Abstract Session-based recommendation (SBR) predicts potential items of interest by analyzing user behavior within sessions. In this work, we explore the continual learning for SBR task, a challenging and practical task closely aligned with real-world online recommendation system due to (1) periodic updates of the model may trigger catastrophic forgetting, and (2) the continuous emergence of new interactions reflects rapidly changing user interests. Although recent studies have mitigated catastrophic forgetting by replaying a small subset of historical data into the model, these samples fail to represent the distribution of the entire dataset. Moreover, the research on SBR when examining changes in user interests is confined to offline settings and does not adequately consider multiple time-correlated user interests. This limitation makes it challenging to finely model the rapid changes in user interests in continual learning. To overcome the limitations of traditional data replay, we propose a data distillation framework for SBR, which synthesizes information-rich samples for replay from the entire dataset instead of relying on simple sampling. Furthermore, to address the changing user interests in continual learning scenarios, we developed an Interest Enhanced Sub-graph Neural Network (IES-GNN), which is capable of efficiently extracting and dynamically modeling the evolution of user interests within sessions. Testing on three real-world datasets demonstrates that our approach outperforms several advanced methods in continual learning for SBR task.

Recent years have seen substantial progress in diffusion-based controllable video generation. However, achieving precise control in complex scenarios, including fine-grained object parts, sophisticated motion trajectories, and coherent background movement, remains a challenge. In this paper, we introduce *TrackGo*, a novel approach that leverages free-form masks and arrows for conditional video generation. This method offers users with a flexible and precise mechanism for manipulating video content. We also propose the *TrackAdapter* for control implementation, an efficient and lightweight adapter designed to be seamlessly integrated into the temporal self-attention layers of a pretrained video generation model. This design leverages our observation that the attention map of these layers can accurately activate regions corresponding to motion in videos. Our experimental results demonstrate that our new approach, enhanced by the TrackAdapter, achieves state-of-the-art performance on key metrics such as FVD, FID, and ObjMC scores.

3D vector graphics play a crucial role in various applications including 3D shape retrieval, conceptual design, and virtual reality interactions due to their ability to capture essential structural information with minimal representation. While recent approaches have shown promise in generating 3D vector graphics, they often suffer from lengthy processing times and struggle to maintain view consistency. To address these limitations, we propose VC3D (**V**iew**C**raft**3D**), an efficient method that leverages 3D priors to generate 3D vector graphics. Specifically, our approach begins with 3D object analysis, employs a geometric extraction algorithm to fit 3D vector graphics to the underlying structure, and applies view-consistent refinement process to enhance visual quality. Our comprehensive experiments demonstrate that VC3D outperforms previous methods in both qualitative and quantitative evaluations, while significantly reducing computational overhead. The resulting 3D sketches maintain view consistency and effectively capture the essential characteristics of the original objects.

Recently, text-guided scalable vector graphics (SVG) synthesis has shown great promise in domains like iconography and sketching. However, existing Text-to-SVG methods often face challenges in editability, visual quality, and diversity. To address these issues, we propose a novel framework for text-guided SVG synthesis that significantly enhances editability, quality, and diversity. To enhance the editability of output SVGs, we introduce a Hierarchical Image VEctorization (HIVE) framework that operates at the semantic object level and supervises the optimization of components within the vector object. This approach facilitates the decoupling of vector graphics into distinct objects and component levels. Our proposed HIVE algorithm, informed by image segmentation priors, not only ensures a more precise representation of vector graphics but also enables fine-grained editing capabilities within vector objects. To improve the diversity of output SVGs, we present a Vectorized Particle-based Score Distillation (VPSD) approach. VPSD addresses over-saturation issues in existing methods and enhances sample diversity. A pre-trained reward model is incorporated to re-weight vector particles, improving aesthetic appeal and enabling faster convergence. Additionally, we design a novel adaptive vector primitives control strategy, which allows for the dynamic adjustment of the number of primitives, thereby enhancing the presentation of graphic details. Extensive experiments validate the effectiveness of the proposed method, demonstrating its superiority over baseline methods in terms of editability, visual quality, and diversity. We also show that our new method supports up to six distinct vector styles, capable of generating high-quality vector assets suitable for stylized vector design and poster design.

