Reviewerly/exHarmony huggingface dataset:
An Informative Path Planner for a Swarm of ASVs Based on an Enhanced PSO with Gaussian Surrogate Model Components Intended for Water Monitoring Applications

Controlling the water quality of water supplies has always been a critical challenge, and water resource monitoring has become a need in recent years. Manual monitoring is not recommended in the case of large water surfaces for a variety of reasons, including expense and time consumption. In the last few years, researchers have proposed the use of autonomous vehicles for monitoring tasks. Fleets or swarms of vehicles can be deployed to conduct water resource explorations by using path planning techniques to guide the movements of each vehicle. The main idea of this work is the development of a monitoring system for Ypacarai Lake, where a fleet of autonomous surface vehicles will be guided by an improved particle swarm optimization based on the Gaussian process as a surrogate model. The purpose of using the surrogate model is to model water quality parameter behavior and to guide the movements of the vehicles toward areas where samples have not yet been collected; these areas are considered areas with high uncertainty or unexplored areas and areas with high contamination levels of the lake. The results show that the proposed approach, namely the enhanced GP-based PSO, balances appropriately the exploration and exploitation of the surface of Ypacarai Lake. In addition, the proposed approach has been compared with other techniques like the original particle swarm optimization and the particle swarm optimization with Gaussian process uncertainty component in a simulated Ypacarai Lake environment. The obtained results demonstrate the superiority of the proposed enhanced GP-based PSO in terms of mean square error with respect to the other techniques.
Automated Data-Driven Generation of Personalized Pedagogical Interventions in Intelligent Tutoring Systems

Abstract Intelligent tutoring systems (ITS) have been shown to be highly effective at promoting learning as compared to other computer-based instructional approaches. However, many ITS rely heavily on expert design and hand-crafted rules. This makes them difficult to build and transfer across domains and limits their potential efficacy. In this paper, we investigate how feedback in a large-scale ITS can be automatically generated in a data-driven way, and more specifically how personalization of feedback can lead to improvements in student performance outcomes. First, in this paper we propose a machine learning approach to generate personalized feedback in an automated way, which takes individual needs of students into account, while alleviating the need of expert intervention and design of hand-crafted rules. We leverage state-of-the-art machine learning and natural language processing techniques to provide students with personalized feedback using hints and Wikipedia-based explanations . Second, we demonstrate that personalized feedback leads to improved success rates at solving exercises in practice: our personalized feedback model is used in , a large-scale dialogue-based ITS with around 20,000 students launched in 2019. We present the results of experiments with students and show that the automated, data-driven, personalized feedback leads to a significant overall improvement of 22.95 % in student performance outcomes and substantial improvements in the subjective evaluation of the feedback.
Guiding Principles for Participatory Design-inspired Natural Language Processing

We introduce 9 guiding principles 1 to integrate Participatory Design (PD) methods in the development of Natural Language Processing (NLP) systems.The adoption of PD methods by NLP will help to alleviate issues concerning the development of more democratic, fairer, less-biased technologies to process natural language data.This short paper is the outcome of an ongoing dialogue between designers and NLP experts and adopts a non-standard format following previous work by Traum (2000); Bender (2013); Abzianidze and Bos (2019).Every section is a guiding principle.While principles 1-3 illustrate assumptions and methods that inform community-based PD practices, we used two fictional design scenarios (Encinas and Blythe, 2018), which build on top of situations familiar to the authors, to elicit the identification of the other 6.Principles 4-6 describes the impact of PD methods on the design of NLP systems, targeting two critical aspects: data collection & annotation, and the deployment & evaluation.Finally, principles 7-9 guide a new reflexivity of the NLP research with respect to its context, actors and participants, and aims.We hope this guide will offer inspiration and a road-map to develop a new generation of PD-inspired NLP.
Graph Entropy Guided Node Embedding Dimension Selection for Graph Neural Networks

