Reviewerly/exHarmony huggingface dataset:
Multi-Scale Shape Adaptive Network for Raindrop Detection and Removal from a Single Image

Removing raindrops from a single image is a challenging problem due to the complex changes in shape, scale, and transparency among raindrops. Previous explorations have mainly been limited in two ways. First, publicly available raindrop image datasets have limited capacity in terms of modeling raindrop characteristics (e.g., raindrop collision and fusion) in real-world scenes. Second, recent deraining methods tend to apply shape-invariant filters to cope with diverse rainy images and fail to remove raindrops that are especially varied in shape and scale. In this paper, we address these raindrop removal problems from two perspectives. First, we establish a large-scale dataset named RaindropCityscapes, which includes 11,583 pairs of raindrop and raindrop-free images, covering a wide variety of raindrops and background scenarios. Second, a two-branch Multi-scale Shape Adaptive Network (MSANet) is proposed to detect and remove diverse raindrops, effectively filtering the occluded raindrop regions and keeping the clean background well-preserved. Extensive experiments on synthetic and real-world datasets demonstrate that the proposed method achieves significant improvements over the recent state-of-the-art raindrop removal methods. Moreover, the extension of our method towards the rainy image segmentation and detection tasks validates the practicality of the proposed method in outdoor applications.
Ontology Application to Construct Inductive Modeling Tools with Intelligent Interface

This paper is devoted to the analysis of sources in the field of development and building intelligent user interfaces. Particular attention is paid to presenting an ontology-based approach to constructing the architecture of the interface, the tasks arising during the development, and ways for solving them. An example of the construction of the intelligent user interface is given for software tools of inductive modeling based on the detailed analysis of knowledge structures in this domain.
WITHDRAWN: Gymnastics training action recognition based on machine learning and wireless sensors

This article has been withdrawn: please see Elsevier Policy on Article Withdrawal (https://www.elsevier.com/about/our-business/policies/article-withdrawal). This article has been withdrawn at the request of the Editor in Chief. Subsequent to acceptance of this special issue paper by the responsible Guest Editor Sundhararajan Mahalingam, the integrity and rigor of the peer-review process was investigated and confirmed to fall beneath the high standards expected by Microprocessors & Microsystems. There are also indications that much of the Special Issue includes unoriginal and heavily paraphrased content. Due to a configuration error in the editorial system, unfortunately the Editor in Chief did not receive these papers for approval as per the journal's standard workflow.
Security in social media policies: guidelines for strategies

Abstract This research investigates the security in social media and provided the guidelines and strategies toward minimizing the security risks in social media to collect parts of information and increase better practices to assist nations with tending to social media security changes adequately. Besides, this work further features more in the writing dependent on social media security dangers and rules to lessen it and furthermore outlined the key bits of knowledge to push our nations to effectively address these issues of social middle security chance. Great associations don’t contain a coercive social media security way set up and are dubious of how to make sturdy social media security systems to forestall social media security risks. This work can be filled in as a trend to associations to moderate social media security hazards that might compromise the associations. The flow investigates further merges the divided conversation in writing and gives a profundity investigation of social media security dangers, rules, and counteraction systems. Reasonable bits of knowledge are distinct and outlined from a wide investigation. Sharing this interesting information could possibly energize more conversation on bestead pursuits for contraction the risks of social media to those associations.
Lower Bounds and a Near-Optimal Shrinkage Estimator for Least Squares Using Random Projections

We consider optimization using random projections as a statistical estimation problem, where the squared distance between the predictions from the estimator and the true solution is the error metric. In approximately solving a large-scale least squares problem using Gaussian sketches, we show that the sketched solution has a conditional Gaussian distribution with the true solution as its mean. Firstly, tight worst-case error lower bounds with explicit constants are derived for any estimator using the Gaussian sketch, and the classical sketching is shown to be the optimal unbiased estimator. For biased estimators, the lower bound also incorporates prior knowledge about the true solution. Secondly, we use the James-Stein estimator to derive an improved estimator for the least squares solution using the Gaussian sketch. An upper bound on the expected error of this estimator is derived, which is smaller than the error of the classical Gaussian sketch solution for any given data. The upper and lower bounds match when the SNR of the true solution is known to be small, and the data matrix is well-conditioned. Empirically, this estimator achieves smaller error on simulated and real datasets, and works for other common sketching methods as well.
A risk-averse approach to mission planning: nupiri muka at the Thwaites Glacier

