With the growing deployment of AI at the edge, running inference on resource-constrained devices increasingly requires efficient access to large models. However, modern models contain billions of parameters, far exceeding local memory, while TCP/IP-based edge networks make bulk transfers inefficient. Existing approaches, such as full downloads or network file systems, incur high latency, memory overhead, and lack fine-grained control. We present ModelMap, a zero-copy remote memory-mapped storage system that enables transparent, on-demand access to large AI models over TCP/IP. Instead of preloading entire models, ModelMap intercepts page faults in the Linux kernel and fetches only the needed parameter pages from a remote server, allowing applications to treat remote models as local. By operating entirely in kernel space, ModelMap reduces latency compared to user-space methods and avoids redundant copies. It further leverages kernel caching, prefetching, and sequential access optimizations to mitigate network bottlenecks. Crucially, ModelMap requires no changes to inference code, offering an efficient, scalable foundation for remote model access in edge environments.

Passive sensing using communication signal waveforms is considered to be a promising technology for target monitoring in Internet-of-Everything. Conventional passive sensing schemes require accurate estimation of the time difference of arrival (TDOA) and frequency difference of arrival (FDOA), which is leading to high complexity but low accuracy. In this paper, a robust passive sensing algorithm using multiple illumination of opportunities is proposed to improve the detection performance while avoiding separate estimation of TDOA and FDOA. The proposed method first combines the linear constrained minimum variance adaptive filter with the wide nulling algorithm to achieve target direction finding while separating the direct wave and suppressing multipath interference. Then, the Linear Canonical Transformation-based Cross Ambiguity Function (LCTCAF) is employed to estimate the distance and radial velocity of the target. Relying on the relationship between distance to time and velocity to Doppler, a Distance-Velocity transformation-based Cross Ambiguity Function (DVCAF) is introduced to characterize the distance and radial velocity of the target. Finally, a spectral peak search scheme is exploited in DVCAF to estimate the time delay and Doppler shift so as to identify the target parameters directly. Its’ Cramer-Rao Low Bound is derived. Simulation results validate that the performance of the proposed algorithm outperforms the conventional estimators based on the cross ambiguity function.

Recently,fake newshas been readily spread by massive amounts of users in social media, and automatic fake news detection has become necessary. The existing works need to prepare the overall data to perform detection, losing important information about the dynamic evolution of crowd opinions, and usually neglect the issue of uneven arrival of data in the real world. To address these issues, in this article, we focus on a kind of approach for fake news detection, namelyprogressive detection, which can be achieved by thedynamic Probabilistic Graphical Model. Based on the observation on real-world datasets, we adaptively improve the Kalman Filter to theLabeled Variable Dimension Kalman Filter(LVDKF) that learns two universal patterns from true and fake news, respectively, which can capture the temporal information of time-series data that arrive unevenly. It can take sequential data as input, distill the dynamic evolution knowledge regarding a post, and utilize crowd wisdom from users’ responses to achieve progressive detection. Then we derive the formulas using the Forward, Backward, and EM Algorithm, and we design a dynamic detection algorithm using Bayes’ theorem. Finally, we design experimental scenarios simulating progressive detection and evaluate LVDKF on two public datasets. It outperforms the baseline methods in these experimental scenarios, which indicates that it is adequate for progressive detection.

In location-based services, such as navigation and ride-hailing, it is an essential function to match a query with Point-of-Interests (POIs) for efficient destination retrieval. Indeed, due to the space limit and real-time requirement, such services usually require intermediate POI matching results when only partial search keywords are typed. While there are numerous retrieval models for general textual semantic matching, few attempts have been made for query-POI matching by considering the integration of rich spatio-temporal factors and dynamic user preferences. To this end, in this paper, we develop a spatio-temporal dual graph attention network ~(STDGAT), which can jointly model dynamic situational context and users' sequential behaviors for intelligent query-POI matching. Specifically, we first utilize a semantic representation block to model semantic correlations among incomplete texts as well as various spatio-temporal factors captured by location and time. Next, we propose a novel dual graph attention network to capture two types of query-POI relevance, where one models global query-POI interaction and another one models time-evolving user preferences on destination POIs. Moreover, we also incorporate spatio-temporal factors into the dual graph attention network so that the query-POI relevance can be generalized to the sophisticated situational context. After that, a pairwise fusion strategy is introduced to extract the salient global feature representatives for both queries and POIs. Finally, several cold-start strategies and training methods are proposed to improve the matching effectiveness and training efficiency. Extensive experiments on two real-world datasets demonstrate the performances of our approach compared with state-of-the-art baselines. The results show that our model achieves significant improvement in terms of matching accuracy even with only partial query keywords are given.

