Reviewerly/exHarmony huggingface dataset:
Improved Crop Productivity Through Optimized Planting Schedules

Problem definition: We study the optimal seeding policy under rainfall uncertainty in rain-fed agriculture and explore its advantage over commonly used heuristics in practice. Academic/practical relevance: Our work is in the area of agriculture operations, and we focus on the improvement of farmer’s expected total profit by optimizing planting schedules. Methodology: We model a farmer’s planting problem under limited planting capacity in a finite-horizon stochastic dynamic program. Results: We show that the optimal planting policy is a time-dependent, threshold-type policy, and the optimal threshold is dependent on the soil water content and planting capacity. In our computational study, we use the well-known Decision Support System for Agrotechnology Transfer simulator used in agriculture to obtain the expected yield when planting in any given period. Utilizing field weather data from Southern Africa, in a real-size, large-scale problem, we demonstrate a significant relative profit advantage of the optimal planting schedule over commonly used heuristics in practice. We show that the relative advantage of the optimal policy increases as the climate condition becomes more severe for planting. We also develop a heuristic based on the secretary problem and demonstrate the increased efficacy of the secretary heuristic. Managerial implications: We show that the farmer should plant down to the optimal threshold of seed amount. However, in practice, farmers start to plant each year after observing enough cumulative rainfall. Utilizing field weather data, in a real-size, large-scale problem, we show significant improvement of the expected total profit if the farmer could adopt the optimal policy.
Does blockchain patent-development influence Bitcoin risk?

The paper presents a novel analysis specifically investigating as to whether stocks associated with leading blockchain patent-developments influence the price volatility of Bitcoin across multiple time frequencies. It is important to further develop our understanding of the inter-dynamics between this relatively youthful financial product and pricing sensitivities associated with corporate technological advancement. Several interesting results are presented. First, Bitcoin is identified as a volatility receiver instead of a transmitter across all of the time frequencies considered during periods of patent development. Secondly, Microsoft, Mastercard, Intel and Visa contribute the largest volatility spillovers to the Bitcoin market due to patent development. Finally, for most of the companies considered, the calculated spillover effects towards Bitcoin markets are found to increase from the short-term to the long-term. These results suggest the existence of an avenue through which large corporations can influence cryptocurrency prices through their announcements of future technological intentions. The inherent risks incorporated with blockchain and cryptocurrency patent-development should be studied in detail, with particular warnings presented to those companies with no evidence of prior exposure and market knowledge.
Pairwise Relation Learning for Semi-supervised Gland Segmentation

Accurate and automated gland segmentation on histology tissue images is an essential but challenging task in the computer-aided diagnosis of adenocarcinoma. Despite their prevalence, deep learning models always require a myriad number of densely annotated training images, which are difficult to obtain due to extensive labor and associated expert costs related to histology image annotations. In this paper, we propose the pairwise relation-based semi-supervised (PRS\(^2\)) model for gland segmentation on histology images. This model consists of a segmentation network (S-Net) and a pairwise relation network (PR-Net). The S-Net is trained on labeled data for segmentation, and PR-Net is trained on both labeled and unlabeled data in an unsupervised way to enhance its image representation ability via exploiting the semantic consistency between each pair of images in the feature space. Since both networks share their encoders, the image representation ability learned by PR-Net can be transferred to S-Net to improve its segmentation performance. We also design the object-level Dice loss to address the issues caused by touching glands and combine it with other two loss functions for S-Net. We evaluated our model against five recent methods on the GlaS dataset and three recent methods on the CRAG dataset. Our results not only demonstrate the effectiveness of the proposed PR-Net and object-level Dice loss, but also indicate that our PRS\(^2\) model achieves the state-of-the-art gland segmentation performance on both benchmarks.
Compact Bilinear Augmented Query Structured Attention for Sport Highlights Classification

