Reviewerly/exHarmony huggingface dataset:
ResTune: Resource Oriented Tuning Boosted by Meta-Learning for Cloud Databases

Modern database management systems (DBMS) contain tens to hundreds of critical performance tuning knobs that determine the system runtime behaviors. To reduce the total cost of ownership, cloud database providers put in drastic effort to automatically optimize the resource utilization by tuning these knobs. There are two challenges. First, the tuning system should always abide by the service level agreement (SLA) while optimizing the resource utilization, which imposes strict constrains on the tuning process. Second, the tuning time should be reasonably acceptable since time-consuming tuning is not practical for production and online troubleshooting.
Detection of geochemical anomalies related to mineralization using the GANomaly network

In this study, a GANomaly network was used to detect geochemical anomalies related to mineralization in the southern part of Jiangxi Province and its adjacent areas in China. The training data used in this study belong to a typical rare-sample category of imbalanced data samples; thus, during the training phase, only non-mineralized dataset randomly selected from the study area was used for training in order to avoid the overfitting problem caused by an imbalance between positive and negative training samples. The established GANomaly network structure can effectively extract anomalous geochemical information from the exploration geochemical data. The geochemical anomalies identified by GANomaly and known tungsten polymetallic deposits show a close spatial correlation. Further, the anomalous high-value areas are located in or around the Yanshanian intrusive rock. The performance of GANomaly for the identification of multivariate geochemical anomalies was compared to that of the deep autoencoder. The comparative results indicated that the GANomaly network can learn the internal connections and characteristics between multivariate geochemical data and can effectively avoid the influence of noise in geochemical data. Therefore, the abnormal areas identified by GANomaly are determined to be significant for mineral exploration.
Performance analysis of a grid-connected rooftop solar PV system in Kuala Terengganu, Malaysia

This paper presents the real performance of a 7.8 kWp grid-connected rooftop photovoltaic (PV) system from a field monitoring at a residential house under the feed-in-tariff scheme. The performance parameters of PV system were assessed based on the two-year energy production in 2018–2019. PVsyst software was used to find the best orientation and tilt angle for the selected area to increase the system performance. Next, the PV derating factor was calculated from the two-day data collection. The derating factor was used in the simulation of the system using HOMER Pro, and the economic parameters for 21 years project lifetime were predicted. The results show that the performance of the installed system was better for 2019 compared to 2018 due to the higher solar irradiance in 2019 and the malfunction of inverter in July to August 2018. The best performance ratio obtained from the recorded data was 75.72% and the average loss was 1.68 kWh/kWp/day. The annual capacity factor and overall system efficiency were in the range of 13%–16% and 10%–12%, respectively. The best tilt angle and orientation was 5° with the increment of annual energy production up to 4.8%. From the simulation, the error between the simulation and measurement results was high when the derating factor was assumed to be 100%. When the value used was based on the calculation obtained from data collection, the annual error was 1.7%, which is acceptable. Thus, the software is considered reliable assuming no component failure in the PV system. From the economic analysis, the annual net profit can be obtained was almost RM 3,000, and the payback period was in the range of 5–7 years. In addition, the installed PV system on the single house has the potential to reduce the CO2 emission of approximately 7.45 tons/year.
UPFlow: Upsampling Pyramid for Unsupervised Optical Flow Learning

We present an unsupervised learning approach for optical flow estimation by improving the upsampling and learning of pyramid network. We design a self-guided upsample module to tackle the interpolation blur problem caused by bilinear upsampling between pyramid levels. Moreover, we propose a pyramid distillation loss to add supervision for intermediate levels via distilling the finest flow as pseudo labels. By integrating these two components together, our method achieves the best performance for unsupervised optical flow learning on multiple leading benchmarks, including MPI-SIntel, KITTI 2012 and KITTI 2015. In particular, we achieve EPE=1.4 on KITTI 2012 and F1=9.38% on KITTI 2015, which outperform the previous state-of-the-art methods by 22.2% and 15.7%, respectively.
Learning Cross-Modal Retrieval with Noisy Labels

Recently, cross-modal retrieval is emerging with the help of deep multimodal learning. However, even for unimodal data, collecting large-scale well-annotated data is expensive and time-consuming, and not to mention the additional challenges from multiple modalities. Although crowd-sourcing annotation, e.g., Amazon’s Mechanical Turk, can be utilized to mitigate the labeling cost, but leading to the unavoidable noise in labels for the non-expert annotating. To tackle the challenge, this paper presents a general Multi-modal Robust Learning framework (MRL) for learning with multimodal noisy labels to mitigate noisy samples and correlate distinct modalities simultaneously. To be specific, we propose a Robust Clustering loss (RC) to make the deep networks focus on clean samples instead of noisy ones. Besides, a simple yet effective multimodal loss function, called Multimodal Contrastive loss (MC), is proposed to maxi-mize the mutual information between different modalities, thus alleviating the interference of noisy samples and cross-modal discrepancy. Extensive experiments are conducted on four widely-used multimodal datasets to demonstrate the effectiveness of the proposed approach by comparing to 14 state-of-the-art methods.
A Comparative Study of Traffic Classification Techniques for Smart City Networks

