Reviewerly/exHarmony huggingface dataset:
Dynamic Target Tracking of Unmanned Aerial Vehicles Under Unpredictable Disturbances

This study proposes an image-based visual servoing (IBVS) method based on a velocity observer for an unmanned aerial vehicle (UAV) for tracking a dynamic target in Global Positioning System (GPS)-denied environments. The proposed method derives the simplified and decoupled image dynamics of underactuated UAVs using a constructed virtual camera and then considers the uncertainties caused by the unpredictable rotations and velocities of the dynamic target. A novel image depth model that extends the IBVS method to track a rotating target with arbitrary orientations is proposed. The depth model ensures image feature accuracy and image trajectory smoothness in rotating target tracking. The relative velocities of the UAV and the dynamic target are estimated using the proposed velocity observer. Thanks to the velocity observer, translational velocity measurements are not required, and the control chatter caused by noise-containing measurements is mitigated. An integral-based filter is proposed to compensate for unpredictable environmental disturbances in order to improve the anti-disturbance ability. The stability of the velocity observer and IBVS controller is analyzed using the Lyapunov method. Comparative simulations and multistage experiments are conducted to illustrate the tracking stability, anti-disturbance ability, and tracking robustness of the proposed method with a dynamic rotating target.
RUMP: Resource Usage Multi-Step Prediction in Extreme Edge Computing

Extreme Edge Computing that leverages the copious yet underutilized computational resources of Extreme Edge Devices (EEDs) has gained significant momentum lately. Estimating the computational capabilities of EEDs can be strenuously challenging since EEDs are user-owned devices, and are thus subject to a highly dynamic user access behavior (i.e., dynamic resource usage). In this paper, we propose the Resource Usage Multi-step Prediction (RUMP) scheme. RUMP is the first scheme that strives to enable multistep-ahead prediction of the dynamic resource usage of EEDs (i.e., workers) in a computationally efficient way, while providing a relatively high prediction accuracy. Towards that end, RUMP exploits the use of the Hierarchical Dirichlet Process-Hidden Semi-Markov Model (HDP-HSMM). In addition, RUMP uses the Simple and Exponential Moving Average (SMA&EMA) and Savitzky-Golay (SG) filters to improve the prediction accuracy. We scrupulously study the trade-off between computational efficiency, prediction accuracy, practicality, and adaptability of the underlying prediction model by conducting complexity analysis, performing various experiments on a testbed of heterogeneous workers, and comparing the HDP-HSMM model used in RUMP to three other prominent prediction models in different dynamic resource usage scenarios. Extensive evaluations show that RUMP achieves a 91% categorical multi-step prediction accuracy and renders a small performance gap of 6% on average in terms of the Mean Absolute Error (MAE) compared to representatives of state-of-the-art prediction models, while yielding a low computational complexity.
An algorithm for training a class of polynomial models

In this paper we propose a new training algorithm for a class of polynomial models. The algorithm is derived using a learning bound for predictors that are convex combinations of functions from simpler classes. In our case, the hypotheses are polynomials over the input features, and they are interpreted as convex combinations of homogeneous polynomials. In addition, the coefficients are restricted to be positive and to sum to 1. This constraint will simplify the interpretation of the model. The training is done by minimizing a surrogate of the learning bound, using an iterative two-phase algorithm. Basically, in the first phase the algorithm decides which monomials of higher degree should be added, and in the second phase the coefficients are recomputed by solving a convex program. We performed several experiments on binary classification datasets from different domains. Experiments show that the algorithm compares favorably in terms of accuracy and speed with other classification methods, including some new interpretable methods like Neural Additive Models and CORELS. In addition, the resulting predictor can sometimes be understood and validated by a domain expert. The code is publicly available.
Stop filtering: Multi-view attribute-enhanced dialogue learning

There is a growing interest in improving the conversational ability of models by filtering dialogue corpora. Previous filtering strategies rely on a scoring method to assess and discard samples from one perspective, enabling the model to enhance the corresponding dialogue attributes (e.g., consistency). However, the discarded samples may achieve high scores in other perspectives and can also provide regularization effects on the model learning. In this work, we propose a multi-view attribute-enhanced dialogue learning framework that can capture the attribute-related features steadily and comprehensively. Instead of filtering the raw dataset, our framework introduces adapters to learn knowledge from the attribute-related sub-sets after pre-training the model on the full dataset. Considering the variety of dialogue attributes, we further design a multi-view enhancement mechanism, including multi-view selection and inter-view fusion. It groups the high-quality samples from multiple perspectives, respectively, and enhances different attributes of responses with the corresponding sub-sets and adapters, keeping knowledge independent and allowing flexible integration. Empirical results and analysis show that our framework outperforms the state-of-the-art data filtering methods significantly in terms of enhancing dialogue attributes and fusing view-specific knowledge.
Load balancing in virtual machines of cloud environments using two-level particle swarm optimization algorithm

