Miracle-dz/UniFAR-FTUs huggingface dataset:
A Lightweight General Adaptive Optimization Tool for Relational DBMSs under HTAP Workloads

It is well-known challenging to operate data-intensive applications on RDBMS(Relational DBMS) because the static storage model does not perform well for real-time processing queries and historical analyses. Existing HTAP systems pursue the robustness and functional completeness of the system excessively, which leads to over-design in the scenario where OLAP and OLTP alternate instead of coming simultaneously. Therefore, we present a lightweight general and adaptive optimization tool for RDBMS to address this issue. By deep learning-based workload forecasting, our tool optimizes the storage model continuously and ensures that the database is always at its best when handling coming HTAP requests. It differs from previous methods in that it adaptively optimizes existing databases as a plug-in but not with intrusive programming. Through experimental validation, our tool has an average performance improvement of 13.1% compared with static row-store RDBMS and 45.3% compared with static column-store RDBMS under a typical HTAP workload. The validation result illustrates that our tool makes RDBMSs more efficient in handling HTAP workloads at a tiny cost.
A Case For Cross-Domain Observability to Debug Performance Issues in Microservices

Many applications deployed in the cloud are usually refactored into small components called microservices that are deployed as containers in a Kubernetes environment. Such applications are deployed on a cluster of physical servers which are connected via the datacenter network.In such deployments, resources such as compute, memory, and network, are shared and hence some microservices (culprits) can misbehave and consume more resources. This interference among applications hosted on the same node leads to performance issues (e.g., high latency, packet loss) in the microservices (victims) followed by a delayed or low-quality response. Given the highly distributed and transient nature of the workloads, it’s extremely challenging to debug performance issues. Especially, given the nature of existing monitoring tools, which collect traces and analyze them at individual points (network, host, etc) in a disaggregated manner.In this paper, we argue toward a case for a cross-domain (network & host) monitoring and debugging framework which could provide the end-to-end observability to debug performance issues of applications and pin-point the root-cause whether it is on the sender-host, receiver-host or the network. We present the design and provide preliminary implementation details using eBPF (extended Berkeley Packet Filter) to elucidate the feasibility of the system.
Study on Performance of Si3N4 Enhanced Microbolometer with Salicided Polysilicon Thermistor in CMOS Technology

In this paper, we studied the enhancement of additional Si3N4 layer on the voltage responsivity and detectivity of Si-based microbolometer in the standard CMOS process, which is composed of a SiO2 absorber coupled with a salicided poly-Si thermistor The FDTD simulation results show that the growth of Si3N4 can improve the absorptivity of the microbolometer at $7-13\mu\mathrm{m}$ wavelength and optimal thickness is 500 nm. The experimental results show that the responsivity and detectivity can be improved after deposition of Si3N4 significantly. Especially, the enhancement of the detectivity can reach the maximum of 122% and 29.6% at $11.5\ \mu \mathrm{m}$ and $8.5\mu \mathrm{m}$, respectively.
Harnessing Confidence for Report Aggregation in Crowdsourcing Environments

Crowdsourcing is an effective means of accomplishing human intelligence tasks by leveraging the collective wisdom of crowds. Given reports of various accuracy degrees from workers, it is important to make wise use of these reports to derive accurate task results. Intuitively, a task result derived from a sufficient number of reports bears lower uncertainty, and higher uncertainty otherwise. Existing report aggregation research, however, has largely neglected the above uncertainty issue. In this regard, we propose a novel report aggregation framework that defines and incorporates a new confidence measure to quantify the uncertainty associated with tasks and workers, thereby enhancing result accuracy. In particular, we employ a link analysis approach to propagate confidence information, subgraph extraction techniques to prioritize workers, and a progressive approach to gradually explore and consolidate workers’ reports associated with less confident workers and tasks. The framework is generic enough to be combined with existing report aggregation methods. Experiments on four real-world datasets show it improves the accuracy of several competitive state-of-the-art methods.
SwiniPASSR: Swin Transformer based Parallax Attention Network for Stereo Image Super-Resolution

