Reviewerly/exHarmony huggingface dataset:
Joint Detection, Tracking, and Classification of Multiple Maneuvering Star-Convex Extended Targets

The extended target joint detection, tracking and classification (JDTC) algorithms based on elliptical shape utilize rough target size information for classification, which is difficult to effectively solve the problem of accurate classification of similarly sized targets. Therefore, this paper proposes a multiple maneuvering star-convex extended target JDTC algorithm called JDTC-MM-CBMeMBer filter. First, the target extent state is modeled as a star-convex shape via the star-convex random hypersurface model (RHM). By modeling the target class-related prior information with vector form, we construct its relationship with the simultaneous extent state, and integrate it into the Bayesian filter framework for joint processing. Second, to solve the implementation difficulty due to the high-dimensional target state and strong nonlinear observation model in the star-convex RHM, we model the target kinematic by two separate vectors, and use the particle filter to update target class probability. Next, we use the multiple model cardinality balanced multi-target multi-Bernoulli (CBMeMBer) filter to derive the JDTC recursion process of the multiple maneuvering star-convex extended targets. At last, the Gamma-Gaussian-Gaussian mixture implementation is present. The simulation results show that: (1) compared with the extended target JDTC algorithm based on an elliptical shape, the proposed filter can accurately classify targets with similar sizes but different shapes (2) compared with the multiple extended target tracking algorithm based on star-convex RHM, the proposed filter can significantly improve the target state estimation results without significantly increasing the running time.
A Sim-to-Real Deep Learning-Based Framework for Autonomous Nano-Drone Racing

Autonomous drone racing competitions are a proxy to improve unmanned aerial vehicles' perception, planning, and control skills.The recent emergence of autonomous nano-sized drone racing imposes new challenges, as their ∼10 cm form factor heavily restricts the resources available onboard, including memory, computation, and sensors.This paper describes the methodology and technical implementation of the system winning the first autonomous nano-drone racing international competition: the "IMAV 2022 Nanocopter AI Challenge."We developed a fully onboard deep learning approach for visual navigation trained only on simulation images to achieve this goal.Our approach includes a convolutional neural network for obstacle avoidance, a sim-to-real dataset collection procedure, and a navigation policy that we selected, characterized, and adapted through simulation and actual in-field experiments.Our system ranked 1 st among seven competing teams at the competition.In our best attempt, we scored 115 m of traveled distance in the allotted 5-minute flight, never crashing while dodging static and dynamic obstacles.Sharing our knowledge with the research community, we aim to provide a solid groundwork to foster future development in this field.
SSE4Rec: Sequential recommendation with subsequence extraction

Sequential recommendation mines the sequential patterns in user behavior data to recommend items to users. Recent studies have mainly followed the language modeling paradigm, on the premise that the next item depends on the sequence of previous items. Notably, differences exist between user behavior and textual data. One key difference is that behavioral sequences can encompass multiple intentions, unlike sentences that typically express a single intention. Furthermore, behavioral sequences emerge freely from users, whereas sentences conform to grammatical rules. This study highlights the risk of treating behavior sequences as a unified sequence, and the resultant potential for overfitting the observed transitions. We mitigated this risk by using subsequence extraction for recommendation (SSE4Rec). This model employs a subsequence extraction module that disperses items into distinct subsequences and groups of related items. Each subsequence is then processed by an independent downstream sequence model, which discourages the memorization of inconsequential transitions. Both the training and inference strategies are inherently integrated into the model. The proposed method was evaluated on four public datasets, whereby it was demonstrated to outperform publicly available alternatives or deliver competitive results. The properties of the model were also explored, further visualizing the output of the subsequence extraction module.
Sentence-level sentiment classification based on multi-attention bidirectional gated spiking neural P systems

The GSNP model is a new recurrent-like network inspired by nonlinear spiking mechanisms in nonlinear spiking neural P systems. In this study, a novel sentiment classification model MA-BiGSNP is established by using bidirectional GSNP model combined with multi-attention mechanism. BiGSNP, which is created by two GSNP models with opposite directions, is used to capture semantic correlations between word contexts in a sentence. The multi-attention mechanism simulates the variety of relationships between sentences as well as the significance of words in sentences. To evaluate the effectiveness of the proposed MA-BiGSNP model, we perform comparative experiments and ablation experiments on five real datasets and twelve baseline models. The experimental results show that the proposed MA-BiGSNP model is effective for sentiment classification task.
Blockchain for the digital twin-driven autonomous optical network

