Reviewerly/exHarmony huggingface dataset:
Pose Invariant Topological Memory for Visual Navigation

Planning for visual navigation using topological memory, a memory graph consisting of nodes and edges, has been recently well-studied. The nodes correspond to past observations of a robot, and the edges represent the reachability predicted by a neural network (NN). Most prior methods, however, often fail to predict the reachability when the robot takes different poses, i.e. the direction the robot faces, at close positions. This is because the methods observe first-person view images, which significantly changes when the robot changes its pose, and thus it is fundamentally difficult to correctly predict the reachability from them. In this paper, we propose pose invariant topological memory (POINT) to address the problem. POINT observes omnidirectional images and predicts the reachability by using a spherical convolutional NN, which has a rotation invariance property and enables planning regardless of the robot’s pose. Additionally, we train the NN by contrastive learning with data augmentation to enable POINT to plan with robustness to changes in environmental conditions, such as light conditions and the presence of unseen objects. Our experimental results show that POINT outperforms conventional methods under both the same and different environmental conditions. In addition, the results with the KITTI-360 dataset show that POINT is more applicable to real-world environments than conventional methods.
A Novel Adaptive Factor-Based H∞ Cubature Kalman Filter for Autonomous Underwater Vehicle

In the navigation of an autonomous underwater vehicle (AUV), the positioning accuracy and stability of the navigation system will decrease due to uncertainties such as mobility, inaccuracy of a priori process noise characteristic, and simplification of a dynamic model. In order to solve the above problems, a new, adaptive factor-based H∞ cubature Kalman filter based on a fading factor (AF-H∞CKF) is proposed in this paper. On the one hand, the H∞ game theory provides AF-H∞CKF good robustness in the worst case; on the other hand, the fading factor makes the innovation orthogonal and inflates the predicted error covariance and the Kalman gain, which avoids a decrease in estimation precision in the case of model uncertainty. The simulation and experiment results show that the AF-H∞CKF filter can deal with AUV navigation better than other existing algorithms in the presence of outliers and model uncertainty, which confirms its effectiveness and superiority.
Who's Next: Rising Star Prediction via Diffusion of User Interest in Social Networks

Finding items with potential to increase sales is of great importance in online market. We propose to study this novel and practical problem: rising star prediction. We call these potential items Rising Star, which implies their ability to rise from low-turnover items to bestsellers in the future. Rising stars can be used to help with unfair recommendation in e-commerce platform, balance supply and demand to benefit the retailers and allocate marketing resources rationally. Although the study of rising star can bring great benefits, it also poses challenges to us. The sales trend of rising star fluctuates sharply in the short-term and exhibits more contingency caused by some external events (e.g., COVID-19 caused increasing purchase of the face mask) than other items, which cannot be solved by existing sales prediction methods. To address above challenges, in this paper, we observe that the presence of rising stars is closely correlated with the early diffusion of user interest in social networks, which is validated in the case of Taocode (an intermediary that diffuses user interest in Taobao). Thus, we propose a novel framework, RiseNet, to incorporate the user interest diffusion process with the item dynamic features to effectively predict rising stars. Specifically, we adopt a coupled mechanism to capture the dynamic interplay between items and user interest, and a special designed GNN based framework to quantify user interest. Our experimental results on large-scale real-world datasets provided by Taobao demonstrate the effectiveness of our proposed framework.
SafeXAI: Explainable AI to Detect Adversarial Attacks in Electronic Medical Records

Explainable Artificial Intelligence (XAI) techniques serve to bridge the gap between Learning based technique’s predictive performance and the model interpretability. In clinical domain, XAI is crucial for healthcare services deploying learning based techniques to retain patient’s trust and being accountable. Learning based techniques are susceptible to adversarial attacks which causes the model to misclassify. The article attempts to exercise XAI techniques as a measure to detect the effectiveness of the perturbations crafted by adversarial attacks on the trained model during run time. Frontline XAI techniques are explored to observe the possible perturbations crafted by Carlini and Wagner (CW) adversarial attack on a Recurrent Neural Network (RNN) trained using clinical informatics retrieved from Electronic Medical Records (EMR) for the binary classification task of Lung Cancer detection. The results manifest the authenticity of the suggested approach and provides guidelines for further research to utilize XAI to detect and reject adversarial samples.
Flamingo Search Algorithm and Its Application to Path Planning Problem

After studying the migratory and foraging behavior of flamingos, this paper proposes a new swarm intelligence optimization algorithm: flamingos search algorithm. At the same time, in order to verify the optimization effect and stability of FSA, this paper conducts tests and comparisons on 20 different benchmark functions, and standard deviation of the proposed FSA outperformed the other algorithms with regard to 20 test functions and path planning applications. Finally, this paper applies FSA to the path optimization problem, and the test results show that FSA has good engineering application potential, especially in the solution of path planning problems with good optimization performance. Experimental code for this article can be obtained from this website: https://github.com/18280426650/FSA.
Electromagnetic Signal Classification Based on Class Exemplar Selection and Multi-Objective Linear Programming