With binocular cameras being widely accepted, the study of stereo image super resolution (Stereo SR) has received increasing attention. Different from single image super resolution (SISR) setting, it is more challenging for utilizing both intra-view and cross-view information. Although prior convolution-based works have achieved admirable progress, few attempts have explored the possibility of the Transformer-based architecture for stereo image SR, which has demonstrated promising performance in several visual tasks. In this paper, we propose a novel approach namely SwiniPASSR, which adopts Swin Transformer as the backbone, meanwhile incorporating it with the Bi-directional Parallax Attention Module (biPAM) to maximize auxiliary information given by the binocular mechanism. Even Transformer and parallax attention mechanism (PAM) have been separately proved usefulness by prior studies, we find that simply integrating convolution-based PAM with Transformer or directly optimizing for stereo SR problem was may not achieve desirable result. We therefore introduced a conversion layer to resolve integration and adopted progressive training strategy to learn disparity correspondence through progressively enlarged receptive fields. Both extensive experiments and ablation studies demonstrate the effectiveness of our proposed SwiniPASSR. In particular, in the NTIRE 2022: Stereo Image Super-Resolution Challenge, we report 23.71dB PSNR and 0.7295 SSIM performance which ranked 2nd place on the leaderboard. Source code is available at https://gj.thub. com/SMI-Lab/SwinlPASSR.
Biochips & Tissue Chips Expression Profile of Angiogenesis Related Genes in Head and Neck Squamous Cell Carcinoma

Objectives: The molecular patterns of tumorigenesis of head and neck squamous cell carcinoma are mostly unknown to date. The concept of this investigation was to expose a genetic fingerprint of angiogenesis in HNSCC with special regard to VEGF as the outstanding mediator of neo-angiogenesis. Material and Methods: A panel of significant transcriptional alterations of VEGF related genes in 83 cancer samples compared to healthy mucosa with microarray technique was established. RT-PCR and immunohistochemistry were performed to confirm the microarray results. Results: In four selected marker genes (VEGFA, RAC2, IL8, PLA2G10) we detected a highly significant transcriptional alteration; three of the genes (VEGFA, RAC2, IL8) showed up-regulation in 88% to 100% of the samples, PLA2G10 down-regulation in 99% of the analyzed tissue samples. Additionally a significant transcription alteration in angio-miRNAs was detected: miR-21 and miR-31 were over-expressed in upto 87% and upto 89% respectively in the analyzed samples. MiR-20a, miR-126, miR-378, miR-17, miR-19a, miR-27b and miR let-7f were down-regulated in 83% to 95% of the specimens. A specific pattern subjected to the different tumor types could not be verified. Conclusion: An expression profile of VEGF related genes was established and selective angio-miRNAs were identified as potential biomarkers on the basis of 83 HNSCC samples. Identification of new relevant oncogenes and insight into the process of tumor vasculogenesis with a highly sensitive method opens new vistas in cancer biology.
Intelligent Spectrum Occupancy Prediction for Realistic Measurements: GRU based Approach

Cognitive radio (CR) technology has always been a research hotspot in the wireless communications field as it has the potential to significantly improve system capacity at the cost of increased processing time and power consumption, which represent highly critical performance indicators (CPI) towards next-generation wireless networks. In particular, the main problem in the CR-based communication links resides in the prediction of spectrum availability in accordance with strict secondary user (SU) CPIs requirements, which is not achievable through the traditional approaches. In this work, we design a novel hierarchical spectrum prediction model, taking advantage from the recurrent neural network (RNN) with the focus on the gated recurrent unit network (GRU). Specifically, the proposed system architecture offers an accrue prediction on the spectrum availability for the SU considering the prior information of the primary user (PU). The performance of the proposed design is illustrated through extensive simulation results. Specifically, real spectrum measurements gathered from Doha, in Qatar are performed to assess the performance accuracy of the designed architecture. In particular different from the conventional scheme that uses a binary representation of spectrum occupancy (idle is “0” and occupied is “1”), we perform training and prediction over the minimum and maximum recorded measurements.
Neural Network-based In-Loop Filter for CLIC 2022

