Reviewerly/exHarmony huggingface dataset:
Information Entropy Based Feature Pooling for Convolutional Neural Networks

In convolutional neural networks (CNNs), we propose to estimate the importance of a feature vector at a spatial location in the feature maps by the network's uncertainty on its class prediction, which can be quantified using the information entropy. Based on this idea, we propose the entropy-based feature weighting method for semantics-aware feature pooling which can be readily integrated into various CNN architectures for both training and inference. We demonstrate that such a location-adaptive feature weighting mechanism helps the network to concentrate on semantically important image regions, leading to improvements in the large-scale classification and weakly-supervised semantic segmentation tasks. Furthermore, the generated feature weights can be utilized in visual tasks such as weakly-supervised object localization. We conduct extensive experiments on different datasets and CNN architectures, outperforming recently proposed pooling methods and attention mechanisms in ImageNet classification as well as achieving state-of-the-arts in weakly-supervised semantic segmentation on PASCAL VOC 2012 dataset.
Improved Black Hole Algorithm Based on Golden Sine Operator and Levy Flight Operator

The black hole (BH) algorithm is a new type of natural heuristic algorithm inspired by the movement law of the ''black hole'' celestial body in the universe.BH algorithm has received extensive attention due to its advantages such as fewer parameters, simple algorithm structure and strong exploitation.For the shortcomings of poor exploaration and premature convergence of BH algorithm, the improved golden sine (G-S) operator is introduced into BH algorithm to greatly improve the exploaration.Then the Levy flight operator with controlled step size has become a better local search operator from the global search operator, so the improved Levy flight operator is introduced to further improve the exploitation of BH algorithm.Ultimately, the golden sine operator and Levy flight operator based black hole (GSLBH) algorithm is able to balance the exploaration with exploitation.GSLBH, BH, Particle Swarm Optimization (PSO), Whale Optimization Algorithm (WAO), Firefly Algorithm (FA), Golden Sine Algorithm (Gold-SA) were adopted to carry out the simulation experiments with 17 benchmark functions, respectively, and the statistical data results are analyzed and compared.Finally, it can be concluded that the proposed improved black hole algorithm has better exploaration, and the convergence speed and accuracy of the algorithm have been further improved.
Anomaly detection of satellite telemetry based on optimized extreme learning machine

In aerospace, anomaly detection based on telemetry data is a critical satellite health monitoring task that is important for identifying unusual or unexpected events and for taking measurements to improve system safety and avoid serious problems. This paper introduces a novel optimized predictive model for detecting anomalies using the Grey Wolf Optimization (GWO) algorithm and an Extreme Learning Machine (ELM), called GWO-ELM. The proposed GWO-ELM is used to find anomalous events by comparing the actual observed values with the predicted intervals of telemetry data; the GWO is applied to optimize the ELM’s input weights and the bias parameters of hidden neurons to improve its prediction accuracy and ability to detect anomalies. A performance evaluation of GWO-ELM is conducted on the NASA shuttle valve benchmark dataset, which contains samples of Labeled anomalies and various metrics are collected. The experimental results for GWO-ELM show that it makes predictions with high efficiency, is stable when detecting anomalies, and requires little computational time. In addition, the results of GWO-ELM compared with those of the basic ELM algorithm with randomized parameter selection and a support vector machine (SVM), demonstrate the effectiveness and superiority of the proposed model.
Some issues in blockchain for accounting and the supply chain, with an application of distributed databases to virtual organizations

Summary This paper reviews some recent blockchain‐based applications for information capture, distribution and preservation. As part of that review, this paper examines two key concerns with current blockchain designs for accounting and supply chain transactions: data independence and multiple semantic models for the same information distribution problem. Blockchain applications typically integrate database, application and presentation tiers all in the same ledger. This results in a general inability to query information in the ledger and other concerns. Further, since most applications appear to be private blockchain applications, there is a concern of agents needing to accommodate multiple blockchains depending on who their trading partners are and what they request. Finally, this paper uses a distributed database to design a ‘blockchain‐like’ system for virtual organizations.
Decentralized Multi-Agent Deep Reinforcement Learning in Swarms of Drones for Flood Monitoring

Multi-Agent Deep Reinforcement Learning is becoming a promising approach to the problem of coordination of swarms of drones in dynamic systems.In particular, the use of autonomous aircraft for flood monitoring is now regarded as an economically viable option and it can benefit from this kind of automation: swarms of unmanned aerial vehicles could autonomously generate nearly real-time inundation maps that could improve relief work planning.In this work, we study the use of Deep Q-Networks (DQN) as the optimization strategy for the trajectory planning that is required for monitoring floods, we train agents over simulated floods in procedurally generated terrain and demonstrate good performance with two different reward schemes.
The Art and Craft of Fraudulent App Promotion in Google Play

