Multimodal sarcasm detection identifies ironic intent by jointly analyzing text and images. It has attracted increasing attention due to its importance in understanding user-generated content on social media. However, multimodal observations are often incomplete due to data loss during transmission or collection, leading to unreliable predictions in real-world multimodal sarcasm detection scenarios. To address incomplete observations, we further propose a Reliability-Aware Dynamic Fusion (RADF) module, which predicts the reliability of the textual, visual, and interactive views from their representations, converts these reliability scores into dynamic fusion weights via a temperature-scaled softmax to control the sharpness of the weight distribution, and refines the fused features through feature-wise scaling. In this way, degraded views are suppressed while more informative views are emphasized under incomplete observations. Extensive experiments on public datasets validate the effectiveness of our approach, which consistently outperforms existing baselines.

Cross-domain sequential recommendation leverages a user’s historical interaction behavior across multiple domains to forecast the subsequent item with which they are likely to engage. Despite the efforts made by previous methods in representing users across domains, we believe they still have three inherent limitations: 1) Ignoring the importance of the relations between items among various domains, leading to suboptimal performance. 2) Most existing cross-domain sequential recommendation models are incapable of making full use of timestamp information, merely representing the sequence of user-item interaction sequence through positional embeddings. 3) Existing cross-domain recommendation methods consider only individual users, failing to recognize that social relations may influence user preferences. To overcome these problems, we propose an innovative approach called Social-Augmented Time-Aware Cross-Domain Sequential Recommendation (STRec). The method leverages the integration of multi-domain data with temporal information, further enriched by social influence, to afford a holistic understanding of user preferences. STRec consists of three main components: Initially, embedding modules are employed to convert timestamps and interaction information into embedding vectors respectively. Subsequently, graph neural network modules to uncover collaborative characteristics among inter-sequence items, while sequence attention encoders extract sequential features within item sequences and uncover the potential dependencies of temporal information in different contexts. Conclusively, a social aware attention network is designed to consider users’ social neighbor information and capture the dynamic patterns of social relationships. Through extensive experimentation on real-world datasets across multiple scenarios, we have demonstrated that STRec surpasses several state-of-the-art approaches.

Quantization methods for convolutional neural network models can be broadly categorized into post-training quantization (PTQ) and quantization aware training (QAT). While PTQ offers the advantage of requiring only a small portion of the data for quantization, the resulting quantized model may not be as effective as QAT. To address this limitation, this paper proposes a novel quantization function named Attention Round. Unlike traditional quantization function that map 32 bit floating-point value w to nearby quantization levels , Attention Round allows w to be mapped to all possible quantization levels in the entire quantization space, expanding the quantization optimization space . The possibilities of mapping w to different quantization levels are inversely correlated with the distance between w and the quantization levels, regulated by a Gaussian decay function. Furthermore, to tackle the challenge of mixed precision quantization, this paper introduces a lossy coding length measure to assign quantization precision to different layers of the model, eliminating the need for solving a combinatorial optimization problem . Experimental evaluations on various models demonstrate the effectiveness of the proposed method. Notably, for ResNet18 and MobileNetV2 , the PTQ approach achieves comparable quantization performance to QAT while utilizing only 1024 training data and 10 min for the quantization process .

Non-coding RNAs are fundamental to the competing endogenous RNA (CeRNA) hypothesis in oncology. Previous work focused on static CeRNA networks. We construct and analyze CeRNA networks for four sequential stages of lung adenocarcinoma (LUAD) based on multi-omics data of long non-coding RNAs (lncRNAs), microRNAs and mRNAs. We find that the networks possess a two-level bipartite structure: common competing endogenous network (CCEN) composed of an invariant set of microRNAs over all the stages and stage-dependent, unique competing endogenous networks (UCENs). A systematic enrichment analysis of the pathways of the mRNAs in CCEN reveals that they are strongly associated with cancer development. We also find that the microRNA-linked mRNAs from UCENs have a higher enrichment efficiency. A key finding is six microRNAs from CCEN that impact patient survival at all stages, and four microRNAs that affect the survival from a specific stage. The ten microRNAs can then serve as potential biomarkers and prognostic tools for LUAD.

Open ports are typically used by server software to serve remote clients, and the usage historically leads to remote exploitation due to insufficient protection. Smartphone operating systems inherit the open port support, but since they are significantly different from traditional server machines in performance and availability guarantees, little is known about how smartphone applications use open ports and what the security implications are. In this paper, we perform the first systematic study of open port usage on mobile platform and their security implications. To achieve this goal, we design and implement OPAnalyzer, a static analysis tool which can effectively identify and characterize vulnerable open port usage in Android applications. Using OPAnalyzer, we perform extensive usage and vulnerability analysis on a dataset with over 100K Android applications. OPAnalyzer successfully classifies 99% of the mobile usage of open ports into 5 distinct families, and from the output, we are able to identify several mobile-specific usage scenarios such as data sharing in physical proximity. In our subsequent vulnerability analysis, we find that nearly half of the usage is unprotected and can be directly exploited remotely. From the identified vulnerable usage, we discover 410 vulnerable applications with 956 potential exploits in total. We manually confirmed the vulnerabilities for 57 applications, including popular ones with 10 to 50 million downloads on the official market, and also an app that is pre-installed on some device models. These vulnerabilities can be exploited to cause highly-severe damage such as remotely stealing contacts, photos, and even security credentials, and also performing sensitive actions such as malware installation and malicious code execution. We have reported these vulnerabilities and already got acknowledged by the application developers for some of them. We also propose countermeasures and improved practices for each usage scenario.

Multiple-Input Multiple-Output (MIMO) technology has become an efficient way to improve the capacity and reliability of wireless networks. Traditional MIMO schemes are designed mainly for the scenario of contiguous spectrum ranges. However, in cognitive radio networks, the available spectrum is discontiguous, making traditional MIMO schemes inefficient for spectrum usage. This motivates the design of new MIMO schemes that apply to networks with discontiguous spectrum ranges. In this paper, we propose a scheme called VSMC MIMO, which enables MIMO nodes to transmit variable numbers of streams in multiple discontinuous spectrum ranges. This scheme can largely improve the spectrum utilization and meanwhile maintain the same spatial multiplexing and diversity gains as traditional MIMO schemes. To implement this spectral-efficient scheme on cooperative MIMO relays in cognitive radio networks, we propose a joint relay selection and spectrum allocation algorithm and a corresponding MAC protocol for the system. We also build a testbed by the Universal Software Radio Peripherals (USRPs) to evaluate the performances of the proposed scheme in practical networks. The experimental results show that VSMC MIMO can efficiently utilize the discontiguous spectrum and greatly improve the throughput of cognitive radio networks.

In this paper, we propose virtual data center (VDC) as the unit of resource allocation for multiple tenants in the cloud. VDCs are more desirable than physical data centers because the resources allocated to VDCs can be rapidly adjusted as tenants' needs change. To enable the VDC abstraction, we design a data center network virtualization architecture called SecondNet. SecondNet achieves scalability by distributing all the virtual-to-physical mapping, routing, and bandwidth reservation state in server hypervisors. Its port-switching based source routing (PSSR) further makes SecondNet applicable to arbitrary network topologies using commodity servers and switches. SecondNet introduces a centralized VDC allocation algorithm for bandwidth guaranteed virtual to physical mapping. Simulations demonstrate that our VDC allocation achieves high network utilization and low time complexity. Our implementation and experiments show that we can build SecondNet on top of various network topologies, and SecondNet provides bandwidth guarantee and elasticity, as designed.