Reviewerly/exHarmony huggingface dataset:
A Sim-to-Real Deep Learning-Based Framework for Autonomous Nano-Drone Racing

Authors:
raw_author_name: Lorenzo Lamberti
raw_affiliation_string: Department of Electrical, Electronic, and Information Engineering, University of Bologna, Bologna, Italy; the Dalle Molle Institute for Artificial Intelligence, USI-SUPSI, Switzerland,
raw_author_name: Elia Cereda
raw_affiliation_string: Dalle Molle Institute for Artificial Intelligence, USI-SUPSI, Z&#x00FC;rich, Switzerland; Department of Electrical, Electronic, and Information Engineering, University of Bologna, Italy,
raw_author_name: Gabriele Abbate
raw_affiliation_string: Dalle Molle Institute for Artificial Intelligence, USI-SUPSI, Z&#x00FC;rich, Switzerland; Department of Electrical, Electronic, and Information Engineering, University of Bologna, Italy,
raw_author_name: Lorenzo Bellone
raw_affiliation_string: are with the Autonomous Robotics Research Center, Technology Innovation Institute, UAE.
raw_author_name: Michał Barciś
raw_affiliation_string: are with the Autonomous Robotics Research Center, Technology Innovation Institute, UAE.
raw_author_name: Alessandro Giusti
raw_affiliation_string: Dalle Molle Institute for Artificial Intelligence, USI-SUPSI, Z&#x00FC;rich, Switzerland; Department of Electrical, Electronic, and Information Engineering, University of Bologna, Italy,
raw_author_name: Francesco Conti
raw_affiliation_string: Department of Electrical, Electronic, and Information Engineering, University of Bologna, Bologna, Italy; the Dalle Molle Institute for Artificial Intelligence, USI-SUPSI, Switzerland,
raw_author_name: Daniele Palossi
raw_affiliation_string: Dalle Molle Institute for Artificial Intelligence, USI-SUPSI, Z&#x00FC;rich, Switzerland; V.

Abstract: 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

Authors:
raw_author_name: Hangyu Deng
raw_affiliation_string: Graduate School of Information, Production and Systems, Waseda University, Fukuoka, 808-0135, Japan

Abstract: 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

Authors:
raw_author_name: Yanping Huang
raw_affiliation_string: School of Computer and Software Engineering, Xihua University, China
raw_author_name: Xinzhu Bai
raw_affiliation_string: School of Computer and Software Engineering, Xihua University, China
raw_author_name: Qian Liu
raw_affiliation_string: School of Computer and Software Engineering, Xihua University, China
raw_author_name: Hong Peng
raw_affiliation_string: School of Computer and Software Engineering, Xihua University, China
raw_author_name: Qian Yang
raw_affiliation_string: School of Computer and Software Engineering, Xihua University, China
raw_author_name: Jun Wang
raw_affiliation_string: School of Electrical Engineering and Electronic Information, Xihua University, China

Abstract: 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.
Style Elimination and Information Restitution for generalizable person re-identification

Authors:
raw_author_name: Qian Zhao
raw_affiliation_string: Shanghai University of Electric Power, College of Electronics and Information Engineering, Shanghai, 201306, China
raw_author_name: Tangyu Ji
raw_affiliation_string: Shanghai University of Electric Power, College of Electronics and Information Engineering, Shanghai, 201306, China

Abstract: 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.
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Authors:
raw_author_name: Jing-Yu Ji
raw_affiliation_string: Department of Computing and Decision Sciences, Lingnan University, Hong Kong
raw_author_name: Zusheng Tan
raw_affiliation_string: Department of Computing and Decision Sciences, Lingnan University, Hong Kong
raw_author_name: Sanyou Zeng
raw_affiliation_string: School of Mechanical Engineering and Electronic Information, China University of Geosciences, Wuhan 430074, China
raw_author_name: Man-Leung Wong
raw_affiliation_string: Department of Computing and Decision Sciences, Lingnan University, Hong Kong

