Reviewerly/exHarmony huggingface dataset:
CGPTuner

Properly selecting the configuration of a database management system (DBMS) is essential to increase performance and reduce costs. However, the task is astonishingly tricky due to a large number of tunable configuration parameters and their inter-dependencies. Also, the optimal configuration depends upon the workload to which the DBMS is exposed. To extract the full potential of a DBMS, we must also consider the entire IT stack on which the DBMS is running, comprising layers like the Java virtual machine, the operating system and the physical machine. Each layer offers a multitude of parameters that we should take into account. The available parameters vary as new software versions are released, making it impractical to rely on historical knowledge bases. We present a novel tuning approach for the DBMS configuration auto-tuning that quickly finds a well-performing configuration of an IT stack and adapts it to workload variations, without having to rely on a knowledge base. We evaluate the proposed approach using the Cassandra and MongoDB DBMSs, showing that it adjusts the suggested configuration to the observed workload and is portable across different IT applications. We try to minimise the memory consumption without increasing the response time, showing that the proposed approach reduces the response time and increases the memory requirements only under heavy-load conditions, reducing it again when the load decreases.
An Underwater Integrated Navigation Algorithm to Deal With DVL Malfunctions Based on Deep Learning

In underwater navigation systems, Global Navigation Satellite System (GNSS) information cannot be used for navigation. The mainstream method of autonomous underwater vehicles (AUV) underwater navigation system is Doppler Velocity Log (DVL) aided strapdown inertial navigation system (SINS). However, because the DVL is an instrument based on Doppler frequency shift to measure velocity, it is easily affected by the external environment. In a complex underwater environment, DVL output is easily polluted by outliers or even interrupted. In this paper, A new integrated navigation algorithm based on deep learning model is proposed to deal with DVL malfunctions. First, use RKF based on Mahalanobis distance algorithm to eliminate outliers, and then train the Nonlinear AutoRegressive with eXogenous input (NARX) model when DVL is available. When DVL is interrupted, use the NARX model to predict the output of DVL and continue integrated navigation. The proposed NARX-RKF scheme’s effectiveness verification was performed on the data set collected by the SINS/DVL ship-mounted experimental system. For comparison, different methods are also compared in the experiment. Experimental results show that NARX-RKF can effectively predict the output of DVL and is significantly better than other methods.
LA-ICP-MS sphalerite and galena trace element chemistry and mineralization-style fingerprinting for carbonate-hosted Pb-Zn deposits: Perspective from early Devonian Huodehong deposit in Yunnan, South China

The Sichuan-Yunnan-Guizhou metallogenic province (SYGMP) in the southwestern margin of the Yangtze block is an important Pb-Zn province in China. In the SYGMP, over 400 Pb-Zn deposits are mostly hosted in the Sinian, Lower Cambrian and Carboniferous sedimentary rocks and rarely in the Devonian carbonate rocks (e.g., Houdehong). For Houdehong, however, its ore-forming conditions and deposit type remain unclear due to limited geological and geochemical data available. In this study, LA-ICP-MS spot analysis and mapping were conducted to determine the trace element features in the sphalerite and galena from Huodehong. The results show that the sphalerite is characterized by enrichments in Ge and Tl but depletions of Fe, Mn, and Co. Most of these elements have widely varying concentrations and occur as solid solutions in the sphalerite crystal lattice. The critical element of Ge enters sphalerite through coupling substitution with Mn2+ for Zn2+ and/or Cd2+. The results calculated by empirical formula show that the sphalerite mineralization occurred under low-temperature conditions (<180℃). Meanwhile, the Houdehong galena contains low Se, Cd, Tl and Bi, and very low Ag (<1.0 ppm) and Sb (<5.0 ppm) concentrations, which is likely controlled by the original ore-fluid compositions. Trace element compositions of the Houdehong sulfides are generally similar to that of the sulfides from Mississippi Valley-type (MVT) deposits and many other Pb-Zn deposits in the SYGMP, but markedly different from those of volcanogenic massive sulfide (VMS), sedimentary exhalative (SEDEX) and skarn Pb-Zn deposits. Considering also the deposit geological and geochemical features, we considered that the Huodehong Pb-Zn deposit is of MVT. Our findings provide further geological and geochemical evidence for understanding the Devonian carbonate-hosted Pb-Zn mineralization in the region.
A Systematic Deep Learning Based Overhead Tracking and Counting System Using RGB-D Remote Cameras

