Cognitive impairment detection through spontaneous speech is a promising avenue for early diagnosis of Alzheimer’s disease (AD) and mild cognitive impairment (MCI), where timely intervention can significantly improve patient outcomes. The PROCESS Grand Challenge at ICASSP 2025 addresses these tasks by promoting innovative classification and regression methods for detecting cognitive decline. In this paper, we propose a multimodal fusion strategy that combines interpretable linguistic features with temporal embeddings extracted from pretrained models. Our approach achieves an F1-score of 0.649 for the classification task (predicting healthy, MCI, dementia) and an RMSE of 2.628 for the regression task (MMSE score prediction), securing the top overall ranking in the competition.

To address issues with Automated Guided Vehicles (AGVs) in ports, such as low utilization, poor flexibility, and weak adaptability, this paper presents a new collaborative transport solution. This solution enables the virtual connection of two AGVs via the Internet of Things (IoT) without physical connections. A hybrid collaborative control framework is designed to implement this solution, integrating feedback and feed-forward control. Within this framework, the transport AGV and container are treated as a single rigid body. A hierarchical decoupling approach is applied to separately design lateral and longitudinal feedback controllers for the leader AGV. Additionally, a centralized feed-forward controller for the follower AGV is developed based on rigid-body consensus theory and the AGV’s kinematic model. Typical port transportation conditions are established on both a high-fidelity dynamics software platform and a self-developed port platform using real-world port data. Tests with two AGVs show a distance error of 0.019 m and an angle error of 0.2∘ at handling points, meeting port operational metrics. These results demonstrate the scheme’s effectiveness in addressing gaps in research on nonholonomic constraint robots in collaborative transport, offering solutions for scenarios with multiple constraints and high precision requirements. Importantly, this work provides insights for developing IoT-enabled smart ports.

The information extracted from waveform data of full-waveform light detection and ranging (LiDAR) has been widely used in applications such as 3D urban modeling, target recognition, and classification However, the presence of weak signals is inevitable in LiDAR systems. To enhance its effective detection capability and extraction accuracy, we propose a multispectral LiDAR (MSL) weak signals extraction (MSL-WSE) method. The measurement data from our MSL system were used to evaluate the performance of the proposed method. The correlation coefficient (R2), root mean square error (RMSE) and effective extraction rate show that the MSL-WSE method accurately detected and extracted the waveform parameters of weak echo signals, providing the more realistic and fine-grained true color 3D point cloud.

Due to the large-scale users and items, industrial recommender systems usually consist of two stages, the matching stage and the ranking stage. The matching stage is responsible for retrieving a small fraction of relevant items from the large-scale item pool which are further selected by the ranking stage. Most of the existing deep learning-based matching models focus on the problem of modeling user interest representation by using inner product between user representation and item representation to obtain the user-to-item relevance. However, the item-to-item relevance between user interacted item and target item is not considered in the deep matching models which is computationally prohibitive for large-scale applications. In this paper, we propose a unified deep matching framework called UDM for the matching stage to mitigate this issue. UDM can model the user-to-item relevance and item-to-item relevance simultaneously with the help of an interest extraction module and interest interaction module, respectively. Specifically, the interest extraction module is used as the main network to extract users' multiple interests with multiple vectors based on users' behavior sequences, while the interest interaction module is used as an auxiliary network to supervise the learning of the interest extraction module, which can model the interaction between user interacted items and target item. In the experiments conducted on two public datasets and a large-scale industrial dataset, UDM achieves consistent improvements over state-of-the-art models. Moreover, UDM has been deployed in the operational system of Alibaba. Online A/B testing results further reveal the effectiveness of UDM. To the best of our knowledge, UDM is the first deep matching framework which combines the user-to-item relevance modeling and item-to-item relevance modeling in the same model.

Entity resolution identifies all records in a database that refer to the same entity. In this paper, we propose an unsupervised framework for entity resolution using blocking and graph algorithms. The records are partitioned into blocks with no redundancy for efficiency improvement. For intra-block data processing, we propose a graph-theoretic fusion framework with two components, namely ITER and CliqueRank. Specifically, ITER constructs a weighted bipartite graph between terms and record-record pairs and iteratively propagates the node salience until convergence. Subsequently, CliqueRank constructs a record graph to estimate the likelihood of two records resident in the same clique. The derived likelihood from CliqueRank is fed back to ITER to rectify the edge weight until a joint optimum can be reached. Experimental evaluation was conducted with 4 real datasets. Results show that our unsupervised framework is comparable or even superior to state-of-the-art deep learning approaches.