Understanding fine-grained activities, such as sport highlights, is a problem being overlooked and receives considerably less research attention. Potential reasons include absences of specific fine-grained action benchmark datasets, research preferences to general super-categorical activities classification, and challenges of large visual similarities between fine-grained actions. To tackle these, we collect and manually annotate two sport highlights datasets, i.e., Basketball-8 & Soccer-10, for fine-grained action classification. Sample clips in the datasets are annotated with professional sub-categorical actions like "dunk", "goalkeeping" and etc. We also propose a Compact Bilinear Augmented Query Structured Attention (CBA-QSA) module and stack it on top of general three-dimensional neural networks in a plug-and-play manner to emphasize important spatio-temporal clues in highlight clips. Specifically, we adapt the hierarchical attention neural networks, which contain learnable query-scheme, on the video to identify discriminative spatial/temporal visual clues within highlight clips. We name this altered attention which separately learns a query for spatial/temporal feature as query structured attention (QSA). Furthermore, we inflate bilinear mapping, which is a mature technique to represent local pairwise interactions for image-level fine-grained classification, on video understanding. In detail, we extend its compact version (i.e., compact bilinear mapping (CBM) based on TensorSketch) to deal with the three-dimensional video signal for modeling local pairwise motion information. We eventually incorporate CBM and QSA together to form CBA-QSA neural networks for fine-grained sport highlights classifications. Experimental results demonstrate that CBA-QSA improves the general state-of-the-arts on Basketball-8 and Soccer-10 datasets.
PyRetri: A PyTorch-based Library for Unsupervised Image Retrieval by Deep Convolutional Neural Networks

Despite significant progress of applying deep learning methods to the field of content-based image retrieval, there has not been a software library that covers these methods in a unified manner. In order to fill this gap, we introduce PyRetri, an open source library for deep learning based unsupervised image retrieval. The library encapsulates the retrieval process in several stages and provides functionality that covers various prominent methods for each stage. The idea underlying its design is to provide a unified platform for deep learning based image retrieval research, with high usability and extensibility. The project source code, with usage examples, sample data and pre-trained models are available at https://github.com/PyRetri/.
A Feature Generalization Framework for Social Media Popularity Prediction

Social media is an indispensable part in modern life and social media popularity prediction can be applied to many aspects of sociality. In this paper, we propose a novel combined framework for social media popularity prediction, which accomplishes feature generalization and temporal modeling based on multi-modal feature extraction. On the one hand, in order to address the generalization problem caused by massive missing data, we train two CatBoost models with different datasets and integrate their outputs with a linear combination. On the other hand, sliding window average is employed to mine potential short-term dependency for each user's post sequence. Extensive experiments show that our proposed framework has superiorities in both feature generalization and temporal modeling. Besides, our approach achieves the 1st place on the leader board of the SMP Challenge in 2020, which proves the effectiveness of our proposed framework.
Towards Lighter and Faster

Due to the significant development of deep learning (DL) techniques, recent advances in the super-resolution (SR) field have achieved a great performance. While seeking for better performance, the later proposed networks prone to be deeper and heavier, which limits the applications of SR algorithms in the resource-constrain devices. Some advances rely on recurrent/recursive learning to reduce the number of network parameters, however, they ignore the caused long inference time, since the more recurrences/recursions are involved, the longer inference time the network needs. To address this trade-off issue between reconstruction performance, the number of network parameters, and inference time, we propose a lightweight and fast network (WSR) to learn wavelet coefficients of the target image progressively for single image super-resolution. More specifically, the network comprises two main branches. One is used for predicting the second level low-frequency wavelet coefficients, and the other one is designed in a recurrent way for predicting the rest wavelet coefficients at the first and second levels. Finally, an inverse wavelet transformation is adopted to reconstruct the SR images from these coefficients. In addition, we propose a deformable convolution kernel (side window) to construct the side-information multi-distillation block (S-IMDB), which is the basic unit of the recurrent blocks (RBs). We train the WSR with loss constraints at wavelet and spatial domains. Comprehensive experiments demonstrate that our WSR achieves a better trade-off than most of the state-of-the-art approaches. Code is available at https://github.com/FVL2020/WSR.
Machine Learning Techniques for Book Recommendation: An Overview

Recommender Systems are intelligent systems which are used as an expert in making decisions in real life problems. They have replicated the human experts and positively affected the e-commerce by changing the behavior of customers and sellers. Book Recommender Systems (BRS) help the librarians in the management of library catalog efficiently. It supports the readers in choosing the best book for them. Merchants implement the BRS to manage their inventory and gain more profit. In this paper, we have discussed traditional techniques of recommendation, machine learning techniques and their categories i.e. supervised, unsupervised, semi-supervised and reinforcement learning. Also, Machine Learning (ML) techniques used for the book recommendation and their effect on book recommender systems have been discussed. The work will help the researchers in exploring new dimension for recommendation technology in general and book recommendation in particular.
Give Me Something to Eat: Referring Expression Comprehension with Commonsense Knowledge

