Task planning under uncertainty is essential for home-service robots operating in the real world. Tasks involve ambiguous human instructions, hidden or unknown object locations, and open-vocabulary object types, leading to significant open-ended uncertainty and a boundlessly large planning space. To address these challenges, we propose Tru-POMDP, a planner that combines structured belief generation using Large Language Models (LLMs) with principled POMDP planning. Tru-POMDP introduces a hierarchical Tree of Hypotheses (TOH), which systematically queries an LLM to construct high-quality particle beliefs over possible world states and human goals. We further formulate an open-ended POMDP model that enables rigorous Bayesian belief tracking and efficient belief-space planning over these LLM-generated hypotheses. Experiments on complex object rearrangement tasks across diverse kitchen environments show that Tru-POMDP significantly outperforms state-of-the-art LLM-based and LLM-tree-search hybrid planners, achieving higher success rates with significantly better plans, stronger robustness to ambiguity and occlusion, and greater planning efficiency.

Multimodal Named Entity Recognition (MNER) is a task that leverages multimodal information (such as text and images) to identify named entities within social media text. Traditional MNER methods primarily rely on simple interactions between text annotations and visual features, thus overlooking the specific correspondence between text and visual objects. Additionally, irrelevant visual noise may interfere with the final recognition results. Therefore, this paper proposes a Entity label-guided Graph Fusion Multi-modal Named Entity Recognition approach(ELGF), which utilizes pre-defined entity label information from input text as a bridge. Firstly, entity label detection tasks are employed to obtain entity label information. Then, the entity label information is utilized as a bridge between the two modalities to construct a multimodal interaction graph. This graph is inputted into a graph neural network, where attention and gate mechanisms are applied to interactively fuse multimodal information. Finally, a Conditional Random Field (CRF) decoding is used to predict the final MNER label sequence. Extensive experimental results demonstrate that compared to mainstream methods, the proposed model achieves competitive recognition accuracy on public datasets.

Due to its excellent interpretability and high accuracy, time series Shapelet has garnered widespread attention in time series classification tasks. However, currently prevalent Shapelet classification methods primarily focus on numerical data, neglecting the common occurrence of categorical feature variables in practical applications. Additionally, existing multivariate time series classification algorithms exhibit short-comings in executing classification tasks following the Shapelet discovery process. Inspired by association rule mining, this paper proposes an innovative Shapelet classification algorithm aimed at addressing both numerical and categorical data in multivariate time series. This algorithm employs a unified representation method to effectively integrate categorical and continuous features, while enhancing existing time series Shapelet discovery methods by independently calculating Shapelets for each variable, making them more suitable for association rule mining. Leveraging the discovered Shapelets and Allen's interval relations, the algorithm constructs temporal relationships among multivariate time series Shapelets, enabling the discovery of frequent patterns and the completion of classification tasks. This study aims to fully leverage the interpretability of time series Shapelets, revealing hidden temporal patterns within time series data. Experimental results demonstrate that this algorithm outperforms existing benchmark algorithms for multivariate time series classification in terms of accuracy, while exhibiting significant interpretability advantages.

Time series shapelets have been widely studied for their optimal interpretability and accuracy in solving time series classification (TSC) tasks. This study determine that similar trends of the same type of time series can be used to reduce the size of the candidate solution set and improve the efficiency of shapelets discovery. However, only a few studies have been conducted on this. Therefore, this study proposes a shapelets discovery algorithm that learns the common trend of the same type of time series to reconstruct the sequence. First, by constructing an autoencoder to fit time series of the same type with similar trends, we reconstruct a sequence that integrates the features of this type, thereby reducing the size of the candidate set. Thereafter, to compress the regenerated sequence and reduce its dimensionality, it is represented by a Symbolic Aggregate approXimation. Furthermore, filtering out impossible and low-quality candidate solutions further reduces the size of the candidate solution set. Finally, this study proposes a candidate solution evaluation method based on the Matrix Profile structure to find the discriminative shapelets that can best represent each type of time series and uses them to construct a classifier to complete the classification task. Extensive experiments demonstrate that the proposed algorithm effectively reduces the candidate solution set size and has advantages in efficiency, accuracy, and interpretability compared to traditional methods.