With the rapid growth of e-commerce in recent years, e-commerce platforms are becoming a primary place for people to find, compare and ultimately purchase products. To improve online shopping experience for consumers and increase sales for sellers, it is important to understand user intent accurately and be notified of its change timely. In this way, the right information could be offered to the right person at the right time. To achieve this goal, we propose a unified deep intent prediction network, named EdgeDIPN, which is deployed at the edge, i.e., mobile device, and able to monitor multiple user intent with different granularity simultaneously in real-time. We propose to train EdgeDIPN with multi-task learning, by which EdgeDIPN can share representations between different tasks for better performance and saving edge resources in the meantime. In particular, we propose a novel task-specific attention mechanism which enables different tasks to pick out the most relevant features from different data sources. To extract the shared representations more effectively, we utilize two kinds of attention mechanisms, where the multi-level attention mechanism tries to identify the important actions within each data source and the inter-view attention mechanism learns the interactions between different data sources. In the experiments conducted on a large-scale industrial dataset, EdgeDIPN significantly outperforms the baseline solutions. Moreover, EdgeDIPN has been deployed in the operational system of Alibaba. Online A/B testing results in several business scenarios reveal the potential of monitoring user intent in real-time. To the best of our knowledge, EdgeDIPN is the first full-fledged real-time user intent understanding center deployed at the edge and serving hundreds of millions of users in a large-scale e-commerce platform.

E-commerce platforms are becoming a primary place for people to find, compare and ultimately purchase products. One of the fundamental questions that arises in e-commerce is to predict user purchasing intent, which is an important part of user understanding and allows for providing better services for both sellers and customers. However, previous work cannot predict real-time user purchasing intent with a high accuracy, limited by the representation capability of traditional browse-interactive behavior adopted. In this paper, we propose a novel end-to-end deep network, named Deep Intent Prediction Network (DIPN), to predict real-time user purchasing intent. In particular, besides the traditional browse-interactive behavior, we collect a new type of user interactive behavior, called touch-interactive behavior, which can capture more fine-grained real-time user features. To combine these behavior effectively, we propose a hierarchical attention mechanism, where the bottom attention layer focuses on the inner parts of each behavior sequence while the top attention layer learns the inter-view relations between different behavior sequences. In addition, we propose to train DIPN with multi-task learning to better distinguish user behavior patterns. In the experiments conducted on a large-scale industrial dataset, DIPN significantly outperforms the baseline solutions. Notably, DIPN gains about 18.96% improvement on AUC than the state-of-the-art solution only using traditional browse-interactive behavior sequences. Moreover, DIPN has been deployed in the operational system of Taobao. Online A/B testing results with more than 12.9 millions of users reveal the potential of knowing users' real-time purchasing intent.

Designing an automatic solver for math word problems has been considered as a crucial step towards general AI, with the ability of natural language understanding and logical inference. The state-of-the-art performance was achieved by enumerating all the possible expressions from the quantities in the text and customizing a scoring function to identify the one with the maximum probability. However, it incurs exponential search space with the number of quantities and beam search has to be applied to trade accuracy for efficiency. In this paper, we make the first attempt of applying deep reinforcement learning to solve arithmetic word problems. The motivation is that deep Q-network has witnessed success in solving various problems with big search space and achieves promising performance in terms of both accuracy and running time. To fit the math problem scenario, we propose our MathDQN that is customized from the general deep reinforcement learning framework. Technically, we design the states, actions, reward function, together with a feed-forward neural network as the deep Q-network. Extensive experimental results validate our superiority over state-of-the-art methods. Our MathDQN yields remarkable improvement on most of datasets and boosts the average precision among all the benchmark datasets by 15\%.

Entity resolution identifies all records in a database that refer to the same entity. The mainstream solutions rely on supervised learning or crowd assistance, both requiring labor overhead for data annotation. To avoid human intervention, we propose an unsupervised graph-theoretic fusion framework with two components, namely ITER and CliqueRank. Specifically, ITER constructs a weighted bipartite graph between terms and record-record pairs and iteratively propagates the node salience until convergence. Subsequently, CliqueRank constructs a record graph to estimate the likelihood of two records resident in the same clique. The derived likelihood from CliqueRank is fed back to ITER to rectify the edge weight until a joint optimum can be reached. Experimental evaluation was conducted among 14 competitors and results show that without any labeled data or crowd assistance, our unsupervised framework is comparable or even superior to state-of-the-art methods among three benchmark datasets.

User-generated trajectories (UGTs), such as travel records from bus companies, capture rich information of human mobility in the offline world. However, some interesting applications of these raw footprints have not been exploited well due to the lack of textual information to infer the subject’s personal interests. Although there is rich semantic information contained in the spatial- and temporal-aware user-generated contents (STUGC) published in the online world, such as Twitter, less effort has been made to utilize this information to facilitate the interest discovery process. In this article, we design an effective probabilistic framework named CO 2 to connect the offline world with the online world in order to discover users’ interests directly from their raw footprints in UGT. CO 2 first infers trip intentions by utilizing the semantic information in STUGC and then discovers user interests by aggregating the intentions. To evaluate the effectiveness of CO 2 , we use two large-scale real-world datasets as a case study and further conduct a questionnaire survey to show the superior performance of CO 2 .