Load balancing in cloud computing refers to dividing computing characteristics and workloads. Distributing resources among servers, networks, or computers enables enterprises to manage workload demands. This paper proposes a novel load-balancing method based on the Two-Level Particle Swarm Optimization (TLPSO). The proposed TLPSO-based load-balancing method can effectively solve the problem of dynamic load-balancing in cloud computing, as it can quickly and accurately adjust the computing resource distribution in order to optimize the system performance. The upper level aims to improve the population’s diversity and escape from the local optimum. The lower level enhances the rate of population convergence to the global optimum while obtaining feasible solutions. Moreover, the lower level optimizes the solution search process by increasing the convergence speed and improving the quality of solutions. According to the simulation results, TLPSO beats other methods regarding resource utilization, makespan, and average waiting time.
HPRE: Leveraging hierarchy-aware paired relation vectors for knowledge graph embedding

Knowledge graphs exhibit a typical hierarchical structure and find extensive applications in various artificial intelligence domains. However, large-scale knowledge graphs need to be completed, which limits the performance of knowledge graphs in downstream tasks. Knowledge graph embedding methods have emerged as a primary solution to enhance knowledge graph completeness. These methods aim to represent entities and relations as low-dimensional vectors, focusing on handling relation patterns and multi-relation types. Researchers need to pay more attention to the crucial feature of hierarchical relationships in real-world knowledge graphs. We propose a novel knowledge graph embedding model called Hierarchy-Aware Paired Relation Vectors Knowledge Graph Embedding (HPRE) to bridge this gap. By leveraging the power of 2D coordinates, HPRE adeptly model relation patterns, multi-relation types, and hierarchical features in the knowledge graph. Specifically, HPRE employs paired relation vectors to capture the distinct characteristics of head and tail entities, facilitating a better fit for relational patterns and multi-relation scenarios. Additionally, HPRE employs angular coordinates to differentiate entities at various levels of the hierarchy, effectively representing the hierarchical nature of the knowledge graph. The experimental results show that the HPRE model can effectively learn the hierarchical features of the knowledge graph and achieve state-of-the-art experimental results on multiple real-world datasets for the link prediction task.
Completely positive graphs and entanglement of quantum states

We present a short survey on completely positive matrices and on their application to quantum information
Blockchain applications for traceability and food safety in agri-food supply chain: cherry product application

Purpose The primary objective of this study is to investigate the application of blockchain technology (BCT) in the agri-food supply chain, focusing on traceability and food safety. Design/methodology/approach The study employed a semi-structured interview method with representatives from the cherry supply chain to evaluate their awareness and acceptance of BCT's impact. Additionally, the analytic hierarchy process (AHP) was utilized to determine digital investment priorities in supply chain strategies. By applying the supply chain operations reference (SCOR) model framework to the cherry supply chain, the study aimed to address the question “Which process model is suitable for implementing BCT in the agri-food supply chain?” Findings The global agri-food supply chains are characterized by significant food losses, escalating prices along the chain, and food safety risks. Concurrently, consumer concerns regarding food safety, quality and transparency are on the rise. BCT, with its ability to ensure data integrity, immutability, and seamless tracking of chain movements, presents immense potential as a secure infrastructure in the agri-food supply chain traceability. Originality/value The developed analytic framework and the study's findings can be adapted to different sectors and different sub-sectors within agri-food supply chains.
SPRA: Scalable policy‐based regulatory architecture for blockchain transactions

