Automatic recognition of depression based on audio and video:A review

Depression is a common mental health disorder.With current depression detection methods,specialized physicians often engage in conversations and physiological examinations based on standardized scales as auxiliary measures for depression assessment.Non-biological markers-typically classified as verbal or non-verbal and deemed crucial evaluation criteria for depression-have not been effectively utilized.Specialized physicians usually require extensive training and experience to capture changes in these features.Advancements in deep learning technology have provided technical support for capturing non-biological markers.Several researchers have proposed automatic depression estimation(ADE)systems based on sounds and videos to assist physicians in capturing these features and conducting depression screening.This article summarizes commonly used public datasets and recent research on audio-and video-based ADE based on three perspectives:Datasets,deficiencies in existing research,and future development directions.

Epoch-based Application Collaboration for Sensing Data Analysis

The application collaboration was addressed to provide energy-efficient data services for distributed sensing applications to collaboratively interacting to achieve a desired global objective not detectable by any single cluster.An epoch-based transaction model was proposed by using the concept of sphere of control(SoC),and a collaborative sensing application can be dynamically formed as a nested architecture composed of time-synchronized applications.By loosening the rigid constraints of ACID to adapt the requirements of sensor networks,the submission,rollback and consistency rules ware educed and a two-phrase submission protocol was designed.Finally,it was illustrated that the model is capable of providing an adaptive formal template for sensing application collaboration.Our performance evaluations show that by applying the two-phrase submission protocol,we can significantly improve the number of reported answers and response time,raise resource utilization,and reduce the energy consumption and data loss.

Efficient Algorithm for the k-Means Problem with Must-Link and Cannot-Link Constraints

Constrained clustering,such as k-means with instance-level Must-Link(ML)and Cannot-Link(CL)auxiliary information as the constraints,has been extensively studied recently,due to its broad applications in data science and AI.Despite some heuristic approaches,there has not been any algorithm providing a non-trivial approximation ratio to the constrained k-means problem.To address this issue,we propose an algorithm with a provable approximation ratio of O(logk)when only ML constraints are considered.We also empirically evaluate the performance of our algorithm on real-world datasets having artificial ML and disjoint CL constraints.The experimental results show that our algorithm outperforms the existing greedy-based heuristic methods in clustering accuracy.

Accelerated Backward Warping

In this paper a plane-based backward warping algorithm is proposed to generate novel views from multiple reference images. First, depth information is employed to reconstruct space planes from individual reference images and calculate the potential occluding relationship between these planes. Then the planes which represent each identical space plane from different reference images are compared with each other to decide the one with the best sample rate to be preserved and used in the later warping period while the other samples are abandoned. While the image of a novel view is produced, traditional methods in computer graphics, such as visibility test and clipping, are used to process the planes reconstructed. Then the planes processed are projected onto the desired image from the knowledge on which plane the desired image pixels are warped from can be acquired. Finally, pixels' depth of the desired image is calculated and then a backward warping is performed from these pixels to the reference images to obtain their colors. The storage requirement in the algorithm is small and increases slowly with the number of reference images increases. By combining the strategy of only preserving the best sample parts and the backward warping algorithm, the sample problem could be well tackled.

VBMq： pursuit baremetal performance by embracing block I/O parallelism in virtualization

Barely acceptable block I/O performance prevents virtualization from being widely used in the HighPerformance Computing field. Although the virtio paravirtual framework brings great I/O performance improvement, there is a sharp performance degradation when accessing high-performance NAND-flash-based devices in the virtual machine due to their data parallel design. The primary cause of this fact is the deficiency of block I/O parallelism in hypervisor, such as KVM and Xen. In this paper, we propose a novel design of block I/O layer for virtualization, named VBMq. VBMq is based on virtio paravirtual I/O model, aiming to solve the block I/O parallelism issue in virtualization. It uses multiple dedicated I/O threads to handle I/O requests in parallel. In the meanwhile, we use polling mechanism to alleviate overheads caused by the frequent context switches of the VM＇s notification to and from its hypervisor. Each dedicated I/O thread is assigned to a non-ovedapping core to improve performance by avoiding unnecessary scheduling. In addition, we configure CPU affinity to optimize I/O completion for each request. The CPU affinity setting is very helpful to reduce CPU cache miss rate and increase CPU efficiency. The prototype system is based on Linux 4.1 kernel and QEMU 2.3.1. Our measurements show that the proposed method scales graciously in the multi-core environment, and provides performance which is 39.6x better than the baseline at most, and approaches bare-metal performance.