In recent years, optical networks have become increasingly large-scale and complex, resulting in mounting pressures on operation and maintenance management. The techniques of the artificial intelligence (AI)-driven digital twin (DT) have been applied to optical networks to pave the way for creating the self-optimizing and self-controlling autonomous optical network (AON). However, the AON is confronted with many security and reliability issues: the centralized architecture is prone to data loss during system disconnection and active/standby switchover, and the massive data, DT models, and control instructions in the AON all face the risk of being tampered with, directly affecting the effectiveness of network regulation. In addition, the reliability of complex autonomous processes should be ensured. To address these challenges, in this paper, we applied blockchain to a DT-driven AON with the designed architecture and deployment scheme to realize data loss prevention, anti-tamper storage, secure autonomous operations, access control, and network element controller decisions. A blockchain-based solution was studied by considering the quality of transmission (QoT) estimation and link optimization as examples. Four chaincodes were designed and developed to perform the model storage, service definition, telemetry subscription, data collection, model implementation, and instruction delivery in the AON. Furthermore, the proposed solution was developed and deployed using Hyperledger Fabric. The Hyperledger Caliper was used to test the performance of the proposed blockchain system. The distributed environment is used for performance testing to simulate the real scenario. The test results are analyzed to indicate the future development direction.
Style Elimination and Information Restitution for generalizable person re-identification

Domain generalizable person re-identification (DG ReID) aims to obtain a model that can be applied directly to unseen domains once trained on a set of source domains (datasets collected from different camera networks). Among current DG ReID methods, instance normalization is a promising solution to reduce the effect of domain bias, but it inevitably filters out some discriminative information. Besides, since most pioneering approaches cannot effectively address the loss of discriminative information, this paper proposes a Style Elimination and Information Restitution (SEIR) module and constructs a generalizable framework. Specifically, we utilize instance normalization to reinforce the generalization capability on unseen domains. Then, the instance-specific mean and variance are employed to construct a feature vector that includes discriminative and style information. Besides, an encoder–decoder structure is designed to restitute the discriminative information to ensure a high recognition rate. Finally, a dual optimization strategy is devised to update the proposed module by simulating a real train-test process to enhance the generalization robustness further and prevent SEIR from overfitting to the source domain. Extensive experimental results demonstrate that the proposed method outperforms the state-of-the-art methods by a large margin on the public DG ReID benchmarks.
An <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si81.svg" display="inline" id="d1e2146"><mml:mi>ɛ</mml:mi></mml:math>-constrained multiobjective differential evolution with adaptive gradient-based repair method for real-world constrained optimization problems

Over the past decade, incorporating information from the objective function into the constraint-handling process has garnered considerable attention in evolutionary algorithm research. Stemming from this, multiobjective optimization has emerged as a promising approach that simultaneously optimizes the objective function and constraints. However, the challenges associated with optimizing objective functions and satisfying constraints exhibit significant variability. Some constraints and/or objective functions can be exceptionally challenging, necessitating specific methods to identify the optimal solution within a limited feasible region. This study proposes an adaptive gradient descent-based repair method to enhance the search capability for both objective function optimization and constraint satisfaction. This method leverages objective function information to rectify infeasible solutions using gradient descent, thereby reducing the limitations of a purely constraint-based approach and automating the application of the repair method. Furthermore, an enhanced variant of the ɛ-constrained multiobjective differential evolution algorithm is developed for solving constrained optimization problems. The efficacy of the proposed approach is assessed using 57 benchmark test functions derived from real-world applications. Empirical results demonstrate that our approach is capable of locating high-quality solutions, outperforming several selected state-of-the-art algorithms.
SDSDet: A real-time object detector for small, dense, multi-scale remote sensing objects

Object detection in remote sensing images (RSIs) plays a crucial role in aerial and satellite image analysis. Existing methods lack the capability to effectively detect small and multi-scale objects in RSIs. Consequently, achieving an optimal trade-off between speed and accuracy remains unattainable. Extensive investigation reveals that state-of-the-art detectors have largely overlooked two critical aspects: Spatial artifacts from convolution operations and gradient confusion caused by neighboring levels in the Feature Pyramid Network. To address the first problem, we propose adopting a non-reorganized patch-embedding layer in the downsampling stage and a dual-path learning network (DPLNet) as the backbone, which can effectively mitigate the adverse effects of the edge pixel feature bias in feature maps. Additionally, using DPLNet as the backbone network can minimize costs while learning the intrinsic feature information of objects in RSI. For the second aspect, we propose a neighbor-erasing module with only one gradient flow (OGF-NEM). This module utilizes deep features to erase large objects to highlight small objects in shallow features and changes the backpropagation path to prevent the backflow of unreasonable gradients and the erosion of information from neighbor scales. Thus, a novel detector, called SDSDet, is proposed, which achieves excellent performance for small, dense, and multi-scale objects in RSIs. We have conducted exhaustive experiments on DOTA and MS COCO datasets. Specifically, the SDSDet achieves 42.8% AP on DOTA and 33.3% AP on MS COCO, together with nearly 4.87 M model size and 95 FPS.
Vendor capabilities development in blockchain sourcing: A parallel play approach