Deploying and AUV in areas such as near and beneath ice is a high-risk activity in which an AUV can be rendered unrecoverable. A risk-averse approach to mission planning and execution can increase the likelihood of vehicle return. This approach relies on a straightforward mission template that defines mission locations and task transitions that prioritizes vehicle recovery among mission objectives. In early 2020, this methodology was put to the test in the Thwaites Glacier region of Antarctica. The execution of this methodology proved highly valuable in conducting reliable AUV operations in a challenging and dynamic environment.Presented are the core ethos and guiding methodology of our risk-averse mission planning strategy.
Vision Based Facial Expression Recognition Using Eigenfaces and Multi-SVM Classifier

Facial Expression Recognition (FER) has become one of the most popular areas of research in computer vision and biometrics authentication and it has achieved a lot of enthusiasm from researchers. The Vision based Facial Expression Recognition system intends to classify the facial expression of a given image. In this paper, the proposed system automatically classifies the facial expression. The system is composed of feature extraction and expression classification. In preprocessing, Hybrid filter (Median and Gabor) and Histogram Equalizations, is used to reduce noise and enhance images. Feature extraction is to extract feature vectors from face images using the Eigenfaces approach, based on Principal Component Analysis (PCA). To classify facial expression, extracted feature vectors are fed into a Multiclass Support Vector Machine (Multi-SVM) classifier. Experiments are performed on the standard dataset of the Japanese Female Facial Expression (JAFFE) and achieved 80% accuracy. The proposed system showed satisfying performance comparing with other methods and effects state-of-the-art performance on the JAFFE dataset.
Semi-Structured Visual Design of Complex Industrial 3D Training Scenes

Designing high-quality interactive 3D spaces, such as those required for professional training, is a complex, expensive, and time-consuming task. Although several approaches have been proposed to simplify the process, the result is always a trade-off between advanced capabilities and the ease of use of the content creation tool. To guarantee high visual quality of the modeling results, no simplification of the geometry detail level is possible. In this paper, we propose a method of visual content design, which simplifies the process by eliminating some of the structural complexity through the introduction of content controllers, which based on the known semantics of content objects introduce specific editing rules and impose a specific structure of their sub-components, thus simplifying and speeding up the content creation process as well as eliminating common mistakes.
Modular Graph Attention Network for Complex Visual Relational Reasoning

Visual Relational Reasoning is crucial for many vision-and-language based tasks, such as Visual Question Answering and Vision Language Navigation. In this paper, we consider reasoning on complex referring expression comprehension (c-REF) task that seeks to localise the target objects in an image guided by complex queries. Such queries often contain complex logic and thus impose two key challenges for reasoning: (i) It can be very difficult to comprehend the query since it often refers to multiple objects and describes complex relationships among them. (ii) It is non-trivial to reason among multiple objects guided by the query and localise the target correctly. To address these challenges, we propose a novel Modular Graph Attention Network (MGA-Net). Specifically, to comprehend the long queries, we devise a language attention network to decompose them into four types: basic attributes, absolute location, visual relationship and relative locations, which mimics the human language understanding mechanism. Moreover, to capture the complex logic in a query, we construct a relational graph to represent the visual objects and their relationships, and propose a multi-step reasoning method to progressively understand the complex logic. Extensive experiments on CLEVR-Ref+, GQA and CLEVR-CoGenT datasets demonstrate the superior reasoning performance of our MGA-Net.
Imputing Block of Missing Data Using Deep Autoencoder

Missing data problems can be seen in almost every field like biology, medicine, sensor networks, survey, etc. Most of the existing algorithms that are used to impute missing data rely on the assumption that there exist some complete data rows. This paper presents a modified deep autoencoder model that can impute even for the case where there are not any complete rows, i.e., a block of data missing. The system is tested in three different datasets: multivariate Gaussian distribution samples, real-world wireless sensor network dataset, and abalone dataset with different percentages of the block of missing values in all the datasets. The performance of the proposed deep autoencoder is compared against k-nearest neighbor (KNN)-based imputation method and mean imputation method, using root mean square error (RMSE) as a performance metric. The results indicate that the proposed system outperforms commonly used mean imputation method. Moreover, for the case having a large sample of datasets and having a good correlation between variables, the deep autoencoder-based imputation method is able to achieve better results than KNN and mean imputation methods.
Object Detection Method Based On Improved One-Stage Detector

We use an efficient way to simulate the two-stage detector, and optimize the algorithm to improve efficiency of object detection as the accuracy of the one-stage detector often lags behind that of the two-stage detector. We have improved the one-stage detector to get higher accuracy on SSD. We mainly adjust the default boxes so that it can well capture the range of the target object in the image, and then the module given the adjusted boxes responds to the alignment of the receptive field of the convolution filter. Test results on the PASCAL VOC dataset show that the final model accuracy can reach that of the advanced detector.
Assessing the Impact of Weather Forecast Models Combination on the AMS Solar Energy Prediction