Recently, text-guided scalable vector graphics (SVGs) synthesis has shown promise in domains such as iconography and sketch. However, existing text-to-SVG generation methods lack editability and struggle with visual quality and result diversity. To address these limitations, we propose a novel text-guided vector graphics synthesis method called SVGDreamer. SVGDreamer incorporates a semantic-driven image vectorization (SIVE) process that enables the decomposition of synthesis into foreground objects and background, thereby enhancing editability. Specifically, the SIVE process introduces attention-based primitive control and an attention-mask loss function for effective control and manipulation of individual elements. Additionally, we propose a Vectorized Particle-based Score Distillation (VPSD) approach to address issues of shape over-smoothing, color over-saturation, limited diversity, and slow convergence of the existing text-to-SVG generation methods by modeling SVGs as distributions of control points and colors. Furthermore, VPSD leverages a reward model to re-weight vector particles, which improves aesthetic appeal and accelerates convergence. Extensive experiments are conducted to validate the effectiveness of SVGDreamer, demonstrating its superiority over baseline methods in terms of editability, visual quality, and diversity. Project page: https://ximinng.github.io/SVGDreamer-project/

Fuel cell vehicles (FCVs) are considered a promising solution for reducing emissions caused by the transportation sector. An energy management strategy (EMS) is undeniably essential in increasing hydrogen economy, component lifetime, and driving range. While the existing EMSs provide a range of performance levels, they suffer from significant shortcomings in robustness, durability, and adaptability, which prohibit the FCV from reaching its full potential in the vehicle industry. After introducing the fundamental EMS problem, this review article provides a detailed description of the FCV powertrain system modeling, including typical modeling, degradation modeling, and thermal modeling, for designing an EMS. Subsequently, an in-depth analysis of various EMS evolutions, including rule-based and optimization-based, is carried out, along with a thorough review of the recent advances. Unlike similar studies, this paper mainly highlights the significance of the latest contributions, such as advanced control theories, optimization algorithms, artificial intelligence (AI), and multi-stack fuel cell systems (MFCSs). Afterward, the verification methods of EMSs are classified and summarized. Ultimately, this work illuminates future research directions and prospects from multi-disciplinary standpoints for the first time. The overarching goal of this work is to stimulate more innovative thoughts and solutions for improving the operational performance, efficiency, and safety of FCV powertrains.

Federated learning (FL), as a machine learning method for multi-client model aggregation, faces performance problems caused by data heterogeneity. At the same time, global model aggregation will also cause customers to forget their own personalized knowledge, resulting in poor local training results. To this end, we propose a double distillation personalized federated learning framework (pFedTD) that combines local self-knowledge distillation and global non-ground truth class knowledge distillation. This method effectively balances personalization and global performance by extracting the personalized and global historical knowledge of each client and using a parameter adaptation method to weigh the intensity of self-distillation and global distillation. pFedTD can also perform stably on large-scale heterogeneous data and can alleviate local and global forgetting problems. Our experiments demonstrate that our method outperforms other baselines even on non-IID data.

Even though trained mainly on images, we discover that pretrained diffusion models show impressive power in guiding sketch synthesis. In this paper, we present DiffSketcher, an innovative algorithm that creates \textit{vectorized} free-hand sketches using natural language input. DiffSketcher is developed based on a pre-trained text-to-image diffusion model. It performs the task by directly optimizing a set of Bézier curves with an extended version of the score distillation sampling (SDS) loss, which allows us to use a raster-level diffusion model as a prior for optimizing a parametric vectorized sketch generator. Furthermore, we explore attention maps embedded in the diffusion model for effective stroke initialization to speed up the generation process. The generated sketches demonstrate multiple levels of abstraction while maintaining recognizability, underlying structure, and essential visual details of the subject drawn. Our experiments show that DiffSketcher achieves greater quality than prior work. The code and demo of DiffSketcher can be found at https://ximinng.github.io/DiffSketcher-project/.