In the increasingly complex electromagnetic environment, a variety of new signal types are appearing; however, existing electromagnetic signal classification (ESC) models cannot handle new signal types. In this context, the emergence of class-incremental learning aims to incrementally update the classification model as new categories emerge. In this paper, an electromagnetic signal classification framework based on class exemplar selection and a multi-objective linear programming classifier (CES-MOLPC) is proposed in order to continuously learn new classes in an incremental manner. Specifically, our approach involves the adaptive selection of class exemplars considering normalized mutual information and a multi-objective linear programming classifier. The former is used to maintain the classification capability of the model for previous categories by selecting key samples, while the latter is used to allow the model to adapt quickly to new categories. Meanwhile, a weighted loss function based on cross-entropy and distillation loss is presented in order to fine-tune the model. We demonstrate the effectiveness of the proposed CES-MOLPC method through extensive experiments on the public RML2016.04c data set and the large-scale real-world ACARS signal data set. The results of the comparative experiments demonstrate that our method can achieve significant improvements over state-of-the-art methods.
The Functional Correspondence Problem

The ability to find correspondences in visual data is the essence of most computer vision tasks. But what are the right correspondences? The task of visual correspondence is well defined for two different images of same object instance. In case of two images of objects belonging to same category, visual correspondence is reasonably well-defined in most cases. But what about correspondence between two objects of completely different category – e.g., a shoe and a bottle? Does there exist any correspondence? Inspired by humans’ ability to: (a) generalize beyond semantic categories and; (b) infer functional affordances, we introduce the problem of functional correspondences in this paper. Given images of two objects, we ask a simple question: what is the set of correspondences between these two images for a given task? For example, what are the correspondences between a bottle and shoe for the task of pounding or the task of pouring. We introduce a new dataset: FunKPoint that has ground truth correspondences for 10 tasks and 20 object categories. We also introduce a modular task-driven representation for attacking this problem and demonstrate that our learned representation is effective for this task. But most importantly, because our supervision signal is not bound by semantics, we show that our learned representation can generalize better on few-shot classification problem. We hope this paper will inspire our community to think beyond semantics and focus more on cross-category generalization and learning representations for robotics tasks.
Hybrid Blind Watermarking Using RDWT-DCT in Singular Value Decomposition Domain

Image watermarking plays a major role in the field of communication. Applying the watermarking on the symbol provides higher imperceptibility and robustness properties with cover data help. Thus, various implementations are focused on non-blind watermarking (NBW) schemes combined with various transformations to perform this watermarking. The NBW methods do not have the accurate functionality to provide the maximum imperceptibility, embedding capacity standards, and the lack of robustness, respectively. Thus, to overcome these problems, this article focuses on implementing the proposed watermarking framework that utilizes the singular value decomposition (SVD), discrete cosine transform (DCT), and redundant discrete wavelet transform (RDWT) jointly, so the properties of the threes methods function together and give the higher performance. The proposed RDWT-DCT-SVD watermarking scheme simulated and compared for the various quality metrics such as normalized correlation coefficient (NCC), root means square error (RMSE), and peak signal to noise ratio (PSNR). Comparing the various existing methods shows that the proposed method gives higher imperceptibility and robustness properties.
Fault-Tolerant Control of Skid Steering Vehicles Based on Meta-Reinforcement Learning with Situation Embedding

Meta-reinforcement learning (meta-RL), used in the fault-tolerant control (FTC) problem, learns a meta-trained model from a set of fault situations that have a high-level similarity. However, in the real world, skid-steering vehicles might experience different types of fault situations. The use of a single initial meta-trained model limits the ability to learn different types of fault situations that do not possess a strong similarity. In this paper, we propose a novel FTC method to mitigate this limitation, by meta-training multiple initial meta-trained models and selecting the most suitable model to adapt to the fault situation. The proposed FTC method is based on the meta deep deterministic policy gradient (meta-DDPG) algorithm, which includes an offline stage and an online stage. In the offline stage, we first train multiple meta-trained models corresponding to different types of fault situations, and then a situation embedding model is trained with the state-transition data generated from meta-trained models. In the online stage, the most suitable meta-trained model is selected to adapt to the current fault situation. The simulation results demonstrate that the proposed FTC method allows skid-steering vehicles to adapt to different types of fault situations stably, while requiring significantly fewer fine-tuning steps than the baseline.
Energy Reduction Methods for Wild Animal Detection Devices