Vendor capabilities development has been an important research stream in the information systems (IS) sourcing literature. The existing research, which was based on the context of implementing centralized enterprise systems, focused on the adoption of deliberate learning and implicit learning, with the assumption that effective selection between these two learning approaches is sufficient for service providers to acquire vendor capabilities. However, this assumption may not hold in implementing a decentralized system based on enterprise blockchain. Because an enterprise blockchain system affords a new way of governing transactions and demands a new governance mode, it creates an uncertain and complex landscape in which the mere selection between deliberate learning and implicit learning becomes insufficient. Addressing this gap is crucial because many enterprise blockchain projects fail due to the incompetence of service providers. By conducting an in-depth case study of a successful blockchain service provider, our study reveals a new mechanism for developing vendor capabilities: parallel play. As with parallel play by preschoolers, the service provider combines a set of learning approaches featuring borrowing from peers and learning through experimentation. Our study extends the IS sourcing literature on vendor capabilities development from the context of implementing centralized enterprise systems to the context of implementing decentralized enterprise blockchain systems. Our findings also provide guidelines for blockchain service providers to follow when acquiring vendor capabilities and for clients to use when selecting service providers.
A Sketch-texture Retrieval Framework using Perceptual Similarity

Sketch-based image retrieval is an important research topic in the field of image processing. Hand-drawn sketches consist only of contour lines, and lack detailed information such as color and textons. As a result, they differ significantly from color images in terms of image feature distribution, making sketch-based image retrieval a typical cross-domain retrieval problem. To solve this problem, we constructed a perceptual space consistent with both textures and sketches, and using perceptual similarity for sketch-based texture retrieval. To implement this approach, we first conduct a set of psychological experiments to analyze the similarity of visual perception of the textures, then we create a dataset of over a thousand hand-drawn sketches according to the textures. We proposed a layer-wise perceptual similarity learning method that integrates perceptual similarity, with which we trained a similarity prediction network to learn the perceptual similarity between hand-drawn sketches and natural texture images. The trained network can be used for perceptual similarity prediction and efficient retrieval. Our experimental results demonstrate the effectiveness of sketch-based texture retrieval using perceptual similarity.
A detailed occupant activity classification model in a residential environment using building monitoring data: Considering occupant characteristics

In this study, we developed a detailed occupant activity classification model to detect occupant activities using various non-intrusive monitoring data in buildings (i.e., indoor environmental data, energy consumption data, motion·door sensor data, and state of indoor climate control devices). To collect building monitoring data, we conducted experiments with five subjects (Person A to E) in an experimental testbed. In total, ten occupant activities (i.e., Sleeping, Eating, Personal hygiene and care, Working, Cooking, Cleaning up after meals, Indoor cleaning, Exercising, Resting, and Away) were selected to predict occupant activity experimentally. Three classification algorithms, including random forest (RF), K-nearest neighbor, and support vector machine, were employed to develop the detailed occupancy activity classification models. In this study, we found that the key variables influencing each occupant activity classification varied significantly for each individual. The lifestyle and characteristics of each occupant were reflected in the building monitoring data. Accordingly, the performance of the model varied depending on each occupant's behavior. In the case of Person A, all classification algorithms displayed a high performance when only environmental datasets were considered; the accuracy was 0.98, and the f1-score was also above 0.98. Person B's classification model required various building monitoring data to ensure optimal performance; the highest accuracy and f1-score was 0.86 using RF. In contrast, Person D's classification model showed a low performance when all monitoring datasets were used; the accuracy was 0.43, and the f1-score was 0.41 using RF. These results support that occupants' characteristics affect the feature variables in an activity classification model, and these feature variables determine the performance of the model of each occupant.
A survey on just noticeable distortion estimation and its applications in video coding

