Reviewerly/exHarmony huggingface dataset:
A Cluster Chip Selection Method for Object Detection in Aerial Images

Although several natural image datasets provide promising detection results, the performance of state-of-the-art detectors on aerial images is unsatisfactory in both accuracy and efficiency. To enhance the detection performance for aerial images, clustered object detection was introduced. It enhanced detection performance by identifying areas where objects are dense, which we call them as cluster chips, and applying fine detectors to those areas to combine them with global detection results. Although it demonstrated enhanced detection performance, still there is a room for improvement in terms of cluster chip selection. In this work, we propose a cluster chip selection scheme which identifies the cluster chip with greater performance improvement. We demonstrate our proposed method through comparison with other methods in terms of detection performance(e.g. mAP, mAP50 and mAP75). Experimental results show that the proposed method has performance gain over baseline methods in mAP, mAP50 and mAP75.
Method and System for Evaluating Tracking Performance of VR/AR/MR Devices

While interest and demand for VR/AR/MR technology are increasing, VR/AR/MR device-related technologies and markets are still in their infancy, and there is no objective standard for evaluating the performance of devices. We are conducting research to develop standards to evaluate the performance of these devices, and in this paper, we propose a method and system for evaluating the tracking performance of VR/AR/MR devices. In consideration of the characteristics of wearable devices, a system for evaluation based on human movement is designed, and a measurement procedure and calculation method are proposed to increase the reliability of evaluation.
Random Forest Approach in Prediction Workers' Stress from Personality Traits

Continuous exposure to stress threatens the physical and mental health of workers and reduces their quality of life. On the business side, stress has negative consequences, such as reduced productivity. Therefore, it needs to assess and manage stress. In this study, we assess stress using workers' personality traits collected from online surveys. Stress was measured by the VAS tool and classified by stress and normal groups. As a result of the experiment, the Random Forest model with the personality traits as input features was able to classify stress with an accuracy of 81 %. It was feasible to improve the accuracy by 90% by adding dynamic factors.
Face Mask Wearing Recognition System for Big Data Video Streaming

With the outbreak of the covid-19 pandemic in recent years, Video Stream Analytics technology quickly became a hot topic of discussion across technology forums. As it has appeared, in the pandemic situation in recent years, the use of masks when interacting with the community is a must, that's why the research works on mask identification today and more. receiving more and more attention. Understanding the situation, the team conducted facial recognition analysis inside the video to determine if the people appearing in the video were wearing masks. to then apply the trained model into practice. After a period of research, the team has also successfully built a mask recognition system that can generate images and can display the results as real-time video. Especially, the model is trained successful using systemml machine learning system. This is considered a positive result with real-time masked face recognition analysis.
Virtualized Infrastructure as an Incentive on AI-Data Commons

There is a growing awareness that in order to develop smarter and automated services using AI technology in various domains, corresponding qualified data is indispensable. In real, rewards from a system has an impact on increasing participants with their data and many systems provide liquidity-typed incentives to gather diverse data. Motivated by a concern that well-schemed incentive mechanism can be crucial to achieve the ultimate goal of Data Commons system, in this paper, we introduce an advanced rewards mechanism especially for non-profit organizations to publish their data in AI-Data Commons which is an ecosystem of trading data using blockchain technology.
Self-Conditional Crowd Activity Detection Network with Multi-label Classification Head

In this paper, we proposed new head network architecture in deep neural networks to classify categories for crowd activity. The proposed network was motivated by multi-label classification and conditional generative adversarial networks. In the head network, latent features were transformed into multi-label embedding vectors using pre-trained deep neural networks. The multi-label embedding vectors were regarded as the probability of relevant objects' existence. Then irrelevant embedding components were eliminated by the threshold layer. The refined multi-label embedding vectors are combined with pure latent feature vectors. Finally, a last linear layer predicted crowd activities. The proposed models configured ResNet back-bones with pre-trained weights. In terms of mean accuracy performances, our proposed models showed 1.55% higher in the best case, whereas 0.38% less in the worst case by comparing with baseline models.
Gait Rehabilitation in Virtual Reality Serious Game Interactive Scenarios