Nowadays, using variable energy resources, particularly renewable but intermittent energy resources such as solar and wind power, have caused an irregular electricity generation which presents technical and financial challenges in the power storage, use and sale. Therefore, solar power forecasting is attracting more and more attention from both industry and academia. It becomes highly relevant in providing the ability to accurately predict power fluctuations of solar energy resources in the power grid in order to maintain a balance between daily supply and demand and to offer an efficient/low-cost storage of energy. For this aim, the American Meteorological Society (AMS) organized a kaggle competition to find the best machine learning and statistical techniques used for solar power forecasting based on weather input data. In this work we focused on the influence of Numerical Weather Prediction (NWP) models on solar power forecast, for this reason six NWP models combinations were examined within seven regressors to ultimately get $2.01\mathrm{E} +6\mathrm{J}/ \mathrm{m}^{2}$ as MAE score in Kaggle leaderboard.
Data-driven rated power prediction of diesel engines using improved multi-class imbalanced learning method

Rated power is one of the most important quality indicators of diesel engines. The effective prediction of the rated power classes is of great significance in controlling the quality of diesel engines. However, the number of samples varies greatly in different classes. Creating an effective machine learning model to predict the rated power classes is difficult due to the imbalanced dataset. This paper develops an improved multi-class imbalanced learning method which integrates dynamic oversampling method with multi-class AdaBoost for producing high predictive accuracy over both the majority classes and the minority classes of the diesel engine dataset. Computational experiments have been performed on four UCI standard datasets and one diesel engine’s rated power dataset, and the experimental results indicate that the proposed algorithm is superior to the compared methods, including resampling methods and boosting-type methods.
Shape-restricted support vector machine (SR-SVM): a SVM classifier taking supplementary shape information of input

Many classification problems contain shape information from input features, such as monotonic, convex, and concave. In this research, we propose a new classifier, called Shape-Restricted Support Vector Machine (SR-SVM), which takes the component-wise shape information to enhance classification accuracy. There exists vast research literature on monotonic classification covering monotonic or ordinal shapes. Our proposed classifier extends to handle convex and concave types of features, and combinations of these types. While standard SVM uses linear separating hyperplanes, our novel SR-SVM essentially constructs non-parametric and nonlinear separating planes subject to component-wise shape restrictions. We formulate SR-SVM classifier as a convex optimization problem and solve it using an active-set algorithm. The approach applies basis function expansions on the input and effectively utilizes the standard SVM solver. We illustrate our methodology using simulation and real world examples, and show that SR-SVM improves the classification performance with additional shape information of input.
Multi-AUV Coverage with Functional Constraints and Currents

Many underwater survey applications such as bathymetry archaeological, mine-counter-measure surveys, etc., can be efficiently performed using autonomous underwater vehicles (AUVs). In all the mentioned survey applications, a region of interest needs to be covered using some sensors. Low cost AUVs have shown promise to perform these applications quickly and with relatively low mission cost. However, these AUVs are affected by the currents present in the region. Currents can help (resp. hinder) in propelling the AUV saving (resp. expending) time and energy and also can hinder with increased endurance cost. During such a process, the results mission time may be reduced significantly affecting the complete mission. If this issue is discovered after deployment then the cost of the mission increases. Additionally, certain areas may need a AUV with specific sensor to visit. For example, AUVs using 3D imaging sonar to map a particular area of interest (archaeology). We term such a constrain as a functional constraint. Further, the total mission time can be reduced significantly by introducing multiple AUVs. Therefore, there is a need develop mission path planners that take current information into account and the functional constraints for multi-AUV deployment. In this paper, we determine paths taking functional constraints (FC) and current information into account. We formulate the problem as a linear program, however, finding routes with the vehicle-target constraints is an NP-Hard problem. Due to the computational complexity we use insertion heuristic methods to determine a fast and simple paths. We also allow for the possibility of multi-depots (base stations for deploy-ments/retrieval) in the problem formulation. The developed insertion heuristics has two stages. The first stage involves determining a feasible solution and in the second stage we perform an iterative search for load balancing which implicitly minimizes the maximum route for the given set of vehicles. Finally, we provide simulations and experiments for the same.
Experience in the use of distance education at the university

