Reviewerly/exHarmony huggingface dataset:
Membership-Function-Dependent Control Design of Interval Type-2 Sampled-Data Fuzzy-Model-Based Output-Feedback Tracking Control System

This article focuses on the stability analysis and control design of an interval type-2 sampled-data fuzzy-model-based output-feedback (IT2SDFMBOF) tracking control system, which is formed by the interval type-2 (IT2) Takagi–Sugeno (T–S) fuzzy model, the stable reference model, and the interval type-2 sampled-data output-feedback (IT2SDOF) fuzzy controller. The design goal is to establish a proper IT2SDOF fuzzy controller that is able to drive the states of the nonlinear plant subject to uncertainties to track those of the stable reference model, and the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$H_\infty$</tex-math></inline-formula> performance index is adopted to optimize the tracking control performance. To improve the robustness of the IT2SDOF fuzzy controller against uncertainties of the nonlinear plant, IT2 fuzzy sets are utilized. Considering that the tracking control system is sampled-data (SD) type, a two-sided looped-functional-based approach exploiting the information on the interval from time <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$t_k$</tex-math></inline-formula> to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$t$</tex-math></inline-formula> and the interval from time <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$t$</tex-math></inline-formula> to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$t_{k+1}$</tex-math></inline-formula> is applied to enhance the stability analysis. The imperfect premise matching (IPM) concept permits the number of rules and the premise membership functions of the IT2 T-S fuzzy model and the IT2SDOF fuzzy controller to be different, which can be employed to promote the design flexibility. The tracking control system with the SD type and IPM design will cause the membership grades between the IT2 T-S fuzzy model and the IT2SDOF fuzzy controller to be mismatched. In addition, the membership-function-dependent stability analysis approach that can introduce the boundary information of membership functions into the stability analysis is used to relax the stability conditions. The relaxed stability conditions subject to the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$H_\infty$</tex-math></inline-formula> performance building on the Lyapunov stability theory are developed in terms of linear matrix inequalities. Simulation results demonstrate the effectiveness of the proposed IT2SDFMBOF tracking control system.
A multi-label emoji classification method using balanced pointwise mutual information-based feature selection

The availability of social media such as twitter allows users to express their feeling, emotions and opinions toward a topic. Emojis are graphic symbols that are regarded as the new generation of emoticons and an effective way of conveying feelings and emotions in social media. With the surging popularity of Emojis, the researchers in the area of Emotion Classification strive to understand the emotion correlated to each Emoji. Two of the most the successful approaches in emoji analysis rely on: 1) official Unicode description and 2) manually built emoji lexicons. Since the use of emoji is socially determined, the former approach is not aligned with intended semantic and usage, which leads researchers to opt for emoji lexicons. To overcome problem of lexicon-based approach, we proposed a method to classify emojis automatically. Therefore, we present a modified Pointwise Mutual Information (PMI) method, called Balanced Pointwise Mutual Information-Based (B-PMI), to develop a balanced weighted emoji classification based on the semantic similarity. Further, deep neural network is used to represent emoji in vector form (emoji embedding) to extend the pre-trained word embeddings. We carefully evaluated the proposed method in multiple twitter datasets that are employed in sentiment and emotion classification using machine learning (ML) and deep learning (DL) approaches. In both approaches, extending word embedding with the proposed emoji embedding improved results. The DL-based approach achieved the highest f1-score of 70.01% for sentiment classification, and accuracy score of 56.36% for emotion classification. ML-based approach obtained accuracy score of 52.17% in emotion classification.
Hiding Messages in Audio using Modulus Operation and Simple Partition

Today's information technology tools allow people to share personal data and information through online media. Data security techniques are also developed to protect confidential messages that are only intended for certain parties, one of which is by embedding those data in a specific medium. The hiding method proposed in this study uses a simple partition to split the 16-bit audio cover file into two groups of the same length. The simple partition technique is applied to improve the quality of the resulting stego file. Furthermore, it is also developing an adaptive modulus operation calculation method, whose implementation on the difference of sample can increase the embedding capacity of the secret messages. The final result of the proposed method shows that all secret messages at the 700 kb payload file size can be successfully embedded, and the PSNR can be maintained up to 120.55 dB.
Mining Reddit as a New Source for Software Requirements