Indoor positioning system plays a key role in location-based services since the widely used Global Navigation Satellite System (GNSS) is denied in indoor scenarios. Crowdsensing or walking-surveying based indoor positioning is proposed aiming at providing low-cost and high-efficient 3D location. This paper proposes a crowdsensing/walking-surveying 3D indoor positioning system by fusing the crowd-sensed inertial data and Wi-Fi fingerprinting samples using deep learning frameworks. A sine-wave-based step detector is used for pedestrian dead-reckoning (PDR) to generate original dense-trajectories. An enhanced optimization-based algorithm (Opt) and a smoothing-based algorithm (Smo) are proposed and evaluated to correct the original dense-trajectories into near-true dense-trajectories which are used to construct the inertial database and Wi-Fi radio map. A ResNet-based inertial neural-network and a BiLSTM-based Wi-Fi fingerprinting neural-network are trained on the constructed navigation database and combined by a Kalman filter to provide accurate and robust 3D localization performance. The realistic experimental results among complex indoor environments demonstrate that the proposed algorithms are proved to achieve a precise 3D indoor localization performance which is superior to several existing relative methods.

Augmented Reality (AR) has great potential in science education, and Collaborative Inquiry-based Learning (CIBL) in the AR environment is of great significance. However, there is a problem of low collaborative performance in technology-based CIBL. This study applied the strategy of role assignment to AR-based CIBL, aiming to explore the effect of role assignment on students’ collaboration. Forty-seven sixth-grade students in elementary school were randomly divided into Group A (without role assignment) and Group B (with role assignment) to participate in AR-based collaborative scientific inquiry activities. Data on students’ scientific knowledge achievement, attitudes toward science learning, cognitive load, and flow experience were collected. In addition, interviews were conducted to investigate students’ opinions on role assignments. It is found that the strategy of role assignment could significantly improve students’ science knowledge achievement. The interview results revealed how role assignments facilitate students’ collaboration from three aspects.

Indoor wireless localization toward the next generation Wi-Fi access point has attracted considerable attention due to the presentation of the state-of-art Wi-Fi fine time measurement (FTM) protocol. In order to improve the autonomy, accuracy, and universality of wireless positioning based on the Internet of Things (IoT) terminals, this article proposes a hybrid wireless positioning system which contains the integration of Wi-Fi FTM, crowdsourced received signal strength indicator (RSSI) fingerprinting and micro-electro-mechanical-system (MEMS) sensors (H-WPS). A light-weight pedestrian aimed inertial navigation system (PINS) is proposed, which contains multilevel constraints and a global optimization model in order to eliminate the cumulative error caused by INS update. A deep-learning-based Wi-Fi fingerprinting database generation framework is developed for crowdsourced trajectories evaluation and selection. In addition, three different multisource integration models are applied to fuse the information of PINS, Wi-Fi FTM and RSSI fingerprinting, and calibrate the Wi-Fi ranging bias in real time, which is further enhanced by a novel misclosure check and the multilayer perceptron contained signal quality evaluation strategy. The comprehensive experiments demonstrate that the proposed H-WPS achieves much more precise and universal indoor positioning performance compared with the single location source, and meter-level localization precision can be realized in the Wi-Fi FTM-covered indoor scenes.

In this paper, the configuration transformation of Wheel-Legged Robot (WLR) is studied, which can enable the robot to change its multilinks configuration on Inverted Equilibrium Manifold (IEM), while keeping balance with a small location drift on the floor. First of all, the general form of dynamics equation of planar Articulated Wheeled Inverted Pendulum (AWIP) with a wheel and n − 1 rigid links, is derived. The Partial Feedback Linearization (PFL) combined with a Sliding Mode Control (SMC) is used to design the inverse dynamics controller of AWIP, while considering full dynamics terms. The well-known WLR model is used as a simple example of AWIP to accomplish the configuration transformation task. An optimization based configuration transformation algorithm is proposed to realize a comprehensive optimization of the shortest path in joint space and the minimum location drift of WLR on the floor. Finally, the effectiveness of the proposed algorithm is demonstrated through simulation to implement the configuration transformation task.

