Reviewerly/exHarmony huggingface dataset:
Sentimental Analysis on Social Media Comments with Recurring Models and Pretrained Word Embeddings in Portuguese

Natural Language Processing (NLP) techniques are increasingly powerful for interpreting a person's feelings and reaction to a product or service. Sentiment analysis has become a fundamental tool for this interpretation, and it has studies in languages other than English. This type of application is uncommon and unheard of in Portuguese. This article presents a sentiment analysis classification based on Portuguese social media comments. Representation of word embeddings with both pre-trained Glove and Word2Vec models were generated through a corpus entirely in Portuguese. This article presents a set of results with different models of pre-trained layers and deep learning models exclusive to the Portuguese language on social networks. Two classification models were used and compared: (i) Bidirectional Long Short-Term Memory (BI-LSTM) and (ii) Bidirectional Gated Recurrent Unit (BI-GRU), achieving accuracy results of 99.1
Automatic Detection and Visualization of Information Structure in English

This paper describes the design and development of an online tool that identifies and visualizes information structure in user-submitted texts written in English. Non-native users of English find it difficult to distinguish between structures that are marked and unmarked. Markedness is evaluated based on acceptability and frequency of a sequence of word tokens. Marked sentences stand out as being unnatural to native speakers, but few native speakers can explain why. Information structure can, however, frequently explain markedness. The tool detects the three principles of information structure: information focus, information flow and end weight. Information focus explains the sequence of elements within sentences. Information flow explains the sequence of elements within paragraphs. End weight explains the relative position of phrases and clauses within a sentence. Through exposure to these principles in context, this tool aims to help writers of English understand which structural language features may be judged as marked.
Predicting sUAS conflicts in the national airspace with interacting multiple models and Haversine-based conflict detection system

In this paper, a conflict detection system for small Unmanned Aerial Vehicles (sUAS), composed of an interacting multiple model state predictor and a Haversine-distance based conflict detector, is proposed. The conflict detection system was developed and tested via a random recursive simulation in the ROS-Gazebo physics engine environment. The simulation consisted of ten small unmanned aerial vehicles flying along randomly assigned way-point navigation missions within a confined airspace. Way-points are generated from a uniform distribution and then sent to each vehicle. The interacting multiple model state predictor runs on a ground-based system and only has access to current vehicle positional information. It does not have access to the future way-points of individual vehicles. The state predictor is based on Kalman filters that utilize constant velocity, constant acceleration, and constant turn models. It generates near-future position estimates for all vehicles operating within an airspace. These models are probabilistically fused together and projected into the near-future to generate state predictions. These state predictions are then passed to the Haversine distance-based conflict detection algorithm to compare state estimates and identify probable conflicts. The conflicts are detected and flagged based on tunable threshold values which compare distances between predictions for the vehicles operating within the airspace. This paper discusses the development of the random recursive simulation for the ROS-Gazebo framework and the derivation of the interacting multiple model along-with the Haversine-based future conflict detector. The results are presented via simulation to highlight mid-air conflict detection application for sUAS operations in the National Airspace.
Fine-Grained Access Control with User Revocation in Smart Manufacturing

Collaborative manufacturing is a key enabler of Industry 4.0 that requires secure data sharing among multiple parties. However, intercompany data-sharing raises important privacy and security concerns, particularly given intellectual property and business-sensitive information collected by many devices. In this paper, we propose a solution that combines four technologies to address these challenges: Attribute-Based Encryption for data access control, blockchain for data integrity and non-repudiation, Hardware Security Modules for authenticity, and the Interplanetary File System for data scalability. We also use OpenID for dynamic client identification and propose a new method for user revocation in Attribute-Based Encryption. Our evaluation shows that the solution can scale up to 2,000,000 clients while maintaining all security guarantees.
A Latent-Variable Model for Intrinsic Probing

The success of pre-trained contextualized representations has prompted researchers to analyze them for the presence of linguistic information. Indeed, it is natural to assume that these pre-trained representations do encode some level of linguistic knowledge as they have brought about large empirical improvements on a wide variety of NLP tasks, which suggests they are learning true linguistic generalization. In this work, we focus on intrinsic probing, an analysis technique where the goal is not only to identify whether a representation encodes a linguistic attribute but also to pinpoint where this attribute is encoded. We propose a novel latent-variable formulation for constructing intrinsic probes and derive a tractable variational approximation to the log-likelihood. Our results show that our model is versatile and yields tighter mutual information estimates than two intrinsic probes previously proposed in the literature. Finally, we find empirical evidence that pre-trained representations develop a cross-lingually entangled notion of morphosyntax.
RelaGraph: Improving embedding on small-scale sparse knowledge graphs by neighborhood relations

