Existing cold-start recommendation methods typically use item-level alignment strategies to align the content feature and collaborative feature of warm items during model training. However, these methods are less effective for cold items with low semantic similarity to the warm items when they first appear in the test stage, as they have no historical interactions to obtain the collaborative feature. In this paper, we propose a preference aware recommendation (PARec) model with hierarchical item alignment to solve the item cold-start issue. Our approach exploits user preference from historical records to achieve group-level alignment with item content feature, enhancing recommendation performance. Specifically, our hierarchical item alignment strategy improves recommendations for both high and low similarity cold items by using item-level alignment for high similarity cold items and introducing group-level alignment for low similarity cold items. Low similarity cold items can be successfully recommended through relationships among items, captured by our group-level alignment, based on their co-occurrence possibilities and semantic similarities. For model training, a hierarchical contrastive objective function is presented to balance the performance of warm and cold items, achieving better overall performance. Extensive experiments demonstrate the effectiveness of our method, with results showing its superiority compared to state-of-the-art approaches.

General matrix multiplication (GEMM) as a fundamental subroutine has been widely used in many applications like scientific computing, machine learning, etc. Although many studies are dedicated to optimizing its performance, they mainly focus on matrices with regular shapes or x86 platforms. The irregularly shaped matrices on GEMM running on modern ARMv8 processors are under-explored. In this paper, we provide a thorough performance analysis of the general block-panel multiplication (GEBP) kernel of GEMM that has irregular shapes. Based on our analysis, we propose a new GEMM algorithm named EPPA with three novel schemes to improve GEMM performance on ARMv8 processors: i) eliminating packing to reduce Ll cache contention, ii) avoiding data eviction and pre-fetching data to reduce the Ll cache miss penalty, and iii) an adaptive selection strategy of the above two and original schemes. We conduct extensive experiments with a large range of irregular matrices on three popular ARMv8 processors compared to seven state-of-the-art GEMM libraries. The experimental results show that our EPPA algorithm outperforms existing ones across workloads and processors and accelerates real-world applications.

With the ubiquity of mobile devices and rapid development of cloud computing, mobile cloud computing (MCC) has been considered as an essential computation setting to support complicated, scalable and flexible mobile applications by overcoming the physical limitations of mobile devices with the aid of cloud. In the MCC setting, since many mobile applications (e.g., map apps) interacting with cloud server and application server need to perform computation with the private data of users, it is important to realize secure computation for MCC. In this article, we propose an efficient server-aided secure two-party computation (2PC) protocol for MCC. This is the first work that considers collusion between a malicious garbled circuit evaluator and a semi-honest server while ensuring privacy and correctness. Also, it can guarantee fairness when collusion does not exist. The security analysis shows that our protocol can securely compute any function f(x, y) against different types of adversaries in the malicious model. Also, the experimental performance analysis shows that this work outperforms the previous works for at least 10 times with the same security level.

How to track the target robustly is a challenging task in the field of computer vision. Occlusion as one of the most difficult problems, occurs due to the information lost when three-dimensional subjects are projected in two-dimensional interface, therefore, the 2D or 3D tracking algorithms which adopted depth information that expects to rely on three-dimensional special structure to resolve these problems and made somewhat progress. The 2D tracking algorithm is not efficient in fully using depth information, and the 3D tracking method is not robust because of the lack of mature 3D feature extraction method, which fairly restricts the actual tracking effect. Responding to above questions, we propose an adoption of adaptive quantified depth information, establish an adaptive hierarchical structure according to various scenarios. Hierarchical structure can filter the foreground and background information to reduce the interference in tracking, at the same time simplify the use of the depth information. Combined with kernel correlation filter tracking method, we design the algorithm using 2D apparent model under the spatial structures, which is efficient to deal with the problems of occlusion and the change of target scale, and prove its effectiveness on Princeton Tracking Dataset.