Conventional referring expression comprehension (REF) assumes people to query something from an image by describing its visual appearance and spatial location, but in practice, we often ask for an object by describing its affordance or other non-visual attributes, especially when we do not have a precise target. For example, sometimes we say 'Give me something to eat'. In this case, we need to use commonsense knowledge to identify the objects in the image. Unfortunately, there is no existing referring expression dataset reflecting this requirement, not to mention a model to tackle this challenge. In this paper, we collect a new referring expression dataset, called KB-Ref, containing $43$k expressions on 16k images. In KB-Ref, to answer each expression (detect the target object referred by the expression), at least one piece of commonsense knowledge must be required. We then test state-of-the-art (SoTA) REF models on KB-Ref, finding that all of them present a large drop compared to their outstanding performance on general REF datasets. We also present an expression conditioned image and fact attention (ECIFA) network that extracts information from correlated image regions and commonsense knowledge facts. Our method leads to a significant improvement over SoTA REF models, although there is still a gap between this strong baseline and human performance. The dataset and baseline models are available at: https://github.com/wangpengnorman/KB-Ref_dataset.
Deep edge map guided depth super resolution

Accurate edge reconstruction is critical for depth map super resolution (SR). Therefore, many traditional SR methods utilize edge maps to guide depth SR. However, it is difficult to predict accurate edge maps from low resolution (LR) depth maps. In this paper, we propose a deep edge map guided depth SR method, which includes an edge prediction subnetwork and an SR subnetwork. The edge prediction subnetwork takes advantage of the hierarchical representation of color and depth images to produce accurate edge maps, which promote the performance of SR subnetwork. The SR subnetwork is a disentangling cascaded network to progressively upsample SR result, where every level is made up of a weight sharing module and an adaptive module. The weight sharing module extracts the general features in different levels, while the adaptive module transfers the general features to the specific features to adapt to different degraded inputs. Quantitative and qualitative evaluations on various datasets with different magnification factors demonstrate the effectiveness and promising performance of the proposed method. In addition, we construct a benchmark dataset captured by Kinect-v2 to facilitate research on real-world depth map SR.
High Accuracy Modeling for Solar PV Power Generation Using Noble BD-LSTM-Based Neural Networks with EMA

More accurate self-forecasting not only provides a better-integrated solution for electricity grids but also reduces the cost of operation of the entire power system. To predict solar photovoltaic (PV) power generation (SPVG) for a specific hour, this paper proposes the combination of a two-step neural network bi directional long short-term memory (BD-LSTM) model with an artificial neural network (ANN) model using exponential moving average (EMA) preprocessing. In this study, four types of historical input data are used: hourly PV generation for one week (168 h) ahead, hourly horizontal radiation, hourly ambient temperature, and hourly device (surface) temperature, downloaded from the Korea Open Data Portal. The first strategy is employed using the LSTM prediction model, which forecasts the SPVG of the desired time through the data from the previous week, which is preprocessed to smooth the dynamic SPVG using the EMA approach. The SPVG was predicted using the LSTM model according to the trend of the previous time-series data. However, slight errors still occur because the weather condition of the time is not reflected at the desired time. Therefore, we proposed a second strategy of an ANN model for more accurate estimation to compensate for this slight error using the four inputs predicted by the LSTM model. As a result, the LSTM prediction model with the ANN estimation model using EMA preprocessing exhibited higher accuracy in performance than other options for SPVG.
Diminishing Returns and Deep Learning for Adaptive CPU Resource Allocation of Containers

Containers provide a lightweight runtime environment for microservices applications while enabling better server utilization. Automatic optimal allocation of CPU pins to the containers serving specific workloads can help to minimize the completion time of jobs. Most of the existing state-of-the-art focused on building new efficient scheduling algorithms for placing the containers on the infrastructure, and the resources to the containers are allocated manually and statically. An automatic method to identify and allocate optimal CPU resources to the containers can help to improve the efficiency of the scheduling algorithms. In this article, we introduce a new deep learning-based approach to allocate optimal CPU resources to the containers automatically. Our approach uses the law of diminishing marginal returns to determine the optimal number of CPU pins for containers to gain maximum performance while maximizing the number of concurrent jobs. The proposed method is evaluated using real workloads on a Docker-based containerized infrastructure. The results demonstrate the effectiveness of the proposed solution in reducing the completion time of the jobs by 23% to 74% compared to commonly used static CPU allocation methods.
Automatic Aerial Victim Detection on Low-Cost Thermal Camera Using Convolutional Neural Network