Mining app stores and social media has proven to be a good source for collecting user feedback to foster requirements engineering and software evolution. Recent literature on mining software-related data from social platforms, such as Twitter and Facebook, shows that it complements app store mining. However, there are many other platforms where users discuss and provide feedback on software applications that are not thoroughly researched and analysed. One of such platforms is reddit. In this paper, we introduce reddit as a new potential data source and explore if and how requirements engineering and software evolution can benefit from obtaining user feedback from reddit. We also present an exploratory study in which we analysed the usage characteristics (i.e., frequency of posts, number of comments, and number of users for each subreddit) of reddit posts about software applications. Furthermore, we examined the content of the posts and the results reveal that almost 54% of posts contain useful information. Finally, we investigated the potential of automatic classification and applied machine learning algorithms to unstructured and noisy reddit data to perform automated classification into the categories of bug reports, feature related, and irrelevant. We found that the Support Vector Machine algorithm with the F1-score of 84% can be effective in categorizing reddit posts. Our results show that reddit posts provide useful feedback on software applications that can foster requirements engineering and software evolution.
Amazon SageMaker Model Monitor: A System for Real-Time Insights into Deployed Machine Learning Models

With the increasing adoption of machine learning (ML) models and systems in high-stakes settings across different industries, guaranteeing a model's performance after deployment has become crucial. Monitoring models in production is a critical aspect of ensuring their continued performance and reliability. We present Amazon SageMaker Model Monitor, a fully managed service that continuously monitors the quality of machine learning models hosted on Amazon SageMaker. Our system automatically detects data, concept, bias, and feature attribution drift in models in real-time and provides alerts so that model owners can take corrective actions and thereby maintain high quality models. We describe the key requirements obtained from customers, system design and architecture, and methodology for detecting different types of drift. Further, we provide quantitative evaluations followed by use cases, insights, and lessons learned from more than two years of production deployment.
Profit Maximizing Smart Manufacturing Over AI-Enabled Configurable Blockchains

Based on the trustless feature of blockchain, this article designs a general configurable blockchain-enabled smart manufacturing system to achieve flexible manufacturing in response to large-scale manufacturing services. With a transaction pool containing all the pending manufacturing tasks but aligning with the logic flow, the complex manufacturing structure can be uniformly tackled. Furthermore, in virtue of the contradiction between large-scale manufacturing and limited blockchain throughput, we formulate a joint optimization of the block size, task scheduling, and the supply-demand configuration to maximize the customers’ net profit with the probabilistic delay requirements, which addresses the critical issue of efficiency and latency in the blockchain-based live manufacturing process. Meanwhile, the production quality and price preference are involved. For solution, a mixed online bipartite matching-based DQN algorithm is proposed, which circumvents the high dimensionality by separating the task-manufacturer matching from the time-correlated problem. Simulation results show that the proposed flexible framework can well adopt to dynamic customer population, and achieves better convergence.
An Extensive Systematic Review of RFID Technology Role in Supply Chain Management (SCM)

To be successful in the supply chain, businesses must completely integrate all departments and partners, rather than just a few. Because of the automation it generates, technology is a “enabler” for SCM systems and improved efficiency. The technology involved in this may bring higher accuracy, better inventory management, and cost savings, all of which partners and customers like, but interacting with them is a smart way to retain the connection and keep them coming back. Many customers are already concerned that technology will take over, and although it does allow us to accomplish a lot more with a lot less, businesses must remember that communication is the cornerstone to all interactions. Technology has helped us to design better supply chain systems, and the following are a few that have proven fairly popular in SCM and connect well to RFID.
True Few-Shot Learning with Prompts—A Real-World Perspective

Abstract Prompt-based approaches excel at few-shot learning. However, Perez et al. (2021) recently cast doubt on their performance as they had difficulty getting good results in a “true” few-shot setting in which prompts and hyperparameters cannot be tuned on a dev set. In view of this, we conduct an extensive study of Pet, a method that combines textual instructions with example-based finetuning. We show that, if correctly configured, Pet performs strongly in true few-shot settings without a dev set. Crucial for this strong performance is a number of design choices, including Pet’s ability to intelligently handle multiple prompts. We put our findings to a real-world test by running Pet on RAFT, a benchmark of tasks taken from realistic NLP applications for which no labeled dev or test sets are available. Pet achieves a new state of the art on RAFT and performs close to non-expert humans for 7 out of 11 tasks. These results demonstrate that prompt-based learners can successfully be applied in true few-shot settings and underpin our belief that learning from instructions will play an important role on the path towards human-like few-shot learning capabilities.
One Metric to Measure Them All: Localisation Recall Precision (LRP) for Evaluating Visual Detection Tasks

