Few-shot learning (FSL) is a machine learning method aimed at learning problem-solving models from a small number of samples. Few-shot classification is a task that utilizes few-shot learning for image classification. However, the existing models have poor classification performance when dealing with datasets with large differences in target scale distribution. In view of the large difference in the scale distribution of the sample target in the few-shot learning dataset, this paper proposes a novel few-shot classification model based on multi-scale Vision Transformer using semantic prompt. This model uses the Transformer block which has more generalization ability and long-range dependencies, and constructs a multi-scale feature fusion model on the large-scale cross-domain and cross-scale dataset Meta-Dataset which consists of 10 datasets, including natural image datasets, handwritten characters, and graffiti datasets. By using an explicit multi-scale feature fusion method, the model can obtain category features with cross scale information, thereby improving the robustness of the model to sample targets of different scales. In addition, the model improved its performance by learning additional semantic prompt, achieving a 2.43% improvement.

In recent years, weakly supervised object detection (WSOD) methods using only image-level labels have received increasing attention, Due to the difficulty of manually labeling large-scale remote sensing images. However, existing methods cannot generate high-quality proposals when applying proposal generation methods to RSIs. Meanwhile, these methods ignore the fact that there are a large number of objects of different scales in RSIs. To address these issues, we propose a unique end-to-end dynamic feature fusion network (DFFNet) for WSOD in RSIs. First, we propose an intersection-over-union selective search (IoU-SS) algorithm to generate high-quality proposals by preferentially merging regions with high IoU. Furthermore, we design a novel and flexible dynamic feature fusion (DFF) module to dynamically acquire features of objects at different scales based on the information of the input image. The performance of WSOD in RSIs is further improved by using high-quality proposals and dynamically fused features. Comprehensive experiments and comparisons with state-of-the-art methods on two datasets of RSIs, i.e., NWPU VHR-10.v2 and DIOR, demonstrate the superiority of our proposed method.

Non-Volatile Memory (NVM) has emerged as an alternative to next-generation main memories. Although many tree indices have been proposed for NVM, they generally use B+-tree-like structures. To further improve the performance of NVM-aware indices, we consider integrating learned indexes into NVM. The challenges of such an integration are two fold: (1) existing NVM indices rely on small nodes to accelerate insertions with crash consistency, but learned indices use huge nodes to obtain a flat structure. (2) the node structure of learned indices is not NVM friendly, meaning that accessing a learned node will cause multiple NVM block misses. Thus, in this paper, we propose a new persistent learned index called PLIN. The novelty of PLIN lies in four aspects: an NVM-aware data placement strategy, locally unordered and globally ordered leaf nodes, a model copy mechanism, and a hierarchical insertion strategy. In addition, PLIN is proposed for the NVM-only architecture, which can support instant recovery. We also present optimistic concurrency control and fine-grained locking mechanisms to make PLIN scalable to concurrent requests. We conduct experiments on real persistent memory with various workloads and compare PLIN with APEX, PACtree, ROART, TLBtree, and Fast&Fair. The results show that PLIN achieves 2.08x higher insertion performance and 4.42x higher query performance than its competitors on average. Meanwhile, PLIN only needs ~30 μs to recover from a system crash.

Join selectivity estimation is a fundamental problem in query optimization, which aims to estimate the cardinality returned by a join query. While join selectivity estimation has been extensively studied in relational databases, there are few studies toward join selectivity estimation for cross-model joins in multi-model databases, such as relation-tree joins between a relational table and a tree-structured document like a BSON file in MongoDB. So far, due to the popularity of MongoDB, many applications use both MongoDB and MySQL to organize heterogeneous data. Thus, it is necessary to devise efficient approaches for processing relation-tree joins running on relational and tree models. In this paper, we present an effective and efficient approach to estimate the join selectivity for relation-tree joins, which consists of a value join estimation and a structural join estimation. In particular, we propose a two-level sampling method that samples the relational tuples and tree nodes at two levels. Then, we apply the discrete learning algorithm to the tree node samples to estimate the join value distribution of the tree nodes. With this mechanism, we can capture the correlation between relational tuples and tree nodes and improve the estimation accuracy. We conduct experiments on the DBLP dataset and compare our approach to existing solutions, and the results suggest the effectiveness and efficiency of our proposal.

Existing approaches based on the SGR (single granularity recognition) improve the recognition accuracy by expanding the dataset, learning more robust feature representation or locating and amplifying significant parts. These approaches often suffer from heavy computational costs, high model complexity or additional non-classsification losses. In this paper, we propose a feature fusion method based on multi-classification losses, which can classify different granularity features and make the network focus on more useful information. The complexity and computational costs of our model have only increased slightly, which can be ignored. The results of comparative experiments show the effectiveness of our proposal on Stanford Cars dataset.

Data-intensive systems are typical applications in the big data era. Most data-intensive systems employ LSM (Log-Structured Merge)-tree-based key-value stores, such as RocksDB and LevelDB. The LSM-tree has a multi-level data structure and can transform random writes into sequential ones by a special operation called compaction. However, compaction operations are costly and will increase the processing latency and lower the throughput of the LSM-tree. In this paper, we propose an adaptive lazy compaction method called ALC for the LSM-tree. The main idea of ALC is to delay compactions operations when the current workload is high, and to perform compactions when the system is low-loaded. With this mechanism, we can lower the impact of compactions on the system's performance and achieve high stability and low latency. Our preliminary experiments show that ALC outperforms RocksDB and the state-of-the-art method SILK in latency and throughput-stability.