In this paper, we study the problem of math formula recognition (MFR) in degraded Chinese document images. Compared to traditional optical character recognition (OCR), the MFR problem brings new challenges in terms of character segmentation and structural analysis, especially in degraded images. To tackle these issues, we propose an over-segmentation strategy to split and recognize adhesive formula elements based on convolutional neural network (CNN). In addition, we propose a hierarchical framework for formula structure analysis that constructs the formula in a top-down manner to iteratively split the regions into recognizable units. Due to the lack of degraded Chinese document images with math formulas in the community, we also harvest a diverse ground-truth dataset containing 100 images submitted from our system users. Extended experiments demonstrate the effectiveness and robustness of our proposed method in comparison with state-of-the-art methods.

Background noise and illumination condition are two primary factors degrading the performance of document image binarization. In this paper, we propose an iterative refinement framework to support robust binarization. Initially, an input image is transformed into a Bhattacharyya similarity matrix with Gaussian kernel, which is subsequently converted into a binary image using maximum entropy classifier. Then, we adopt the run-length histogram to estimate the character stroke width, an important indicator to determine the length of filter window. After noise elimination, the output image is used for the next round of refinement and the process terminates when the estimated stroke width is stable. Extensive experiments were conducted on the standard DIBCO datasets as well as a new benchmark harvested from our user query log. Results show that our proposed method outperforms state-of-the-art methods and is more robust to handle low-quality images.

We study a novel problem of influence maximization in trajectory databases that is very useful in precise locationaware advertising. It finds k best trajectories to be attached with a given advertisement and maximizes the expected influence among a large group of audience. We show that the problem is NP-hard and propose both exact and approximate solutions to find the best set of trajectories. We also extend our problem to support the scenario when there are a group of advertisements. We validate our approach via extensive experiments with real datasets.

User-generated trajectories (UGT), such as GPS footprints from wearable devices or travel records from bus companies, capture rich information of human mobility and urban dynamics in the offline world. In this paper, our objective is to enrich these raw footprints and discover the users' personal interests by utilizing the semantic information contained in the spatial-and temporal-aware user-generated contents (STUGC) published in the online world. We design a novel probabilistic framework named CO2to connect the offline world with the online world in order to discover the users' interests directly from their raw footprints in UGT. In particular, we first propose a latent probabilistic generative model named STLDA to infer the intention attached with each trip, and then aggregate the extracted trip intentions to discover the users' personal interests. To tackle the inherent sparsity and noisiness problems of the tags in STUGC, STLDA considers the inner correlation between tags (i.e., semantic, spatial and temporal correlation) on the topic-level. To evaluate the effectiveness of CO2, we utilize a dataset containing three months of data with 5.3 billion bus records and a Twitter dataset with 1.5 million tweets published in 6 months in Singapore as a case study. Experimental results on these two real-world datasets show that CO2is effective in discovering user interests and improves the precision of the state-of-the-art method by 280%. In addition, we also conduct a questionnaire survey in Singapore to evaluate the effectiveness of CO2. The results further validate the superiority of CO2.

In this paper, we study a novel problem of influence maximization in trajectory databases that is very useful in precise location-aware advertising. It finds k best trajectories to be attached with a given advertisement and maximizes the expected influence among a large group of audience. We show that the problem is NP-hard and propose both exact and approximate solutions to find the best set of trajectories. In the exact solution, we devise an expansion-based framework that enumerates trajectory combinations in a best-first manner and propose three types of upper bound estimation techniques to facilitate early termination. In addition, we propose a novel trajectory index to reduce the influence calculation cost. To support large k, we propose a greedy solution with an approximation ratio of (1 - 1/e), whose performance is further optimized by a new proposed cluster-based method. We also propose a threshold method that can support any approximation ratio ϵ ∈ (0, 1]. In addition, we extend our problem to support the scenario when there are a group of advertisements. In our experiments, we use real datasets to construct user profiles, motion patterns, and trajectory databases. The experimental results verified the efficiency of our proposed methods.

In spite of vast business potential, targeted advertising in public transportation systems is a grossly unexplored research area. For instance, SBS Transit in Singapore can reach 1 billion passengers per year but the annual advertising revenue contributes less than $35 million. To bridge the gap, we propose a probabilistic data model that captures the motion patterns and user interests so as to quantitatively evaluate the impact of an advertisement among the passengers. In particular, we leverage hundreds of millions of bus/train boarding transaction records to quantitatively estimate the probability as well as the extent of a user being influenced by an ad. Based on the influence model, we study a top-kretrieval problem for bus/train ad recommendation, which acts as a primitive operator to support various advanced applications. We solve the retrieval problem efficiently to support real-time decision making. In the experimental study, we use the dataset from SBS Transit as a case study to verify the effectiveness and efficiency of our proposed methodologies.