Black Hat App Search Optimization (ASO) in the form of fake reviews and sockpuppet accounts, is prevalent in peer-opinion sites, e.g., app stores, with negative implications on the digital and real lives of their users. To detect and filter fraud, a growing body of research has provided insights into various aspects of fraud posting activities, and made assumptions about the working procedures of the fraudsters from online data. However, such assumptions often lack empirical evidence from the actual fraud perpetrators. To address this problem, in this paper, we present results of both a qualitative study with 18 ASO workers we recruited from 5 freelancing sites, concerning activities they performed on Google Play, and a quantitative investigation with fraud-related data collected from other 39 ASO workers. We reveal findings concerning various aspects of ASO worker capabilities and behaviors, including novel insights into their working patterns, and supporting evidence for several existing assumptions. Further, we found and report participant-revealed techniques to bypass Google-imposed verifications, concrete strategies to avoid detection, and even strategies that leverage fraud detection to enhance fraud efficacy. We report a Google site vulnerability that enabled us to infer the mobile device models used to post more than 198 million reviews in Google Play, including 9,942 fake reviews. We discuss the deeper implications of our findings, including their potential use to develop the next generation fraud detection and prevention systems.
Online Book Recommendation System using Collaborative Filtering (With Jaccard Similarity)

Abstract Recommendation System (RS) is software that suggests similar items to a purchaser based on his/her earlier purchases or preferences. RS examines huge data of objects and compiles a list of those objects which would fulfil the requirements of the buyer. Nowadays most ecommerce companies are using Recommendation systems to lure buyers to purchase more by offering items that the buyer is likely to prefer. Book Recommendation System is being used by Amazon, Barnes and Noble, Flipkart, Goodreads, etc. to recommend books the customer would be tempted to buy as they are matched with his/her choices. The challenges they face are to filter, set a priority and give recommendations which are accurate. RS systems use Collaborative Filtering (CF) to generate lists of items similar to the buyer’s preferences. Collaborative filtering is based on the assumption that if a user has rated two books then to a user who has read one of these books, the other book can be recommended (Collaboration). CF has difficulties in giving accurate recommendations due to problems of scalability, sparsity and cold start. Therefore this paper proposes a recommendation that uses Collaborative filtering with Jaccard Similarity (JS) to give more accurate recommendations. JS is based on an index calculated for a pair of books. It is a ratio of common users (users who have rated both books) divided by the sum of users who have rated the two books individually. Larger the number of common users higher will be the JS Index and hence better recommendations. Books with high JS index (more recommended) will appear on top of the recommended books list.
Adversarial Training of Gradient-Boosted Decision Trees

Adversarial training is a prominent approach to make machine learning (ML) models resilient to adversarial examples. Unfortunately, such approach assumes the use of differentiable learning models, hence it cannot be applied to relevant ML techniques, such as ensembles of decision trees. In this paper, we generalize adversarial training to gradient-boosted decision trees (GBDTs). Our experiments show that the performance of classifiers based on existing learning techniques either sharply decreases upon attack or is unsatisfactory in absence of attacks, while adversarial training provides a very good trade-off between resiliency to attacks and accuracy in the unattacked setting.
Towards an Efficient Accelerator for DNN-Based Remote Sensing Image Segmentation on FPGAs

Among popular techniques in remote sensing image (RSI) segmentation, Deep Neural Networks (DNNs) have gained increasing interest but often require high computation complexity, which largely limits their applicability in on-board space platforms. Therefore, various dedicated hardware designs on FPGAs have been developed to accelerate DNNs. However, it imposes difficulty on the design of efficient accelerators for DNN-based segmentation algorithms, since they need to perform both convolution and deconvolution which are two fundamentally different types of operations. This paper proposes a uniform architecture to efficiently implement both convolution and deconvolution in one vector multiplication module. This architecture is further optimized through exploiting different levels of parallelism and layer fusion to achieve low latency for RSI segmentation tasks. Moreover, an optimized DNN model is developed for real-time RSI segmentation, which shows preferable accuracy compared to other methods. The proposed hardware accelerator efficiently implements the DNN model on Intel's Arria 10 device, demonstrating 1578 GOPS of throughput and 17.4 ms of latency, i.e., 57 images per second.
Semi-Supervised Monocular 3D Face Reconstruction With End-to-End Shape-Preserved Domain Transfer