The first thing to do by the search-and-rescue (SAR) team after the disaster occurred is to find the location of the victim quickly. Thus the loss of lives can be reduced. After the disaster, the environment usually very messy, containing debris from building, soil, and gravel, which makes it harder to find the victims. By detecting the temperature using a thermal camera, it can easily be distinguished between the victims and the other background. Previous work, the technology to detect a person using a thermal camera from aerial has been developed, but it is only working with the most nearly uniform background. In this paper, we developed an automatic aerial (drones) victim detection using a thermal camera. A low-cost thermal camera has been used so that anyone can quickly implement in the real situation. By combining CNN as its algorithm that widely uses for its excellent performance on object detection, it can easily detect victims from the low-cost thermal camera and distinguished from complex background. Experiments show very well that the proposed method able to detect victims from aerial thermal view with accuracy AP = 82.49%. We believe it could bring benefits for future work with the related field and able to help search-and-rescue team to find and evacuate the victims quickly.
Prediction of Traffic Density Using YOLO Object Detection and Implemented in Raspberry Pi 3b + and Intel NCS 2

Increasing the number of vehicles as the population increases causes traffic congestion, thus indirectly hampering community activities. One way to unravel traffic jams is to do activities outside of peak hours. This study discusses how to determine vehicle traffic density using the Double Exponential Smoothing forecasting model, which is to estimate in quantity, what will happen in the future based on relevant data in the past. While testing the forecasting model using the MAPE (Mean Absolute Percentage Error) method, it aims to measure how the error rate obtained from the results of forecasting. Vehicle identification as the object causing the traffic jam uses the Convolutional Neural Network (CNN) object detection method, especially the You Only Look Once (YOLO) method. The system design involves a Raspberry Pi 3 device, an Intel NCS 2 device, and a website application. Based on the test results obtained, accurate forecasting accuracy of more than 86%.
The Lockdown Effect: Implications of the COVID-19 Pandemic on Internet Traffic

Due to the COVID-19 pandemic, many governments imposed lock-downs that forced hundreds of millions of citizens to stay at home. The implementation of confinement measures increased Internet traffic demands of residential users, in particular, for remote working, entertainment, commerce, and education, which, as a result, caused traffic shifts in the Internet core.In this paper, using data from a diverse set of vantage points (one ISP, three IXPs, and one metropolitan educational network), we examine the effect of these lockdowns on traffic shifts. We find that the traffic volume increased by 15-20% almost within a week---while overall still modest, this constitutes a large increase within this short time period. However, despite this surge, we observe that the Internet infrastructure is able to handle the new volume, as most traffic shifts occur outside of traditional peak hours. When looking directly at the traffic sources, it turns out that, while hypergiants still contribute a significant fraction of traffic, we see (1) a higher increase in traffic of non-hypergiants, and (2) traffic increases in applications that people use when at home, such as Web conferencing, VPN, and gaming. While many networks see increased traffic demands, in particular, those providing services to residential users, academic networks experience major overall decreases. Yet, in these networks, we can observe substantial increases when considering applications associated to remote working and lecturing.
X-AWARE: ConteXt-AWARE Human-Environment Attention Fusion for Driver Gaze Prediction in the Wild

Reliable systems for automatic estimation of the driver's gaze are crucial for reducing the number of traffic fatalities and for many emerging research areas aimed at developing intelligent vehicle-passenger systems. Gaze estimation is a challenging task, especially in environments with varying illumination and reflection properties. Furthermore, there is wide diversity with respect to the appearance of drivers' faces, both in terms of occlusions (e.g. vision aids) and cultural/ethnic backgrounds. For this reason, analysing the face along with contextual information - for example, the vehicle cabin environment - adds another, less subjective signal towards the design of robust systems for passenger gaze estimation. In this paper, we present an integrated approach to jointly model different features for this task. In particular, to improve the fusion of the visually captured environment with the driver's face, we have developed a contextual attention mechanism, X-AWARE, attached directly to the output convolutional layers of InceptionResNetV2 networks. In order to showcase the effectiveness of our approach, we use the Driver Gaze in the Wild dataset, recently released as part of the Eighth Emotion Recognition in the Wild Challenge (EmotiW) challenge. Our best model outperforms the baseline by an absolute of 15.03% in accuracy on the validation set, and improves the previously best reported result by an absolute of 8.72% on the test set.
Multilabel Deep Learning-Based Side-Channel Attack

In recent years, deep learning methods make a big difference in side-channel attack (SCA) community especially in the profiled scenario. Multiclass classification method is the common way to complete such classification task. In this article, we propose a novel SCA method utilizing multilabel classification from bit-to-byte view. Accordingly, each leakage trace has eight labels when considering a byte. The experimental results on several datasets show that our multilabel classification method is efficient and even performs better in some situations compared with the original multiclass classification model while model complexity is much reduced. Besides, our multilabel model can be seen as ensemble of monobit models and we verify the ensemble effect experimentally.