The prevalence of social networks and mobile devices has facilitated the real-time dissemination of local events such as sales, shows and exhibitions. To explore nearby events, mobile users can query a location based search engine for the desired data. However, operating under such a pull based model means that users may miss interesting events (because no explicit queries are issued) or processing/communication overheads may be high (because users have to continuously issue queries). In this demo, we present Elaps, an efficient location-aware publish/subscribe system that can effectively disseminate interesting events to moving users. Elaps is based on the push model and notifies mobile users instantly whenever there is a matching event around their locations. Through the demo, we will demonstrate that Elaps is scalable to a large number of subscriptions and events. Moreover, Elaps can effectively monitor the subscribers without missing any event matching, and incur low communication overhead.

In this paper, we propose a new location-aware pub/sub system, Elaps, that continuously monitors moving users subscribing to dynamic event streams from social media and E-commerce applications. Users are notified instantly when there is a matching event nearby. To the best of our knowledge, Elaps is the first to take into account continuous moving queries against dynamic event streams. Like existing works on continuous moving query processing,Elaps employs the concept of safe region to reduce communication overhead. However, unlike existing works which assume data from publishers are static, updates to safe regions may be triggered by newly arrived events. In Elaps, we develop a concept called \textit{impact region} that allows us to identify whether a safe region is affected by newly arrived events. Moreover, we propose a novel cost model to optimize the safe region size to keep the communication overhead low. Based on the cost model, we design two incremental methods, iGM and idGM, for safe region construction. In addition, Elaps uses boolean expression, which is more expressive than keywords, to model user intent and we propose a novel index, BEQ-Tree, to handle spatial boolean expression matching. In our experiments, we use geo-tweets from Twitter and venues from Foursquare to simulate publishers and boolean expressions generated from AOL search log to represent users intentions. We test user movement in both synthetic trajectories and real taxi trajectories. The results show that Elaps can significantly reduce the communication overhead and disseminate events to users in real-time.

Research on cognitive science indicates that humans often use different criteria for route selection. An alternative type of spatial proximity search on road networks recently has been proposed to find the easiest-to-reach neighboring object with the smallest navigation complexity. This paper presents an evaluation to compare the effectiveness of easiest-to-reach neighbor query against a classic nearest neighbor query in a real-world setting. Our user study demonstrates usability of the new spatial query type and suggests people may not always care about travel distance most. To provide flexibility to accommodate different requirements, we also show how to achieve tradeoff between navigation complexity and travel distance for advanced navigational assistance.

Conflict based clause learning is known to be an important component in Modern SAT solving. Because of the exponential blow up of the size of learnt clauses database, maintaining a relevant and polynomially bounded set of learnt clauses is crucial for the efficiency of clause learning based SAT solvers. In this paper, we first compare several criteria for selecting the most relevant learnt clauses with a simple random selection strategy. We then propose new criteria allowing us to select relevant clauses w.r.t. A given search state. Then, we use such strategies as a means to diversify the search in a portfolio based parallel solver. An experimental evaluation comparing the classical Many SAT solver with the one augmented with multiple deletion strategies, shows the interest of such approach.

With the rapid development of GPS-enabled mobile devices, huge amounts of user-contributed data with location information can be collected from the Internet. With this kind of data, one promising application is travel recommendation, which has attracted a considerable number of researches recently. However, most of the previous studies only focus on one aspect of the relations among users and locations or make a coarse linear combination of the relations. Moreover, all the existing work on travel recommendation do not consider recommendation to groups, which is an important characteristic of travelers' behavior. In this paper, we present a personalized travel recommendation system named Where to Go. The novelty of the system is a 3R model which can unify user-location relation, user-user relation and location-location relation into a single framework and perform random walk with restart to analyze the model. We further extend our approach to provide recommendations for groups. To the best of our knowledge, this is the first work to use random walk with restart for group recommendation. We conduct a comprehensive performance evaluation using a real dataset collected from Flickr, which is one of the most popular online photo-sharing sites. Experimental results show that our approach provides significantly superior recommendation quality compared to other state-of-the-art travel recommendation approaches for both individuals and groups.

In this paper, a new heuristic for polarity selection is proposed. This heuristic is defined for parallel SAT solvers. The selected polarity is an important component of modern SAT solvers, in particular in portfolio ones. Indeed, these solvers are often based on the cooperation/competition principle. In this case, the polarity can be used to guide the solver in the search space. A criterion based on the intention notion is proposed in order to evaluate whether two solvers are to study the same search space or not. Once this criterion defined, a dynamical heuristic polarity is proposed for tuning the different solvers. Experimental results show that our approach is efficient and provides significant improvements on a range of industrial instances.