Monocular face reconstruction is a challenging task in computer vision, which aims to recover 3D face geometry from a single RGB face image. Recently, deep learning based methods have achieved great improvements on monocular face reconstruction. However, for deep learning-based methods to reach optimal performance, it is paramount to have large-scale training images with ground-truth 3D face geometry, which is generally difficult for human to annotate. To tackle this problem, we propose a semi-supervised monocular reconstruction method, which jointly optimizes a shape-preserved domain-transfer CycleGAN and a shape estimation network. The framework is semi-supervised trained with 3D rendered images with ground-truth shapes and in-the-wild face images without any extra annotation. The CycleGAN network transforms all realistic images to have the rendered style and is end-to-end trained within the overall framework. This is the key difference compared with existing CycleGAN-based learning methods, which just used CycleGAN as a separate training sample generator. Novel landmark consistency loss and edge-aware shape estimation loss are proposed for our two networks to jointly solve the challenging face reconstruction problem. Extensive experiments on public face reconstruction datasets demonstrate the effectiveness of our overall method as well as the individual components.
New Associative Classification Method Based on Rule Pruning for Classification of Datasets

In data mining, a rule-based classification approach called Associative Classification (AC) normally builds accurate classifiers from supervised learning data sets.It extracts ''If-Then'' rules and associates each of the generated rules with two computed parameters; support and confidence.These two parameters are utilized to differentiate the rules' superiority during the building of a classifier's step.In current AC algorithms, whenever a rule is inserted into a classifier, all of its corresponding training data is discarded.However, the discarded data actually are used to compute support and confidence of other rules and will affect other lower ranked rules since rules normally have common training data examples.The use of static support and confidence may result in very large less-accurate classifiers.Thus, a procedure that amends other rules' support and confidence is important.This paper proposes a new procedure named Active Pruning Rules (APR) to overcome the above problem so then the classifiers' performance -especially predictive accuracy and reducing rule redundancy -will be further improved.The experimental results obtained from a number of University of California Irvine (UCI) data sets and real adult autism classification data set showed that APR is highly competitive to other AC and rule-based classifiers and often produces smaller yet more predictive classifiers.
Short-term solar PV forecasting using computer vision: The search for optimal CNN architectures for incorporating sky images and PV generation history

Cloud movement makes short-term forecasting of solar photovoltaic (PV) panel output challenging. A better PV forecast can realize value for both grid operators and commercial or industrial customers with solar assets. In this study, we build convolutional neural network (CNN) based models to forecast power output from PV panels 15 min into the future. Model inputs are the PV power output history and ground-based sky images for the past 15 min. The key challenge is ensuring that due importance is given to each type of input. We systematically explore 28 methods of “fusing” these heterogeneous inputs in our CNN. These methods of fusion (MoF) belong to 4 families. We also systematically explore the many hyperparameters related to model training and tuning. Limited resources preclude an exhaustive search. We apply a three-stage “funnel” approach instead, wherein we narrow our search to the most promising one of these 28 MoF. We find that a two-step autoregression-CNN MoF has the best performance followed closely by a “mix-in” MoF that performs feature expansion and reduction to give appropriate importance to the two types of inputs. The two-step autoregression-CNN model has a forecast skill (FS) of 17.1% relative to smart persistence on the test set comprising 20 complete days (9 sunny, FS = 22%; 11 cloudy, FS = 16.9%). This optimization results in the improvement of FS from 14.1% for a previously published nonoptimized “baseline” model, a CNN wherein the PV history was simply concatenated to the end of the image-sourced vector obtained after convolution, pooling, and flattening operations.
A Global-Local Embedding Module for Fashion Landmark Detection

Detecting fashion landmarks is a fundamental technique for visual clothing analysis. Due to the large variation and non-rigid deformation of clothes, localizing fashion landmarks suffers from large spatial variances across poses, scales, and styles. Therefore, understanding contextual knowledge of clothes is required for accurate landmark detection. To that end, in this paper, we propose a fashion landmark detection network with a global-local embedding module. The global-local embedding module is based on a non-local operation for capturing long-range dependencies and a subsequent convolution operation for adopting local neighborhood relations. With this processing, the network can consider both global and local contextual knowledge for a clothing image. We demonstrate that our proposed method has an excellent ability to learn advanced deep feature representations for fashion landmark detection. Experimental results on two benchmark datasets show that the proposed network outperforms the state-of-the-art methods.
Multi-Agent Path Finding with Priority for Cooperative Automated Valet Parking