Automatic head tracking and counting using depth imagery has various practical applications in security, logistics, queue management, space utilization and visitor counting. However, no currently available system can clearly distinguish between a human head and other objects in order to track and count people accurately. For this reason, we propose a novel system that can track people by monitoring their heads and shoulders in complex environments and also count the number of people entering and exiting the scene. Our system is split into six phases; at first, preprocessing is done by converting videos of a scene into frames and removing the background from the video frames. Second, heads are detected using Hough Circular Gradient Transform, and shoulders are detected by HOG based symmetry methods. Third, three robust features, namely, fused joint HOG-LBP, Energy based Point clouds and Fused intra-inter trajectories are extracted. Fourth, the Apriori-Association is implemented to select the best features. Fifth, deep learning is used for accurate people tracking. Finally, heads are counted using Cross-line judgment. The system was tested on three benchmark datasets: the PCDS dataset, the MICC people counting dataset and the GOTPD dataset and counting accuracy of 98.40%, 98%, and 99% respectively was achieved. Our system obtained remarkable results.
Blockchain Applications in Health Care and Public Health: Increased Transparency

Background Although big data and smart technologies allow for the development of precision medicine and predictive models in health care, there are still several challenges that need to be addressed before the full potential of these data can be realized (eg, data sharing and interoperability issues, lack of massive genomic data sets, data ownership, and security and privacy of health data). Health companies are exploring the use of blockchain, a tamperproof and distributed digital ledger, to address some of these challenges. Objective In this viewpoint, we aim to obtain an overview of blockchain solutions that aim to solve challenges in health care from an industry perspective, focusing on solutions developed by health and technology companies. Methods We conducted a literature review following the protocol defined by Levac et al to analyze the findings in a systematic manner. In addition to traditional databases such as IEEE and PubMed, we included search and news outlets such as CoinDesk, CoinTelegraph, and Medium. Results Health care companies are using blockchain to improve challenges in five key areas. For electronic health records, blockchain can help to mitigate interoperability and data sharing in the industry by creating an overarching mechanism to link disparate personal records and can stimulate data sharing by connecting owners and buyers directly. For the drug (and food) supply chain, blockchain can provide an auditable log of a product’s provenance and transportation (including information on the conditions in which the product was transported), increasing transparency and eliminating counterfeit products in the supply chain. For health insurance, blockchain can facilitate the claims management process and help users to calculate medical and pharmaceutical benefits. For genomics, by connecting data buyers and owners directly, blockchain can offer a secure and auditable way of sharing genomic data, increasing their availability. For consent management, as all participants in a blockchain network view an immutable version of the truth, blockchain can provide an immutable and timestamped log of consent, increasing transparency in the consent management process. Conclusions Blockchain technology can improve several challenges faced by the health care industry. However, companies must evaluate how the features of blockchain can affect their systems (eg, the append-only nature of blockchain limits the deletion of data stored in the network, and distributed systems, although more secure, are less efficient). Although these trade-offs need to be considered when viewing blockchain solutions, the technology has the potential to optimize processes, minimize inefficiencies, and increase trust in all contexts covered in this viewpoint.
A BERT based Sentiment Analysis and Key Entity Detection Approach for Online Financial Texts

The emergence and rapid progress of the Internet have brought ever-increasing impact on financial domain. How to rapidly and accurately mine the key information from the massive negative financial texts has become one of the key issues for investors and decision Rakers. Aiming at the issue, we propose a sentiment analysis and key entity detection approach based on BERT, which is applied in online financial text mining and public opinion analysis in social media. By using pre-train model, we first study sentiment analysis, and then we consider key entity detection as a sentence matching or Machine Reading Comprehension (MRC) task in different granularity. Among them, we mainly focus on negative sentimental information. We detect the specific entity by using our approach, which is different from traditional Named Entity Recognition (NER). In addition, we also use ensemble learning to improve the performance of proposed approach. Experimental results show that the performance of our approach is generally higher than SVM, LR, NBM, and BERT for two financial sentiment analysis and key entity detection datasets.
A lightweight multi-scale channel attention network for image super-resolution

In recent years, deep learning techniques have significantly improved the performance of single image super-resolution (SISR). However, this improvement is often achieved at the cost of introducing a large amount of parameters, which limits the real-world applications for SISR. In this paper, we propose a lightweight SISR network called Multi-scale Channel Attention Network for Image Super-Resolution (MCSN). Our contributions are threefold. First of all, the multi-scale feature fusion block (MSFFB) can extract multi-scale features by filters with different receptive fields. Secondly, the channel shuffle attention mechanism (CSAM) encourages the flow of the information across feature channels and enhances the ability of feature selection. Thirdly, the global feature fusion connection (GFFC) can effectively improve feature utilization. Extensive experiments demonstrate that the parameter amount of our method is reduced by 3/4 compared with the current state-of-the-art MSRN method, while both the subjective visual effect and objective quality of the reconstructed high-resolution images are significantly better.
Simplified Deep Forest Model Based Just-in-Time Defect Prediction for Android Mobile Apps