Despite being widely used as a performance measure for visual detection tasks, Average Precision (AP) is limited in (i) reflecting localisation quality, (ii) interpretability and (iii) robustness to the design choices regarding its computation, and its applicability to outputs without confidence scores. Panoptic Quality (PQ), a measure proposed for evaluating panoptic segmentation (Kirillov et al., 2019), does not suffer from these limitations but is limited to panoptic segmentation. In this paper, we propose Localisation Recall Precision (LRP) Error as the average matching error of a visual detector computed based on both its localisation and classification qualities for a given confidence score threshold. LRP Error, initially proposed only for object detection by Oksuz et al. (2018), does not suffer from the aforementioned limitations and is applicable to all visual detection tasks. We also introduce Optimal LRP (oLRP) Error as the minimum LRP Error obtained over confidence scores to evaluate visual detectors and obtain optimal thresholds for deployment. We provide a detailed comparative analysis of LRP Error with AP and PQ, and use nearly 100 state-of-the-art visual detectors from seven visual detection tasks (i.e. object detection, keypoint detection, instance segmentation, panoptic segmentation, visual relationship detection, zero-shot detection and generalised zero-shot detection) using ten datasets to empirically show that LRP Error provides richer and more discriminative information than its counterparts. Code available at: https://github.com/kemaloksuz/LRP-Error.
Multi-Dimensional Cooperative Network for Stereo Matching

The dimensions of cost volumes and corresponding aggregation networks play a critical role in balancing the speed and accuracy for stereo matching. Current most 2D stereo networks based on 3D cost volumes are speedy but poor in precision, while 3D stereo networks with 4D volumes are slow but precise. To tackle this problem, we propose a novel stereo framework for combining 2D and 3D networks. The disparity range generated by the 2D network is used to guide the 3D network and reduce the computational complexity. Meanwhile, the 3D network can refine the rough disparity from coarse to fine. Through the cooperation of 2D and 3D networks, our method can achieve accurate stereo results at a fast speed. We evaluate our method on three popular public benchmark datasets. Experimental results from the KITTI official website show that, our method can achieve similar accuracy with other 3D stereo networks (PSMNet, GCNet, GwcNet, etc.) at a significantly faster speed. The ablation studies further demonstrate the facticity and reasonability of our propose method.
A Shared Cyber Threat Intelligence Solution for SMEs

Small- and medium-sized enterprises (SMEs) frequently experience cyberattacks, but often do not have the means to counter these attacks. Therefore, cybersecurity researchers and practitioners need to aid SMEs in their defence against cyber threats. Research has shown that SMEs require solutions that are automated and adapted to their context. In recent years, we have seen a surge in initiatives to share cyber threat intelligence (CTI) to improve collective cybersecurity resilience. Shared CTI has the potential to answer the SME call for automated and adaptable solutions. Sadly, as we demonstrate in this paper, current shared intelligence approaches scarcely address SME needs. We must investigate how shared CTI can be used to improve SME cybersecurity resilience. In this paper, we tackle this challenge using a systematic review to discover current state-of-the-art approaches to using shared CTI. We find that threat intelligence sharing platforms such as MISP have the potential to address SME needs, provided that the shared intelligence is turned into actionable insights. Based on this observation, we developed a prototype application that processes MISP data automatically, prioritises cybersecurity threats for SMEs, and provides SMEs with actionable recommendations tailored to their context. Subsequent evaluations in operational environments will help to improve our application, such that SMEs are enabled to thwart cyberattacks in future.
Practice of Streaming Processing of Dynamic Graphs: Concepts, Models, and Systems

Graph processing has become an important part of various areas of computing, including machine learning, medical applications, social network analysis, computational sciences, and others. A growing amount of the associated graph processing workloads are <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">dynamic</i> , with millions of edges added or removed per second. Graph streaming frameworks are specifically crafted to enable the processing of such highly dynamic workloads. Recent years have seen the development of many such frameworks. However, they differ in their general architectures (with key details such as the support for the concurrent execution of graph updates and queries, or the incorporated graph data organization), the types of updates and workloads allowed, and many others. To facilitate the understanding of this growing field, we provide the first analysis and taxonomy of dynamic and streaming graph processing. We focus on identifying the fundamental system designs and on understanding their support for concurrency, and for different graph updates as well as analytics workloads. We also crystallize the meaning of different concepts associated with streaming graph processing, such as dynamic, temporal, online, and time-evolving graphs, edge-centric processing, models for the maintenance of updates, and graph databases. Moreover, we provide a bridge with the very rich landscape of graph streaming theory by giving a broad overview of recent theoretical related advances, and by discussing which graph streaming models and settings could be helpful in developing more powerful streaming frameworks and designs. We also outline graph streaming workloads and research challenges. <!--?query id="Q1"--> Author: Please confirm or add details for any funding or financial support for the research of this article. ?>
Poligraph: Intrusion-Tolerant and Distributed Fake News Detection System