Since the financial crisis in late 2008-2009, several global regulatory authorities have mandated stress-testing exercises to evaluate the potential capital shortfalls & systemic impacts that large banks may face during adverse economic conditions. Thus, having the ability to analyze economic conditions & banking performance profiles together to determine relationships among their respective features may provide insights for stress testing tasks. In this paper, we propose an Integrated Multimodal Bank Stress Test Prediction (IMBSTP) model framework consisting of a two-stages; (1) economic conditions estimator to approximate joint representation among the exogenous factors using generative models, (2) bank capital & loss forecaster to project stress test measures based on dimensional & temporal features selected from the exogenous economic conditions & banking performance profiles using a dual-attention recurrent neural network. Extensive experimentation is performed on historical economic conditions & consolidated financial statements of U.S. bank holdings companies to show the effectiveness of our approach when compared to state-of-the-art baseline methods.

Crime prediction has always been a crucial issue for public safety, and recent works have shown the effectiveness of taking spatial correlation, such as region similarity or interaction, for fine-grained crime modeling. In our work, we seek to reveal the relationship across regions for crime prediction using Continuous Conditional Random Field (CCRF). However, conventional CCRF would become impractical when facing a dense graph considering all relationship between regions. To deal with it, in this paper, we propose a Neural Network based CCRF (NN-CCRF) model that formulates CCRF into an end-to-end neural network framework, which could reduce the complexity in model training and improve the overall performance. We integrate CCRF with NN by introducing a Long Short-Term Memory (LSTM) component to learn the non-linear mapping from inputs to outputs of each region, and a modified Stacked Denoising AutoEncoder (SDAE) component for pairwise interactions modeling between regions. Experiments conducted on two different real-world datasets demonstrate the superiority of our proposed model over the state-of-the-art methods.

User behaviors in online social networks convey not only literal information but also one’s emotional attitudes towards the information. To compute this attitude, we define the concept of emotion role as the concentrated reflection of a user’s online emotional characteristics. Emotion role detection aims to better understand the structure and sentiments of online social networks and support further analysis, e.g., revealing public opinions, providing personalized recommendations, and detecting influential users. In this article, we first introduce the definition of a fine-grained emotion role, which consists of two dimensions: emotion orientation (i.e., positive, negative, and neutral) and emotion influence (i.e., leader and follower). We then propose a Multi-dimensional Emotion Role Mining model (MERM) to determine a user’s emotion role in online social networks. Specifically, we tend to identify emotion roles by combining a set of features that reflect a user’s online emotional status, including degree of emotional characteristics, accumulated emotion preference, structural factor, temporal factor, and emotion change factor. Experiment results on a real-life micro-blog reposting dataset show that the classification accuracy of the proposed model can achieve up to 90.1%.

Given its importance, crime prediction has attracted a lot of attention in the literature, and several methods have been proposed to discover different aspects of characteristics for crime prediction. In this paper, we propose a Clustered Continuous Conditional Random Field (Clustered-CCRF) model which is able to effectively exploit both spatial and temporal factors for crime prediction in an integrated way. In particular, we observe that the crime number at one specific area is not only conditioned on its own historical records but also has high correlation to crime records from similar areas. Therefore, we propose two factors: an auto-regressed temporal correlation and a feature-based inter-area spatial correlation, to measure such patterns for crime prediction. Further, we present a tree-structured clustering algorithm to discover high similar areas based on spatial characteristics to improve the performance of our proposed model. Experiments on real-world crime dataset demonstrate the superiority of our proposed model over the state-of-the-art methods.