Abstract Hardware Trojans are usually implanted by making malicious changes to a chip circuit, which can destroy chip functions or expose sensitive information once activated. The hardware Trojan detection method based on side channel information has now become one of the most widely used detection methods. However, due to the influence of the deviation of the acquisition equipment and the noise of the actual chip working environment, insufficient acquisition of useful information of the collected side channel information occurs, affecting the final results. To address the problem, this paper proposes a detection method based on a dual discriminator assisted conditional generation adversarial network (D2ACGAN), which combines the benefits of CGAN, ACGAN, and D2GAN models and can learn a variety of valid information of the tested chip. It can distinguish between side channel data with and without hardware Trojan and classify hardware Trojan using the extended data. Furthermore, to compare the performance of the proposed model, we use the existing CGAN and ACGAN models equally for side channel information expansion and hardware Trojan detection. Finally, the designed hardware Trojan is implanted in an encryption chip for generating data quality evaluation experiments and model method performance experiments. The results show that the average detection accuracy of the D2ACGAN-based hardware Trojan classification model can reach 97.08%, which is better than the detection models based on CNN, SVM, etc. The D2ACGAN model also outperforms the CGAN and ACGAN models in terms of generated data and hardware Trojan classification.

Gaze-based target suffers from low input precision and target occlusion. In this paper, we explored to leverage the continuous eyelid movement to support high-efficient and occlusion-robust dwell-based gaze pointing in virtual reality. We first conducted two user studies to examine the users’ eyelid movement pattern both in unintentional and intentional conditions. The results proved the feasibility of leveraging intentional eyelid movement that was distinguishable with natural movements for input. We also tested the participants’ dwelling pattern for targets with different sizes and locations. Based on these results, we propose DEEP, a novel technique that enables the users to see through occlusions by controlling the aperture angle of their eyelids and dwell to select the targets with the help of a probabilistic input prediction model. Evaluation results showed that DEEP with dynamic depth and location selection incorporation significantly outperformed its static variants, as well as a naive dwelling baseline technique. Even for 100% occluded targets, it could achieve an average selection speed of 2.5s with an error rate of 2.3%.

Abstract As a combination of broadcast encryption and signcryption, broadcast signcryption enables a broadcaster to simultaneously achieve encryption and signature for a specific set of receivers in a single logical step. And it can provide a very efficient solution to achieve confidentiality and authentication of message source during information distribution. Recently, to achieve both confidentiality and authenticity simultaneously, Kim et al. proposed two source‐authenticated broadcast encryption schemes and claimed that their two schemes could provide both authentication and confidentiality simultaneously. Despite being proven to secure, their schemes are shown to be insecure in this paper, namely, they cannot achieve authentication and confidentiality of message. An attacker can forge a ciphertext and convince a specified receiver that the ciphertext is from a valid broadcaster. 3 After the corresponding attacks are given, the reasons to produce such attacks are analyzed at last. Copyright © 2014 John Wiley & Sons, Ltd.

As the focus of networking research shifts from raw performance to the delivery of advanced network services, there is a growing need for open-platform systems for extensible networking research. The Applied Research Laboratory at Washington University in Saint Louis has developed a flexible network services platform (NSP) to meet this need. The NSP provides an extensible platform for prototyping next-generation network services and applications. The paper describes the design of a system-on-chip packet processor for the NSP which performs all core packet processing functions, including segmentation and reassembly, packet classification, route lookup, and queue management. Targeted to a commercial configurable logic device, the system is designed to support gigabit links and switch fabrics with a 2:1 speed advantage. We provide resource consumption results for each component of the packet processor design.