Abstract Gesture recognition is a fundamental technology in nearly all Human-Computer Interaction (HCI) devices, drawing significant attention from engineers and researchers worldwide. Fine-grained air-writing recognition within a small spatial range remains a challenging yet crucial research topic. In this paper, we propose a method, namely RingRT, which uses an inertial measurement unit (IMU) integrated in the ring to identify character air-writing actions within an area as big as the palm of one’s hand. RingRT leverages the neural network that integrates both the Transformer architecture and the Focused Linear Attention mechanism, enabling it to effectively analyze 6-axis motion signals and accurately recognize air-written characters. In experiments, we collected 24,120 air-writing samples within a 6 cm $$\times$$ × 6 cm area, comprising 67 characters from 24 participants across three scenarios. RingRT achieved a user-independent accuracy of 90.08% in offline evaluation and maintained over 86% accuracy in real-world scenarios.

Voice-face association is generally specialized as a cross-modal cognitive matching problem, and recent attention has been paid on the feasibility of devising the computational mechanisms for recognizing such associations. Existing works are commonly resorting to the combination of contrastive learning and classification-based loss to correlate the heterogeneous datas. Nevertheless, the reliance on typical features of each category, known as archetypes, derived from the combination suffer from the weak invariance of modality-specific features within the same identity, which might induce a cross-modal joint feature space with calibration deviations. To tackle these problems, this paper presents an efficient Archetype-agnostic framework for reliable voice-face association. First, an Archetype-agnostic Subspace Merging (AaSM) method is carefully designed to perform feature calibration which can well get rid of the archetype dependence to facilitate the mutual perception of datas. Further, an efficient Bilateral Connection Re-gauging scheme is proposed to quantitatively screen and calibrate the biased datas, namely loose pairs that deviate from joint feature space. Besides, an Instance Equilibrium strategy is dynamically derived to optimize the training process on loose data pairs and significantly improve the data utilization. Through the joint exploitation of the above, the proposed framework can well associate the voice-face data to benefit various kinds of cross-modal cognitive tasks. Extensive experiments verify the superiorities of the proposed voice-face association framework and show its competitive performances with the state-of-the-arts.

Depth estimation is a critical problem in robotics applications especially autonomous driving. Currently, depth prediction based on binocular stereo matching and depth completion based on fusion of monocular image and laser point cloud are two mainstream methods. However, the former usually suffers from lack of constraint while building cost volume, and the latter could not be trained in self-supervised way and haven’t utilized the geometric constraint of stereo matching, which we think will further improve the performance. Therefore, we propose a novel multimodal neural network, namely UAMD-Net, for dense depth completion based on fusion of binocular stereo matching and the weak constraint from the sparse point clouds. Specifically, the sparse point clouds are converted to sparse depth map and filled to the multimodal feature encoder (MFE) with binocular image, constructing a cross-modal cost volume. Then, it will be further processed by the multimodal feature aggregator (MFA) and the depth regression layer. Furthermore, since previous multimodal depth estimation methods ignore the problem of modality dependence, we propose a new training strategy called random modality dropout (RMD) which enables the network to be adaptively trained with multiple modality inputs and inference with specific modality inputs. Benefiting from the flexible network structure and adaptive training method, our proposed network can realize unified training under various modality input conditions. Comprehensive experiments conducted on KITTI and DrivingStereo depth completion datasets demonstrate that our method produces robust results and outperforms other state-of-the-art methods.

Much research work devotes to tuning big data analytics in modern data centers, since %the truth that even a small percentage of performance improvement immediately translates to huge cost savings because of the large scale. Simultaneous multithreading (SMT) receives great interest from data center communities, as it has the potential to boost performance of big data analytics by increasing the processor resources utilization. For example, the emerging processor architectures like POWER8 support up to 8-way multithreading. However, as different big data workloads have disparate architectural characteristics, how to identify the most efficient SMT configuration to achieve the best performance is challenging in terms of both complex application behaviors and processor architectures. In this paper, we specifically focus on auto-tuning SMT configuration for Spark-based big data workloads on POWE-R8. However, our methodology could be generalized and extended to other programming software stacks and other architectures.

Big Data is changing this world at a surprising speed, and MapReduce plays a critical role in finding insights in Big Data. However, to efficiently extract insights from Big Data, performance optimization of MapReduce applications is a challenging task. To facilitate the full-system optimization of MapReduce applications, we propose a correlation-based performance analysis approach to efficiently identify critical outliers. The basic intuition is that critical outliers are key to the overall performance and they can only be accurately identified by correlating different phases, tasks and resources. Based on the proposed approach, we further implement a correlation-based performance analysis tool, called Sonata. It can efficiently identify critical outliers, and then, recommend optimization suggestions for practitioners based on embedded rules. Since the performance overhead is key to the applicability of a performance tool, we conduct experiments to demonstrate that Sonata is a practical tool with less than 5% overhead and good scalability. To demonstrate the effectiveness of Sonata, we share several cases during the performance tuning of IBM Platform SymphonyTM with the help of Sonata.

MapReduce plays an critical role in finding insights in Big Data. The performance optimization of MapReduce programs is challenging because it requires a comprehensive understanding of the whole system including both hardware layers (processors, storages, networks and etc), and software stacks (operating systems, JVM, runtime, applications and etc). However, most of the existing performance tuning and optimization are based on empirical and heuristic attempts. It remains a blank on how to build a systematical framework which breaks the boundary of multiple layers for performance optimization. In this paper, we propose a performance evaluation framework by correlating performance metrics from different layers, which provides insights to efficiently pinpoint the performance issue. This framework is composed of a series of predefined patterns. Each pattern indicates one or more potential issues. The behavior of a MapReduce program is mapped to the corresponding resource utilization. The framework provides a holistic approach which allows users at different levels of experience to conduct MapReduce program performance optimization. We use Terasort benchmark running on a 10-node Power7R2 cluster as a real case to show how this framework improves the performance. By this framework, we finally get the Terasort result improved from 47 mins to less than 8 mins. In addition to the best practice on performance tuning, several key findings are summarized as valuable workload analysis for JVM, MapReduce runtime and application design.

Critical sections are well known potential performance bottlenecks in multithreaded applications and identifying the ones that inhibit scalability are important for performance optimizations. While previous approaches use idle time as a key measure, we show such a measure is not reliable. The reason is that idleness does not necessarily mean the critical section is on the critical path. We introduce critical lock analysis, a new method for diagnosing critical section bottlenecks in multithreaded applications. Our method firstly identifies the critical sections appearing on the critical path, and then quantifies the impact of such critical sections on the overall performance by using quantitative performance metrics. Case studies show that our method can successfully identify critical sections that are most beneficial for improving overall performance as well as quantify their performance impact on the critical path, which results in a more reliable establishment of the inherent critical section bottlenecks than previous approaches.