Recently, automatic wild animal detection methods using deep learning for taken images by camera traps have been reported. Energy consumption is important for edge devices that include deep learning because such devices are required to use outside where commercial power is not supplied. In this paper, we propose energy reduction methods for a wild animal detection device. The proposed methods are sensitivity adjustment for the motion sensor, attachment of a hat, motion detection by a frame difference method, and separation of functions on the device. The sensitivity adjustment for the motion sensor reduces the number of taking images by the camera. The attachment of a hat reduces the number of sensings by the motion sensor. The frame difference method reduces the number of inferences by deep learning. The separation of functions on the device reduces the power consumption in both operation time and idle time. In the experiments, we evaluate the effect of the proposed four methods by applying them to a wild animal detection device which we proposed previously. We compare the energy reduction ratio when each method is applied and all methods are combined. Compared to the device without the proposed methods, we can reduce the energy consumption by more than half when we combined all methods.
Performance Evaluation of Machine Learning Classifiers for Face Recognition

The digital world, especially image processing, has been evolving due to the needs of society and the importance of digital-based system security. One of the rapidly progressing technologies is the face recognition system using artificial intelligence. It recognizes a person’s face registered in the database for verification purposes. In this study, we evaluate the face recognition systems based on machine learning classifier algorithms and Principal Component Analysis (PCA) for feature extraction. Seven machine learning algorithms were considered, i.e., Support Vector Machine (SVM), Decision Tree, K-Nearest Neighbour (KNN), Logistic Regression, Naïve Bayes, Multi-Layer Perceptron (MLP), and Convolutional Neural network (CNN). In the CNN scenario, PCA was not used since it has its feature extraction capability. The first six classifiers were set to the most optimal settings. At the same time, CNN used the LeNet-5 architecture trained with a dropout rate of 0.25, 60 epochs, batch size of 20, Adam optimizer, and cross-categorical entropy for the loss function. The input image size was 64×64×1 obtained from the Olivetti faces database. CNN, SVM, and LR achieved the three highest accuracies, i.e., 98.75%, 98.75%, and 97.50%, respectively.
Text Classification Method Based on BiGRU-Attention and CNN Hybrid Model

Aiming at the problem that traditional Gated Recurrent Unit (GRU) and Convolution Neural Network (CNN) can not reflect the importance of each word in the text when extracting features, a text classification method based on BiGRU Attention and CNN is proposed. Firstly, CNN was used to extract the local information of the text, and then the full-text semantics was integrated. Secondly, BiGRU was used to extract the context features of the text, and attention mechanism was used after BiGRU to extract the attention score of the output information. Finally, the output of BiGRU attention was fused with the output of CNN to realize the effective extraction of text features and focused on the important content words. Experimental results on three public datasets showed that the proposed model was better than GRU, CNN and other models, which can effectively improve the effect of text classification.
EOP: efficient operator partition for deep learning inference over edge servers

Recently, Deep Learning (DL) models have demonstrated great success for its attractive ability of high accuracy used in artificial intelligence Internet of Things applications. A common deployment solution is to run such DL inference tasks on edge servers. In a DL inference, each operator takes tensors as input and run in a tensor virtual machine, which isolates resource usage among operators. Nevertheless, existing edge-based DL inference approaches can not efficiently use heterogeneous resources (e.g., CPU and low-end GPU) on edge servers and result in sub-optimal DL inference performance, since they can only partition operators in a DL inference with equal or fixed ratios. It is still a big challenge to support partition optimizations over edge servers for a wide range of DL models, such as Convolution Neural Network (CNN), Recurrent Neural Network (RNN) and Transformers.
Attention‐guided duplex adversarial U‐net for pancreatic segmentation from computed tomography images

Segmenting the organs from computed tomography (CT) images is crucial to early diagnosis and treatment. Pancreas segmentation is especially challenging because the pancreas has a small volume and a large variation in shape.To mitigate this issue, an attention-guided duplex adversarial U-Net (ADAU-Net) for pancreas segmentation is proposed in this work. First, two adversarial networks are integrated into the baseline U-Net to ensure the obtained prediction maps resemble the ground truths. Then, attention blocks are applied to preserve much contextual information for segmentation. The implementation of the proposed ADAU-Net consists of two steps: 1) backbone segmentor selection scheme is introduced to select an optimal backbone segmentor from three two-dimensional segmentation model variants based on a conventional U-Net and 2) attention blocks are integrated into the backbone segmentor at several locations to enhance the interdependency among pixels for a better segmentation performance, and the optimal structure is selected as a final version.The experimental results on the National Institutes of Health Pancreas-CT dataset show that our proposed ADAU-Net outperforms the baseline segmentation network by 6.39% in dice similarity coefficient and obtains a competitive performance compared with the-state-of-art methods for pancreas segmentation.The ADAU-Net achieves satisfactory segmentation results on the public pancreas dataset, indicating that the proposed model can segment pancreas outlines from CT images accurately.
Ethereum MongoDB: Integrating Blockchain with Non-relational Databases