Fall accidents are generally very harmful and can be the consequence of gait impairments, that’s why it’s very important for a person to have regular checkups if they notice any unusual differences in the way they use to walk. The way a person walks can at times be characterized by a certain anomaly that can compromise their everyday routines and be an enormous risk to their state of health. These types of anomalies are known as Gait disorders. Inside the healthcare areas of physiotherapy and rehabilitation, the growing demand to improve the quality and efficiency of treatments, has led to several experimentations, developments, and implementations of various types of technologies and innovations in the past few years, to allow patients with these types of conditions to reach a faster and more motivating recovery. In this paper is presented a solution designed and implemented to be used for gait based on the concept of 3D serious games. Virtual reality interactive scenarios were considered and implemented using the software platform Unity 3D and the interaction between the user and the VR scenario is carried out by Azure Kinect. Microsoft Azure Kinect SDK is used to extract the body joints tracking. The proposed solution includes a Web application designed for physiotherapists to access the electronic health record of their patients but also to prescribe the training plan supported by the high interactive VR scenarios of the proposed serious game. Based on the joins acquired data different metrics are considered and used to validate the capabilities of proposed system.
On the Hardness of Pruning NASNet

NASNet is one of the famous convolutional neural networks generated by a neural architecture search algorithm. Its topological structure consists of skip connections like ResNet and DenseNet, but it also has a different aspect, which is called inner channel coupling. The inner channel coupling is a big obstacle to pruning internal channels in NASNet, so NASNet was not studied in the literature for channel pruning. Motivated by this, we present what the inner channel coupling is and why it happens for NASNet. Then, we analyze how it makes pruning NASNet especially hard. Finally, we conduct experiments to explore the changes of NASNet over different pruning ratios. To the best of our knowledge, this is the first work for introducing the inner channel coupling and channel pruning on NASNet.
An Image Resizing Function Embedded in an Inverse Discrete Cosine Transform

Image resizing is an everyday activity and can be done in either a spatial domain or a transform domain. This work realizes that an image is commonly stored in the transform domain and the transformed image must be converted back to the spatial domain to display. This work hence proposes embedding an image resizing function based on a scaled frequency (SF) in a matrix operation of inverse discrete cosine transform (IDCT) for the sake of convenience. By adopting the proposed technique called as the SF-IDCT is not only capable of resizing the transformed image to a given resolution, but also acquiring the IDCT as a by-product. Image data can be shortened or lengthened in proportion to their frequencies by varying the frequencies of the DCT basis functions. Besides, the effects of modifying the pattern of the image data are also studied. The experimental results indicate that the SF-IDCT functions well. The proposed technique reconstructs an upsized image of equal or better visual quality than those produced by existing methods. For a downsized image, the performance of the presented approach is competitive with other available methods.
Multi-Stage Instance Segmentation

Two-stage instance segmentation relies on the correctness of the Region Proposal Network (RPN). To improve the accuracy of RPN, we propose a slight modification of the two-stage object detection network Faster R-CNN as a region proposal network. First, we remove the class branch of the second stage of Faster R-CNN and use the box regression as the output of the entire proposal network. Then we add a new box regression branch, classification branch, and segmentation branch as the final output. Finally, we train the entire network. The results show a great improvement in segmentation accuracy.
PCIe Side-channel Attack on I/O Device via RDMA-enabled Network Card

Microarchitectural side-channel attacks aim to steal sensitive information by exploiting various shared resources of systems. Recently, several studies proposed PCIe-based microar-chitectural side-channel attacks. Existing studies evaluated such attacks on the limited environments that utilize special-purpose system hardwares. In this study, we analyze the operating mechanism of IOMMU which manages direct memory access of PCIe devices in general system environments. Based on the result, we discuss the possibility of exploiting IOMMU to conduct a PCIe-based microarchitectural side-channel attack.
Lightweight framework for the violence and falling-down event occurrence detection for surveillance videos

Recently, there are huge advances in the computer vision and deep-learning technology. However, the practical and light-weight solution for capturing the abnormal event is still less researched, especially in bad weather conditions such as snow and rain. In this paper, we propose the entire pipeline of the abnormal event occurrence detection system for realistic surveillance videos. The proposed system can detect two abnormal behavior: violence and falling down. While modern object detection models accomplish remarkable performance, pedestrian detection and tracking with fast processing speed still have problems, primarily when abnormal behaviors such as falling down occur. In this paper several tracking enhancement method is proposed. The proposed methods improve the F1 score of 21.21% of the detection of the falling down on the largescale CCTV dataset named KISA overseas. Finally, the proposed system achieved the reliable performance of 91% averaged F1 score on the KISA-v2 test set which address the various weather condition and places.
UD-assisted Multi-path Transport in RDMA

Although Remote Direct Memory Access (RDMA) has become one of the most promising networking technologies in data centers, it is prone to link failures and several schemes have been proposed to utilize redundant network paths in RDMA for mitigating the problem. However, they still do not scale well since their schemes rely on a connection-based transport mode which uses more cache memory than a datagram-based mode in RNICs. In this paper, we propose a novel UD-based multi-path transport in RDMA (MPRUD). MPRUD not only addresses the scalability problem by employing UD QPs which occupy only a small amount of cache memory but also obtains robustness with failure detection and recovery algorithms. Our evaluation shows that MPRUD can achieve the line rate bandwidth with utilizing multi-paths and successfully recover from link failures about 72x faster compared to a default single-path flow, significantly decreasing data loss to 0.12% (from 4.5GB to 5.6MB).
Federated Learning via Local Update with Uploading Zone