A hybrid video codec comprised of an optimized VVC codec and a convolutional neural network-based loop filter (CNNLF), was submitted in the video compression track in Challenge on Learned Image Compression (CLIC) 2022[1]. This paper presents the traditional methods and deep learning scheme in video coding optimization, which were adopted in the hybrid codec based on VTM-15.0. Traditional methods include QP adaptive adjustment of I frame and rate-distortion optimization based on SSIM. Meanwhile, the deep learning scheme proposes an adaptive CNNLF, which is turned on / off based on the rate-distortion optimization at CTU and frame level. The network architecture mainly consists of the attention residual module and the convolution feature maps module, which help extract image features and improve image quality. To balance performance and complexity, the proposed scheme sets different training parameters for 0.1 Mbps and 1 Mbps, respectively. The experimental results show that compared with VTM-15.0, the proposed traditional methods and adding CNNLF improve the PSNR by 0.4dB and 0.8dB at 0.1Mbps, respectively; 0.2dB and 0.5dB at 1Mbps, respectively, which proves the superiority of our method.
Autonomous Maneuver Decisions via Transfer Learning Pigeon-Inspired Optimization for UCAVs in Dogfight Engagements

This paper proposes an autonomous maneuver decision method using transfer learning pigeon-inspired optimization (TLPIO) for unmanned combat aerial vehicles (UCAVs) in dogfight engagements. Firstly, a nonlinear F-16 aircraft model and automatic control system are constructed by a MATLAB/Simulink platform. Secondly, a 3-degrees-of-freedom (3-DOF) aircraft model is used as a maneuvering command generator, and the expanded elemental maneuver library is designed, so that the aircraft state reachable set can be obtained. Then, the game matrix is composed with the air combat situation evaluation function calculated according to the angle and range threats. Finally, a key point is that the objective function to be optimized is designed using the game mixed strategy, and the optimal mixed strategy is obtained by TLPIO. Significantly, the proposed TLPIO does not initialize the population randomly, but adopts the transfer learning method based on Kullback-Leibler (KL) divergence to initialize the population, which improves the search accuracy of the optimization algorithm. Besides, the convergence and time complexity of TLPIO are discussed. Comparison analysis with other classical optimization algorithms highlights the advantage of TLPIO. In the simulation of air combat, three initial scenarios are set, namely, opposite, offensive and defensive conditions. The effectiveness performance of the proposed autonomous maneuver decision method is verified by simulation results.
Layered Contention Mitigation for Cloud Storage

We introduce an ecosystem of contention mitigation supports within the operating system, runtime and library layers. This ecosystem provides an end-to-end request abstraction that enables a uniform type of contention mitigation capabilities, namely request cancellation and delay prediction, that can be stackable together across multiple resource layers. Our evaluation shows that in our ecosystem, multi-resource storage applications are faster by 5-70% starting at 90P (the 90thpercentile) compared to popular practices such as speculative execution and is only 3% slower on average compared to a best-case (no contention) scenario.
Cloud Data Center Fabric Virtualization