Abstract: 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

Authors:
raw_author_name: Kunyi Zheng
raw_affiliation_string: College of Information Science and Engineering, Hunan Normal University, Changsha, Hunan 410081, China
raw_author_name: Xianyi Liu
raw_affiliation_string: College of Information Science and Engineering, Hunan Normal University, Changsha, Hunan 410081, China
raw_author_name: Pengfei Xu
raw_affiliation_string: College of Information Science and Engineering, Hunan Normal University, Changsha, Hunan 410081, China
raw_author_name: Ying Zhou
raw_affiliation_string: Data and Information Management Center, Hunan Children's Hospital, Changsha, Hunan 410007, China

Abstract: 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.
A Sketch-texture Retrieval Framework using Perceptual Similarity

Authors:
raw_author_name: Yan Liu
raw_affiliation_string: Department of Business, Qingdao Vocational and Technical College of Hotel Management, No. 599 Jiushuidong Road, Qingdao, China
raw_author_name: Ying Gao
raw_affiliation_string: College of Computer Science & Technology, Ocean University of China, No. 238 Songling Road, Qingdao, China; College of Data Science, Qingdao University of Science and Technology, No. 99 Songling Road, Qingdao, China
raw_author_name: Lin Qi
raw_affiliation_string: College of Computer Science & Technology, Ocean University of China, No. 238 Songling Road, Qingdao, China
raw_author_name: Junyu Dong
raw_affiliation_string: College of Computer Science & Technology, Ocean University of China, No. 238 Songling Road, Qingdao, China

Abstract: 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

Authors:
raw_author_name: Sun Ho Kim
raw_affiliation_string: Department of Architectural Engineering, Dankook University, Yongin 448-701, Korea
raw_author_name: Hyeun Jun Moon
raw_affiliation_string: Department of Architectural Engineering, Dankook University, Yongin 448-701, Korea

Abstract: 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

Authors:
raw_author_name: Guoxiang Wang
raw_affiliation_string: Lishui University, Lishui, China
raw_author_name: Hongkui Wang
raw_affiliation_string: Lishui Institute of Hangzhou Dianzi University, China; School of Communication Engineering, Hangzhou Dianzi University, China
raw_author_name: Hui Li
raw_affiliation_string: Zhejiang Dahua Technology co., LTD, Hangzhou, China
raw_author_name: Li Yu
raw_affiliation_string: College of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan, China
raw_author_name: Haibing Yin
raw_affiliation_string: Lishui Institute of Hangzhou Dianzi University, China; School of Communication Engineering, Hangzhou Dianzi University, China
raw_author_name: Haifeng Xu
raw_affiliation_string: Lishui Institute of Hangzhou Dianzi University, China; School of Communication Engineering, Hangzhou Dianzi University, China
raw_author_name: Zhen Ye
raw_affiliation_string: Lishui University, Lishui, China
raw_author_name: Junfeng Song
raw_affiliation_string: Lishui University, Lishui, China

Abstract: 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”

Authors:
raw_author_name: Syh-Yuan Tan
raw_affiliation_string: Faculty of Information Science and Technology, Multimedia University, Melaka, Malaysia
raw_author_name: Swee-Huay Heng
raw_affiliation_string: Faculty of Information Science and Technology, Multimedia University, Melaka, Malaysia

Abstract: 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

Authors:
raw_author_name: Bowen Wang
raw_affiliation_string: Control Science and Engineering, Bohai University, Jinzhou 121013, China
raw_author_name: Shuxian Lun
raw_affiliation_string: Control Science and Engineering, Bohai University, Jinzhou 121013, China
raw_author_name: Ming Li
raw_affiliation_string: Control Science and Engineering, Bohai University, Jinzhou 121013, China
raw_author_name: Xiaodong Lu
raw_affiliation_string: College of Information Engineering, College of Suqian, Jiangsu 223800, China

Abstract: 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.