Emotional Support Conversation (ESC) aims to alleviate individuals’ negative emotions through multi-turn dialogues, where effective strategy planning and response generation are essential. However, existing methods often suffer from limitations in both planning reasonable support strategies and effectively expressing them in responses. To the end, we propose a novel LLM-based Emotional Support Conversation Agent (ESCA) with a plug-in strategy planner and a strategy-aligned prompt generator. The strategy planner cooperates with four aspects of the seeker’s state, including emotion intensity, trust degree, dialogue behavior, and stage of change, to enhance the rationality and effectiveness of the strategy prediction. To ensure that predicted strategies are better conveyed, the prompt generator integrates strategy-aligned instructions, knowledge, and context to generate the soft prompt for guiding the LLM to generate supportive responses. In addition to supervised fine-tuning, the prompt generator is further optimized by reinforcement learning. Experimental results demonstrate that ESCA significantly improves both response quality and the success rate of achieving the ESC task goal.

Although camouflaged object segmentation has advanced rapidly in recent years, existing methods are still confined to visual mask prediction under fixed task assumptions. They cannot interactively respond to user requests, nor can they proactively understand and reason about the user’s intent. Our work tackles this issue by proposing a novel task, Language-Guided Reasoning Camouflaged Object Segmentation (LRCOS). Given a camouflaged image and an implicit query text instruction that requires reasoning, LRCOS aims to output intent-consistent segmentation mask. To establish a benchmark for this task, we build CamoQuery, comprising 12,437 image–mask samples and 25971 implicit query text instructions. To better reflect real-world camouflaged scenarios, we additionally collect MCD, a multi-instance camouflage dataset where multiple camouflaged targets co-exist within the same scene, increasing the need for reasoning. Building on CamoQuery, we further propose COSA, a vision–language segmentation assistant that segments the intended camouflaged object from implicit queries and produces a reasoning explanation. Experiments on CamoQuery demonstrate that COSA has strong reasoning segmentation capability in camouflaged scenes and exhibits zero-shot capability.

Heterogeneous behavioral data provides comprehensive insights into user intentions and decision-making patterns. Contemporary multi-behavior recommendation models, which leverage such data to infer user preferences, typically capture high-order collaborative signals through graph neural networks on a multi-behavior heterogeneous graph or multiple behavior-specific subgraphs. However, auxiliary behaviors (e.g., view, cart) inherently contain noise that can mislead target behavior (e.g., purchase) prediction, and the incorporation of high-order collaborative signals further amplify such noise. Moreover, these approaches fail to adequately explore cross-behavior item dependencies, leading to inadequate modeling of dependencies across heterogeneous behaviors. To address these limitations, we propose Cross-behavior Item DEpendency modeling for multi-behavior Recommendation (CIDER) , a novel framework that explicitly models item dependencies across multiple types of behaviors for target behavior prediction (e.g., purchase). Specifically, our framework introduces the Hierarchical Behavior Sequence (HBS) , a data structure to systematically organize multi-behavior user–item interactions. Based on the HBS, we design a Cross-behavior Item Dependency Modeling (CIDM) module coupled with a multi-behavior cascading learning scheme to capture item-level dependencies. To enhance the robustness of the representations learned from the CIDM module, we develop an HBS-based denoising module that filters out noise inherent in auxiliary behaviors. Empirical evaluation on three benchmark datasets demonstrates the effectiveness of our model in harnessing multi-behavior data. The implementation is publicly available at https://github.com/SunJianier/CIDER .

FPGAs are widely used for efficient CNN inference acceleration but designing high-performance accelerators demands significant hardware expertise. Existing solutions face limitations: hardware designs are often model/chip-specific with suboptimal resource efficiency, and compiler support is typically framework-restricted. To overcome these, we propose a generalized and flexible high-performance FPGA accelerator architecture and a flexible end-to-end compilation toolflow based on ONNX IR. The architecture features an optimized uint8 systolic array for high compute density and a dedicated X-bus module handling diverse convolution parameters. On-chip buffers and allocation algorithms enhance memory efficiency. Configurable design variables enable architectural adaptation and fine-tuning. Deploying four accelerator variants on a VCU118 board and compiling 17 CNN models demonstrated a peak convolutional throughput of 5,792.19 GOPS (99.82% of theoretical peak, 5,825.42 GOPS) and overall throughput up to 3,311.48 GOPS. Compared to prior work, our solution offers superior usability, greater flexibility, and higher performance under comparable DSP usage. Furthermore, across most tested models, it provides significantly lower latency and higher energy efficiency versus CPUs and GPUs.