Blockchain is a decentralized distributed digital ledger that provides tamper-resistance but is very slow in search query processing with a considerable amount of transaction cost associated with it. With the advent of newer technologies and large-scale data collections, it is extremely important to increase the security level of databases. Thus, enabling the features of non-relational databases that support immutable transactions. In this paper, we propose an open-source system, Ethereum MongoDB, which is developed by integrating Ethereum blockchain features with the MongoDB database. The non-relational database uses a storage model optimized for specific requirements of the type of data being stored. It provides well-designed data formatting and cost-effective, quick query processing but suffers from data reliability. The proposed Ethereum MongoDB, on the other hand, is a blockchain-enabled database that stores data in a non-relational database with the transaction log being stored in blockchain thus guaranteeing the consistency, immutability, and auditability of transaction log along with the faster query processing of databases in a cost-effective manner. In a blockchain, every transaction has some cost associated with it so, mapping the transactions of the database onto Ethereum's smart contract is not a straightforward process. By modularizing the code in certain ways, we can bring down the transaction cost considerably. A comprehensive real-time application has been developed to demonstrate the effectiveness of the system.
Simplified Particle Swarm Optimization Algorithm with Improved Learning Factor and Search Method

In view of the disadvantages such as easy to fall into local optimum and slow convergence of PSO, a simplified particle swarm optimization algorithm based on improved learning factor and search method (LSSPSO) is proposed. Based on the simplified particle swarm optimization algorithm, the values of learning factor are dynamically changed by using the sine-cosine strategy, which promotes the learning ability of particle. Meanwhile, a new search method is proposed to enhance the sufficiency of particle learning. Experimental results of eight test functions show that the proposed algorithm has better comprehensive optimization performance than other six compared optimization algorithms.
Enregistering emojis in online socialization: linguaculturally diverse university students’ affective translingual practices

This article examines linguaculturally diverse university students’ affective translingual practices of using emojis and languages in computer-mediated communication. Research studies have investigated the sociopragmatic uses of emojis from the perspectives of social semiotics, language socialisation, and translingual practices. This study expands on these perspectives and further adopts enregisterment as a conceptual framework to explore the emergence of emoji variations and other affective expressions in online discussions about news and social media content. The primary data were collected from written discourses on Facebook across 8 weeks and through follow-up data-focused interviews. The results present the multilingual participants’ metapragmatic commentaries on particular uses of face emojis in relation to emotive discourses, enregistered voices, and embodied contextualisation. The results exemplify the multimodal enregisterment of affective translingual practices through which emoji users’ playful performances can be recognised and enacted in online socialisation.
Optimal Low-Hit-Zone Frequency-Hopping Sequence Sets With Wide-Gap for FHMA Systems Under Follower Jamming

This letter is concerned with tackling both of the multi-access interference (MAI) and the follower jamming (FJ) in frequency-hopping multiple-access (FHMA) systems via novel FH sequence (FHS) design. We consider low-hit-zone (LHZ) FHS which provides lower MAIs in quasi-synchronous FHMA systems but suffers from FJ attack. We introduce an improved LHZ-FHS with wide-gap (i.e., WG-LHZ-FHS) for the neighboring carrier frequencies so as to combat the MAI and the FJ. We first present a general method to generate new WG-FHS sets and then construct the first known WG-LHZ-FHS sets with optimal periodic Hamming correlation (PHC) properties. Besides, the error-rate performance of the constructed WG-LHZ-FHS set imposed in an FHMA system under FJ is investigated. The numerical analysis and the system simulation results indicate that the proposed WG-LHZ-FHS set provides the better error-rate performance to combat the FJ as well as satisfactory capability of MAI suppression due to its combined merits of WG and LHZ.
Complex Embedding with Type Constraints for Link Prediction

Large-scale knowledge graphs not only store entities and relations but also provide ontology-based information about them. Type constraints that exist in this information are of great importance for link prediction. In this paper, we proposed a novel complex embedding method, CHolE, in which complex circular correlation was introduced to extend the classic real-valued compositional representation HolE to complex domains, and type constraints were integrated into complex representational embeddings for improving link prediction. The proposed model consisted of two functional components, the type constraint model and the relation learning model, to form type constraints such as modulus constraints and acquire the relatedness between entities accurately by capturing rich interactions in the modulus and phase angles of complex embeddings. Experimental results on benchmark datasets showed that CHolE outperformed previous state-of-the-art methods, and the impartment of type constraints improved its performance on link prediction effectively.