In this paper, to improve the read performance of LSM-tree, we propose an enhanced LSM-tree called HotKey-LSM. The key idea of HotKey-LSM is to put hot keys and cold keys in two separated column families. Thus, when a hot key is not in the block cache, LSM-tree only needs to access a small hot-key LSM-tree to read the key. With this mechanism, most hot-key requests will be answered with low latency: if the hot key is in the block cache, we can return the memory address of the key; if it is not in the cache, we search the small hot-key LSM-tree. This differs from the traditional LSM-tree in that a hot-key request may traverse a large LSM-tree, which causes a high read latency. We implement HotKey-LSM on RocksDB and compare HotKey-LSM with the original RocksDB. The result in terms of QPS suggests the efficiency of our proposal.

Classification for big image data is an essential field of big data technology. Big image data classification has a wide range of applications, such as face recognition, object detection, and human posture recognition. Traditional methods for big image data classification usually ignore the close connection between different levels of features in neural networks. In this paper, we propose a new way for big image data classification that improves the previous method through the Multi-Level Feature Connection Network (MFC-Net). MFC-Net presents a multi-level feature connection module to mine more useful features for classification. Experiments show that MFC-Net significantly improves its baseline SSD (Single Shot Multibox Detector) and other advanced classification neural networks in the classic datasets Pascal VOC and Microsoft COCO.

Non-Volatile Memory (NVM) has emerged as an alternative to next-generation memories. Compared to the traditional DRAM, NVM offers data persistency and higher density. However, so far, NVM has higher accessing latency than DRAM. Therefore, to ensure the high performance of data accessing, we still need to consider using DRAM in memory architecture. This leads to the hybrid memory architecture involving DRAM and NVM. Some previous benchmark works have shown that such hybrid memory architecture is more efficient than NVM-only architecture. Due to NVM's unique properties, the traditional memory B+-tree becomes unsuitable for NVM because of its high cost of maintaining node orderliness and high space-filling feature. In this paper, we propose to optimize the Adaptive Radix Tree (ART) for the hybrid memory architecture and offer a new index called HART (Hybrid Adaptive Radix Tree). HART takes advantage of ART's deterministic structure to get good query performance. Meanwhile, we only selectively persist linked list to reduce NVM access cost. In particular, we exploit the compression path to improve the leaf node's space utilization, making the subtree shorter. We run a preliminary experiment on a server with Intel Optane DC Persistent Memory and compare HART with several NVM-aware indexes. The results suggest the efficiency of our proposal.

Non-volatile memory (NVM) as a new kind of future memory has several special properties such as non-volatility, read/write asymmetry, and byte address-ability. This makes it difficult to directly replace DRAM with NVM in the current memory hierarchy. Thus, a practical way is to construct a hybrid memory composed of both NVM and DRAM. Such hybrid memory architecture introduces many new challenges for existing algorithms. In this paper, we focus on the data placement issue in NVM-based hybrid memory systems, i.e., how to place the data on DRAM and NVM for a data-centric algorithm so that it can achieve high performance on hybrid memory. Particularly, we propose an optimal data placement model (ODP) to properly store data structures on DRAM and NVM during the execution of an algorithm. We present the theoretical proof to ODP to ensure the correctness of the model. To demonstrate the efficiency of ODP, we apply the ODP to two kinds of data-centric algorithms, namely sorting and database join. For sorting algorithms, we implement four ODP-based sorting algorithms, including Insertion Sort, Selection Sort, Heapsort, and Merge Sort. For join algorithms, we implement four ODP-based join strategies, including Nested Loops Join, Sort Join, Hash Join, and Virtual Partitioning Join. We conduct comparative experiments to evaluate the performance of the sorting/join algorithms. The results show that the ODP-based sorting/join strategies are much faster than the classical sorting/join algorithms that are not NVM-aware. In addition, the ODP-based implementation can reduce more NVM writes, showing that it is more NVM-friendly.

Indoor moving objects management has been a research focus in recent years. In order to get the trajectory data of indoor moving objects, people have to deploy a lot of positioning equipment, such as RFID readers and tags, which takes lots of money, time, and other costs. In addition, it is a very complex and costly process to construct different environment settings for various indoor applications. Aiming to provide experimental trajectory data for various indoor operations and mining algorithms, in this paper we present a flexible tool to generate trajectories for indoor moving objects, which is named IndoorSTG (Indoor Spatiotemporal Trajectory Generator). IndoorSTG can simulate different indoor environments using various elements including rooms, doors, corridors, stairs, elevators, and virtual positioning devices such as RFID or Bluetooth readers. Meanwhile, it can generate semantic-based trajectories for indoor moving objects in a specific indoor space. After an overview of the general features of IndoorSTG, we discuss the architecture and implementation of IndoorSTG. And finally, a case study of IndoorSTG's demonstration is presented.

In this paper, we present a CASE tool supporting conceptual modeling for moving objects database applications, which is called STXER (spatio-temporal extended entity-relational model). The main purpose of STXER is to support the database design for moving objects applications on typical object-relational DBMS. The STXER tool enhances the traditional ER model with moving characteristics. It can express rich spatiotemporal semantics for moving objects applications. After an overview of the general features of STXER, we discuss the architecture of STXER. And finally, a case study of STXER's demonstration is presented.