Unmanned aerial vehicles (UAVs) have opened new opportunities for crowd-sensing enabling the execution of sensing tasks in remote or rural regions by leveraging data sensing and computation offloading capabilities. However, UAVs often lack the incentive to actively engage in crowd-sensing tasks. Auction has been proven as an effective strategy to boost participant motivation and engagement. By incentivizing UAV owners, high-quality crowd-sensing tasks can be completed through increased task completion rates and improved data quality achieved by UAVs. In this paper, we formally model this QoE-aware UAV-enabled crowd-sensing problem as an optimization problem with the aim of maximizing the overall Quality of Experience (QoE) under resource and budget constraints. To solve this problem, we propose a QoE-aware online auction algorithm named CERA, based on the primal-dual technique and theoretically prove its competitive ratio. We conducted both small-scale and large-scale experiments to evaluate the performance of our approach, and the experimental results demonstrate that our CERA outperforms other algorithms significantly.

There have been many studies on developing automatic tools for mapping CNN models onto FPGAs. However, challenges remain in designing an easy-to-use toolflow. First, the toolflow should be able to handle models exported from various deep learning frameworks and models with different topologies. Second, the hardware architecture should make better use of on-chip resources to achieve high performance. In this work, we build a toolflow upon Open Neural Network Exchange (ONNX) IR to support different DL frameworks. We also try to maximize the overall throughput via multiple hardware-level efforts. We propose to accelerate the convolution operation by applying parallelism not only at the input and output channel level, but also at the output feature map level. Several on-chip buffers and corresponding management algorithms are also designed to leverage abundant memory resources. Moreover, we employ a fully pipelined systolic array running at 400 MHz as the convolution engine, and develop a dedicated bus to implement the im2col algorithm and provide feature inputs to the systolic array. We generated 4 accelerators with different systolic array shapes and compiled 12 CNN models for each accelerator. Deployed on a Xilinx VCU118 evaluation board, the performance of convolutional layers can reach 3267.61 GOPS, which is 99.72% of the ideal throughput (3276.8 GOPS). We also achieve an overall throughput of up to 2424.73 GOPS. Compared with previous studies, our toolflow is more user-friendly. The end-to-end performance of the generated accelerators is also better than that of related work at the same DSP utilization.

In-memory databases (IMDBs) are gaining increasing popularity in big data applications, where clients commit updates intensively. Specifically, it is necessary for IMDBs to have efficient snapshot performance to support certain special applications (e.g., consistent checkpoint, HTAP). Formally, the in-memory consistent snapshot problem refers to taking an in-memory consistent time-in-point snapshot with the constraints that 1) clients can read the latest data items and 2) any data item in the snapshot should not be overwritten. Various snapshot algorithms have been proposed in academia to trade off throughput and latency, but industrial IMDBs such as Redis adhere to the simple fork algorithm. To understand this phenomenon, we conduct comprehensive performance evaluations on mainstream snapshot algorithms. Surprisingly, we observe that the simple fork algorithm indeed outperforms the state-of-the-arts in update-intensive workload scenarios. On this basis, we identify the drawbacks of existing research and propose two lightweight improvements. Extensive evaluations on synthetic data and Redis show that our lightweight improvements yield better performance than fork, the current industrial standard, and the representative snapshot algorithms from academia. Finally, we have opensourced the implementation of all the above snapshot algorithms so that practitioners are able to benchmark the performance of each algorithm and select proper methods for different application scenarios.

In-memory databases (IMDBs) are gaining increasing popularity in big data applications, where clients commit updates intensively. Consistent snapshot is a key step in backup and recovery of IMDBs, thus an important factor for system performance of IMDBs. Formally, the in-memory consistent snapshot problem refers to taking an in-memory consistent time-in-point snapshot with the constraints that 1) clients can read the latest data items, and 2) any data item in the snapshot should not be overwritten. Various snapshot algorithms have been proposed in the academia to trade off throughput and latency, yet industrial IMDBs such as Redis still stick to the simple fork algorithm. As an understanding of this phenomenon, we conduct comprehensive performance evaluations on mainstream snapshot algorithms. Surprisingly, we observe that the simple fork algorithm indeed outperforms the state-of-the-arts in update-intensive workload scenarios. On this basis, we identify the drawbacks of existing research and propose two lightweight improvements. Extensive evaluations on synthetic data and Redis show that our lightweight improvements yield better performance than fork, the current industrial standard, and the representative snapshot algorithms from the academia. Finally, we have opensourced the implementation of all the above snapshot algorithms to facilitate practitioners to benchmark the performance of each algorithm and select proper methods for different application scenarios.