Smart city networks involve many applications that impose specific Quality of Service (QoS) requirements, thus representing a challenging scenario for network management. Solutions aiming to guarantee QoS support have not been deployed in large-scale networks. Traffic classification is a mechanism used to manage different aspects, including QoS requirements. However, conventional traffic classification methods, such as the port-based method, are inefficient because of their inability to handle dynamic port allocation and encryption. Traffic classification using machine learning has gained research interest as an alternative method to achieve high performance. In fact, machine learning embeds intelligence into network functions, thus improving network management. In this study, we apply machine learning algorithms to predict network traffic classification. We apply four supervised learning algorithms: support vector machine, random forest, k-nearest neighbors, and decision tree. We also apply a port-based method of traffic classification based on applications’ popular assigned port numbers. Then, we compare the results of this method to those obtained from the machine learning algorithms. The evaluation results indicate that the decision tree algorithm provides the highest average accuracy among the evaluated algorithms, at 99.18%. Moreover, network traffic classification using machine learning provides more accurate results and higher performance than the port-based method.
Edge-Aware Multiscale Feature Integration Network for Salient Object Detection in Optical Remote Sensing Images

The optical remote sensing images (RSIs) show various spatial resolutions and cluttered background, where salient objects with different scales, types, and orientations are presented in diverse RSI scenes. Therefore, it is inappropriate to directly extend cutting-edge saliency detection methods for conventional RGB images to optical RSIs. Besides, the existing saliency models targeting RSIs often render imperfect saliency maps, where some of them are with coarse boundary details. To solve this problem, this article attempts to introduce the edge information to precisely detect salient objects in RSIs. Accordingly, we propose an edge-aware multiscale feature integration network (EMFI-Net) for salient object detection by conducting multiscale feature integration under the explicit and implicit assistance of salient edge cues. Specifically, our network contains two parts including the encoder and decoder. First, the encoder extracts multiscale deep features from three RSIs with different resolutions, where the high-level deep semantic features from three RSIs are integrated using a cascaded feature fusion module. Second, the encoder explicitly enriches the multiscale deep features by integrating the salient edge cues extracted by a salient edge extraction module. Meanwhile, we also implicitly deploy an edge-aware constraint to the supervision of the saliency map prediction by introducing a hybrid loss function. Finally, the decoder integrates the enriched multiscale deep features in a coarse-to-fine way, yielding a high-quality saliency map. The experiments conducted on two public optical RSI datasets clearly prove the effectiveness and superiority of the proposed EMFI-Net against the state-of-the-art saliency models.
Reflections on dynamics, adaptation and control: A cognitive architecture for mental models

In this paper, an overview of the wide variety of occurrences of mental models in the literature is discussed. They are classified according to two dimensions obtaining four categories of them: static-dynamic and world-mental, where static refers to mental models for static world states or for static mental states and dynamic refers to mental models for world processes or for mental processes. In addition, distinctions are made for what can be done by mental models: they can, for example, be (1) used for internal simulation, they can be (2) adapted, and these processes can be (3) controlled. This leads to a global three-level cognitive architecture covering these three ways of handling mental models. It is discussed that in this cognitive architecture reflection principles play an important role to define the interactions between the different levels.
TextOCR: Towards large-scale end-to-end reasoning for arbitrary-shaped scene text

A crucial component for the scene text based reasoning required for TextVQA and TextCaps datasets involve detecting and recognizing text present in the images using an optical character recognition (OCR) system. The current systems are crippled by the unavailability of ground truth text annotations for these datasets as well as lack of scene text detection and recognition datasets on real images disallowing the progress in the field of OCR and evaluation of scene text based reasoning in isolation from OCR systems. In this work, we propose TextOCR, an arbitrary-shaped scene text detection and recognition with 900k annotated words collected on real images from TextVQA dataset. We show that current state-of-the-art text-recognition (OCR) models fail to perform well on TextOCR and that training on TextOCR helps achieve state-of-the-art performance on multiple other OCR datasets as well. We use a TextOCR trained OCR model to create PixelM4C model which can do scene text based reasoning on an image in an end-to-end fashion, allowing us to revisit several design choices to achieve new state-of-the-art performance on TextVQA dataset.
A Transfer Learning Architecture Based on a Support Vector Machine for Histopathology Image Classification