More and more applications of location-based services lead to the development of indoor positioning technology. As a part of the Internet-of-Things ecosystem, most existing indoor positioning algorithms are applied to specific situations, e.g., pedestrian navigation and target detection. To meet the high-precision indoor localization requirement, IEEE 802.11 included the Wi-Fi fine-time measurement (FTM) protocol in 2016, which provides a novel approach for Wi-Fi ranging between the mobile terminal and Wi-Fi access point (AP). This article proposes a precise 3-D indoor localization algorithm based on Wi-Fi FTM and smartphone built-in sensors (3D-WFBS). The adaptive extended Kalman filter (AEKF) is used to estimate the pedestrian's real-time heading and walking speed, and the received signal strength indication and round-trip time collected from Wi-Fi APs are combined for proximity detection and providing more accurate ranging results. In addition, the unscented particle filter is applied to fuse the results of AEKF, proximity detection, and Wi-Fi ranging. The experimental results show that compared with the existing dead reckoning method and the other fusion methods, the proposed 3D-WFBS algorithm is proved to achieve meter-level indoor positioning accuracy in typical indoor scenes.

Quantum wireless communications (QWC) is gaining much attention for its incomparable advantages of secure key transmission in free-space scenarios. In this paper, a novel iterative multi-user quantum key distribution (MQKD) non-orthogonal multiple-access (NOMA) system over Poisson atmospheric channels is proposed. Considering the atmospheric attenuation and quantum direct product operation, a MQKD parallel interference cancellation (PIC) approach is designed at the receiver. In particular, a quantum nonlinear Poisson shot noise limited sum-product interference cancellation algorithm is derived. Simulation results show that the proposed scheme is capable of mitigating the multi-user interference effectively, even considering the polarization mismatch. Quantitatively, for the 4-user scenario, it can achieve the classical Bit Error Rate (BER) of 3 x 10-6 at 2.5 km distance with polarization mismatch factor of 0.05. Moreover, the proposed NOMA-MQKD scheme can have obviously higher secure key rate than the conventional WDMA-MQKD approach.

The performance of traditional hydraulic robots is often limited by their hoses across moving joints or connecting hydraulic drive units, which would reduce their mobility and impede their ability to operate in complex environment. In response to this deficiency, this paper introduces the WLR-II (the second generation of wheel-legged robot), a novel hydraulic wheel-legged robot developed by using hose-less design approach which is focused on improving the reliability of the hydraulic system and perfecting the appearance of the robot. As its notable features, seven Hydraulic Hose-less Joints (HHJ) that include a pair of high and also low pressure oil pipes based on rotary seal, Cylinder-Valve-Skeleton (CVS) integration thighs and arms which are produced by subtractive manufacturing as well as oscillating cylinders driven by gear rack transmission are included. In addition to a description of its design, experimental characterizations of rough pavement adaptability and payload capability together with the achievement of the reliability of hydraulic system are also demonstrated. As a result, we confirmed effectiveness of the hose-less design by moving on the rugged ground, climbing slope, squatting with load, dragging and picking up a heavy load. To the authors' best knowledge, this is the first time that the design of a hose-less hydraulic wheel-legged robot has been presented.

Wheel-legged hybrid robot with multi-modal locomotion can efficiently adapt to different terrain environments, as well as realize rapid maneuver on flat ground. We have developed a novel hydraulic wheel-legged robot (WLR) combined with a humanoid structural design. This robot can assist to emergency scenarios where the high mobility, adaptability and robustness are required. The paper introduces the details of the WLR, highlighting the innovative design and optimization of physical construction which is considered to maximize the mobile abilities, enhance the environmental adaptability and improve the reliability of hydraulic system. Firstly, maximizing the mobile abilities includes optimizing the configuration of each actuator and integrating them with the structure, so as to achieve a large range of movement and also reduce the mass and inertia of the legs. Secondly, the environmental adaptability can be ensured with a magnetorheological (MR) fluid-based damper and direct-drive wheels. Thirdly, improving the reliability of hydraulic system involves using the selective laser melting (SLM) technology to integrate hydraulic system and reducing the number of exposed tubes. The maneuverability of the WLR is demonstrated with a series of experiments. At present, the WLR can perform the following operations, including moving on the flat ground, squatting, and picking up a heavy load.