In this demonstration, we present a system that recommends to the user the locations and activities she/he might be interested in according to history GPS trajectories and public places of interest (POI) data. Its innovation lies in the acceptable performance of recommendations in cases where no user comments on activity types are available. Such situations are more realistic considering the restrictions on mobile devices' abilities, users' privacies, or business secret. For this purpose, we first extract stay points according to uses' trajectories, and label them with the top-k common activities which have the most possibility in terms of the POI dataset. Then, by taking stay points as observations, and activities as hidden states, a Hidden Markov model is built to learn the transfer possibilities between activities and the generation probabilities between activities and stay points. Finally, with the obtained model, our system can perform two types of recommendation, i.e. the history based recommendation and the similarity based recommendation. The results of former type are those stay points from user's own history positions. While, the latter one conducts collaborative filtering by taking history based recommendation results from similar users. The demonstration shows the running effects of the implemented prototype system, in which the Microsoft GeoLife trajectories dataset and the "DianPing.com" POI dataset were loaded. The preliminary experimental results demonstrate the feasibility.

To obtain comparable high query performance with relational databases, diverse database technologies have to be adapted to confront the complexity posed by both Resource Description Framework (RDF) data and SPARQL query. Database caching is one of such technologies that improves the performance of database with reasonable space expense based on the spatial/temporal/semantic locality principle. However, existing caching schemes exploited in RDF stores are found to be dysfunctional for complex query semantics. Although semantic caching approaches work effectively in this case, little work has been done in this area. In this paper, we try to improve SPARQL query performance with semantic caching approaches, i.e., SPARQL algebraic expression tree (AET) based caching and entity caching. Successive queries with multiple identical sub-queries and star-shaped joins can be efficiently evaluated with these two approaches. The approaches are implemented on a two-level-storage structure. The main memory stores the most frequently accessed cache items, and items swapped out are stored on the disk for future possible reuse. Evaluation results on three mainstream RDF benchmarks illustrate the effectiveness and efficiency of our approaches. Comparisons with previous research are also provided.

Finding all justifications of an OWL entailment is an important reasoning service for explaining logical inconsistencies. In this paper, we consider finding all justifications of an entailment in OWL pD* fragment, which is a fragment of OWL that makes possible decidable rule extensions of OWL. We first propose a novel approach to find all justifications of OWL pD* entailments using TMS and show the complexity of this approach. This approach is limited by the hardware capabilities of standalone systems. In order to improve its scalability to handle large scale semantic data, we optimize the proposed approach by exploiting the MapReduce technology. We implement our approach and the optimization, and do experiments on synthetic and real world data sets. Evaluation results show that our approach has the ability to scale to more than one billion triples.

Falconer is a semantic Web search engine enhanced SIOC (Semantically-Interlinked Online Communities) application, which is designed to demonstrate the ability of accelerating the creation and reuse process of semantic Web data with easy-to-use user interfaces. In this process, semantic Web search engines feed existing semantic data into the SIOC framework, where new semantic data are composed by the community and indexed again by those search engines. Compared to existing social (semantic) Web applications, Falconer inherently conforms to SIOC specification. It provides semantic search engine based user registration suggestion, friends auto-discovery, and semantic annotation for forum post content. Another distinctive feature is that it enables users to subscribe any resource having a URI as the topic they are interested in. The relationships among users, topics, and posts are further visualized for analyzing the topic trends in the community. As all semantic data are formatted in RDF and RDFa, they can be queried with SPARQL query language.

The basic idea behind parallel database systems is to perform operations in parallel to reduce the response time and improve the system throughput. Data placement is a key factor on the performance of parallel database systems. This paper proposes two data partition strategies to decluster XML documents with very large size, path schema based path instance balancing (PSPIB) strategy, in which all path instances with the same path schema in a data tree are declustered evenly over all sites, and node schema based node round-robin (NSNRR) strategy, in which all node objects with the same node schema in a data tree are declustered over all sites in a round-robin way. Accordingly, two query processing algorithms are proposed based on the two partition methods, parallel path merge (PPM) algorithm and parallel pipelining path join (PPPJ) algorithm. The performance analysis and evaluation on the two data placement strategies and corresponding query processing algorithms are given in this paper.