Monitoring group mobility and structure is crucial for understanding group activities and social relations. In this paper, we develop algorithms for fine-grained mobility classification and structure recognition of social groups utilizing mobile devices. First, we present a method that recognizes four levels of group mobility, including stationary, strolling, walking, and running. Second, using multiple types of mobile sensors, a novel relative position relationship estimation algorithm is developed to understand different moving group structures. We have conducted real-life experiments in which 12 volunteers moved in different small groups either in an office building or a shopping mall with various speeds and structures. Experimental results show that our approach achieves an accuracy of 99.5 percent in group mobility level classification and about 80 percent in group structure recognition.

The wide spread use of social network services, especially location based services, has transformed social networks into an important information source of real-world events. Many event detection systems using geo-tagged posts from social networks have been developed in recent years. Besides detecting real-world events, it is also desirable for government officials, news media, and police, etc., to identify on-site users of an event, from whom we could gather valuable information regarding the process of events and investigate suspects when an event is associated with crime or terrorist. However, due to the high uncertainty of human mobility patterns and the low probability of users sharing their location information, it is difficult to identify on-site users while a social event unfolds, and research work in this area is still in its infancy. In this paper, we propose a Fused fEature Gaussian prOcess Regression (FEGOR) model, which exploits three influential factors in social networks for on-site user identification: mobility influence, content similarity, and social relationship. By integrating these factors, we are able to estimate the distance between a user and a social event even when the user's location profile is unknown, thus identify on-site users. Experiments on a real-world Twitter dataset demonstrate the effectiveness of our model, achieving a minimum mean absolute error of 1.7km and outperforming state-of-the-art methods.

The rapid development of social networks has woven themselves into people's daily life and become indispensable platforms with superior commercial and scientific values. Both companies and governments have discovered the potential effectiveness of employing social network users for information diffusion. Rather than only selecting a group of users who are interested in target topic, it is more beneficial to choose users with desired emotion preference to help diffuse information under certain emotional expectation. In this paper, we propose an emotional participant selection system that not only considers user's topic preference, but also more importantly takes user's emotional influence into account. Specifically, a dynamic forgetting mechanism is applied to learn user's topic preference, and independent cascade model is leveraged to construct emotional influence. Combining these two features, we develop an algorithm that can accomplish the task for emotional participant selection. Experimental results on a real-world data set validate the effectiveness of our proposed method.

Moving destination prediction offers an important category of location-based applications and provides essential intelligence to business and governments. In existing studies, a common approach to destination prediction is to match the given query trajectory with massive recorded trajectories by similarity calculation. Unfortunately, due to privacy concerns, budget constraints, and many other factors, in most circumstances, we can only obtain a sparse trajectory dataset. In sparse dataset, the available moving trajectories are far from enough to cover all possible query trajectories; thus the predictability of the matching-based approach will decrease remarkably. Toward destination prediction with sparse dataset, instead of searching similar trajectories over the sparse records, we alternatively examine the changes of distances from sampling locations to final destination on query trajectory. The underlying idea is intuitive: It is directly motivated by travel purpose, people always get closer to the final destination during the movement. By borrowing the conception of gradient descent in optimization theory, we propose a novel moving destination prediction approach, namely MGDPre. Building upon the mobility gradient descent, MGDPre only investigates the behavior characteristics of query trajectory itself without matching historical trajectories, and thus is applicable for sparse dataset. We evaluate our approach based on extensive experiments, using GPS trajectories generated by a sample of taxis over a 10-day period in Shenzhen city, China. The results demonstrate that the effectiveness, efficiency, and scalability of our approach outperform state-of-the-art baseline methods.

Monitoring group mobility and structure is crucial for public safety management and emergency evacuation. In this paper, we propose a fine-grained mobility classification and structure recognition approach for social groups based on hybrid sensing using mobile devices. First, we present a method which classifies group mobility into four levels, including stationary, strolling, walking and running. Second, by combining mobile sensing and Wi-Fi signals, a novel relative position relationship estimation algorithm is developed to understand moving group structures of different shapes. We have conducted real-life experiments in which eight volunteers form two to three small groups moving in a teaching building with different speed and structures. Experimental results show that our approach achieves an accuracy of 99.5% in mobility classification and about 80% in group structure recognition.