In this paper, we propose a novel idea of Uploading Zone for Federated Learning (FL) which largely affects transmission time and the number of participating devices. In order for sufficient devices to join FL, we assume heterogeneous quantization (HQ) where each device adapts its quantization level according to its signal-to-noise ratio (SNR). The devices located in the inner uploading zone utilize more bits for quantization because they have enough power to transmit gradients. However, the devices located in the outer uploading zone use fewer bits for quantization because of their limited transmit power. Since the quantization heterogeneity affects learning performance, we propose how to optimize the number of local updates under this HQ condition. Further, we consider the mobility condition where devices move freely and change their locations, which renders to increase traffic diversity. To look into these effects, we compare two types of uploading zone, small and large zones, without and with mobility. Finally, we optimize the size of the uploading zone and compare the two zones with the optimal uploading zone without mobility.
Virtual Skinner Box for the Test of Operant Conditioning

In this paper, authors evaluate the progress of the artificial intelligence research according to the levels of intelligence. Current model-free reinforcement learning is similar to the Level 1 intelligence or instincts that is refined with evolution. Animals with Level 2 intelligence can learn with operant conditioning where behavior policy can be updated with a few trials. Therefore, authors claim that next mile-stone should be the operant conditioning and propose an experiment framework motivated by the skinner box experiment.
A Study on Liver Segments Separation and Hologram Visualization Using Deep Learning-Based Liver Vascular in CT Images

In this paper, we propose a system that uses an artificial intelligence-based automatic segmentation function on a liver with 3D visualization. After segmenting liver, tumor and hepatic vessels from the input abdominal CT volume, repeat further separation on segments of liver based on the segmented blood vessels. Finally, generate segments as volumes to visualize holograms. Use 3D-UNet to segment the required portion and calculate the volume of the reconstructed one. Only the volume validated by the medical staff is displayed on the medical image viewer, and simultaneously, it can be transmitted to the hologram device to visualize a three-dimensional image with a realistic space. If this system becomes the standard for holographic visualization of brain images, this will assist in studying the field of brain science.
Blockchain and IPFS-based Data Storage for VANET

CCTV cameras and black boxes in vehicles play an important role in road condition and accident management. CCTV laws require that data recorded by CCTV cameras be kept securely and only authorized people have access to it. CCTV footage and blackbox data are saved locally to the camera's microSD card, a separate hard drive, or the cloud. Security and data integrity are issues with both local and cloud storage. In this paper, we propose an IPFS and blockchain-based Decentralized Storage Application that stores files in a decentralized way. The proposed scheme is implemented using the private Ethereum blockchain, Smart Contacts, and IPFS.
Design Autonomous Drone Control For Delivery Package using Prim Algorithm and Waypoint Method

The shipping industry assigns an immense potential and future competitive advantage to using a drone as deliveries courier. 57.5% Compound Annual Growth Rate (CAGR) is expected for the global commercial drone market from 2021 to 2028. Design autonomous drone control for the delivery package is a design for delivering packages using drone technology. We use drones with a prim algorithm for path decisions and the waypoint method as a method to determine the point. We compare the power efficiency of UAVs between implementing the prim algorithm and without this algorithm. From our experiments, the calculation using the prim algorithm impacts power consumption, the power efficiency is 43%.
AI-Based Modeling Architecture to Detect Traffic Anomalies from Dashcam Videos

This paper introduces a new modeling architecture to detect traffic anomalies using AI techniques. This study intends to reveal the effectiveness of merging extracted features which may be changed over predefined time period from dashcam video datasets. Relevant features are extracted by using a convolutional learning method and their temporal occurrence is modeled with a self-attention model. Segmented traffic accidents are classified into a couple of pre-defined groups indicating different traffic accident types. The analysis results show that the proposed modeling architecture is quite effective to identify traffic anomalies from dashcam video datasets. Additional issues for future analysis and implementations are discussed briefly as well.
Application of Deep Learning Methods to Improve the Resolution of Small Objects Images in Aerial Photographs