Graph representation learning has achieved great success in many areas, including e-commerce, chemistry, biology, etc. However, the fundamental problem of choosing the appropriate dimension of node embedding for a given graph still remains unsolved. The commonly used strategies for Node Embedding Dimension Selection (NEDS) based on grid search or empirical knowledge suffer from heavy computation and poor model performance. In this paper, we revisit NEDS from the perspective of minimum entropy principle. Subsequently, we propose a novel Minimum Graph Entropy (MinGE) algorithm for NEDS with graph data. To be specific, MinGE considers both feature entropy and structure entropy on graphs, which are carefully designed according to the characteristics of the rich information in them. The feature entropy, which assumes the embeddings of adjacent nodes to be more similar, connects node features and link topology on graphs. The structure entropy takes the normalized degree as basic unit to further measure the higher-order structure of graphs. Based on them, we design MinGE to directly calculate the ideal node embedding dimension for any graph. Finally, comprehensive experiments with popular Graph Neural Networks (GNNs) on benchmark datasets demonstrate the effectiveness and generalizability of our proposed MinGE.
Adversarial Feature Disentanglement for Long-Term Person Re-identification

Most existing person re-identification methods are effective in short-term scenarios because of their appearance dependencies. However, these methods may fail in long-term scenarios where people might change their clothes. To this end, we propose an adversarial feature disentanglement network (AFD-Net) which contains intra-class reconstruction and inter-class adversary to disentangle the identity-related and identity-unrelated (clothing) features. For intra-class reconstruction, the person images with the same identity are represented and disentangled into identity and clothing features by two separate encoders, and further reconstructed into original images to reduce intra-class feature variations. For inter-class adversary, the disentangled features across different identities are exchanged and recombined to generate adversarial clothes-changing images for training, which makes the identity and clothing features more independent. Especially, to supervise these new generated clothes-changing images, a re-feeding strategy is designed to re-disentangle and reconstruct these new images for image-level self-supervision in the original image space and feature-level soft-supervision in the disentangled feature space. Moreover, we collect a challenging Market-Clothes dataset and a real-world PKU-Market-Reid dataset for evaluation. The results on one large-scale short-term dataset (Market-1501) and five long-term datasets (three public and two we proposed) confirm the superiority of our method against other state-of-the-art methods.
Explainable recommendation: when design meets trust calibration

Abstract Human-AI collaborative decision-making tools are being increasingly applied in critical domains such as healthcare. However, these tools are often seen as closed and intransparent for human decision-makers. An essential requirement for their success is the ability to provide explanations about themselves that are understandable and meaningful to the users. While explanations generally have positive connotations, studies showed that the assumption behind users interacting and engaging with these explanations could introduce trust calibration errors such as facilitating irrational or less thoughtful agreement or disagreement with the AI recommendation. In this paper, we explore how to help trust calibration through explanation interaction design. Our research method included two main phases. We first conducted a think-aloud study with 16 participants aiming to reveal main trust calibration errors concerning explainability in AI-Human collaborative decision-making tools. Then, we conducted two co-design sessions with eight participants to identify design principles and techniques for explanations that help trust calibration. As a conclusion of our research, we provide five design principles: Design for engagement, challenging habitual actions, attention guidance, friction and support training and learning. Our findings are meant to pave the way towards a more integrated framework for designing explanations with trust calibration as a primary goal.
Assessment of Axillary Lymph Nodes for Metastasis on Ultrasound Using Artificial Intelligence

The purpose of this study was to evaluate an artificial intelligence (AI) system for the classification of axillary lymph nodes on ultrasound compared to radiologists. Ultrasound images of 317 axillary lymph nodes from patients referred for ultrasound guided fine needle aspiration or core needle biopsy and corresponding pathology findings were collected. Lymph nodes were classified into benign and malignant groups with histopathological result serving as the reference. Google Cloud AutoML Vision (Mountain View, CA) was used for AI image classification. Three experienced radiologists also classified the images and gave a level of suspicion score (1–5). To test the accuracy of AI, an external testing dataset of 64 images from 64 independent patients was evaluated by three AI models and the three readers. The diagnostic performance of AI and the humans were then quantified using receiver operating characteristics curves. In the complete set of 317 images, AutoML achieved a sensitivity of 77.1%, positive predictive value (PPV) of 77.1%, and an area under the precision recall curve of 0.78, while the three radiologists showed a sensitivity of 87.8% ± 8.5%, specificity of 50.3% ± 16.4%, PPV of 61.1% ± 5.4%, negative predictive value (NPV) of 84.1% ± 6.6%, and accuracy of 67.7% ± 5.7%. In the three external independent test sets, AI and human readers achieved sensitivity of 74.0% ± 0.14% versus 89.9% ± 0.06% ( p = .25), specificity of 64.4% ± 0.11% versus 50.1 ± 0.20% ( p = .22), PPV of 68.3% ± 0.04% versus 65.4 ± 0.07% ( p = .50), NPV of 72.6% ± 0.11% versus 82.1% ± 0.08% ( p = .33), and accuracy of 69.5% ± 0.06% versus 70.1% ± 0.07% ( p = .90), respectively. These preliminary results indicate AI has comparable performance to trained radiologists and could be used to predict the presence of metastasis in ultrasound images of axillary lymph nodes.
Detecting Adversarial DDoS Attacks in Software- Defined Networking Using Deep Learning Techniques and Adversarial Training