Learning a continuous dense low-dimensional representation of knowledge graphs (KGs), known as knowledge graph embedding (KGE), has been viewed as the key to intelligent reasoning for deep learning (DL) and gained much attention in recent years. To address the problem that the current KGE models are generally ineffective on small-scale sparse datasets, we propose a novel method RelaGraph to improve the representation of entities and relations in KGs by introducing neighborhood relations. RelaGraph extends the neighborhood information during entity encoding, and adds the neighborhood relations to mine deeper level of graph structure information, so as to make up for the shortage of information in the generated subgraph. This method can well represent KG components in a vector space in a way that captures the structure of the graph, avoiding underlearning or overfitting. KGE based on RelaGraph is evaluated on a small-scale sparse graph KMHEO, and the MRR reached 0.49, which is 34 percentage points higher than that of the SOTA methods, as well as it does on several other datasets. Additionally, the vectors learned by RelaGraph is used to introduce DL into several KG-related downstream tasks, which achieved excellent results, verifying the superiority of KGE-based methods.
Losses over Labels: Weakly Supervised Learning via Direct Loss Construction

Owing to the prohibitive costs of generating large amounts of labeled data, programmatic weak supervision is a growing paradigm within machine learning. In this setting, users design heuristics that provide noisy labels for subsets of the data. These weak labels are combined (typically via a graphical model) to form pseudolabels, which are then used to train a downstream model. In this work, we question a foundational premise of the typical weakly supervised learning pipeline: given that the heuristic provides all “label” information, why do we need to generate pseudolabels at all? Instead, we propose to directly transform the heuristics themselves into corresponding loss functions that penalize differences between our model and the heuristic. By constructing losses directly from the heuristics, we can incorporate more information than is used in the standard weakly supervised pipeline, such as how the heuristics make their decisions, which explicitly informs feature selection during training. We call our method Losses over Labels (LoL) as it creates losses directly from heuristics without going through the intermediate step of a label. We show that LoL improves upon existing weak supervision methods on several benchmark text and image classification tasks and further demonstrate that incorporating gradient information leads to better performance on almost every task.
Research on Improving Gray Wolf Algorithm Based on Multi-Strategy Fusion

To address the shortcomings of the basic Gray Wolf Optimization (GWO) algorithm in solving complex problems, such as relying on the initial population, converging too early, and easily falling into local optimality, a chaotic reverse learning initialization strategy, a nonlinear control parameter convergence strategy, and a dynamic position update strategy are introduced to develop a multi-strategy fusion Improved Gray Wolf Optimization (IGWO) algorithm, and this method is used to solve function optimization problems.First, a chaotic backward learning initialization strategy, based on logistic mapping and backward learning, is adopted to improve the random initialization of the GWO algorithm and enhance the traversal and diversity of the initial population.Second, a nonlinear control parameter for local perturbation is constructed to avoid the problem of premature convergence of the GWO algorithm due to linear convergence and to balance the exploration and exploitation ability of the GWO algorithm.Finally, a location guidance strategy based on dynamic weights and individual memory is proposed to effectively improve the algorithm's optimization accuracy and computational efficiency; meanwhile, the Gaussian-Cauchy mutation strategy of superior selection is introduced to optimize the location update of optimal individual α wolves and improve the ability of the population to jump out of local extremes.Simulation experiments are conducted for 11 classical test functions, and the results show that the proposed improved algorithm IGWO for gray wolves is superior to 10 other standard swarm intelligence optimization algorithms and 4 other improved optimization algorithms in terms of solution accuracy, convergence speed, and algorithm stability.It provides a new optimization algorithm for solving complex optimization problems.
Quantum Computing to Study Cloud Turbulence Properties

The analysis and investigation of the data obtained from Direct Numerical (DNS) simulation of droplet dynamics in cloud turbulence is a complex and time-consuming task when performaed on traditional computers.The DNS data generally have, a high spatial resolution ≈ 1mm and require considerable space to store.It is tedious to find specific features of this data, such as locating high and low vortex areas in cloud turbulence using machine learning algorithms.In this research, we employ quantum computing to examine and analyze cloud droplet dynamics data and present a quantum supervised machine learning algorithm, namely, a support vector machine (SVM) to segregate low and high vortex regions and investigate the droplet characteristics in those regions.The result show that use of quantum computers can accelerate the entire process, and quantum mechanics tools, such as quantum kernels and quantum circuits can better manage the complex nature of data than traditional methods.
Dynamic Neural Network Models for Time-Varying Problem Solving: A Survey on Model Structures