Cloud networks support workloads with diverse characteristics. A key challenge facing cloud providers is how to meet the stringent performance and security needs of these diverse applications running over a shared infrastructure. To address this challenge, some providers build for peak capacity or even build dedicated clusters with specialized networks, which may be under-utilized at times. We propose a virtualization approach that customizes data center network resources to the needs of applications. Our approach is based on slicing data center network resources on-demand and customizing these slices to target workloads. Such slices can grow or shrink dynamically and programmatically based on workload demands. This elasticity provides a more efficient solution over building dedicated clusters. In our approach, a slice can be customized to a given set of workloads with similar security and performance requirements carved out of the underlying network. It leverages a software-defined underlay network controller and segment routing for fine-grained path control and service chaining. We have implemented a prototype of our fabric virtualization solution based on network slicing. In this paper, we first present the architecture of our prototype. Second, we present empirical results of slice provisioning times in networks of varying sizes and switch operating systems. Empirical results indicate that our prototype can support slice provisioning in the order of tens to hundreds of seconds and can meet the provisioning requirements of production networks.
Learning Effect of Strength Training System for Beginners Using Motion Capture Device*

We aim to make strength training safe and efficient for beginners. We developed a strength training system that can judge the correctness of the posture of a bodyweight squat movement in real-time. In addition, we combined two teaching methods, “Step by Step” and “Overall”, and compared their learning effects on beginners' performances. As a result, we found it effective to estimate the correctness of the movement with features determined based on the joint angles. We also found a significant difference between the two groups, who were trained with two different approaches, “Step by Step” or “Overall”, respectively. The result shows that the learning effects of the “Overall” method outperform the “Step by Step” method. Our findings will enable to develop a strength training support system, which allows people to safely train themselves.
An Empirical Study on How Well Do COVID-19 Information Dashboards Service Users’ Information Needs

The ongoing COVID-19 pandemic highlights the importance of dashboards for providing critical real-time information. In order to enable people to obtain information in time and to understand complex statistical data, many developers have designed and implemented public-oriented COVID-19 “information dashboards” during the pandemic. However, development often takes a long time and developers are not clear about many people’s information needs, resulting in gaps between information needs and supplies. According to our empirical study and observations with popular developed COVID-19 dashboards, this seriously impedes information acquirement. Our study compares people’s needs on Twitter with existing information suppliers. We determine that despite the COVID-19 information that is currently on existing dashboards, people are also interested in the relationship between COVID-19 and other viruses, the origin of COVID-19, vaccine development, fake new about COVID-19, impact on women, impact on school/university, and impact on business. Most of these have not yet been well addressed. We also summarise the visualization and interaction patterns commonly applied in dashboards, finding key patterns between data and visualization as well as visualization and interaction. Our findings can help developers to better optimize their dashboard to meet people’s needs and make improvements to future crisis management dashboard development.
Voltage Stability Constrained Moving Target Defense Against Net Load Redistribution Attacks

Moving target defense (MTD) using distributed flexible AC transmission system (D-FACTS) devices is a promising defense strategy to detect stealthy false data injection (FDI) attacks against the power system state estimation. However, all existing studies myopically perturb the reactance of D-FACTS lines without considering the system voltage stability. In this paper, we first illustrate voltage instability induced by MTDs in a three-bus system. To address this issue, we further propose a novel MTD framework that explicitly considers system voltage stability by using continuation power flow and voltage stability indices. We mathematically derive the sensitivity matrix of voltage stability index to line impedance, on which an optimization problem for maximizing voltage stability index is formulated. This framework is tested on the IEEE 14-bus and the IEEE 118-bus transmission systems, in which net load redistribution attacks are launched by sophisticated attackers. The simulation results show the effectiveness of the proposed framework in circumventing the voltage instability while maintaining the detection effectiveness of MTD. We conduct case studies with and without the proposed framework under different MTD planning and operational methods. The impacts of the proposed two methods on attack detection effectiveness and system economic metrics are also revealed.
Power-Aware Checkpointing for Multicore Embedded Systems