The popularity of mobile devices has led to an explosive growth in the number of mobile apps in which Android mobile apps are the mainstream. Android mobile apps usually undergo frequent update due to new requirements proposed by users. Just-in-time (JIT) defect prediction is appropriate for this scenario for quality assurance because it can provide timely feedback by determining whether a new code commit will introduce defects into the apps. As defect-prediction performance usually relies on the quality of the data representation and the used classification model, in this work, we propose a model, called Simplified Deep Forest (SDF), to conduct JIT defect prediction for Android mobile apps. SDF modifies a state-of-the-art deep forest model by removing the multigrained scanning operation that is designed for data with a high-dimensional feature space. It uses a cascade structure with ensemble forests for representation learning and classification. We conduct experiments on 10 Android mobile apps and experimental results show that SDF performs significantly better than comparative methods in terms of 3 performance indicators.
Fed2

Federated learning learns from scattered data by fusing collaborative models from local nodes. However, conventional coordinate-based model averaging by FedAvg ignored the random information encoded per parameter and may suffer from structural feature misalignment. In this work, we propose Fed2, a feature-aligned federated learning framework to resolve this issue by establishing a firm structure-feature alignment across the collaborative models. Fed2 is composed of two major designs: First, we design a feature-oriented model structure adaptation method to ensure explicit feature allocation in different neural network structures. Applying the structure adaptation to collaborative models, matchable structures with similar feature information can be initialized at the very early training stage. During the federated learning process, we then propose a feature paired averaging scheme to guarantee aligned feature distribution and maintain no feature fusion conflicts under either IID or non-IID scenarios. Eventually, Fed2 could effectively enhance the federated learning convergence performance under extensive homo- and heterogeneous settings, providing excellent convergence speed, accuracy, and computation/communication efficiency.
Fusion of Skeleton and RGB Features for RGB-D Human Action Recognition

The output of Microsoft Kinect is a multimodal signal, which provides RGB videos, depth sequences and skeleton information at the same time, opening up a new opportunity for the research of human action recognition. However, for different single modalities of the signals, how to exploit and fuse useful features of these various sources remains a very challenging problem. Most of the methods based on RGB-D action recognition simply fuse the multimodal features, ignoring the potential semantic relationship between different models. In this paper, we propose a multi-modal action recognition model based on Bilinear Pooling and Attention Network (BPAN), which could effectively fuse multi-modal for RGB-D action recognition. Firstly, we adopt the efficient data preprocessing methods for RGB and skeleton data. Then, we propose a multimodal fusion network combining RGB video and skeleton sequences. The proposed BPAN module could effectively compress the features of RGB and skeleton, and project them into latent subspace to get the fusion features. In the end, a fully connected three-layer perceptron is adopted to obtain the final classification decision. Experimental results on three public datasets demonstrate that our proposed method leads to a more favorable performance compared with the state-of-the-art methods.
Fast Weakly Supervised Action Segmentation Using Mutual Consistency

Action segmentation is the task of predicting the actions for each frame of a video. As obtaining the full annotation of videos for action segmentation is expensive, weakly supervised approaches that can learn only from transcripts are appealing. In this paper, we propose a novel end-to-end approach for weakly supervised action segmentation based on a two-branch neural network. The two branches of our network predict two redundant but different representations for action segmentation and we propose a novel mutual consistency (MuCon) loss that enforces the consistency of the two redundant representations. Using the MuCon loss together with a loss for transcript prediction, our proposed approach achieves the accuracy of state-of-the-art approaches while being 14 times faster to train and 20 times faster during inference. The MuCon loss proves beneficial even in the fully supervised setting.
FPGA Implementation of Object Detection Accelerator Based on Vitis-AI

The emergence of YOLOv3 makes it possible to detect small targets. Due to the characteristics of the YOLO network itself, the YOLOv3 network has exceptionally high requirements for computing power and memory bandwidth and it usually needs to be deployed on a dedicated hardware acceleration platform. FPGAs is a logically reconfigurable hardware chip with substantial advantages in terms of performance and power consumption, so it is a good choice to deploy a deep convolutional network. In the research of this paper, we proposed a reconfigurable YOLOv3 FPGA hardware accelerator based on the AXI bus ARM+FPGA architecture. The YOLOv3 network quantifies through Vitis AI, and a series of operations such as model compression and data pre-processing can save accelerator chips and the access time of external storage. Pipeline operation enables FPGAs to achieve higher throughput. Compared with the GPU implementation of the YOLOv3 model, it is found that the hardware implementation of the FPGA-based YOLOv3 accelerator has lower energy consumption and can achieve higher throughput.
Going to Extremes: Weakly Supervised Medical Image Segmentation