With the developing explosion in video data delivery, perceptual video coding (PVC) plays an increasingly significant role in video compression. The just noticeable distortion (JND) reflects the tolerance limit of human visual system (HVS) to coding distortion directly, resulting in that JND-based PVC is the most important branch of video coding. This paper provides an extensive overview of JND estimation and JND-based PVC, so as to make interested readers aware of the status quo. The main contribution of this article can be briefly outlined as follows. Firstly, the general description of JND concepts and most existing computation models for JND are to be reviewed systematically. Secondly, most related works about JND-based perceptual image and video coding schemes are introduced, including JND-based coding preprocessing and JND-based codec embedding. Thirdly, in addition to a thorough summary of JND estimation and JND-based PVC, possible future directions and opportunities are analyzed and discussed.
Comment on “An efficient identity-based signature scheme with provable security”

In this comment paper, we reveal a security issue in an efficient identity-based signature (IBS) scheme that has been proven secure in the standard model. More specifically, we perform a key-only attack on the IBS scheme to break its universal unforgeability. Since the security notion of universal unforgeability under key-only attack (uuf-koa) is a weaker notion than the existential unforgeability under chosen message attack (euf-cma), this invalidates the euf-cma security claimed by the IBS scheme. Subsequently, we propose a fix to achieve a stronger seuf-cma security without a random oracle.
Echo state network structure optimization algorithm based on correlation analysis

Echo State Network (ESN) is an effective variant of Recurrent Neural Network (RNN). However, it is difficult for traditional ESN to determine the reservoir size that matches a given task. In this paper, an ESN structure optimization pruning algorithm based on correlation analysis, called PCESN, is proposed to design the size of the reservoir automatically. First, a characteristic matrix is constructed utilizing probability theory and information theory to measure the correlation between each neuron in the reservoir and the output neuron. On this basis, a pruning criterion is proposed to achieve a sparse reservoir structure by dynamically removing neurons with low correlation. Second, in order to retain the sample information of the neurons in the removed reservoir during the network reduction, the input weights of the retained reservoir are updated by means of the average transverse propagation of the weights. Finally, the performance of PCESN is tested on multiple time series. Simulation results show that the proposed PCESN outperforms some fixed size ESN and other dynamic ESN in terms of prediction accuracy, generalization performance and model complexity.
Influence of the operating regime of the magnonic active ring oscillator on the performance of the magnonic reservoir computer based on it

A magnonic active ring oscillator based on an yttrium iron garnet (YIG) is a promising hardware platform for physical reservoir computing. It meets the required conditions for the availability of fading memory and nonlinearity. However, its performance still needs further improvement. Two methods can be employed to improve the performance. One is increasing the time of establishing steady self-oscillations in the auto-oscillator. The second one is increasing the amplitude of spin waves propagating in the YIG film. In this work, we achieve an increase of the amplitude of the reservoir response by broadening the available range for the ring gain coefficient. We use a metallized YIG film as a waveguide in which spin waves propagate. This shifts up the amplitude threshold for the development of spin wave modulation instability in the ring. The threshold defines the upper limit of the gain range for the ring. Binary pulse sequence is entered into the ring by switching its gain between upper and lower levels. The modulation instability threshold then sets the upper limit for the higher gain level. In addition, in this work, we set the lower level of the gain to negative values. This is one more improvement with respect to earlier works. Switching the gain between a negative and a positive value increases the amplitude of the reservoir response and the depth of its fading memory. We employ the standard short-term memory and parity-check tests to quantify the performance of the reservoir computer. Both tests demonstrate an increase in the computational efficiency of the magnonic reservoir.
Mission-Oriented Gaussian Process Motion Planning for UUVs Over Complex Seafloor Terrain and Current Flows

We present a novel motion planning framework for unmanned underwater vehicles (UUVs) - the first framework that applies Gaussian process motion planning to solve a 3D path planning problem for a 6-DoF robot in underwater environments. We address missions requiring UUVs to remain in close proximity to seafloor terrain, which must be achieved alongside collision avoidance. Our framework also considers the influence of current flows as part of the cost function, allowing for more accurate planning. To evaluate the performance of our proposed framework, we compare it with the widely used RRT* and STOMP algorithms over a range of underwater environments. Our experimental results demonstrate the stability and time efficiency of our framework.
Hermitian self-dual 2-quasi-abelian codes

In this paper, we construct a class of Hermitian self-dual 2-quasi-abelian codes over a finite field. Based on counting the number of such codes and estimating the number of the codes in this class whose relative minimum weights are small, we prove that the class of Hermitian self-dual 2-quasi-abelian codes over any finite field is asymptotically good. The existence of such codes is unconditional, which is different from the case of Euclidean self-dual 2-quasi-abelian codes over a special finite field.