Shortage of parking lots has been a serious problem due to the rapid population growth in urban areas. Cooperative Automated Valet Parking (Co-AVP) is a promising approach to mitigate the problem. Co-AVP system would realize high density and efficient parking by automatically navigating vehicles to vacant parking spaces. Cooperative path planning has been extensively studied as Multi-Agent Path Finding (MAPF). In Co-AVP, vehicles would be often prioritized. For instance, vehicles would be able to spend more time to park in vacant spaces than moving from parking spaces to waiting drivers. Previous MAPF settings, however, cannot treat such priorities. In this paper, we formulate MAPF considering each agent's priority. We also develop the optimal method based on Conflict-Based Search (named CBS-Pri) and the heuristic method based on Cooperative A* (named CA*-Pri). We verified that higher prioritized vehicles tended to preferentially arrive at waiting drivers by our methods in numerical experiments. We also found that CBS-Pri always searched better solutions than or equal to CA*-Pri but requires more computational time.
Practical Cover Selection for Steganography

This letter focus on a practical scenario of cover selection for steganography, in which a part of the available images of the sender have been processed to improve visual quality, e.g., contrast enhancement, image denoising. In this case, not only the embedding distortion (caused by steganography), but also the processing distortion (caused by processing) should be considered when selecting cover image. We propose a cover selection method to combine the two kinds of distortion together to measure the suitability for steganography of the available images. To calculate the processing distortion, a classifier is trained to distinguish the processed images with the original ones. The classifier is then used to measure the possibility of the existence of processing. High possibility means high processing distortion. In addition, the current distortion minimization framework designed for steganography is employed to calculate the embedding distortion. Finally, both kinds of distortion are combined to form the total distortion, and the image with the minimal total distortion is selected as cover. Using the selected image for embedding, high undetectability can be achieved.
A Color Enhancement Scene Estimation Approach for Single Image Haze Removal

The presence of suspended particles, such as fog, smoke, and dust, in the atmosphere reduces the quality of the captured image. It is essential to overcome these particles, because these particles have a very dire effect on different applications of image processing. We propose a new image dehazing method for remote sensing (RS) applications. Since the hazy RS image is affected by multiple colors and contrast reduction, our goal is to focus on degraded objects, including color correction and color-contrast enhancement. A “Piecewise Linear Transformation (PWLT)” is used to correct the color distortion, and then the color contrast is improved by applying the proposed method. The intensity distribution manipulation is one of the most common methods used to strengthen image contrast in the past. Compared with advanced techniques, the proposed method is easy to implement and suitable for real-time applications. Moreover, it is not necessary to require prior imaging conditions. The experimental results show that, in terms of subjective and visual quality, the color, contrast, naturalness, and high brightness of the object increase in the image to be improved.
Moving object detection under different weather conditions using full-spectrum light sources

The moving object detection always remains an active field of research given the variety of challenges related to this topic. In fact, most of the challenges related to the low illumination and weather conditions (fog, snow, rain, etc.) remain unresolved and require more developments. In this paper, our intrinsic objective is to overcome these challenges using an effective moving object detection method. Unlike most works in the literature that use one of the two infrared or visible spectra independently, we proposed a Moving Object Detection method based on background modeling using the Full-Spectrum Light Sources (FSLS-MOD). To better ensure the adaptability and independence of the moving object speeds and sizes, the principle of the inter-frame differences’ methods is introduced in the background modeling stage. Furthermore, we applied a new strategy to switch between the spectra allowing us to benefit from the advantages of each spectrum and carry out a better moving object detection even in bad weather conditions. An experimental study by quantitative and qualitative evaluations proved the robustness and effectiveness of our proposed method of moving object detection using the switching strategy between full-spectrum light sources under different illuminations and weather conditions.
Using avatars in weight management settings: A systematic review

Obesity interventions rely predominantly on managing dietary intake and/or increasing physical activity but sustained adherence to behavioural regimens is often poor. Avatar technology is well established within the computer gaming industry and evidence suggests that virtual representations of self may impact real-world behaviour, acting as a catalyst for sustained weight loss behaviour modification. However, the effectiveness of avatar technology in promoting weight loss is unclear.We aimed to assess the quantity and quality of empirical support for the use of avatar technologies in adult weight loss interventions.A systematic review of empirical studies was undertaken. The key objectives were to determine if: (i) the inclusion of avatar technology leads to greater weight loss achievement compared to routine intervention; and (ii) whether weight loss achievement is improved by avatar personalisation (avatar visually reflects self).We identified 6 papers that reported weight loss data. Avatar-based interventions for weight loss management were found to be effective in the short (4-6 weeks) and medium (3-6 months) term and improved weight loss maintenance in the long term (12 months). Only 2 papers included avatar personalisation, but results suggested there may be some added motivational benefit.The current evidence supports that avatars may positively impact weight loss achievement and improve motivation. However, with only 6 papers identified the evidence base is limited and therefore findings need to be interpreted with caution.