Medical image annotation is a major hurdle for developing precise and robust machine-learning models. Annotation is expensive, time-consuming, and often requires expert knowledge, particularly in the medical field. Here, we suggest using minimal user interaction in the form of extreme point clicks to train a segmentation model which, in effect, can be used to speed up medical image annotation. An initial segmentation is generated based on the extreme points using the random walker algorithm. This initial segmentation is then used as a noisy supervision signal to train a fully convolutional network that can segment the organ of interest, based on the provided user clicks. Through experimentation on several medical imaging datasets, we show that the predictions of the network can be refined using several rounds of training with the prediction from the same weakly annotated data. Further improvements are shown using the clicked points within a custom-designed loss and attention mechanism. Our approach has the potential to speed up the process of generating new training datasets for the development of new machine-learning and deep-learning-based models for, but not exclusively, medical image analysis.
Eye See What You See: Exploring How Bi-Directional Augmented Reality Gaze Visualisation Influences Co-Located Symmetric Collaboration

Gaze is one of the predominant communication cues and can provide valuable implicit information such as intention or focus when performing collaborative tasks. However, little research has been done on how virtual gaze cues combining spatial and temporal characteristics impact real-life physical tasks during face to face collaboration. In this study, we explore the effect of showing joint gaze interaction in an Augmented Reality (AR) interface by evaluating three bi-directional collaborative (BDC) gaze visualisations with three levels of gaze behaviours. Using three independent tasks, we found that all bi-directional collaborative BDC visualisations are rated significantly better at representing joint attention and user intention compared to a non-collaborative (NC) condition, and hence are considered more engaging. The Laser Eye condition, spatially embodied with gaze direction, is perceived significantly more effective as it encourages mutual gaze awareness with a relatively low mental effort in a less constrained workspace. In addition, by offering additional virtual representation that compensates for verbal descriptions and hand pointing, BDC gaze visualisations can encourage more conscious use of gaze cues coupled with deictic references during co-located symmetric collaboration. We provide a summary of the lessons learned, limitations of the study, and directions for future research.
OWLW: Ontology Focused User Centric Architecture for Web Service Recommendation Based on LSTM and Whale Optimization

Web services are software packages proposed to facilitate machine-to-machine connectivity on an interoperable network. They are the web application elements, and it is possible to post, search, and use them on the Web. The use of the web service has grown and diversified in recent years as the Internet expands. This paper offers an innovative approach for recommending web services. The intention of the user is captured by the queries input by the user searches. The search queries are collected as input and preprocessed. Query topics are generated by formalizing the input queries. The formalized queries, along with the domain knowledge, are classified using LSTM. Upon classification, the top 10% of the search results’ semantic similarity is computed using Jaccard and Lin similarity. Web service repositories such as UDDI and WSDL have been incorporated with similar terms in the dataset to recommend web services.
Multi-View Attention-Guided Multiple Instance Detection Network for Interpretable Breast Cancer Histopathological Image Diagnosis

Deep learning approaches have demonstrated significant progress in breast cancer histopathological image diagnosis.Training an interpretable diagnosis model using high-resolution histopathological image is still challenging.To alleviate this problem, a novel multi-view attention-guided multiple instance detection network (MA-MIDN) is proposed.The traditional image classification problem is framed as a weakly supervised multiple instance learning (MIL) problem.We first divide each histopathology image into instances and form a corresponding bag to fully utilize high-resolution information through MIL.Then a new multiple-view attention (MVA) algorithm is proposed to learn attention on the instances from the image to localize the lesion regions in this image.A MVA-guided MIL pooling strategy is designed for aggregating instance-level features to obtain bag-level features for the final classification.The proposed MA-MIDN model performs lesion localization and image classification, simultaneously.Particularly, we train the MA-MIDN model under the deep mutual learning (DML) schema.This transfers DML to a weakly supervised learning problem.Three public breast cancer histopathological image datasets are chosen to evaluate classification and localization results.The experimental results demonstrate that the MA-MIDN model is superior to the latest baselines in terms of diagnosis accuracy, AUC, Precision, Recall, and F1.Notably, it achieves better localization results without compromising classification performance, thereby proving its higher practicality.The code for the MA-MIDN model is available at https://github.com/lcxlcx/MA-MIDN.