We present Poligraph, an intrusion-tolerant and decentralized fake news detection system. Poligraph aims to address architectural, system, technical, and social challenges of building a practical, long-term fake news detection platform. We first conduct a case study for fake news detection at authors’ institute, showing that machine learning-based reviews are less accurate but timely, while human reviews, in particular, experts reviews, are more accurate but time-consuming. This justifies the need for combining both approaches. At the core of Poligraph is two-layer consensus allowing seamlessly combining machine learning techniques and human expert determination. We construct the two-layer consensus using Byzantine fault-tolerant (BFT) and asynchronous threshold common coin protocols. We prove the correctness of our system in terms of conventional definitions of security in distributed systems (agreement, total order, and liveness) as well as new review validity (capturing the accuracy of news reviews). We also provide theoretical foundations on parameter selection for our system. We implement Poligraph and evaluate its performance on Amazon EC2 using a variety of news from online publications and social media. We demonstrate Poligraph achieves throughput of more than 5,000 transactions per second and latency as low as 0.05 second. The throughput of Poligraph is only marginally ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${4\%}$ </tex-math></inline-formula> – <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${7\%}$ </tex-math></inline-formula> ) slower than that of an unreplicated, single-server implementation. In addition, we conduct a real-world case study for the review of fake and real news among both experts and non-experts, which validates the practicality of our approach.
Clipped Hyperbolic Classifiers Are Super-Hyperbolic Classifiers

Hyperbolic space can naturally embed hierarchies, unlike Euclidean space. Hyperbolic Neural Networks (HNNs) exploit such representational power by lifting Euclidean features into hyperbolic space for classification, outperforming Euclidean neural networks (ENNs) on datasets with known semantic hierarchies. However, HNNs underperform ENNs on standard benchmarks without clear hierarchies, greatly restricting HNNs' applicability in practice. Our key insight is that HNNs' poorer general classification performance results from vanishing gradients during backpropagation, caused by their hybrid architecture connecting Euclidean features to a hyperbolic classifier. We propose an effective solution by simply clipping the Euclidean feature magnitude while training HNNs. Our experiments demonstrate that clipped HNNs become super-hyperbolic classifiers: They are not only consistently better than HNNs which already outperform ENNs on hierarchical data, but also on-par with ENNs on MNIST, CIFAR10, CIFAR100 and ImageNet benchmarks, with better adversarial robustness and out-of-distribution detection.
CAViaR-WS-based HAN: conditional autoregressive value at risk-water sailfish-based hierarchical attention network for emotion classification in COVID-19 text review data

The Corona Virus Disease-2019 (COVID-19) pandemic has made a remarkable impact on economies and societies worldwide. With numerous procedures of social distancing and lockdowns, it becomes essential to know people's emotional responses on a very large scale. Thus, an effective emotion classification approach is developed using the proposed Conditional Autoregressive Value at Risk-Water Sailfish-based Hierarchical Attention Network (CAViaR-WS-based HAN) for classifying the emotions in the COVID-19 text review data. The proposed approach, named CAViaR-WS, is designed by the incorporation of Conditional Autoregressive Value at Risk-Sail Fish (CAViaR-SF) and Water Cycle Algorithm (WCA). Here, the significant features, such as mean, variance, entropy, Term Frequency-Inverse Document Frequency (TF-IDF), SentiWordNet features, and spam word-based features, are extracted to further processing. Based on the extracted features, feature fusion is accomplished using the RideNN. In addition, CAViaR-SF-based GAN is used to perform the spam classification, and then, the emotion classification is carried out using Hierarchal Attention Networks (HAN), where the training procedure of HAN is performed using proposed CAViaR-WS. Furthermore, the developed CAViaR-WS-based HAN offers effective performance results concerning precision, recall, and f-measure with the maximal values of 0.937, 0.958, and 0.948, respectively.