Increasing the number of cores integrated on a single chip offers a great potential for the implementation of fault-tolerant techniques to achieve high reliability in real-time embedded systems. Checkpointing with rollback-recovery is a well-established technique to tolerate transient faults in multicore platforms. To consider the worst-case fault occurrence scenario, checkpointing technique requires to re-execute some parts of the tasks, and that might lead to simultaneous execution of task parts with high power consumptions, which eventually might result in a peak power increase beyond the thermal design power (TDP). Exceeding TDP can elevate on-chip temperatures beyond safe limits, and thereby triggering countermeasures that throttle down the voltage and frequency levels or power gate the cores. Such countermeasures might lead to violating task deadlines and degrading the system's reliability. To avoid such severe scenarios, it is inevitable to consider the impact of applying fault-tolerant techniques on the power consumption and prevent violating the power constraint of the chip, i.e., TDP. This article presents for the first time, a peak-power-aware checkpointing (PPAC) technique that tolerates a given number of faults, k, while at the same time meets the power constraint in hard real-time embedded systems. To do this, our proposed technique (PPAC) adjusts the timing of the checkpoints, which have lower power consumption than the tasks to the execution time points that have power spikes beyond TDP. Moreover, PPAC exploits the available slack times on the cores to delay the execution of some tasks to avoid the remaining power spikes beyond TDP, which could not be mitigated by solely adjusting checkpoints. To evaluate our technique, we extend the state-of-the-art system-level simulator, gem5, with the state-of-the-art checkpointing module in Linux. Our experimental results show that our proposed technique is able to tolerate a given number of faults without exceeding the timing and power constraints in hard real-time embedded systems. The resulting peak power reduction achieved by our technique compared to state-of-the-art techniques is an average of 23%. Moreover, our technique employs the Dynamic Power Management (DPM) during the slack times resulting at runtime in the case of fault-free scenarios, which provides energy savings with an average of 17.28% and up to 61.1%.
Delineation of agricultural fields using Psi-Net from high-resolution remote sensing images

Boundary information of agricultural fields is essential to many agricultural applications, particularly at the field level. This paper investigates the use of high-resolution remote sensing images to delineate the boundaries of agricultural fields. We consider the delineation task a multi-task semantic segmentation problem and use a recent deep neural network, i.e., Psi-Net, to do the semantic segmentation. The structure of a Psi-Net consists of an encoder and three decoders. The decoders learn three parallel tasks, corresponding to a primary task (i.e., mask prediction) and two additional tasks (i.e., contour detection and distance map estimation). The additional tasks are used to regularize the mask prediction to produce a refined mask with smooth boundaries. We conducted experiments on a GF1 PMS satellite image (2m) acquired in the 21st regiment of the 2nd agricultural division of Xinjiang. To evaluate the effectiveness of the proposed method, we compared it with existing single task semantic segmentation using UNet. Our results show that the proposed method using Psi-Net performed better than the existing method from the perspective of geometric and attribute accuracies. We conclude that the proposed Psi-Net method has a high potential for extracting field boundaries from high-resolution remote sensing images.
Regularized DTW in Offline Music Score-Following for Sight-Singing Based on Sol-fa Name Recognition

The automatic scoring of singing evaluation is a hot topic in recent years. Improving the score following effect is the first step to improve the accuracy of evaluation. Most of the commonly used methods are based on DTW, but for audios with low singing quality and inaccurate pitch, DTW often predicts the onset incorrectly. In order to solve the above problems, this paper focus on the offline following, mainly improves from two aspects: 1. Sol-fa name recognition is done before pitch tracking as preprocess. We cannot guarantee that the pitch of the singer is correct, but we can assume that the singer pronounces the sol-fa name correctly, so we use sol-fa name recognition as preprocessing; 2. Regularized DTW is proposed based on the basis of sol-fa name recognition. The results show that for general audio, under the condition of a tolerance of 20ms, compared with about 86% accuracy of ordinary DTW algorithm, our algorithm has improved to about 92%, while the average error of predicted notes is reduced by about 23ms. For audio with low signal-to-noise ratio and unstable voice frequency, the alignment effect is improved by about 20% compared with ordinary DTW.