In recent years, neural networks have become a common practice in academia for handling complex problems.Numerous studies have indicated that complex problems can generally be formulated as a single or a set of time-varying equations.Dynamic neural networks, as powerful tools for processing time-varying problems, play an essential role in their online solution.This paper reviews recent advances in real-valued, complex-valued, and noise-tolerant dynamic neural networks for solving various time-varying problems, discusses the finite-time convergence, fixed/varying parameters, and noise tolerance properties of dynamic neural network models.This review is highly relevant for researchers and novices interested in using dynamic neural networks to solve time-varying problems.
Improving 3-D Zebrafish Tracking With Multiview Data Fusion and Global Association

Zebrafish behavioral patterns reveal valuable insights for biomedical research. To accurately identify these patterns, visual tracking systems need to reconstruct 3-D trajectories from multiview video sequences. However, 3-D zebrafish tracking faces challenges such as the dynamics in movements, the similarity in appearances, and the distortion caused by different viewpoints. In this article, we propose a new method for robust 3-D zebrafish trajectory reconstruction based on multiview data fusion and global association. Our method generates reliable segments of 2-D/3-D trajectories, called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">tracklets</i> , where we consider short-term cues of appearance similarity and motion consistency and propose corresponding scoring metrics. Moreover, we use a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">lazy-reconstruction</i> strategy to enhance the overall accuracy of 3-D trajectories by taking into account the global context. Extensive experiments on the public 3D-ZeF20 dataset demonstrate the effectiveness of the proposed method, achieving 67.9% multiple object tracking accuracy (MOTA), 64.3% ID F1 Score (IDF1), and 55.0 MTBFm.
PCLDet: Prototypical Contrastive Learning for Fine-Grained Object Detection in Remote Sensing Images

The capacity of satellites to supply high-resolution imaging has promoted the fine-grained object detection task in remote sensing images. However, this type of object detection is challenging due to low interclass feature differences in objects. To address this issue, we propose a prototypical contrastive learning-based detector (PCLDet) for fine-grained object detection in remote sensing images. The PCLDet first introduces the prototype to learn the fine-grained objects’ features, and then adopts contrastive learning to compare the target and the learned features, thus improving the differentiability of the fine-grained object. Specifically, we first introduce the prototype, which represents the feature centers of each class, and then construct a prototype bank to store the feature prototypes of each class. Then, we introduce contrastive learning to extract the discriminative features by maximizing the interclass distance and minimizing the intraclass distance. Furthermore, we propose the ProtoCL loss as a part of the model optimization, which enables more representative prototypes to be learned. Finally, to address the long-tail problem in the remote sensing fine-grained object detection dataset, we propose a new proposal sampler, the class-balanced sampler (CBS) that can sample each class equally. Extensive experiments demonstrate that our method can achieve state-of-the-art performance on a commonly used aerial fine-grained object dataset (Fair1M) and aerial fine-grained ship dataset (OFSD) while maintaining high efficiency. The code will be available at https://github.com/G-Naughty/PCLDet.
Solving Time-Varying Nonsymmetric Algebraic Riccati Equations With Zeroing Neural Dynamics

The problem of solving algebraic Riccati equations (AREs) and certain linear matrix equations which arise from the ARE frequently occur in applied and pure mathematics, science, and engineering applications. In this article, by considering the nonsymmetric ARE (NARE) as a general form of ARE, the time-varying NARE (TV-NARE) problem is proposed and investigated. As a particular case of TV-NARE, the time-invariant NARE (TI-NARE) problem is investigated too. Then, by employing the zeroing (or Zhang) neural dynamics (ZND) design, a ZND TV-NARE (ZNDTV-NARE) model and a ZND TI-NARE (ZNDTI-NARE) model are proposed and investigated. Also, by combining the ZNDTV-NARE model with the frozen-time Riccati equation (FTRE) approach to optimal control of linear time-varying (LTV) systems based on the state-dependent Riccati equation (SDRE) process, a hybrid ZND FTRE control (HZND-FTREC) model is developed and investigated. The effectiveness of the proposed dynamical systems is proven in ten numerical experiments, three of which include applications to LTV and nonlinear systems.
Creating Star Worlds: Reshaping the Robot Workspace for Online Motion Planning

