guarantee the verifiability of the global model. G-VCFL does not require any complex cryptographic primitives and does not introduce noise, but enables verifiable privacy-preserving federated learning by utilizing lightweight pseudorandom generators. We conduct extensive experiments on real-world datasets by comparing G-VCFL with other state-of-the-art approaches. The experimental results and functional evaluation indicate that G-VCFL is efficient in the six experimental cases and satisfies all the intended design goals.
Semisupervised Deep Learning for Image Classification With Distribution Mismatch: A Survey

Deep learning methodologies have been employed in several different fields, with an outstanding success in image recognition applications, such as material quality control, medical imaging, autonomous driving, etc. Deep learning models rely on the abundance of labeled observations to train a prospective model. These models are composed of millions of parameters to estimate, increasing the need of more training observations. Frequently, it is expensive to gather labeled observations of data, making the usage of deep learning models not ideal, as the model might overfit data. In a semisupervised setting, unlabeled data are used to improve the levels of accuracy and generalization of a model with small labeled datasets. Nevertheless, in many situations different unlabeled data sources might be available. This raises the risk of a significant distribution mismatch between the labeled and unlabeled datasets. Such phenomena can cause a considerable performance hit to typical semisupervised deep learning (SSDL) frameworks, which often assume that both labeled and unlabeled datasets are drawn from similar distributions. Therefore, in this article we study the latest approaches for SSDL for image recognition. Emphasis is made in SSDL models designed to deal with a distribution mismatch between the labeled and unlabeled datasets. We address open challenges with the aim to encourage the community to tackle them, and overcome the high data demand of traditional deep learning pipelines under real-world usage settings.
ARC: An Asynchronous Consensus and Relay Chain-based Cross-chain Solution to Consortium Blockchain

Cross-chain technology has become an important research direction of blockchain, which realizes value transfer and intercommunication between different blockchain systems. The latency and the throughput of the existing cross-chain systems hinder further expansion due to the limitation of synchronous consensuses. In order to address the low performance and limited reliability of cross-chain systems, this paper proposes a relay chain-based cross-chain system, called ARC, with the asynchronous consensus, which ensures the global consensuses and improves the robustness and scalability of the cross-chain systems. We also conduct a security analysis to show the security property of ARC when deployed in adversarial scenarios.