The article analyzes personal experience in distance learning using the Microsoft Teams application. The author defines distance education as a form of interaction between a teacher and a student and a student’s independent work using information technology in accordance with the educational program. The article presents the results of the author’s sociological study, conducted with the help of qualitative and quantitative methods of information collection. 30 free unstructured interviews and a small questionnaire survey were conducted. There were 250 respondents. The results of the survey lead to the conclusion that as a forced measure, most participants in the educational process took this type of training positively. In the future, distance education can be recommended for use in full-time and part-time educational formats.
A Tensor-Based Markov Chain Model for Heterogeneous Information Network Collective Classification

Heterogeneous Information Network (HIN) collecitve classification studies the problem of predicting labels for one type of nodes in a HIN which contains multiple types of nodes multiple types of links among them. Previous studies have revealed that exploiting relative importance of links is quite useful to improve node classification performance as connected nodes tend to have similar labels. Most existing approaches exploit the relative importance of links either by directly counting the number of connections among nodes or by learning the weight of each type of link from labeled data only. However, these approaches either neglect the importance of types of links to the class labels or may lead to overfitting problem. We propose a <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">T</b> ensor-based <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Mark</b> ov chain (T-Mark) approach, which is able to automatically and simultaneously predict the labels for unlabeled nodes and give the relative importance of types of links that actually improve the classification accuracy. Specifically, we build two tensor equations by using the HIN and features of nodes from both labeled and unlabeled data. A Markov chain-based model is proposed and it is solved by an iterative process to obtain the stationary distributions. Theoretical analyses of the existence and uniqueness of such probability distributions are given. Extensive experimental results demonstrate that T-Mark is able to achieve superior performance in the comparison and obtain reasonable relative importance of links.
Self-adapting Confidence Estimation for Stereo

Estimating the confidence of disparity maps inferred by a stereo algorithm has become a very relevant task in the years, due to the increasing number of applications leveraging such cue. Although self-supervised learning has recently spread across many computer vision tasks, it has been barely considered in the field of confidence estimation. In this paper, we propose a flexible and lightweight solution enabling self-adapting confidence estimation agnostic to the stereo algorithm or network. Our approach relies on the minimum information available in any stereo setup (i.e., the input stereo pair and the output disparity map) to learn an effective confidence measure. This strategy allows us not only a seamless integration with any stereo system, including consumer and industrial devices equipped with undisclosed stereo perception methods, but also, due to its self-adapting capability, for its out-of-the-box deployment in the field. Exhaustive experimental results with different standard datasets support our claims, showing how our solution is the first-ever enabling online learning of accurate confidence estimation for any stereo system and without any requirement for the end-user.
A Blockchain Based Framework for Complete Secure Data Outsourcing with Malicious Behavior Prevention

Outsourcing information on the cloud server is a method to reduce the computational and storage overhead resulted from data owners' huge amount of information. Considering the fact that this information could be sensitive, encryption is used before outsourcing. But this prevents efficient searching. Searchable encryption is an approach that provides security alongside efficiency. So far, many searchable encryption schemes have been presented in which the server is modeled as an honest but curious entity. But in case the server is malicious, the accuracy of the information will be at stake. For this purpose, we propose a multi-server searchable symmetric encryption scheme without the need to trust the cloud servers, using blockchain and smart contract, which works efficiently in addition to ensuring the accuracy of information. Moreover, this scheme involves a fair mechanism to preserve the interests of users and servers and prevent their malicious actions.
SmartTuning: Selecting Hyper-Parameters of a ConvNet System for Fast Training and Small Working Memory

It is desirable to deploy a ConvNet system with high inference accuracy, as well as fast training and small inference memory. However, existing approaches to hyper-parameter tuning only focus on high accuracy. Although achieving high accuracy, tuning poorly can significantly increase the performance burden, and thus degrade the overall performance of a ConvNet system. In this article, we propose SmartTuning, an approach to identifying the hyper-parameters of a ConvNet system for high training speed and small working memory, with the restriction of high inference accuracy. The key idea of SmartTuning is to build a new performance model for a ConvNet system, and to integrate Bayesian Optimization to learn the relationship between the overall performance and the hyper-parameters of a ConvNet system. In this way, SmartTuning can balance inference accuracy, training speed and inference memory usage during the tuning process, and thus maximizes the overall performance of a ConvNet system. Our experiments show that SmartTuning can stably identify the hyper-parameter sets that offer very close accuracy with faster training speed (i.e., 7×-11× over MNIST and 2×-3× over CIFAR-10) and much less inference memory usage (i.e., 17×-23× over MNIST and 4×-9× over CIFAR-10), compared with existing tuning approaches.