Recently, digital pathology is an essential application for clinical practice and medical research. Due to the lack of large annotated datasets, the deep transfer learning technique is often used to classify histopathology images. A softmax classifier is often used to perform classification tasks. Besides, a Support Vector Machine (SVM) classifier is also popularly employed, especially for binary classification problems. Accurately determining the category of the histopathology images is vital for the diagnosis of diseases. In this paper, the conventional softmax classifier and the SVM classifier-based transfer learning approach are evaluated to classify histopathology cancer images in a binary breast cancer dataset and a multiclass lung and colon cancer dataset. In order to achieve better classification accuracy, a methodology that attaches SVM classifier to the fully-connected (FC) layer of the softmax-based transfer learning model is proposed. The proposed architecture involves a first step training the newly added FC layer on the target dataset using the softmax-based model and a second step training the SVM classifier with the newly trained FC layer. Cross-validation is used to ensure no bias for the evaluation of the performance of the models. Experimental results reveal that the conventional SVM classifier-based model is the least accurate on either binary or multiclass cancer datasets. The conventional softmax-based model shows moderate classification accuracy, while the proposed synthetic architecture achieves the best classification accuracy.
Local Semantic Enhanced ConvNet for Aerial Scene Recognition

Aerial scene recognition is challenging due to the complicated object distribution and spatial arrangement in a large-scale aerial image. Recent studies attempt to explore the local semantic representation capability of deep learning models, but how to exactly perceive the key local regions remains to be handled. In this paper, we present a <i>local semantic enhanced ConvNet</i> (LSE-Net) for aerial scene recognition, which mimics the human visual perception of key local regions in aerial scenes, in the hope of building a discriminative local semantic representation. Our LSE-Net consists of a context enhanced convolutional feature extractor, a local semantic perception module and a classification layer. Firstly, we design a multi-scale dilated convolution operators to fuse multi-level and multi-scale convolutional features in a trainable manner in order to fully receive the local feature responses in an aerial scene. Then, these features are fed into our two-branch local semantic perception module. In this module, we design a <i>context-aware class peak response</i> (CACPR) measurement to precisely depict the visual impulse of key local regions and the corresponding context information. Also, a spatial attention weight matrix is extracted to describe the importance of each key local region for the aerial scene. Finally, the refined class confidence maps are fed into the classification layer. Exhaustive experiments on three aerial scene classification benchmarks indicate that our LSE-Net achieves the state-of-the-art performance, which validates the effectiveness of our local semantic perception module and CACPR measurement.
Video Prediction Recalling Long-term Motion Context via Memory Alignment Learning

Our work addresses long-term motion context issues for predicting future frames. To predict the future precisely, it is required to capture which long-term motion context (e.g., walking or running) the input motion (e.g., leg movement) belongs to. The bottlenecks arising when dealing with the long-term motion context are: (i) how to predict the long-term motion context naturally matching input sequences with limited dynamics, (ii) how to predict the long-term motion context with high-dimensionality (e.g., complex motion). To address the issues, we propose novel motion context-aware video prediction. To solve the bottle-neck (i), we introduce a long-term motion context memory (LMC-Memory) with memory alignment learning. The pro-posed memory alignment learning enables to store long-term motion contexts into the memory and to match them with sequences including limited dynamics. As a result, the long-term context can be recalled from the limited in-put sequence. In addition, to resolve the bottleneck (ii), we propose memory query decomposition to store local motion context (i.e., low-dimensional dynamics) and recall the suitable local context for each local part of the input individually. It enables to boost the alignment effects of the memory. Experimental results show that the proposed method outperforms other sophisticated RNN-based methods, especially in long-term condition. Further, we validate the effectiveness of the proposed network designs by conducting ablation studies and memory feature analysis. The source code of this work is available <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">†</sup> .
MR Image Super-Resolution with Squeeze and Excitation Reasoning Attention Network

High-quality high-resolution (HR) magnetic resonance (MR) images afford more detailed information for reliable diagnosis and quantitative image analyses. Deep convolutional neural networks (CNNs) have shown promising ability for MR image super-resolution (SR) given low-resolution (LR) MR images. The LR MR images usually share some visual characteristics: repeating patterns, relatively simpler structures, and less informative background. Most previous CNN-based SR methods treat the spatial pixels (including the background) equally. They also fail to sense the entire space of the input, which is critical for high-quality MR image SR. To address those problems, we propose squeeze and excitation reasoning attention networks (SERAN) for accurate MR image SR. We propose to squeeze attention from global spatial information of the input and obtain global descriptors. Such global descriptors enhance the network’s ability to focus on more informative regions and structures in MR images. We further build relationship among those global descriptors and propose primitive relationship reasoning attention. The global descriptors are further refined with learned attention. To fully make use of the aggregated information, we adaptively recalibrate feature responses with learned adaptive attention vectors. These attention vectors select a subset of global descriptors to complement each spatial location for accurate details and texture reconstruction. We propose squeeze and excitation attention with residual scaling, which not only stabilizes the training but also makes it flexible to other basic networks. Extensive experiments show the effectiveness of our proposed SERAN, which clearly surpasses state-of-the-art methods on benchmarks quantitatively and visually.
Exploring the application of blockchain to humanitarian supply chains: insights from Humanitarian Supply Blockchain pilot project