Abstract Implementing effective regulation of blockchain transactions has become a research hotspot in recent years. However, most of the current regulatory schemes are customized for specific blockchain applications and lack versatility and scalability. Meanwhile, these schemes cannot guarantee fairness due to the non‐disclosure of regulatory policies and regulatory processes. To address these issues, a scalable policy‐based regulatory architecture (SPRA) is proposed for blockchain transactions that separates regulation and application to provide sufficient scalability. SPRA is a four‐layer model (permission layer, regulation layer, bridge layer, and business layer). A regulatory policy description language (XRPL) is designed to define the regulatory rules and specifications for interoperability between the layers. A decentralized jury mechanism (JuryBC) based on the Shamir threshold secret sharing algorithm and Pedersen commitment is proposed at the regulation layer to avoid regulatory arbitrariness and unfairness. We also construct a secure and efficient regulatory data sharing scheme (RDShare) at the business layer using an attribute‐based encryption algorithm. The key parameters in both JuryBC and RDShare can be specified in the regulatory policy to suit different application scenarios. Finally, the security of the architecture is analyzed and the feasibility and scalability of the architecture through simulation experiments are demonstrated.
Overlap Analysis: A Case Study

In a landscape of uncertain budgets, it is more important than ever that libraries make the best decisions for their collections. Overlap analysis is an essential part of that decision-making process. In the previous decades, overlap analysis and tools have evolved to meet the challenges associated with analyzing today’s complex resources. This case study outlines the overlap analysis workflows and tools utilized by the University of Arkansas Libraries. The paper also discusses the Libraries’ processes for creating overlap reports for both full-text and citation-only resources and provides an appendix that details how the Libraries completed select functions outside of vendor-supplied tools.
Chat agents respond more empathetically by using hearsay experience

As the responses of chat dialogue systems have become more natural, the empathy skill of dialogue systems has become an important new issue. In text-based chat dialogue systems, the definition of empathy is not precise, and how to design the kind of utterance that improves the user's impression of receiving empathy is not clear since the main method used is to imitate utterances and dialogues that humans consider empathetic. In this study, we focus on the necessity of grasping an agent as an experienceable Other, which is considered the most important factor when empathy is performed by an agent, and propose an utterance design that directly conveys the fact that the agent can experience and feel empathy through text. Our system has an experience database including the system's pseudo-experience and feelings to show empathetic feelings. Then, the system understands the user's experiences and empathizes with the user on the basis of the system's experience database, in line with the dialogue content. As a result of developing and evaluating several systems with different ways of conveying the aforementioned rationale, we found that conveying the rationale as a hearsay experience improved the user's impression of receiving empathy more than conveying it as the system's own experience. Moreover, an exhaustive evaluation shows that our empathetic utterance design using hearsay experience is effective to improve the user's impression about the system's cognitive empathy.
The single robot line coverage problem: Theory, algorithms, and experiments

Abstract Line coverage is the task of servicing a given set of one‐dimensional features in an environment. It is important for the inspection of linear infrastructure such as road networks, power lines, and oil and gas pipelines. This paper addresses the single robot line coverage problem for aerial and ground robots by modeling it as an optimization problem on a graph. The problem belongs to the broad class of arc routing problems and is closely related to the rural postman problem (RPP) on asymmetric graphs. The paper presents an integer linear programming formulation with proofs of correctness. Using the minimum cost flow problem, we develop approximation algorithms with guarantees on the solution quality. These guarantees also improve the existing results for the asymmetric RPP. The main algorithm partitions the problem into three cases based on the structure of the required graph , that is, the graph induced by the features that require servicing. We evaluate our algorithms on road networks from the 50 most populous cities in the world, consisting of up to 730 road segments. The algorithms, augmented with improvement heuristics, run within 3 s and generate solutions that are within 10% of the optimum. We experimentally demonstrate our algorithms with commercial UAVs on the UNC Charlotte campus road network.
ASPER: Answer Set Programming Enhanced Neural Network Models for Joint Entity-Relation Extraction

Abstract A plethora of approaches have been proposed for joint entity-relation (ER) extraction. Most of these methods largely depend on a large amount of manually annotated training data. However, manual data annotation is time-consuming, labor-intensive, and error-prone. Human beings learn using both data (through induction) and knowledge (through deduction). Answer Set Programming (ASP) has been a widely utilized approach for knowledge representation and reasoning that is elaboration tolerant and adept at reasoning with incomplete information. This paper proposes a new approach, ASP-enhanced Entity-Relation extraction (ASPER), to jointly recognize entities and relations by learning from both data and domain knowledge. In particular, ASPER takes advantage of the factual knowledge (represented as facts in ASP) and derived knowledge (represented as rules in ASP) in the learning process of neural network models. We have conducted experiments on two real datasets and compare our method with three baselines. The results show that our ASPER model consistently outperforms the baselines.
Stabilizing deep Q-learning with Q-graph-based bounds