Super-resolution video sequence techniques aim to create a high spatial resolution frame from multiple low resolution frames in some local time window. The inter-frame temporal relationship is just as important as the intra-frame spatial relationship for solving this problem. However, how to use temporal information effectively remains a challenge, as complex movements are difficult to model, which can lead to adverse effects if not properly processed. The paper presents the results of a study on testing and analyzing the best methods to date for increasing the resolution of a video sequence. Research is aimed at restoring the resolution of a video sequence with minimal loss of quality of perception. The article presents the results of the proposed algorithms in several aspects. In particular, the quality of restoration of specific objects is considered. Since this study is more focused on further research in the field of compatibility of methods for increasing the detail of objects and their detection.
PID controller‐based adaptive gradient optimizer for deep neural networks

Abstract Due to improper selection of gradient update direction or learning rate, SGD optimization algorithms for deep learning suffer from oscillation and slow convergence. Although Adam algorithm can adaptively adjust the update direction and learning rate at the same time, it still has the overshoot phenomenon, and hence suffers from wasting computing resources and slow convergence. In this work, the PID controller from the feedback control area is borrowed to re‐express the adaptive optimization algorithm (the Adam optimization algorithm is derived into the integral I component form) of deep learning. In order to alleviate the overshoot phenomenon and hence speed up the convergence of Adam, a complete adaptive PID optimizer (adaptive‐PID) is proposed by incorporating the proportional P and derivative D component. Extensive experiments on standard data sets verify that the proposed adaptive‐PID algorithm significantly outperforms Adam algorithm in terms of convergence rate and accuracy.
Block coordinate type methods for optimization and learning

We study nonconvex (composite) optimization and learning problems where the decision variables can be split into blocks of variables. Random block projection is a popular technique to handle this kind of problem for its remarkable reduction of the computational cost from the projection. This powerful method has not been well proposed for the situation that first-order information is prohibited and only zeroth-order information is available. In this paper, we propose to develop different classes of zeroth-order stochastic block coordinate type methods. Zeroth-order block coordinate descent (ZS-BCD) is proposed for solving unconstrained nonconvex optimization problem. For composite optimization, we establish the zeroth-order stochastic block mirror descent (ZS-BMD) and its associated two-phase method to achieve the complexity bound for the composite optimization problem. Furthermore, we also establish zeroth-order stochastic block coordinate conditional gradient (ZS-BCCG) method for nonconvex (composite) optimization. By implementing ZS-BCCG method, in each iteration, only (approximate) linear programming subproblem needs to be solved on a random block instead of a rather costly projection subproblem on the whole decision space, in contrast to the existing traditional stochastic approximation methods. In what follows, an approximate ZS-BCCG method and corresponding two-phase ZS-BCCG method are proposed. This is also the first time that a two-phase BCCG method has been developed to carry out the complexity analysis of nonconvex composite optimization problem.
Installation on Heterogeneous Operating Systems

The last two major releases of SQL Server have focused heavily on providing capability to install SQL Server in a variety of nontraditional environments. In this chapter, we will explore how to install SQL Server on Linux and how to build and run Docker images and containers that include SQL Server.
An efficient and high-speed two-stage decoding scheme for continuous-variable quantum key distribution system

The throughput of error correction is the main bottleneck of continuous variable quantum key distribution (CV-QKD) postprocessing. Implementing the decoder of low-density parity-check (LDPC) codes based on FPGA with limited precision can improve the decoding throughput significantly. However, the limited precision on FPGA results in the existence of residual error-bits after decoding, which lowers the secret key rate and restricts the application of high-rate real-time CVQKD system. In this paper, an efficient decoding scheme is proposed to erase the residual error-bits and decrease the frame errors rate (FER), where the decoding process into two stages and some values of initial Log Likelihood Ratio (LLR) are adjusted according to the proposed principles before starting the second-stage decoding. For the rates 0.2 and 0.1 LDPC codes, numerical results demonstrate that the proposed decoding scheme decreases the FER obviously and the throughputs of 152.47Mbps and 88.32Mbps are achieved, which can be applied to support high-speed CV-QKD system under transmission distance of 25km and 50km respectively.
Experimental demonstration of 5 GBaud four-state continuous-variable quantum key distribution with digital signal processing

In this paper, we experimentally demonstrate a 5 GBaud four-state continuous-variable quantum key distribution with digital signal processing. By employing a frequency- and polarization-multiplexing quantum key transceiver, the modulation noise and DAC quantization noise in quantum state preparation, the photo-leakage noise in co-fiber transmission, the detection noise and ADC quantization noise in polarization diversity detection can be effectively reduced for achieving an ultra-low level of excess noise. Moreover, the main polarization variation and phase noise can be accurately compensated by designing a precise digital compensation scheme including the pilot-assisted polarization and phase compensation algorithm and the data-assisted equalized compensation algorithm. Besides, the explicit asymptotic secure key rate is evaluated by using an improved semidefinite programming security analysis method, which achieves a 100 Mbps level of secure key rate within 10 km distance.