In recent years, Deep Learning (DL) has been utilized for cyber-attack detection mechanisms as it offers highly accurate detection and is able to overcome the limitations of standard machine learning techniques. When applied in a Software-Defined Network (SDN) environment, a DL-based detection mechanism shows satisfying detection performance. However, in the case of adversarial attacks, the detection performance deteriorates. Therefore, in this paper, first, we outline a highly accurate flooding DDoS attack detection framework based on DL for SDN environments. Second, we investigate the performance degradation of our detection framework when being tested with two adversary traffic datasets. Finally, we evaluate three adversarial training procedures for improving the detection performance of our framework concerning adversarial attacks. It is shown that the application of one of the adversarial training procedures can avoid detection performance degradation and thus might be used in a real-time detection system based on continual learning.
Adaptive Prototypical Networks With Label Words and Joint Representation Learning for Few-Shot Relation Classification

Relation classification (RC) task is one of fundamental tasks of information extraction, aiming to detect the relation information between entity pairs in unstructured natural language text and generate structured data in the form of entity–relation triple. Although distant supervision methods can effectively alleviate the problem of lack of training data in supervised learning, they also introduce noise into the data and still cannot fundamentally solve the long-tail distribution problem of the training instances. In order to enable the neural network to learn new knowledge through few instances such as humans, this work focuses on few-shot relation classification (FSRC), where a classifier should generalize to new classes that have not been seen in the training set, given only a number of samples for each class. To make full use of the existing information and get a better feature representation for each instance, we propose to encode each class prototype in an adaptive way from two aspects. First, based on the prototypical networks, we propose an adaptive mixture mechanism to add label words to the representation of the class prototype, which, to the best of our knowledge, is the first attempt to integrate the label information into features of the support samples of each class so as to get more interactive class prototypes. Second, to more reasonably measure the distances between samples of each category, we introduce a loss function for joint representation learning (JRL) to encode each support instance in an adaptive manner. Extensive experiments have been conducted on FewRel under different few-shot (FS) settings, and the results show that the proposed adaptive prototypical networks with label words and JRL has not only achieved significant improvements in accuracy but also increased the generalization ability of FSRC.
Synthetic faces: how perceptually convincing are they?

Recent advances in machine learning, specifically generative adversarial networks (GANs), have made it possible to synthesize highly photo-realistic faces. Such synthetic faces have been used in the creation of fraudulent social media accounts, including the creation of a fictional candidate for U.S. Congress. It has been shown that deep neural networks can be trained to discriminate between real and synthesized faces; it remains unknown, however, if humans can. We examined people’s ability to discriminate between synthetic and real faces. We selected 400 faces synthesized using the state of the art StyleGAN2, further ensuring diversity across gender, age, and race. A convolutional neural network descriptor was used to extract a low-dimensional, perceptually meaningful, representation of each face. For each of the 400 synthesized faces, this representation was used to find the most similar real faces in the Flickr-Faces-HQ (FFHQ) dataset. From these, we manually selected a matching face that did not contain additional discriminative cues (e.g., complex background, other people in the scene). Participants (N=315) were recruited from Mechanical Turk and given a brief tutorial consisting of examples of synthesized and real faces. Each participant then saw 128 trials, each consisting of a single face, either synthesized or real, and had unlimited time to classify the face accordingly. Although unknown to the participant, half of the faces were real and half were synthesized. Across the 128 trials, faces were equally balanced in terms of gender and race. Average performance was close to chance with no response bias (d-prime = -0.09; beta = 0.99). These results suggest that StyleGAN2 can successfully synthesize faces that are realistic enough to fool naive observers. We are examining whether a more detailed training session, raising participants’ awareness of some common synthesis artifacts, will improve their ability to detect synthetic faces.
Extracting Event Temporal Relations via Hyperbolic Geometry