Purpose Some studies and reports have recently suggested using blockchain technology to improve transparency and trust in humanitarian supply chains (HSCs). However, evidence-based studies to display the utility and applicability of blockchains in HSCs are missing in the literature. This paper aims to investigate the key drivers and barriers of blockchain application to HSCs and explore whether evidence could support that the application of blockchain improves transparency and trust in HSCs. Design/methodology/approach This paper puts forward a two-stage approach to explore the blockchain application in HSCs: an initial exploration of humanitarian practitioners and academicians interested in blockchain through focus group discussions; semi-structured interviews with practitioners involved at the UK Department for International Development's Humanitarian Supply Blockchain pilot project. Findings First, we found that main drivers include accountability, visibility, traceability, trust, collaboration, time efficiency, reducing administrative work and cross-sector partnership. Main barriers, however, are composed of engagement issues, lack of technical skills and training, lack of resources, privacy concerns, regulatory problems, pilot scalability issues and governance challenges. Second, evidence from our case study revealed the blockchain application could have added value to improve visibility and traceability, thus contributing to improve transparency. Concerning trust, evidence supports that blockchain could enhance both commitment and swift trust in the pilot study. Practical implications Our study contributes to a more understanding of added values and challenges of blockchain application to HSCs and creates a perspective for humanitarian decision-makers. Originality/value This study provides the first evidence from the actual application of blockchain technology in HSCs. The study discovered that it is still less evident in many humanitarian organizations, including medium- and small-sized nongovernmental organizations, that they engage in a direct deployment of in-house or customized blockchain-based HSC. Instead, these actors are more likely to indirectly use blockchain in HSCs through a private commercial partner.
Two-Stage Attention Over LSTM With Bayesian Optimization for Day-Ahead Solar Power Forecasting

The penetration of PVs into the power grid is increasing day by day, as they are more economical and environment-friendly. However, due to the intrinsic intermittency in solar radiation and other meteorological factors, the generated power from PVs is uncertain and unstable. Therefore, accurate forecasting of power generation is considered one of the fundamental challenges in power system. In this paper, a deep-learning model based on two-stage attention mechanism over LSTM is proposed to forecast a day-ahead PV power. In addition, the Bayesian optimization algorithm is applied to obtain the optimal combination of hyper-parameters for the proposed deep-learning model. Various input features that can affect the PV power generation such as solar radiation, temperature, humidity, snowfall, albedo etc. are considered and their impact with respect to the attention mechanisms on the forecasted PV power is analyzed. The input consists of data from 21 PVs installed at different geographical locations in Germany. The proposed model is compared with state-of-the-art models such as LSTM-Attention, CNN-LSTM, and Ensemble model for day-ahead forecasting. The model is also compared with various single attention mechanisms such as Input-attention, SNAIL, Raffel, and Hierarchical attention etc. The proposed model outperforms the traditional methods in terms of accuracy, hence proving its efficiency. Forecasting Skill (FS) score of the proposed model is 0.4813 whereas 0.4427 is for the Ensemble model, which is the best among other state-of-the-art models. Root Mean Square (RMSE) and Mean Absolute Error (MAE) of the proposed model is 0.0638 and 0.0324 respectively, whereas those of the Ensemble model are 0.0685 and 0.0369 respectively.
RAFT-3D: Scene Flow using Rigid-Motion Embeddings

We address the problem of scene flow: given a pair of stereo or RGB-D video frames, estimate pixelwise 3D motion. We introduce RAFT-3D, a new deep architecture for scene flow. RAFT-3D is based on the RAFT model developed for optical flow but iteratively updates a dense field of pixelwise SE3 motion instead of 2D motion. A key innovation of RAFT-3D is rigid-motion embeddings, which represent a soft grouping of pixels into rigid objects. Integral to rigid-motion embeddings is Dense-SE3, a differentiable layer that enforces geometric consistency of the embeddings. Experiments show that RAFT-3D achieves state-of-the-art performance. On FlyingThings3D, under the twoview evaluation, we improved the best published accuracy (δ < 0.05) from 34.3% to 83.7%. On KITTI, we achieve an error of 5.77, outperforming the best published method (6.31), despite using no object instance supervision.