Closed-loop motion planning is suitable for obstacle avoidance in dynamically changing environments due to its reactive nature, and various methods have been presented to provide (almost) global convergence. A common assumption in the control design is that the robot operates in a disjoint star world, i.e., all obstacles are strictly starshaped and mutually disjoint. However, in real-life scenarios obstacles may intersect due to expanded obstacle regions corresponding to robot radius or safety margins. To broaden the applicability of closed-loop motion planning methods, such as harmonic potential fields, we propose a method to reshape a workspace of intersecting obstacles into a disjoint star world. The algorithm is based on two novel concepts presented here, namely, admissible kernel and starshaped hull with specified kernel, which are closely related to the notion of starshaped hull. The utilization of the proposed method is illustrated with examples of a robot operating in a 2-D workspace using a harmonic potential field approach in combination with the developed algorithm.
Leveraging Ensembles and Self-Supervised Learning for Fully-Unsupervised Person Re-Identification and Text Authorship Attribution

Learning from fully-unlabeled data is challenging in Multimedia Forensics problems, such as Person Re-Identification and Text Authorship Attribution. Recent self-supervised learning methods have shown to be effective when dealing with fully-unlabeled data in cases where the underlying classes have significant semantic differences, as intra-class distances are substantially lower than inter-class distances. However, this is not the case for forensic applications in which classes have similar semantics and the training and test sets have disjoint identities. General self-supervised learning methods might fail to learn discriminative features in this scenario, thus requiring more robust strategies. We propose a strategy to tackle Person Re-Identification and Text Authorship Attribution by enabling learning from unlabeled data even when samples from different classes are not prominently diverse. We propose a novel ensemble-based clustering strategy whereby clusters derived from different configurations are combined to generate a better grouping for the data samples in a fully-unsupervised way. This strategy allows clusters with different densities and higher variability to emerge, reducing intra-class discrepancies without requiring the burden of finding an optimal configuration per dataset. We also consider different Convolutional Neural Networks for feature extraction and subsequent distance computations between samples. We refine these distances by incorporating context and grouping them to capture complementary information. Our method is robust across both tasks, with different data modalities, and outperforms state-of-the-art methods with a fully-unsupervised solution without any labeling or human intervention.
Event-Trigger-Based Finite-Time Adaptive Fuzzy Control for Stochastic Nonlinear Systems With Unmeasured States

In this article, the issue of an event-triggered finite-time control is tackled for a category of stochastic nonlinear systems with unmeasured states. Initially, the capacity of fuzzy logic systems (FLSs) to approximate unknown nonlinear functions is fully utilized. After that, the immeasurable states are measured by the constructed state observer. In addition, via integrating the backstepping approach with a relative-threshold event-triggered condition, an original event-triggered adaptive fast finite-time control scheme is further developed. Theoretically speaking, the presented strategy could ensure that all signals in the closed-loop system are bounded. Most important of all, the Zeno behavior could be successfully excluded upon associated analysis. Last but not least, a simulation is provided to give a surely support of the correctness and the practicability of the theoretical analysis.
Self-Admitted Technical Debt in Ethereum Smart Contracts: A Large-Scale Exploratory Study

Programmable blockchain platforms such as Ethereum offer unique benefits to application development, including a decentralized infrastructure, tamper-proof transactions, and auditability. These benefits enable new types of applications that can bring competitive advantage to several business segments. Nonetheless, the pressure of time-to-market combined with relatively immature development technologies (e.g., the Solidity programming language), lack of high-quality training resources, and an unclear roadmap for Ethereum creates a context that favors the introduction of technical debt (e.g., code hacks, workarounds, and suboptimal implementations) into application code. In this paper, we study self-admitted technical debt (SATD) in smart contracts. SATD refers to technical debt that is explicitly acknowledged in the source code by developers via code comments. We extract 726 k real-world contracts from Ethereum and apply both quantitative and qualitative methods in order to (i) determine SATD prevalence, (ii) understand the relationship between code cloning and SATD prevalence, and (iii) uncover the different categories of SATD. Our findings reveal that, while SATD is not a widespread phenomenon (1.5% of real-world contracts contain SATD), SATD does occur in extremely relevant contracts (e.g., multi-million contracts). We also observed a strong connection between SATD prevalence and code cloning activities, leading us to conclude that the former cannot be reliably studied without taking the latter into consideration. Finally, we produced a taxonomy for SATD that consists of 6 major and 26 minor categories. We note that several minor categories are bound to the domain of blockchain and smart contracts, including gas-inefficient implementations and Solidity-induced workarounds. Based on our results, we derive a set of practical recommendations for contract developers and introduce open research questions to guide future research on the topic.