Detecting events and their evolution through time is a crucial task in natural language understanding.Recent neural approaches to event temporal relation extraction typically map events to embeddings in the Euclidean space and train a classifier to detect temporal relations between event pairs.However, embeddings in the Euclidean space cannot capture richer asymmetric relations such as event temporal relations.We thus propose to embed events into hyperbolic spaces, which are intrinsically oriented at modeling hierarchical structures.We introduce two approaches to encode events and their temporal relations in hyperbolic spaces.One approach leverages hyperbolic embeddings to directly infer event relations through simple geometrical operations.In the second one, we devise an end-to-end architecture composed of hyperbolic neural units tailored for the temporal relation extraction task.Thorough experimental assessments on widely used datasets have shown the benefits of revisiting the tasks on a different geometrical space, resulting in state-of-the-art performance on several standard metrics.Finally, the ablation study and several qualitative analyses highlighted the rich event semantics implicitly encoded into hyperbolic spaces.
Development of Mathematical Models of Problems of Management the Production Division with a Discrete Unit Type Production

This article discusses the problem of managing production systems, the relationship of management of the organization of production, operational management with the efficiency of industrial production, formulates the tasks of managing the organization of production and operational management, solving the problem of increasing production efficiency, and also gives a brief overview of existing methods for solving the scheduling problem, how key task of managing the production unit. The developed mathematical models of the tasks of managing a production unit based on problem statements are considered. To solve the scheduling problem in the conditions of discrete unit production, it is necessary to develop a mathematical model that takes into account the specifics of this production. The developed information model of the control object allows you to fully describe the production process in the tool shop. The developed mathematical models of the problems of managing the organization of production and the tasks of scheduling take into account the specifics of unit production and an order-based planning system for the tool shop of the enterprise.
An Anti-Counterfeit and Traceable Management System for Brand Clothing with Hyperledger Fabric Framework

Counterfeit products are internationally regarded as “the world’s second greatest public health hazards after drugs”. Counterfeiters produce counterfeit brand clothing and then sell them to consumers through unofficial channels; thus, consumers spend a lot of money without getting the value they deserve. With the rise of e-shopping, the safety and security of branded clothing supply chains are also under threat. Counterfeit branded apparel manufacturers generate profits while genuine manufacturers suffer, which ultimately violates the interests of the public. This study proposes a traceable anti-counterfeit management system for branded clothing based on Hyperledger Fabric technology. This system can achieve full traceability of the production information of branded clothing. It uses the unique characteristics of blockchain, such as being unforgeable, traceable, open, and transparent, and collectively ‘maintaining’, to record the specific production processes of the brand clothing, and ensure the authenticity and legitimacy of the production information of brand clothing. The end-user can self-verify the product’s authenticity by sharing the product’s details on the immutable framework. It solves problems surrounding information asymmetry, opaque supply chain data, and easy falsification in the production process of branded clothing in traditional branded clothing supply chains.
UniPose+: A Unified Framework for 2D and 3D Human Pose Estimation in Images and Videos

We propose UniPose+, a unified framework for 2D and 3D human pose estimation in images and videos. The UniPose+ architecture leverages multi-scale feature representations to increase the effectiveness of backbone feature extractors, with no significant increase in network size and no postprocessing. Current pose estimation methods heavily rely on statistical postprocessing or predefined anchor poses for joint localization. The UniPose+ framework incorporates contextual information across scales and joint localization with Gaussian heatmap modulation at the decoder output to estimate 2D and 3D human pose in a single stage with state-of-the-art accuracy, without relying on predefined anchor poses. The multi-scale representations allowed by the waterfall module in the UniPose+ framework leverage the efficiency of progressive filtering in the cascade architecture, while maintaining multi-scale fields-of-view comparable to spatial pyramid configurations. Our results on multiple datasets demonstrate that UniPose+, with a HRNet, ResNet or SENet backbone and waterfall module, is a robust and efficient architecture for single person 2D and 3D pose estimation in single images and videos.