State-of-the art deep reinforcement learning has enabled autonomous agents to learn complex strategies from scratch on many problems including continuous control tasks. Deep Q-networks (DQN) and deep deterministic policy gradients (DDPGs) are two such algorithms which are both based on Q-learning. They therefore all share function approximation, off-policy behavior, and bootstrapping—the constituents of the so-called deadly triad that is known for its convergence issues. We suggest to take a graph perspective on the data an agent has collected and show that the structure of this data graph is linked to the degree of divergence that can be expected. We further demonstrate that a subset of states and actions from the data graph can be selected such that the resulting finite graph can be interpreted as a simplified Markov decision process (MDP) for which the Q-values can be computed analytically. These Q-values are lower bounds for the Q-values in the original problem, and enforcing these bounds in temporal difference learning can help to prevent soft divergence. We show further effects on a simulated continuous control task, including improved sample efficiency, increased robustness toward hyperparameters as well as a better ability to cope with limited replay memory. Finally, we demonstrate the benefits of our method on a large robotic benchmark with an industrial assembly task and approximately 60 h of real-world interaction.
Image classification adversarial attack with improved resizing transformation and ensemble models

Convolutional neural networks have achieved great success in computer vision, but incorrect predictions would be output when applying intended perturbations on original input. These human-indistinguishable replicas are called adversarial examples, which on this feature can be used to evaluate network robustness and security. White-box attack success rate is considerable, when already knowing network structure and parameters. But in a black-box attack, the adversarial examples success rate is relatively low and the transferability remains to be improved. This article refers to model augmentation which is derived from data augmentation in training generalizable neural networks, and proposes resizing invariance method. The proposed method introduces improved resizing transformation to achieve model augmentation. In addition, ensemble models are used to generate more transferable adversarial examples. Extensive experiments verify the better performance of this method in comparison to other baseline methods including the original model augmentation method, and the black-box attack success rate is improved on both the normal models and defense models.
Perturbed peer model with joint confidence for semi-supervised object-based classification in urban area

The application of deep learning (DL) improves the accuracy of object-based classification in urban area, but the huge numbers of labelled samples required for training DL models are difficult to obtain. To address that, we propose a novel semi-supervised object-based classification method that combines pseudo-labelling method and consistency regularization method, named as perturbed peer model (PPM). To evaluate the quality of unlabelled object samples, a new object-level joint confidence is designed to assess the confidence of the samples’ prediction, which provides guidance for selecting unlabelled object samples in semi-supervised object-based classification. Instead of only using the high-confidence samples as usual, both the high-confidence samples with discriminative power and the low-confidence samples with a large range of structural features are exploited using pseudo-labelling method and consistency regularization method, respectively, facilitating the fusion of the two methods in the proposed PPM. In addition, two types of perturbations, dropout and difference augmentation, are integrated into the model to drive the consistency regularization method as well as to enhance the difference to facilitate the pseudo-labelling method. Experimental results show that the classification accuracy of PPM is better than the widely-used self-training, co-training, noisy-student, unsupervised data augmentation for consistency training, and FreeMatch methods in urban area.
Learning parallel and hierarchical mechanisms for edge detection

Edge detection is one of the fundamental components of advanced computer vision tasks, and it is essential to preserve computational resources while ensuring a certain level of performance. In this paper, we propose a lightweight edge detection network called the Parallel and Hierarchical Network (PHNet), which draws inspiration from the parallel processing and hierarchical processing mechanisms of visual information in the visual cortex neurons and is implemented via a convolutional neural network (CNN). Specifically, we designed an encoding network with parallel and hierarchical processing based on the visual information transmission pathway of the "retina-LGN-V1" and meticulously modeled the receptive fields of the cells involved in the pathway. Empirical evaluation demonstrates that, despite a minimal parameter count of only 0.2 M, the proposed model achieves a remarkable ODS score of 0.781 on the BSDS500 dataset and ODS score of 0.863 on the MBDD dataset. These results underscore the efficacy of the proposed network in attaining superior edge detection performance at a low computational cost. Moreover, we believe that this study, which combines computational vision and biological vision, can provide new insights into edge detection model research.